OpenRaiser/Intern-Atlas huggingface dataset:
Online interpretation of numeric sign language using 2-d skeletal model
Venue: arXiv cs.CV 2014
Year: 2014

Gesturing is one of the natural modes of human communication. Signs produced by gestures can have a basic meaning coupled with additional information that is layered over the basic meaning of the sign. Sign language is an important example of communicative gestures that are highly structured and well accepted across the world as a communication medium for deaf and dumb. In this paper, an online recognition scheme is proposed to interpret the standard numeric sign language comprising of 10 basic hand symbols. A web camera is used to capture the real time hand movements as input to the system. The basic meaning of the hand gesture is extracted from the input data frame by analysing the shape of the hand, considering its orientation, movement and location to be fixed. The input hand shape is processed to identify the palm structure, fingertips and their relative positions and the presence of the extended thumb. A 2-dimensional skeletal model is generated from the acquired shape information to represent and subsequently interpret the basic meaning of the hand gesture.
A two-pass fuzzy-geno approach to pattern classification
Venue: arXiv cs.CV 2014
Year: 2014

The work presents an extension of the fuzzy approach to 2-D shape recognition [1] through refinement of initial or coarse classification decisions under a two pass approach. In this approach, an unknown pattern is classified by refining possible classification decisions obtained through coarse classification of the same. To build a fuzzy model of a pattern class horizontal and vertical fuzzy partitions on the sample images of the class are optimized using genetic algorithm. To make coarse classification decisions about an unknown pattern, the fuzzy representation of the pattern is compared with models of all pattern classes through a specially designed similarity measure. Coarse classification decisions are refined in the second pass to obtain the final classification decision of the unknown pattern. To do so, optimized horizontal and vertical fuzzy partitions are again created on certain regions of the image frame, specific to each group of similar type of pattern classes. It is observed through experiments that the technique improves the overall recognition rate from 86.2%, in the first pass, to 90.4% after the second pass, with 500 training samples of handwritten digits.
Unified Analysis of Collapsible and Ordered Pushdown Automata via Term Rewriting
Venue: arXiv cs.FL 2014
Year: 2014

We model collapsible and ordered pushdown systems with term rewriting, by encoding higher-order stacks and multiple stacks into trees. We show a uniform inverse preservation of recognizability result for the resulting class of term rewriting systems, which is obtained by extending the classic saturation-based approach. This result subsumes and unifies similar analyses on collapsible and ordered pushdown systems. Despite the rich literature on inverse preservation of recognizability for term rewrite systems, our result does not seem to follow from any previous study.
Unified mobile public health care system (UMPHCS) for underdeveloped countries
Venue: arXiv cs.CY 2014
Year: 2014

In this paper, we have proposed a new smartphone based system for health care, monitoring and diagnosis, which is specially designed to efficiently increase the public health care system in the distant, rural, unreached areas of the underdeveloped and developing countries. In this all-in-one system, we have digitized the health monitoring and diagnostic devices in a way so that each device works as a minimum `plug and play' sensor module of the total system, reducing the cost radically. Besides, the easy-to-use smartphone application for operating the whole system reduces the necessity of skilled and trained manpower, making it a perfect toolbox for the government health workers in the unreached rural areas.
Wireless Mobility Management with Prediction, Delay Reduction and Resource Management in 802.11 Networks
Venue: arXiv cs.NI 2014
Year: 2014

In 802.11 wireless infrastructure networks, as the mobile node moves from one access point to another, the active connections will not be badly dropped if the handoff is smooth and if there are sufficient resources reserved in the target access point. In a 5x5 grid of access points, within a 6x6 grid of regions, by location tracking and data mining, we predict the mobility pattern of mobile node with good accuracy. The pre-scanning of mobile nodes, along with pre-authenticating neighbouring access points and pre-reassociation is used to reduce the scan delay, authentication delay and re-association delay respectively. The model implements first stage reservation by using prediction results and does second stage reservation based on the packet content type, so that sufficient resources can be reserved when the mobile node does the handoff to the next access point. The overall mobility management scheme thus reduces the handoff delay. The performance simulations are done to verify the proposed model.
LP/SDP Hierarchy Lower Bounds for Decoding Random LDPC Codes
Venue: arXiv cs.CC 2014
Year: 2014

Random (dv,dc)-regular LDPC codes are well-known to achieve the Shannon capacity of the binary symmetric channel (for sufficiently large dv and dc) under exponential time decoding. However, polynomial time algorithms are only known to correct a much smaller fraction of errors. One of the most powerful polynomial-time algorithms with a formal analysis is the LP decoding algorithm of Feldman et al. which is known to correct an Omega(1/dc) fraction of errors. In this work, we show that fairly powerful extensions of LP decoding, based on the Sherali-Adams and Lasserre hierarchies, fail to correct much more errors than the basic LP-decoder. In particular, we show that: 1) For any values of dv and dc, a linear number of rounds of the Sherali-Adams LP hierarchy cannot correct more than an O(1/dc) fraction of errors on a random (dv,dc)-regular LDPC code. 2) For any value of dv and infinitely many values of dc, a linear number of rounds of the Lasserre SDP hierarchy cannot correct more than an O(1/dc) fraction of errors on a random (dv,dc)-regular LDPC code. Our proofs use a new stretching and collapsing technique that allows us to leverage recent progress in the study of the limitations of LP/SDP hierarchies for Maximum Constraint Satisfaction Problems (Max-CSPs). The problem then reduces to the construction of special balanced pairwise independent distributions for Sherali-Adams and special cosets of balanced pairwise independent subgroups for Lasserre. Some of our techniques are more generally applicable to a large class of Boolean CSPs called Min-Ones. In particular, for k-Hypergraph Vertex Cover, we obtain an improved integrality gap of $k-1-ε$ that holds after a \emph{linear} number of rounds of the Lasserre hierarchy, for any k = q+1 with q an arbitrary prime power. The best previous gap for a linear number of rounds was equal to $2-ε$ and due to Schoenebeck.
Optimal Simultaneous Detection and Signal and Noise Power Estimation
Venue: arXiv cs.IT 2014
Year: 2014

Simultaneous detection and estimation is important in many engineering applications. In particular, there are many applications where it is important to perform signal detection and Signal-to-Noise-Ratio (SNR) estimation jointly. Application of existing frameworks in the literature that handle simultaneous detection and estimation is not straightforward for this class of application. This paper therefore aims at bridging the gap between an existing framework, specifically the work by Middleton et al., and the mentioned application class by presenting a jointly optimal detector and signal and noise power estimators. The detector and estimators are given for the Gaussian observation model with appropriate conjugate priors on the signal and noise power. Simulation results affirm the superior performance of the optimal solution compared to the separate detection and estimation approaches.
Approximate Bayesian algorithms for multiple target tracking with binary sensors
Venue: arXiv cs.SY 2014
Year: 2014

In this paper, we propose an approximate Bayesian computation approach to perform a multiple target tracking within a binary sensor network. The nature of the binary sensors (getting closer - moving away information) do not allow the use of the classical tools (e.g. Kalman Filter, Particle Filer), because the exact likelihood is intractable. To overcome this, we use the particular feature of the likelihood-free algorithms to produce an efficient multiple target tracking methodology.
Achieving High Resolution for Super-resolution via Reweighted Atomic Norm Minimization
Venue: arXiv cs.IT 2014
Year: 2014

The super-resolution theory developed recently by Candès and Fernandes-Granda aims to recover fine details of a sparse frequency spectrum from coarse scale information only. The theory was then extended to the cases with compressive samples and/or multiple measurement vectors. However, the existing atomic norm (or total variation norm) techniques succeed only if the frequencies are sufficiently separated, prohibiting commonly known high resolution. In this paper, a reweighted atomic-norm minimization (RAM) approach is proposed which iteratively carries out atomic norm minimization (ANM) with a sound reweighting strategy that enhances sparsity and resolution. It is demonstrated analytically and via numerical simulations that the proposed method achieves high resolution with application to DOA estimation.
Ultra-Reliable Communication in 5G Wireless Systems
Venue: arXiv cs.IT 2014
Year: 2014

Wireless 5G systems will not only be "4G, but faster". One of the novel features discussed in relation to 5G is Ultra-Reliable Communication (URC), an operation mode not present in today's wireless systems. URC refers to provision of certain level of communication service almost 100 % of the time. Example URC applications include reliable cloud connectivity, critical connections for industrial automation and reliable wireless coordination among vehicles. This paper puts forward a systematic view on URC in 5G wireless systems. It starts by analyzing the fundamental mechanisms that constitute a wireless connection and concludes that one of the key steps towards enabling URC is revision of the methods for encoding control information (metadata) and data. It introduces the key concept of Reliable Service Composition, where a service is designed to adapt its requirements to the level of reliability that can be attained. The problem of URC is analyzed across two different dimensions. The first dimension is the type of URC problem that is defined based on the time frame used to measure the reliability of the packet transmission. Two types of URC problems are identified: long-term URC (URC-L) and short-term URC (URC-S). The second dimension is represented by the type of reliability impairment that can affect the communication reliability in a given scenario. The main objective of this paper is to create the context for defining and solving the new engineering problems posed by URC in 5G.
Principles for Verification Tools: Separation Logic
Venue: arXiv cs.LO 2014
Year: 2014

A principled approach to the design of program verification and con- struction tools is applied to separation logic. The control flow is modelled by power series with convolution as separating conjunction. A generic construction lifts resource monoids to assertion and predicate transformer quantales. The data flow is captured by concrete store/heap models. These are linked to the separation algebra by soundness proofs. Verification conditions and transformation laws are derived by equational reasoning within the predicate transformer quantale. This separation of concerns makes an implementation in the Isabelle/HOL proof as- sistant simple and highly automatic. The resulting tool is correct by construction; it is explained on the classical linked list reversal example.
Map Matching based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories
Venue: arXiv cs.NI 2014
Year: 2014

In order to improve offline map matching accuracy of low-sampling-rate GPS, a map matching algorithm based on conditional random fields (CRF) and route preference mining is proposed. In this algorithm, road offset distance and the temporal-spatial relationship between the sampling points are used as features of GPS trajectory in CRF model, which can utilize the advantages of integrating the context information into features flexibly. When the sampling rate is too low, it is difficult to guarantee the effectiveness using temporal-spatial context modeled in CRF, and route preference of a driver is used as replenishment to be superposed on the temporal-spatial transition features. The experimental results show that this method can improve the accuracy of the matching, especially in the case of low sampling rate.
Analysis of incremental augmented affine projection algorithm for distributed estimation of complex signals
Venue: arXiv cs.DC 2014
Year: 2014

This paper considers the problem of distributed estimation in an incremental network when the measurements taken by the node follow a widely linear model. The proposed algorithm which we refer to it as incremental augmented affine projection algorithm (incAAPA) utilizes the full second order statistical information in the complex domain. Moreover, it exploits spatio-temporal diversity to improve the estimation performance. We derive steady-state performance metric of the incAAPA in terms of the mean-square deviation (MSD). We further derive sufficient conditions to ensure mean-square convergence. Our analysis illustrate that the proposed algorithm is able to process both second order circular (proper) and noncircular (improper) signals. The validity of the theoretical results and the good performance of the proposed algorithm are demonstrated by several computer simulations.
Proof-Carrying Hardware via IC3
Venue: arXiv cs.LO 2014
Year: 2014

Proof-carrying hardware (PCH) is an approach to achieving safety of dynamically reconfigurable hardware, transferring the idea of proof-carrying code to the hardware domain. Current PCH approaches are, however, either limited to combinational and bounded unfoldings of sequential circuits, or only provide semi-automatic proof generation. We propose a new approach to PCH which employs IC3 as proof generator, making automatic PCH applicable to sequential circuits in their full generality. We demonstrate feasibility of our approach by showing that proof validation is several orders of magnitude faster than original proof generation while (most often) generating smaller proofs than current PCHs.
Providing Higher Throughput for a Single User with M-ary Orthogonal Walsh Codes
Venue: arXiv cs.IT 2014
Year: 2014

Reliable communication is a challenge in a very noisy RF channel further corrupted by severe, multiple narrowband interference. Code Division Multiple Access (CDMA) is a widely used method to both mitigate such interference and support multiple in-band users. The ability to time-control multiple receptions at a receiver permits use of deterministically orthogonal codes. Achieving full capacity of the available frequency band depends, in part, on ensuring that all signals in the band are from the agreed set of orthogonal signals. Each unwanted signal consumes a fraction of the available capacity. In the extreme, as more interferers are added the available bandwidth can support only one user. We adapt CDMA techniques to a single user by assigning a constellation of M symbols built around Walsh codes. This M-ary constellation encodes K bits of data into one of the length N codewords, and gives preference to a non-power-of-two Hadamard length, e.g., 12, 20, and higher multiples. The tradeoff between the modified and conventional Walsh code designs is that one user sends K bits per Walsh symbol interval as opposed to many users sending one bit each per symbol. The throughput sacrifice is a consequence of the severe external interference environment.
Efficient Distance Computation Algorithm between Nearly Intersected Objects Using Dynamic Pivot Point in Virtual Environment Application
Venue: arXiv cs.GR 2014
Year: 2014

Finding nearly accurate distance between two or more nearly intersecting three-dimensional (3D) objects is vital especially for collision determination such as in virtual surgeon simulation and real-time car crash simulation. Instead of performing broad phase collision detection, we need to check for accuracy of detection by running narrow phase collision detection. One of the important elements for narrow phase collision detection is to determine the precise distance between two or more nearly intersecting objects or polygons in order to prepare the area for potential colliding. Distance computation plays important roles in determine the exact point of contact between two or more nearly intersecting polygons where the preparation for collision detection is determined at the earlier stage. In this paper, we describes our current works of determining the distance between objects using dynamic pivot point that will be used as reference point to reduce the complexity searching for potential point of contacts. By using Axis-Aligned Bounding Box for each polygon, we calculate a dynamic pivot point that will become our reference point to determine the potential candidates for distance computation. The test our finding distance will be simplified by using our method instead of performing unneeded operations. Our method provides faster solution than the previous method where it helps to determine the point of contact efficiently and faster than the other method.
KCRC-LCD: Discriminative Kernel Collaborative Representation with Locality Constrained Dictionary for Visual Categorization
Venue: arXiv cs.CV 2014
Year: 2014

We consider the image classification problem via kernel collaborative representation classification with locality constrained dictionary (KCRC-LCD). Specifically, we propose a kernel collaborative representation classification (KCRC) approach in which kernel method is used to improve the discrimination ability of collaborative representation classification (CRC). We then measure the similarities between the query and atoms in the global dictionary in order to construct a locality constrained dictionary (LCD) for KCRC. In addition, we discuss several similarity measure approaches in LCD and further present a simple yet effective unified similarity measure whose superiority is validated in experiments. There are several appealing aspects associated with LCD. First, LCD can be nicely incorporated under the framework of KCRC. The LCD similarity measure can be kernelized under KCRC, which theoretically links CRC and LCD under the kernel method. Second, KCRC-LCD becomes more scalable to both the training set size and the feature dimension. Example shows that KCRC is able to perfectly classify data with certain distribution, while conventional CRC fails completely. Comprehensive experiments on many public datasets also show that KCRC-LCD is a robust discriminative classifier with both excellent performance and good scalability, being comparable or outperforming many other state-of-the-art approaches.
Low-Rank Spatial Channel Estimation for Millimeter Wave Cellular Systems
Venue: arXiv cs.IT 2014
Year: 2014

The tremendous bandwidth available in the millimeter wave (mmW) frequencies between 30 and 300 GHz have made these bands an attractive candidate for next-generation cellular systems. However, reliable communication at these frequencies depends extensively on beamforming with very high-dimensional antenna arrays. Estimating the channel sufficiently accurately to perform beamforming can thus be challenging both due to low coherence time and large number of antennas. Also, the measurements used for channel estimation may need to be made with analog beamforming where the receiver can "look" in only direction at a time. This work presents a novel method for estimation of the receive-side spatial covariance matrix of a channel from a sequence of power measurements made at different angular directions. The method reduces the spatial covariance estimation to a matrix completion optimization problem. To reduce the number of measurements, the optimization can incorporate the low-rank constraints in the channels that are typical in the mmW setting. The optimization is convex and fast, iterative methods are presented to solving the problem. Simulations are presented for both single and multi-path channels using channel models derived from real measurements in New York City at 28 GHz.
Aspects and challenges of mashup creator design
Venue: arXiv cs.CY 2014
Year: 2014

With the advent of Web 2.0, an increasing number of web sites has started offering their data over the web in standard formats and exposed their functionality as APIs. A new type of applications has taken advantage of the new data and services available by mixing them, in order to generate new applications fast and efficiently, getting its name from its own architectural style: mashups. A set of applications that aims to help a user create, deploy and manage his mashups has also emerged, using various approaches. In this paper we discuss the key factors that should be taken into consideration when designing a mashup creator, along with the most important challenges that offer a field for research.
Semantic Gateway as a Service architecture for IoT Interoperability
Venue: arXiv cs.NI 2014
Year: 2014

The Internet of Things (IoT) is set to occupy a substantial component of future Internet. The IoT connects sensors and devices that record physical observations to applications and services of the Internet. As a successor to technologies such as RFID and Wireless Sensor Networks (WSN), the IoT has stumbled into vertical silos of proprietary systems, providing little or no interoperability with similar systems. As the IoT represents future state of the Internet, an intelligent and scalable architecture is required to provide connectivity between these silos, enabling discovery of physical sensors and interpretation of messages between things. This paper proposes a gateway and Semantic Web enabled IoT architecture to provide interoperability between systems using established communication and data standards. The Semantic Gateway as Service (SGS) allows translation between messaging protocols such as XMPP, CoAP and MQTT via a multi-protocol proxy architecture. Utilization of broadly accepted specifications such as W3C's Semantic Sensor Network (SSN) ontology for semantic annotations of sensor data provide semantic interoperability between messages and support semantic reasoning to obtain higher-level actionable knowledge from low-level sensor data.
Learning Vague Concepts for the Semantic Web
Venue: arXiv cs.AI 2014
Year: 2014

Ontologies can be a powerful tool for structuring knowledge, and they are currently the subject of extensive research. Updating the contents of an ontology or improving its interoperability with other ontologies is an important but difficult process. In this paper, we focus on the presence of vague concepts, which are pervasive in natural language, within the framework of formal ontologies. We will adopt a framework in which vagueness is captured via numerical restrictions that can be automatically adjusted. Since updating vague concepts, either through ontology alignment or ontology evolution, can lead to inconsistent sets of axioms, we define and implement a method to detecting and repairing such inconsistencies in a local fashion.