Sentimental equilibria with optimal control The ubiquitous phenomenon of marital dissolution in the West poses a major theoretical problem that is not well understood. The fact that most couples declare that they are committed to an enduring and happy relationship allows us to model their sentimental relationship as a simple optimal control problem. A state variable monitors the wellness of the relationship whose natural decay in time must be counteracted with effort -the control variable -according to a widely accepted principle in marital psychology. Equilibria are basic desirable solutions of this control problem. In this note key properties of equilibria -in particular, existence, dynamical instability and sensitivity analysis -are obtained for a class of decaying sentimental dynamics. An underlying mechanism combining the existence of an effort stress with the instability of the model may be identified as a main cause of distress for the couple relationship. There is an epidemic of failure among couple relationships in the West. In 1989 demographers Martin and Bumpass [1] predicted that two thirds of all US marriages would end up in separation or divorce within a 40-year span. At present the divorce rate in the US could be at about 50%. The same phenomenon occurs in western countries. In the European Union, there is a marital breakdown every 30 s. The last available data shows that the divorce rate in EU27 is about 0.43. In Spain there are 2 divorces for every 3 marriages [2] . In general cohabitation is less stable than marriage [3] .. A second piece of evidence concerns the attitudes of each partner, rather than demographic data. First, people typically consider that a stable relationship is a main ingredient of happiness. This has been evidenced in different surveys. In Spain, for instance, most college students in a recent survey declared that they absolutely want to have a stable relationship [4] . Second, most people consider their own relationship stable. Also in Spain, the majority of respondents in another survey qualified as definite their project of life in common with their partners [5] .. The two facts above pose together what is called in [6] the failure paradox: how is it that couples that are ready to commit to a lasting happy relationship will probably fail? The failure paradox is related to an old problem in marital psychology: why are some marriages happy while others are miserable? Gottman et al. [7] claim that mathematical theory is desperately needed in the field of marital research, in particular to answer the previous question. In their pioneer work [7] , they develop a model to analyze marital instability, by calibrating a system of difference equations for the evolution of partners' emotions during a conversation. The interaction dynamics they prescribe is descriptive, based on experience and their intuitive understanding of the influence among partners during conflict.. In [6] a different mathematical modeling approach is proposed to analyze marital dynamics. It is based on a behavioral assumption: couples design their relationships to last forever with the objective to be as happy as possible. Notice that this seems consistent with the sociological evidence quoted above. According to a widely accepted principle in the psychology of marital relationships, marriages starts off happy but over time a reinforcement erosion occurs that is the source of marital dysfunction [8] . Also, the erosion can be counterbalanced with effort, in the form of conscious practices to keep the relationship alive and well (see e.g. [7] ). Thus the couple's problem can be naturally seen as an optimal control problem where marital status -measured in terms of attachment or feeling -is the state variable and effort making is the control variable.. Sentimental equilibria -stationary feeling levels sustained by a routine effort -are considered key solutions for the control problem. Assuming that the feeling variable decays at a constant rate, it is shown in [6] that a unique sentimental equilibrium exists. It is also proved that the equilibrium is of saddle type -in turn unstable -and such that there exists an effort gap, that is, the effort level at equilibrium is always higher than the favorite effort level defined by the couple a priori. These two facts were identified as a basic underlying mechanism that may lie behind the distress and eventual disruption of many sentimental relationships that were initially planned to last forever. In turn, the model findings may account for the failure paradox.. In this note, the simple model in [6] is further explored. Assuming a more general decaying law for the feeling, the key issues of existence and instability of equilibria -as well as the presence of the effort gap -are considered. The sensitivity of equilibria with respect to changes in the significant parameters of the model is also analyzed. Human relationships usually pose difficult problems that may appear very complex when sentiments are a driving component of the interaction. This seems particularly true in the case of romantic relationships. Given the high failure rates among couple relationships in the West, understanding what makes an enduring and happy couple is a substantial issue. The fact that couple break-up is such a pervasive phenomenon suggests that a simple deterministic mechanism may be acting behind many sentimental failures. Long-term relationships, like marriages, are fueled by a complex mix of emotions -notably romance, lust or attachment, among others [13] . These are amalgamated in a unique overall feeling, which is represented by the (time dependent) feeling variable x(t) in this paper. According to psychological evidence, the level of the fuel variable x(t) naturally decays in time and must be recovered through effort. This fact is combined with a behavioral rule -couples intend to stay together to be as happy as possible -to modeling a couple's relationship as a simple optimal control problem. Solving the control problem determines the required effort levels to sustain the relationship in the long term. The model simplifies by considering homogamous couples, composed by partners that share feeling and effort levels and have the same valuation scheme about their relationship.. In terms of individual behavior, the symmetry due to the homogamy assumption implies that each partner solves the same problem. As a consequence, when solving his problem a partner can think of x(t) as the feeling of the other rather than his own. His mate's feeling is thus a main source of his satisfaction. This interpretation matches with a more interactive -disinterested -form of love. Disinterested love, according to Leibniz, consists of ''not thinking about or asking for any pleasure of one's own except what one can get from the happiness or pleasure of the loved one'' [14] . Of course, disinterested love truly produces an interesting case study when both partners differ in the variables or in the utility structure of the model. This is the case of heterogamous couples, which requires an extension of the optimal control model in this paper. In Western society human mate choice is based on a strong form of homogamy.. Within the simplified setting described above, optimal control theory provides a natural framework to model long-term sentimental relationships. In particular, this paper is concerned with sentimental equilibria -stationary solutions for the couple's control problem. These are particularly appealing, either as states of relationships already settled or as sustainable destinies in the long-term for newlywed couples. For a class of diminishing laws for the feeling, the mathematical analysis reveals a basic obstacle for the sustainability of a relationship in equilibrium, namely the existence of an effort gap along with dynamical instability. The mechanism goes as follows. First, to sustain equilibrium, a couple must fine-tune the correct effort only to find that is bigger than the effort level that they prefer. This may conceivably lead to relaxing the required effort level. Then, local instability operates against restoring things back to equilibrium.. A natural way out of the pessimistic scenario above consists of assuming some resilience in the feeling to degrade. This can be introduced in the model by considering a different class of dynamics for the feeling, e.g. consistent with decaying in a concave fashion for large levels and in a convex way at lower levels. The analysis in this note shows that this kind of resilience might avoid the effort gap at equilibrium but not the instability. A sentimental equilibrium with optimal control is unstable regardless of the decaying dynamics of the model.

O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O B I O O O O O O O O O O


Engaging consumers online through websites and social media: A gender study of Italian Generation Y clothing consumers Abstract: Consumers increasingly search for, evaluate, and buy items via social media and websites, but little is known about how these activities affect their level of trust, attitudes toward online retailing, and online shopping behaviors. Therefore, this study focuses on how online shopping via Facebook, peer recommendations, and website service quality affect consumer trust, attitudes and loyalty intentions in e-tailing. An online survey was conducted with Generation Y Italian consumers who used Facebook searches of various websites to shop for clothing online. Confirmatory factor analysis was used to validate the constructs, and structural equation modeling (SEM) was employed to test the hypotheses. Findings confirm that website service quality and consumers' predispositions to use Facebook for online shopping directly and positively affect consumer trust towards an e-tailer. In contrast, peer recommendations affect attitude directly rather than indirectly via trust. The results further indicate that peer recommendations have a significantly stronger influence on attitudes of females than they do on attitudes of males.We study consumers' engagement online through websites and social networking sites and related peer recommendations.Data has been collected from Generation Y consumer segments who shop online.Online shopping via Facebook and website service quality has positive effect on trust.Peer recommendations have a significantly stronger influence on attitudes of females than they do on attitudes of males.Trust has a positive and significant effect on attitude towards e-tailer and loyalty intentions. In order to test the hypothesized effects and the moderating role of gender, the authors used the following four steps. First, we conducted a confirmatory factor analysis (CFA), confirming the factor structure and deleting the items that contributed to the poor fit of the measurement model. Second, we conducted an invariance analysis to ensure that the measurement model provides a comparable representation among both genders. Third, we evaluated the discriminant validity of the constructs, and fourth, we conducted a multigroup analysis to assess the moderator effect of gender.

O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B I O O O O O O O B I O B B I O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B I O O O O O B O O O O O B I O O O O O O O B O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B


Representing Popov v Hayashi with dimensions and factors Abstract Modelling reasoning with legal cases has been a central concern of AI and Law since the 1980s. The approach which represents cases as factors and dimensions has been a central part of that work. In this paper I consider how several varieties of the approach can be applied to the interesting case of Popov v Hayashi. After briefly reviewing some of the key landmarks of the approach, the case is represented in terms of factors and dimensions, and further explored using theory construction and argumentation schemes approaches. One of the distinctive features of legal reasoning, especially in Common Law jurisdictions such the US and UK, but relevant also to Civil Law jurisdictions, is the use of precedent cases in decision making. This aspect of legal reasoning has been a focus of work in Artificial Intelligence and Law, and approaches to this problem based on dimensions and factors have been one of the major successes of that field.. In this paper I will first describe some of the key work which uses dimensions and factors to model reasoning with legal cases. In Sect. 3, I will then illustrate these approaches, and some of the differences between them, using the case of Popov v Hayashi with four much discussed cases relating to the possession of wild animals as the background precedents. The facts of these five cases are summarised at the beginning of Sect. 3. In Sect. 4 I will look at Popov in terms of an approach based on the idea that reasoning with legal cases is a process of theory construction.. In Sect. 5 I apply an reconstruction of factor based reasoning in terms of argumentation schemes to the case. Finally Sect. 6 offers a brief conclusion. The above discussion has been intended to draw out a number of points relating to case representation:. 1. That new cases may require us to reinterpret our analysis of precedent cases, throwing new light on how we should identify factors.. 2. In consequence, systems such as HYPO and CATO, which presuppose an existing analysis, can best be applied in domains in which the case law is regarded as stable and relatively well understood. Even then, however, we need to be mindful that at some point a case may appear which upsets this stability, and so take care that a new case does not introduce a new issue that means that its strengths or weaknesses cannot be done justice by an existing analysis. 3. That the granularity of the analysis needs to be sufficiently fine if blurring important distinctions is to be avoided. 4. That it is possible that that theories should be seen in terms of some nonclassical logic such as DL rather than in terms of classical logic, so that the notion of defeaters can be captured.. Use of dimensions and factors to explicate and model computational reasoning with legal cases, has been an important strand in AI and Law. The above analysis shows that some insights have been gained, even though it demonstrates the importance of the way in which the cases are represented in determining what will be produced. Moreover it points to some of the difficulties in producing a definitive representation, especially in advance of a new case. Additionally it indicates the importance of a phenomenon hinted at in the knock out factors of IBP, but deserving of a more systematic treatment, whereby the effect of some factors is not susceptible to a treatment using factors as premises of standard rules. This last would certainly merit further investigation. Thus far, we have seen that, applying the analysis of existing papers to Popov v Hayashi, we find that the factors based approach aligns the case with Pierson, and the dimensions approach with Young. Neither of these would be good news for Alex Popov, and on these analyses he would have no way of distinguishing the cited cases. If, however, we consider the perspective just developed, Popov (and Pierson) has a dimension on which Young can be distinguished: the interference was illegal. Although this was not something that the analyses is previous papers recognised, it was a clear consideration of the judge in the Popov case. Thus we can see Popov as requiring us to modify the analysis to include this additional dimension.. We should perhaps not be too surprised that a new case can lead us to reinterpret existing cases. Case law should not be seen as a static body of knowledge, but as something which evolves and adapts. For example Levi (1948) writes:. The movement of common or expert concepts into the law may be followed. The concept is suggested in arguing difference or similarity in a brief, but it wins no approval from the court. The idea achieves standing in society. It is suggested again to a court. The court this time reinterprets the prior case and in so doing adopts the rejected idea.. The adaptive nature of case law was explored in Henderson and Bench-Capon (2001) , where the authors considered how understanding of a case law domain would evolve differently depending on the sequence in which cases were presented.. Of course, this does have some implications for the use of systems such as HYPO and CATO: those systems presuppose that case law can be seen as a static body of analysis which can be applied to a new case without adaptation. How can we plausibly make the assumption of a static domain? First there is the nature of the domain. In Levi's model (1948) a period of fluctuation and development is followed by a period of stability, in which the law seems to be well understood and settled. During this period, cases tend to retain a fixed interpretation. Eventually tensions will develop and this will break down, typically through a landmark case. That eventually a settled interpretation would break down was recognised, and signs that suggest that a change is imminent were discussed, in Berman and Hafner (1995) and Rissland and Friedman (1995) . So one thing that HYPO and CATO require is that the law be in its period of stability. It is also necessary to recognise that any analysis will have a lifetime and then need revisiting when the understanding of the domain is changed by some landmark case. A second consideration is the granularity of the analysis. The more abstract the level of analysis, the more likely we are to be able to fit a new case into it. On the other hand, results using this coarser classification may be less reliable. The abstract factor hierarchy of CATO helps with this: one could hope that new aspects can be incorporated as leaves in the abstract factor hierarchy, while retaining the structure. This is made all the more likely in CATO, which deals with the domain of US Trade Secrets Law because, for that domain, the Restatement of Torts provides an authoritative high level set of abstract factors. This was used to structure the abstract factor hierarchy in Aleven (1997) , and explicitly as a 'logical model' in IBP (Brüninghaus and Ashley 2005) , a system based on CATO designed to predict the outcome of cases based on the strength of the parties arguments relating to the relevant issues. Finally there is the precaution of basing the analysis on a sufficiently large number of cases: it was perhaps unrealistic to expect all the issues to be identified in the four cases we used as background here.

O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Coprime

sampling

for

nonstationary

signal

in

radar

signal

processing

Estimating

the

spectrogram

of

non-stationary

signal

relates

to

many

important

applications

in

radar

signal

processing.

In

recent

years,

coprime

sampling

and

array

attract

attention

for

their

potential

of

sparse

sensing

with

derivative

to

estimate

autocorrelation

coefficients

with

all

lags,

which

could

in

turn

calculate

the

power

spectrum

density.

But

this

theoretical

merit

is

based

on

the

premise

that

the

input

signals

are

wide-sense

stationary.

In

this

article,

we

discuss

how

to

implement

coprime

sampling

for

non-stationary

signal,

especially

how

to

attain

the

benefits

of

coprime

sampling

meanwhile

limiting

the

disadvantages

due

to

lack

of

observations

for

estimations.

Furthermore,

we

investigate

the

usage

of

coprime

sampling

for

calculating

ambiguity

function

of

matched

filter

in

radar

system.

We

also

examine

the

effect

of

it

and

conclude

several

useful

guidelines

of

choosing

configuration

to

conduct

the

sparse

sensing

while

retain

the

detection

quality.

Both

of

the

designs

of

radar

system

and

sensor

network

could

be

attribute

to

obtaining

sufficient

samples

to

generate

the

correlation

function

so

that

a

good

ambiguity

scale

or

spectrum

estimation

could

be

obtained

The

design

of

radar

system

needs

to

take

advantage

of

the

ambiguity

function

(AF)

between

received

signal

and

transmitted

signal

to

determine

the

resolution

of

the

radar,

side

lobe

behavior,

and

ambiguities

in

both

time

and

Doppler

domains.

AF

is

calculated

via

the

convolution

of

transmitted

signal

with

received

signal,

which

contains

the

copy

of

transmitted

signal,

noise,

and

Doppler

shift

caused

by

the

movement

of

the

target.

Furthermore,

considering

cost

of

deployment

in

broad

range,

many

applications

of

sensor

network

require

to

distribute

the

sensor

elements

sparsely.

The

power

spectral

density

(PSD)

acquired

by

these

sensors

could

describe

the

power

incidents

for

the

given

direction

and

area,

and

PSD

is

the

Fourier

transform

of

autocorrelation

function

of

received

signal

or

correlation

function

among

the

signal

received

in

different

sensors

in

the

array.

Hence,

both

scenarios

could

benefit

from

sparse

sensing

a

rapidly

changing

signal

sequence

with

*Correspondence:

liang@uta.edu

Department

of

Electrical

Engineering,

University

of

Texas

at

Arlington,

Arlington,

Texas,

USA

optimal

performance

in

terms

of

retaining

the

resolution

or

detecting

ability

compared

with

dense

sampling..

The

degree

of

freedom

(DoF)

of

sampling

defined

the

minimum

number

of

sample

points,

which

could

specify

certain

properties

of

the

sequence

as

a

whole

Before

the

research

of

coprime

samplers,

the

available

sensors

were

considered

as

a

signal

array

and

increasing

DoF

could

be

achieved

by

performing

an

augmentation

algorithm

on

the

covariances

obtained

via

minimum

redundancy

arrays

(MRA)

which

consisted

uniformly

linear

arrays

with

maximum

possible

aperture.

Bedrosian

extended

the

linear

array

to

non-uniformly

distribution

such

that

their

pairwise

differences

could

generate

full

coverage

for

certain

span,

the

article

also

enumerated

the

array

size

M

from

to

to

achieve

full

coverage

as

much

as

M(M

The

algorithm

proposed

in

could

find

near-optimal

integer

sensor

locations

that

maximized

the

number

of

distinct

nonnegative

integers,

but

it

also

restated

the

fact

that

location

of

elements

in

an

MRA

could

only

be

approximated

rather

than

specified

in

closed

form.

Besides,

there

were

other

ways

to

generate

extra

freedoms,

including

higher

order

statistics

based

methods,

Khatri-Rao

product

based

methods,

and

nested

array

Besides,

the

article

developed

the

application

of

nested

array

beyond

focusing

on

the

DoF,

finding

nested

array

could

improve

the

spectrum

efficiency.

Coprime

sampling

first

had

been

used

for

identifying

sinusoids

in

noise

along

with

other

methods

proposed

for

synthetic

aperture

radar

locating

and

imaging

of

moving

targets

Further

research

explored

the

properties

and

applications

of

coprime

sampling

and

array

in

both

time

and

frequency

domains.

The

article

used

coprime

samplers

to

increase

the

dimensions

of

DFT

filter

banks

after

sensor

arrays

as

well

as

to

estimate

the

power

spectrum

density

of

received

signal.

In

the

article

the

multidimensional

coprime

sensing

extended

the

previous

implementations

to

acquire

densely

sampled

domain.

The

article

proposed

spatial

smoothing

algorithm

together

with

coprime

sampling

to

estimate

frequencies

of

sinusoids

buried

in

noise

and

directions-of-arrival

of

impinging

signals

on

a

sensor

array..

Note

that

the

article

presenting

coprime

sampling

strictly

confines

discussion

within

the

underlying

assumption

of

wide-sense

stationary

signal

so

that

the

expectation

of

autocorrelation

could

approach

the

real

value

via

multitimes

averaging.

This

increased

delay

is

used

to

compensate

the

variation

introduced

by

sub-Nyquist

sampling.

On

the

other

hand,

however,

in

the

real-world

application,

just

as

the

description

in

the

first

paragraph,

the

working

scenarios

of

many

applications

involves

non-stationary

signal.

The

sampled

points

could

not

simply

ascribe

to

independent

and

identical

distribution

either.

Consequently,

the

autocorrelation

coefficients

might

change

dramatically

during

a

short

period.

In

this

article,

we

deal

with

this

inconsistency

and

discuss

the

coprime

sampling

for

non-stationary

signal

to

obtain

its

second

order

statistic

properties.

In

general,

the

classic

point

of

view

for

processing

non-stationary

signal

regards

it

as

piece-wise

stationary

signal,

but

as

these

two

theories

combining

together

there

are

many

research

problems

such

as

stability

of

estimation,

coverage

of

second

order

derivatives,

and

so

on.

In

the

following

content,

we

will

discuss

these

problems

and

our

tentative

solutions

in

detail..

The

rest

of

this

article

is

organized

as

follows,

we

first

quickly

revisit

the

basic

concepts

and

properties

about

coprime

sampling

in

Section

Theory

and

properties

for

coprime

sampling.

In

Section

STFT

for

coprime

sampling

non-stationary

signal,

we

propose

and

simulate

the

algorithm

of

two-steps

coprime

sampling

especially

used

for

the

non-stationary

signal.

In

Section

Implementation

in

radar

signal

processing,

we

extend

the

implementation

scenario

to

radar

signal

processing

and

discuss

several

critical

trade-offs

in

designing

the

radar

signal

processing

system

with

coprime

sampling.

Finally,

we

conclude

the

research

discussion

in

Section

Conclusions

and

future

research.

B I O O O O B I I O O O O B I O O O O O O B I I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O B I B I O O O O O O O O B I O O O O B I O O O O B I I O O O O O O B I I O O O O O O O O B I I O O B I O O O O O O O O O O O O


Efficient MR image reconstruction for compressed MR imaging In this paper, we propose an efficient algorithm for MR image reconstruction. The algorithm minimizes a linear combination of three terms corresponding to a least square data fitting, total variation (TV) and L1 norm regularization. This has been shown to be very powerful for the MR image reconstruction. First, we decompose the original problem into L1 and TV norm regularization subproblems respectively. Then, these two subproblems are efficiently solved by existing techniques. Finally, the reconstructed image is obtained from the weighted average of solutions from two subproblems in an iterative framework. We compare the proposed algorithm with previous methods in term of the reconstruction accuracy and computation complexity. Numerous experiments demonstrate the superior performance of the proposed algorithm for compressed MR image reconstruction. Magnetic Resonance (MR) imaging has been widely used in medical diagnosis because of its non-invasive manner and excellent depiction of soft tissue changes. Recent developments in compressive sensing theory (Candes et al., 2006; Donoho, 2006) show that it is possible to accurately reconstruct the Magnetic Resonance (MR) images from highly undersampled K-space data and therefore significantly reduce the scanning duration.. Suppose x is a MR image and R is a partial Fourier transform, the sampling measurement b of x in K-space is defined as b = Rx. The compressed MR image reconstruction problem is to reconstruct x given the measurement b and the sampling matrix R. Motivated by the compressive sensing theory, Lustig et al. (Lustig et al., 2007) proposed their pioneering work for the MR image reconstruction. Their method can effectively reconstruct MR images with only 20% sampling. The improved results were obtained by having both a wavelet transform and a discrete gradient in the objective, which is formulated as follows:. x ¼ arg min. where a and b are two positive parameters, b is the undersampled measurements of K-space data, R is a partial Fourier transform and U is a wavelet transform. It is based on the fact that the piecewise smooth MR images of organs can be sparsely represented by the wavelet basis and should have small total variations. The TV was defined discretely as kxk TV ¼ P i P j ððr 1 x ij Þ 2 þ ðr 2 x ij Þ 2 Þ where r 1 and r 2 denote the forward finite difference operators on the first and second coordinates, respectively. Since both L1 and TV norm regularization terms are nonsmooth, this problem is very difficult to solve. The conjugate gradient (CG) (Lustig et al., 2007) and PDE (He et al., 2006) methods were used to attack it. However, they are very slow and impractical for real MR images. Computation became the bottleneck that prevented this good model (1) from being used in practical MR image reconstruction. Therefore, the key problem in compressed MR image reconstruction is thus to develop efficient algorithms to solve problem (1) with nearly optimal reconstruction accuracy.. Other methods tried to reconstruct compressed MR images by performing L p -quasinorm (p < 1) regularization optimization Chartrand, 2007; Chartrand, 2009 ). Although they may achieve a little bit of higher compression ratio, these nonconvex methods do not always give global minima and are also relatively slow. Trzasko et al. (Trzasko et al., 2009 ) used the homotopic nonconvex L 0 -minimization to reconstruct MR images. They created a gradual nonconvex objective function which may allow global convergence with designed parameters. It was faster than those L p -quasinorm regularization methods. However, it still needed 1-3 min to obtain reconstructions of 256 Â 256 images in MATLAB on a 3 GHz desktop computer. Recently, two fast methods were proposed to directly solve (1). In (Ma et al., 2008) , Ma et al. proposed an operator-splitting algorithm (TVCMRI) to solve the MR image reconstruction problem. In Yang et al. (2010) , a variable splitting method (RecPF) was proposed to solve the MR image reconstruction problem. Both of them can replace iterative linear solvers with Fourier domain computations, which can gain substantial time savings. In MATLAB on a 3 GHz desktop computer, they can be used to obtain good reconstructions of 256 Â 256 images in ten seconds or less. They are two of the fastest MR image reconstruction methods so far.. Model (1) can be interpreted as a special case of general optimization problems consisting of a loss function and convex functions as priors. Two classes of algorithms to solve this generalized problem are operator-splitting algorithms and variable-splitting algorithms. Operator-splitting algorithms search for an x that makes the sum of the corresponding maximal-monotone operators equal to zero. These algorithms widely use the Forward-Backward schemes (Gabay, 1983; Combettes and Wajs, 2008; Tseng, 2000) , Douglas-Rachford splitting schemes (Spingarn, 1983) and projective splitting schemes (Eckstein and Svaiter, 2009 ). The Iterative Shrinkage-Thresholding Algorithm (ISTA) and Fast ISTA (FISTA) (Beck and Teboulle, 2009b) are two well known operator-splitting algorithms. They have been successfully used in signal processing (Beck and Teboulle, 2009b; Beck and Teboulle, 2009a) and multitask learning (Ji and Ye, 2009) . Variable splitting algorithms, on the other hand, are based on combinations of alternating direction methods (ADM) under an augmented Lagrangian framework. It is firstly used to solve the PDE problem in Gabay and Mercier (1976) , Glowinski and Le Tallec (1989) . Tseng and He et al. extended it to solve variational inequality problems (Tseng, 1991; He et al., 2002) . Wang et al. (2008) showed that the ADMs are very efficient for solving TV regularization problems. They also outperform previous interior-point methods on some structured SDP problems (Malick et al., 2009) . Two variable-splitting algorithms, namely the Multiple Splitting Algorithm (MSA) and Fast MSA (FaMSA), have been recently proposed to efficiently solve (1), while all convex functions are assumed to be smooth (Goldfarb and Ma, 2009) .. However, all these above-mentioned algorithms can not efficiently solve (1) with provable convergence complexity. Moreover, none of them can provide the complexity bounds of iterations for their problems, except ISTA/FISTA in Beck and Teboulle (2009b) and MSA/FaMSA in Goldfarb and Ma (2009 Nesterov (1983 Nesterov ( , 2007 . However, both ISTA and FISTA are designed for simpler regularization problems and can not be applied efficiently to the composite regularization problem (1) using both L1 and TV norm. While the MSA/FaMSA assume that all convex functions are smooth, it makes them unable to directly solve the problem (1) as we have to smooth the nonsmooth function first before applying them. Since the smooth parameters are related to , the FaMSA with complexity bound Oð1= ffiffi ffi p Þ requires O(1/) iterations to compute an -optimal solution, which means that it is not optimal for this problem. Section 2.1 will further introduce the related algorithms in details.. In this paper, we propose a new optimization algorithm for MR image reconstruction method. It is based on the combination of both variable and operator splitting techniques. We decompose the hard composite regularization problem (1) into two simpler regularization subproblems by: (1) splitting variable x into two variables {x i } i=1,2 ; (2) performing operator splitting to minimize total variation regularization and L1 norm regularization subproblems over {x i } i=1,2 respectively and (3) obtaining the solution x by linear combination of {x i } i=1,2 . This includes both variable splitting and operator splitting. We call it the Composite Splitting Algorithm (CSA). Motivated by the effective acceleration scheme in FISTA (Beck and Teboulle, 2009b) , the proposed CSA is further accelerated with an additional acceleration step. Numerous experiments have been conducted on real MR images to compare the proposed algorithm with previous methods. Experimental results show that it impressively outperforms previous methods for the MR image reconstruction in terms of both reconstruction accuracy and computation complexity.. The remainder of the paper is organized as follows. Section 2.1 briefly reviews the related acceleration algorithm FISTA which motivates our method. In Section 2.2, our new MR image reconstruction methods are proposed to solve problem (1). Numerical experiment results are presented in Section 3. Finally, we provide our conclusions in Section 4.. The conference version of this submission has appeared in MIC-CAI'10 (Huang et al., 2010) . This submission has undergone substantial revisions and offers new contributions in the following aspects:. 1. The introduction section is rewritten to provide an extensive review of relevant work and to make our contributions clear. Two important optimization techniques are explained for the motivation purpose. Their convergence complexity is also analyzed. 2. We provide the theoretical proof of the algorithm convergence in the methodology section. It mathematically demonstrates the benefit of the proposed method. 3. The experiment section is substantially extended by adding numerous comparisons, such as new data, CPU time, SNR and sample ratio. The extensive comparisons further demonstrate the superior performance of the proposed method. We have proposed an efficient algorithm for the compressed MR image reconstruction. Our work has the following contributions. First, the proposed FCSA can efficiently solve a composite regularization problem including both TV term and L1 norm term, which can be easily extended to other medical image applications. Second, the computational complexity of the FCSA is only Oðp logðpÞÞ in each iteration where p is the pixel number of the reconstructed image. It also has strong convergence properties. These properties make the real compressed MR image reconstruction much more feasible than before. Finally, we conduct numerous experiments to compare different reconstruction methods. Our method is shown to impressively outperform the classical methods and two of the fastest methods so far in terms of both accuracy and complexity. The experimental results reported above validate the effectiveness and efficiency of the proposed composite splitting algorithms for compressed MR image reconstruction. Our main contributions are:. We propose an efficient algorithm (FCSA) to reconstruct the compressed MR images. It minimizes a linear combination of three terms corresponding to a least square data fitting, total variation (TV) and L1 norm regularization. The computational complexity of the FCSA is Oðp logðpÞÞ in each iteration (p is the pixel number in reconstructed image). It also has fast convergence properties. It has been shown to significantly outperform the classic CG methods (Lustig et al., 2007) and two state-of-the-art methods (TVCMRI (Ma et al., 2008) and RecPF (Yang et al., 2010) ) in terms of both accuracy and complexity. The step size in the FCSA is designed according to the inverse of the Lipschitz constant L f . Actually, using larger values is known to be a way of obtaining faster versions of the algorithm (Wright et al., 2009) . Future work will study the combination of this technique with the CSD or FCSA, which is expected to further accelerate the optimization for this kind of problems. In this paper, the proposed methods are developed to efficiently solve model (1), which has been addressed by SparseMRI, TVCMRI and RecPF. Therefore, with enough iterations, Sparse-MRI, TVCMRI and RecPF will obtain the same solution as that obtained by our methods. Since all of them solve the same formulation, they will lead to the same gain in information content. In our future work, we will develop new effective models for compressed MR image reconstruction, which can lead to more information gains.

O B I I O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O


The Thummer Mapping Project (ThuMP) This paper presents the Thummer Mapping Project (ThuMP), an industry partnership project between ThumMotion P/L and The University of Western Sydney (UWS). ThuMP sought to developing mapping strategies for new interfaces for musical expression (NIME), specifically the Thummer™, which provides thirteen simultaneous degrees of freedom. This research presents a new approach to the mapping problem resulting from a primary design research phase and a prototype testing and evaluation phase. In order to establish an underlying design approach for the Thummer™ mapping strategies, a number of interviews were carried out with high-level acoustic instrumental performers, the majority of whom play with the Sydney Symphony Orchestra, Sydney, Australia. Mapping strategies were developed from analysis of these interviews and then evaluated in trial usability testing. The ThuMP project is engaging in the development of a new electronic musical interface/instrument based on a re-evaluation of the performer's relationship with the performance interface. It sought to go back to examine musical interfaces that are broadly agreed to be successful and have persisted for a long time; acoustic instruments, namely, string and wind instruments and because of the nature of the Thummer™ interface, the piano accordion and concertina.. The ThuMP project posits that approaching the challenge of musical interface design from the musician's perspective might enable a detailed understanding of the subtle mechanisms of feedback and control that allow and support virtuosic technique.. There exists a need to combine the valuable research outcomes from the computer sciences community and the musician's perspective at a semantic level -one approach to this problem is based on temporal data, such as the measuring of sensor pressure and angle over time, rates of velocity, acceleration and other quantifiable, measurable characteristics, however, the data itself is already conditioned by interface design decisions. In order to address these inbuilt biases, the ThuMP project has asked several questions of highly skilled acoustic musicians and analysed the interview data using qualitative software tools.. Approaches to evaluating the resultant mappings have been explored in a pilot playability study. The ThuMP project commenced with performance practice analysis of highly experiences players of exceedingly successful acoustic instruments. The project sought to ground itself therefore in centuries of instrument design and performance practice, and to leverage this knowledge as the foundation for mapping strategies for the Thummer instrument.. Qualitative analysis outcomes proved initially difficult to defined, however the change to the NVivo tool and the support of the third author provided useful detailed taxonomic diagrams of control parameter inter-relationships which formed the basis of the second round of interviews and the resultant mapping strategies for the Thummer and the usability studies.. The mapping strategies used in the playability testing were kept to simple single voice sounds in order to focus on the mapping relationships and the link between mapping and morphology of the sound.. The design recommendations hint at generalized design considerations to which the NIME community may like to contribute further. This Stage of the project set out to answer the following questions:. 1) What are the useable number and range of control parameters offered by the prototype Thummer™? This question defines the playable range or gesture space of the prototype Thummer™?. 2) What influence do control-mapping strategies have on the players ability to complete a simple performance gesture task?. 3) What factors influence the "playability" of these prototype control mappings, from the performer's perspective?. Control data from the orientation sensor was calibrated to provide angles of orientation. In normal hand-held use, the pitch is limited by the articulation of the wrist, elbows and shoulders to a range of about 200 degrees. The pitch angle parameter exhibits a range of about 100 degrees during the experiments. In this implementation, the floating-point data from the sensor was reduced to 7-bit integers for compatibility with MIDI controller data. While the full 360 degrees of yaw can be achieved in normal performance by articulating the wrist, arms and rotating the body, the algorithm used to capture angular velocity was optimized to give a full 7-bit range for a comfortable range of gestures. The maximum range captured during the experiment was 68/128 against a target range of 100/128. The maximum roll angle that can be achieved while holding the instrument with two hands is about 180 degrees. However, it should be noted that the range for each angle is dependent on the absolute angle of the other axes. For example, with the wrists flexed to their maximum vertical deflection the range of roll available in the wrists is limited to about 40 degrees.. It is likely that intensive use with any given mapping will reveal aspects of the available gesture space not considered during the design phase. This development of "extended technique" is characteristic of the use of all musical instruments in contemporary performance.. Treating the mapping selection as an independent variable in this quasi-experimental design, changes in the closeness of fit between target control data and that recorded during the experiments was observed. Calculating relative mean-square error, time difference, accuracy and precision values for each participant and each mapping created simple measures for playability. Predictably, the joystick mapping showed much higher scores for each of these measures for all subjects except one. Precision represents the repeatability of the control gestures across attempts. It is propose that low accuracy coupled with high precision scores may indicate factors outside the mapping as indicated in figure 7 . This effect may be attributed to perceptual factors related for example to auditory perception. Further work is intended in extracting features from the data. It is proposed that further modeling may reveal a method to match the quantitative playability scores with subjective evaluation of the performance as discussed above.. Several observations were made regarding the variability in participant's performance. The most significant factor would appear to be the experiences the player brings to the instrument. This is likely to have more impact on an experiment with a limited training period than the results of continued practice. It was noted that the only string player was almost immediately able to produce sustained sounds smoothing out the fluctuations at the end of each "bow stroke" whereas others (including the researchers) were rarely able to achieve this effect. One participant showed very subtle control of the joystick almost immediately. The participant revealed that he was a computer game enthusiast who spent more time gaming than playing his instrument. The joysticks on the Thummer are of a similar form and operated with the thumbs in the manner of a typical game controller. It was found that the foot pedal used to initiate recording caused some participant's considerable difficulty and others none at all. This was attributed to the relative experience of pedal use for keyboard or guitar performance. The pedal was chosen as it was felt that this control would be less likely to influence performance than the use of the Thummer keyboard itself.. All subjects agreed that the second orientation sensor based configuration was more musically satisfying and showed the greatest potential. Four of the participants indicated that they considered this configuration easier to play although the results do not correlate with this observation.. In conclusion, the sample size in this pilot study does not support generalisable claims but it has provided a basis for useful approaches to evaluating playability that can be pursued in future work. The second stage addressed practical aspects of the Thummer™ instrument itself. During the project two prototypes were received. Changes in the design of the instrument reflected responses to early user testing, developments in the concept of the instrument, and refinements influenced by production considerations. Notable changes included a dramatic weight reduction in the later prototype to better support handheld performance; the development of single piece pressure sensitive conductive plastic key field technology (in response to both user feedback on the tactile properties of the earlier button/spring design and cost imperatives for mass manufacture); and the most significant change being the division of the instrument into two halves each housing a complete key field, joystick and associated buttons. This change dramatically expanded the potential application of the instrument in performance. The manufacturer intends to allow the user to define the way in which the instrument is used. Possible approaches include table or stand top use with the key fields placed side-by-side, handheld in the manner of an accordion (but without the bellows) or using a neck strap and mounting support so the two hands can be used in the manner of a guitar. While this open approach to the development of the instrument is to be applauded and may be a contributing factor to the potential commercial success of the instrument, it presented some challenges in the design of this research. Rather than assessing all possibilities we chose to adopt a fixed arrangement with the two halves joined back to back.. The basic unit was augmented by a commercial gyro enhanced orientation sensor [11] .. Thumtronics have put significant effort into assessing and designing solutions to problems of musical ergonomics based around the requirements of tonal music and keyboard performance [12] . This work is reflected in the isomorphic keyboard design. Further evaluation of these features was outside the scope of this research.

O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O B O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O


A Stochastic Model for Quantitative Security Analyses of Networked Systems Traditional security analyses are often geared towards cryptographic primitives or protocols. Although such analyses are necessary, they cannot address a defender's need for insight into which aspects of a networked system having a significant impact on its security, and how to tune its configurations or parameters so as to improve security. This question is known to be notoriously difficult to answer, and the state-of-the-art is that we know little about it. Towards ultimately addressing this question, this paper presents a stochastic model for quantifying security of networked systems. The resulting model captures two aspects of a networked system: (1) the strength of deployed security mechanisms such as intrusion detection systems, and (2) the underlying vulnerability graph, which reflects how attacks may proceed. The resulting model brings the following insights: (1) How should a defender "tune" system configurations (e.g., network topology) so as to improve security? (2) How should a defender "tune" system parameters (e.g., by upgrading which security mechanisms) so as to improve security? (3) Under what conditions is the steady-state number of compromised entities of interest below a given threshold with a high probability? Simulation studies are conducted to confirm the analytic results, and to show the tightness of the bounds of certain important metric that cannot be resolved analytically. Traditional security analyses are often geared towards cryptographic primitives (e.g., how one should pad a message before encrypting it using the RSA function) or protocols (e.g., how a password-based authentication protocol should operate so that it is immune to the off-line dictionary attack). Although such analyses are necessary, they do not provide much insight into answering an equally important, if not more important, question: which aspects of a networked system having a significant impact on its security, and how to tune system configurations or parameters so as to improve security. The state-of-the-art is that we know little about the answer to this question. Nevertheless, resolving this question would represent a significant step towards fulfilling full-fledged quantitative security analyses of networked systems -a notoriously difficult problem but of important practical value (cf. [5] , [20] ). There have been some attempts to understand security of networked systems by considering a system as a whole. Nevertheless, our approach can address some unique and important questions.. Existing epidemic-like models vs. our model. Epidemic models [17] , [14] , [6] , [4] have been adopted to investigate the spreading of computer viruses or worms pioneered by [13] .. As mentioned before, our model can be seen as a generalization of traditional epidemic models because traditional models often (the exceptions are discussed below) assume that every node or vertex has equal contact with everyone else (i.e., effectively regular graphs), and that the rate of infection is largely determined by the density of the infected individuals. Such homogeneous models cannot capture the heterogeneity of the vertex degrees in vulnerability graphs. Whereas, our model can.. The aforementioned exceptions that considered non-homogeneous graphs are due to the following. Epidemic spreading in the Barabasi-Albert power-law networks [7] is investigated in [19] , [24] , [25] , [23] . More general non-homogeneous graphs are investigated in [34], [33], [11] .. The differences between these models and ours are the following.. • All of the models in [34], [33], [11] can only capture the spreading behavior of attacks, and cannot capture the fact that computers can get compromised because of their own reasons (e.g., a user downloads and executes a malicious code). In contrast, our model captures this (through the parameter α > 0).. • None of the models in [34], [33], [11] can answer questions our model deals with, namely "How a defender should "tune" system configurations or parameters so as to improve security." This is true even though [33] indicated that indicates that CAN [28] might be more secure than Chord [31] against attacks, because the former is a lower degree regular graph.. Privilege graph and attack graph based approaches vs. our approach. In a privilege graph [9] , [21] , a vertex represents a set of privileges on some objects and an arc represents a. vulnerability. An arc exists from one vertex to another if there is a method allowing a user owning the former vertex's privileges to obtain those of the latter. In an attack graph, a vertex represents the state of a network (i.e., the values assigned to relevant system attributes such as specific vulnerabilities on various hosts and connectivity between hosts), and an edge represents a step in an attack (cf. [27] , [12] , [3] and the references therein). A designated vertex (or set of vertices) represents the initial state(s), and each transition represents a specific exploit that an attack can carry out.. The main difference between privilege and attack graphs and ours lies in the model purpose and scalability. From a purpose perspective, privilege graphs can be used to estimate the effort an attacker, in order to defeat the system security objectives, might expend to exploit the vulnerabilities. Attack graphs can be used to identify end-to-end attack paths by chaining together the vulnerabilities uncovered by the vulnerability scanners. In contrast, our model focuses on investigating the impact of system attributes, such as topologies and vertex properties, on the security of the system. Moreover, our approach suggests methods to "tune" the parameters to lead to more secure systems. From a scalability perspective, privilege and attack graph based approaches suffer from limited scalability, because of their inherent exponential state explosion [3] , [8] . In contrast, our approach is scalable because there is no issue of state explosion.. Key challenge graph based approach vs. our approach. In a key challenge graph [8] , a vertex represents a host, and an arc represents a key challenge -an abstraction to capture access control. A key challenge is, for instance, a password authentication prior to accessing to a resource. The starting point of an attack could be one or more vertices, which are assumed to be initially in the control of the attacker. The target of an attack could be one or more vertices, for which the attacker knows the location and the paths to reach them. A successful attack is a sequence of zero or more vertices not in the initial set but eventually containing all the target vertices. The cost of an attack is measured as the sum of the effort required to compromise individual vertices by attempting to counter the key challenges on the edges. Since there are multiple paths from the starting point to the target, a problem of particular interest is to find an attack path of minimum cost.. The main difference between the key challenge graph based approach and ours lies in the model purpose and capability. From a purpose perspective, the key challenge graph based approach emphasizes an algorithmic aspect of finding an optimal attack path, namely that the adversary can achieve its goal with minimal effort or cost. As noted before, our model focuses on the impact of system attributes on the security of the system, and suggests methods to "tune" the attributes to lead to more secure systems. From a capability perspective, the key challenge graph based approach can only capture the attack behaviors with respect to some specific starting points (i.e., the vertices initially compromised); otherwise, it will encounter the exponential explosion problem. In contrast, our modeling approach does not need to know the initially compromised vertices. Instead, it can accommodate the scenario of no initially compromised vertices.. Connectivity-oriented analysis vs. our security-oriented analysis. Connectivity-oriented analysis of networked systems can be traced back to the early days of random graph theory [10] . A central problem in this context is to investigate the network reliability subject to edge removals, namely the probability that graph remains connected after removing some edges. Recently, such analysis has been extended to explore the impact of topologies of complex communication networks, including the impact of removing vertices, which is more damaging because removal of a vertex implies the removal of all its edges as well. It turns out that there is a strong correlation between connectivity and network topology [1] . For example, consider networks that have the same number of vertices and edges, and differ only in their degree distributions. Then, power-law networks are more robust than random networks against random vertex failures, but are more vulnerable when the most connected vertices are targeted [2] .. Connectivity-oriented analysis does not necessarily bring much insight for security analysis. This is because a malicious attacker would be more likely to use the compromised vertices as "stepping stones" to attack some more vertices, rather than simply undermining the network connectivity. Therefore, the connectivity of a system may never be jeopardized, but the security has been undermined. Our model focuses on the security aspect by taking into consideration the perspective of topologies as well as vertex properties. Moreover, our model suggests methods to "tune" the parameters to lead to more secure systems.

O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


ON ASSESSING THE DISCLOSURE RISK OF CONTROLLED ADJUSTMENT METHODS FOR STATISTICAL TABULAR DATA Minimum distance controlled tabular adjustment is a recent perturbative approach for statistical disclosure control in tabular data. Given a table to be protected, it looks for the closest safe table, using some particular distance. Controlled adjustment is known to provide high data utility. However, the disclosure risk has only been partially analyzed using theoretical results from optimization. This work extends these previous results, providing both a more detailed theoretical analysis, and an extensive empirical assessment of the disclosure risk of the method. A set of 25 instances from the literature and four different attacker scenarios are considered, with several random replications for each scenario, both for L 1 and L 2 distances. This amounts to the solution of more than 2000 optimization problems. The analysis of the results shows that the approach has low disclosure risk when the attacker has no good information on the bounds of the optimization problem. On the other hand, when the attacker has good estimates of the bounds, and the only uncertainty is in the objective function (which is a very strong assumption), the disclosure risk of controlled adjustment is high and it should be avoided. Statistical data has to be protected before publication to guarantee that sensitive and confidential information of individuals is not released. There are several techniques available, both for microdata and for tabular data. Formally, a microdata set can be defined as a function. that maps individuals of set I to an array of t values for variables V 1 ,. . . , V t , D() being the domain of those variables. Categorical variables have a discrete domain, whereas numerical variables can be both discrete or continuous. Tabular data is obtained by crossing one or more categorical variables. Formally, a table is a function. l being the number of categorical variables that were crossed. The result of function T (cell values) belongs to N for a frequency table, and to R for a magnitude table.. For instance, the two-dimensional frequency table crossing the categorical variables "sex" and "smoke?" could be defined as T : {"male", "female"}×{"yes", "no"} → N. Recent surveys and monographs provide detailed information about the different table structures, and, in general, about the statistical disclosure field. The disclosure risk of controlled adjustment methods for statistical tabular data never before had been analyzed empirically, and few results only based on the theory of optimization could be found in the literature. This work presented such an empirical assessment of the disclosure risk of controlled adjustment methods. The main conclusion is that, as observed from the extensive computational results, if the attacker does not have good knowledge on the original data, he/she could hardly obtain good estimates of the sensitive cells, in general. However, if the attacker has good information about the protection levels and which are the sensitive cells, or he/she knows the lower and upper bounds of the optimization problem (which is a stronger condition), then the method has a high disclosure risk. We also observed, unexpectedly, that L 2 in general provides solutions of lower disclosure risk, which combined with the good data utility exhibited by this norm, 5 makes it a suitable choice for controlled tabular adjustment.

O O O B I O O O O O O O O O O B I I O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B I O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I I


A Bayesian non-linear method for feature selection in machine translation quality estimation We perform a systematic analysis of the effectiveness of features for the problem of predicting the quality of machine translation (MT) at the sentence level. Starting from a comprehensive feature set, we apply a technique based on Gaussian processes, a Bayesian non-linear learning method, to automatically identify features leading to accurate model performance. We consider application to several datasets across different language pairs and text domains, with translations produced by various MT systems and scored for quality according to different evaluation criteria. We show that selecting features with this technique leads to significantly better performance in most datasets, as compared to using the complete feature sets or a state-of-the-art feature selection approach. In addition, we identify a small set of features which seem to perform well across most datasets. We have presented a number of experiments showing the potential of a promising feature-ranking technique based on Gaussian Processes for translation-quality estimation. Using an oracle approach to select the number of top-ranked features to train quality estimation models, this technique has been shown to outperform all feature sets available with only a small fraction of their features on a number of datasets with common feature sets. More importantly, we were able to obtain the same or comparable performance with this technique when selecting the number of features based on a development set, or even based on an empirically pre-defined threshold. The proposed feature selection technique has also been shown to improve the performance of all participating systems in the WMT12 shared task on quality estimation. Finally, our analysis led to the identification of a set of features which perform well on average across many datasets with different language pairs, MT systems, text domains and quality labels. We note that preliminary results for some of the datasets used here have been reported in Shah et al. (2013b) . The following results include additional datasets and further analysis. Examples of successful cases of QE include improving post-editing efficiency by filtering out low-quality segments which would require more effort or time to correct than translating from scratch (Specia et al. 2009; Specia 2011) , selecting high-quality segments to be published as they are, without post-editing (Soricut and Echihabi 2010) , selecting a translation from either an MT system or a TM for post-editing (He et al. 2010) , selecting the best translation from multiple MT systems (Specia et al. 2010) , and highlighting sub-segments that need revision (Bach et al. 2011) . For an overview of various algorithms and features we refer the reader to the WMT12-13 shared tasks on QE (Callison-Burch et al. 2012; Bojar et al. 2013) .. Most previous work on QE uses machine-learning algorithms such as support vector machines (SVM), which are fairly robust to redundant/noisy features, and so feature selection is often neglected. Work using explicit feature selection methods rely mostly on forward/backward selection approaches, but the order in which features are added/removed is not informed by any prior knowledge. In what follows we summarise recent work using explicit feature-selection methods. González-Rubio et al. (2012) performed feature selection on a set of 475 sentenceand subsentence-level features. Principal component analysis and a greedy selection algorithm to iteratively create subsets of increasing size with the best-scoring individual features were exploited. Both selection methods yielded better performance than all features, with greedy selection achieving the best mean absolute error (MAE) scores with 254 features. Langlois et al. (2012) reported positive results with a greedy backward selection algorithm that removes 21 poor features from an initial set of 66 features based on error minimisation on a development set.. In an oracle-like experiment, Felice et al. (2012) use a sequential forward selection method, which starts from an empty set and adds one feature at a time as long as it decreases the model's error, evaluating the performance of the feature subsets on the test set directly. 37 features out of 147 are selected, and these significantly improve the overall performance.. Avramidis (2012) tested a few feature selection methods using both greedy stepwise and best-first search to select among their 266 features with tenfold cross-validation on the training set. These resulted in sets of 30-80 features, all outperforming the complete feature set. Correlation-based selection with best-first search strategy was reported to perform the best. Conversely, Moreau and Vogel (2012) reported no improvements in performance in experiments with several selection methods.. Finally, the winning system in the WMT12 QE shared task ) used a computationally intensive method on a development set. For each of the official evaluation metrics (e.g. MAE), from an initial set of 24 features, all 2 24 possible combinations were tested, followed by an exhaustive search to find the best combinations. The 15 features belonging to most of the top combinations were selected. Other rounds were added to deal with POS features, but the final feature sets included 14-15 features depending on the evaluation metric. This technique outperformed the complete feature set by a large margin.

O O O O O O O O B I B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Image contour based on context aware in complex wavelet domain Active contours are used in the image processing application including edge detection, shape modeling, medical image-analysis, detectable object boundaries, etc. Shape is one of the important features for describing an object of interest. Even though it is easy to understand the concept of 2D shape, it is very difficult to represent, define and describe it. In this paper, we propose a new method to implement an active contour model using Daubechies complex wavelet transform combined with B-Spline based on context aware. To show the superiority of the proposed method, we have compared the results with other recent methods such as the method based on simple discrete wavelet transform, Daubechies complex wavelet transform and Daubechies complex wavelet transform combined with B-Spline. Contours are used extensively in image processing applications. Active contours can be classified according to several different criteria. One of the classifications is based on the flexibility of the active contour and is proposed in a slightly modified form by Jain [1] . The active contour models can be accordingly partitioned in two classes: free form of active contour models and limited form of active contour models.. The free form of active contour models constrained by local continuity and smoothness constraints [2] [3] [4] [5] [6] [7] . Its limit uses a priori information about the geometrical shape directly. This information is available in the form of a sketch or a parameter vector that encodes the shape of interest. The geometric shape of the contour is adjusted by varying the parameters [8] [9] [10] [11] [12] [13] . They cannot take any arbitrary shapes.. The snake has found wide acceptance and has proven extremely useful in the applications for medical analysis, feature tracking in the video sequences, three-dimensional object recognition [14] , and stereo matching [15] . To take active contour, there are many methods to take it.. In the past, many algorithms have been built to find object contour. The dual-tree Complex Wavelet Transform (DTCWT) was proposed by Kingsbury [16] . In DTCWT, he used two trees of real filters for the real and imaginary parts of the wavelet coefficients. Recently, Bharath [17] has presented a framework for the construction of steerable complex wavelet.. This transform also avoids the shortcomings of discrete wavelet transform, but it uses a non-separable and highly redundant implementation. The redundancy of this transform is even higher than that of DTCWT.. In the entire complex transforms above, use of real filters make them not a true complex wavelet transform and due to the presence of redundancy, they are computationally costly. Lawton [18] and Lina [19] used an approximate shift-invariant Daubechies complex wavelet transform for avoiding redundancy and providing phase information. Shensa [20] and Ansari [21] use Lagrange filters, Akansu [22] uses binomial filters. Shen [22] used the Daubechies filter roots. Goodman [23] considered them as the roots of a Laurent polynomial. Temme [24] described the asymptotic of the roots in terms of a representation of the incomplete beta function. Almost of that method related Daubechies filters.. The wavelet transform for contour has serious disadvantages, such as shift-sensitivity [25] and poor directionality [26] . Several researchers have provided solutions for minimizing these disadvantages. Some of them have suggested the other method such as: local binary fitting [27, 28] , local region descriptors [29] , local region [30] , local region based [31] , local intensity clustering method. There exist some drawbacks with local regions. In [32] , the problem is how to define the degree of overlap.. The local region based method has two drawbacks: (i) the Dirac functional is restricted to a neighborhood around the zero level set. (ii) Region descriptors only based on regions mean information without considering region variance [33] .. Use of complex-valued wavelet can minimize these disadvantages. The DCWT uses complex filters and can be made symmetric, thus leading to symmetric DCWT, and it is more useful for image contour.. In this paper, we propose a new method to implement an active contour model using Daubechies complex wavelet transform combined with B-Spline based on context-aware (DCWTBCA). To show the superiority of the proposed method, we have compared the results with the other recent methods such as the method based on simple discrete wavelet transform (DWT), Daubechies complex wavelet transform (DCWT) and Daubechies complex wavelet transform combined with B-Spline (DCWTB). The rest of the paper is organized as follows: in section 2, we described the basic concepts of Daubechies complex wavelet transform. Details of the proposed algorithm have been given in section 3. In section 4, the results of the proposed method for contour have been shown and compared with other methods. Finally in section 5, we presented our conclusions. In this paper, the image contour model with Daubechies complex wavelet transform combined with B-Spline based on context-aware is proposed. The proposed technique allows estimating the contour location of a target object along an image. The contribution in the use of Daubechies complex wavelet transform for image was discussed. Mathematical basis of the Daubechies complex wavelet transform and B-Spline proved that image features based on the wavelet transform coefficients can be used very efficiently for image contour classification.. From the results shown in the above section, we see that the proposed method performs better in case of both strong and weak objects. The proposed method can be applied on any modality of images. However, in the case of weak objects, the proposed method finds approximate boundaries. Therefore, if the quality of the image is very bad due to heavy noise or heavy blur, etc., then the estimation ability is reduced because of the effect to edge the object. To avoid this problem, we can reduce noise and blur before applying the proposed method. In the future work, the method is going to be compared with some other methods to evaluate its results in different cases and the complexity of them. In this section, we present the theory related to the work such as: Complex Daubechies Wavelet and advantages of B-Spline for Snakes.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O B I I I O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Flexible TDMA/WDMA passive optical network: Energy efficient next-generation optical access solution Abstract Access networks must further advance to address the intensification of the requirements of growing speeds and the usage of Internet applications, and time and wavelength division multiple access (TDMA/WDMA) based passive optical networks (TWDM-PONs) have been widely considered as one of the evolutionary steps of next-generation optical access (NGOA) networks. TWDM-PON combines the flexibility of TDMA with an increased capacity offered by the use of a WDM layer. Moreover, it offers interesting and challenging avenues to minimize energy consumption: especially, with current access networks consuming about 80% of the energy consumed in the Internet. Along with other efforts, reducing energy consumption of central offices is conspicuous as it directly minimizes the operational expenditures of network providers. In this paper, we explore the new paradigms to conserve energy at the central offices in TWDM-PONs. By extensive simulations, we evaluate the possible energy savings in the various flavors of TWDM-PON. Based on the findings, we propose a new architectural flavor of TWDM-PON and benchmark the architecture for cost, power consumption and reach. We also propose a novel energy saving scheme for the proposed architecture and evaluate the impact of the proposed algorithm on energy savings by extensive simulations. In this section, we have evaluated through simulation the gains of using flexible architectures in next-generation optical access (NGOA) networks, and more specifically in TWDM-PONs. In particular, we have evaluated two types of flexibility gains, due to multicasting and switching. We conclude that a partially flexible network can already provide enough multicasting gain compared to a fully flexible network. Moreover, we have shown through simulations that in long reach PON in which the traffic can be highly bursty, a partially flexible network further benefits from the switching flexibility, which can be delivered by a reconfigurable switch like WSSs. For short reach PON solutions, however, because of presumably less bursty traffic, switching wavelengths does not lead to any significant advantages in PON performance. Thus, if an operator is aiming for higher reaches, then using a reconfigurable remote node solution looks as a promising one. For short reach PON solutions, however, reconfigurability at the remote 23 node may not be the way forward. Our study will help network operators and service providers to choose the suitable architecture for NGOA networks. In this section, we have proposed a novel architecture for a TWDM-PON using WSS as the key component. Through a detailed reach and cost evaluation, we have shown that it provides the best combination of reach, flexibility, security and cost among all the existing TWDM-PON technologies.

O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O