Query: Control theoretic approach to tracking radar: First step towards cognition

Relevant passages:
Dynamic Programming and Optimal Control <=SEP=> This is an updated version of Chapter 4 of the author’s Dynamic Programming and Optimal Control, Vol. II, 4th Edition, Athena Scientific, 2012. It includes new material, and it is substantially revised and expanded (it has more than doubled in size). The new material aims to provide a unified treatment of several models, all of which lack the contractive structure that is characteristic of the discounted problems of Chapters 1 and 2: positive and negative cost models, deterministic optimal control (including adaptive DP), stochastic shortest path models, and risk-sensitive models. Here is a summary of the new material:

Cognitive radar: a way of the future <=SEP=> This article discusses a new idea called cognitive radar. Three ingredients are basic to the constitution of cognitive radar: 1) intelligent signal processing, which builds on learning through interactions of the radar with the surrounding environment; 2) feedback from the receiver to the transmitter, which is a facilitator of intelligence; and 3) preservation of the information content of radar returns, which is realized by the Bayesian approach to target detection through tracking. All three of these ingredients feature in the echo-location system of a bat, which may be viewed as a physical realization (albeit in neurobiological terms) of cognitive radar. Radar is a remote-sensing system that is widely used for surveillance, tracking, and imaging applications, for both civilian and military needs. In this article, we focus on future possibilities of radar with particular emphasis on the issue of cognition. As an illustrative case study along the way, we consider the problem of radar surveillance applied to an ocean environment.

Dynamic programming. <=SEP=> Little has been done in the study of these intriguing questions, and I do not wish to give the impression that any extensive set of ideas exists that could be called a "theory." What is quite surprising, as far as the histories of science and philosophy are concerned, is that the major impetus for the fantastic growth of interest in brain processes, both psychological and physiological, has come from a device, a machine, the digital computer. In dealing with a human being and a human society, we enjoy the luxury of being irrational, illogical, inconsistent, and incomplete, and yet of coping. In operating a computer, we must meet the rigorous requirements for detailed instructions and absolute precision. If we understood the ability of the human mind to make effective decisions when confronted by complexity, uncertainty, and irrationality then we could use computers a million times more effectively than we do. Recognition of this fact has been a motivation for the spurt of research in the field of neurophysiology. The more we study the information processing aspects of the mind, the more perplexed and impressed we become. It will be a very long time before we understand these processes sufficiently to reproduce them. In any case, the mathematician sees hundreds and thousands of formidable new problems in dozens of blossoming areas, puzzles galore, and challenges to his heart's content. He may never resolve some of these, but he will never be bored. What more can he ask?

Do Personal Ethics Influence Corporate Ethics ? <=SEP=> In this paper, we introduce a new measure of personal ethics in the form of marital cheating to examine the relationship between personal ethics and corporate misconduct. Firms with CEOs and CFOs who use a marital infidelity website are more than twice as likely to engage in two forms of corporate misconduct. The relationship is not explained by a wide range of regional, firm, and executive characteristics or by the infidelity website usage of other executives. Additionally, white-collar SEC defendants also have elevated levels of infidelity website usage. Our findings suggest that personal and professional ethics are closely related. (JEL G30, M14)

IGBT and Diode Behavior During Short-Circuit Type 3 <=SEP=> A short circuit during inverter operation can result in the so-called short-circuit types 2 and 3. The short-circuit type 3 results in an interaction with several current commutations between the Insulate-Gate Bipolar Transistor (IGBT) and the antiparallel diode. The switched ON IGBT, conducting no current before the short circuit occurs, has no plasma inside and can block any voltage. It behaves like a voltage-controlled current source, which can stress the diode by commutating the complete short-circuit current into the diode. An avalanche arises inside the diode, which can lead to a destruction of the diode. The current through the diode determines dv/dt and makes the diode a dominating semiconductor during the short circuit. The current source behavior of the IGBT can be verified with semiconductor simulations and measurements on a high-power 3.3-kV IGBT. In the simulations, the IGBT is replaced by a current source, which gives similar results. For the measurements, the gate-emitter voltage is varied to change the IGBT current without influencing the collector-emitter voltage.

Educational data mining and its role in determining factors affecting students academic performance: A systematic review <=SEP=> Education helps people develop as individuals. It not only helps build social skills but also enhances the problem solving and decision making skills of individuals. With the growing number of schools, colleges and universities around the globe, education now has taken new dimension. The main focus of higher Educational Institutes is to improve the overall quality and effectiveness of education. Predicting the performance of student and Predicting the Future in this world of Big Data is the need of Today in Education. The crude data from educational organizations act as a gold mine which facilitate the analysts and designers to make a system that will improve the overall teaching and learning process. This new field coined as Educational Data mining helps in Predicting the Future and Changing the Future. In this paper we have studied what is Educational Data Mining and what are the Application areas of EDM, various techniques of EDM and the factors affecting the students' Academic Performance and the Teaching-Learning process.

Maternal Control of Child Feeding During the Weaning Period: Differences Between Mothers Following a Baby-led or Standard Weaning Approach <=SEP=> A controlling maternal feeding style has been shown to have a negative impact on child eating style and weight in children over the age of 12 months. The current study explores maternal feeding style during the period of 6–12 months when infants are introduced to complementary foods. Specifically it examines differences between mothers who choose to follow a traditional weaning approach using spoon feeding and pureés to mothers following a baby-led approach where infants are allowed to self feed foods in their solid form. Seven hundred and two mothers with an infant aged 6–12 months provided information regarding weaning approach alongside completing the Child Feeding Questionnaire. Information regarding infant weight and perceived size was also collected. Mothers following a baby-led feeding style reported significantly lower levels of restriction, pressure to eat, monitoring and concern over child weight compared to mothers following a standard weaning response. No association was seen between weaning style and infant weight or perceived size. A baby-led weaning style was associated with a maternal feeding style which is low in control. This could potentially have a positive impact upon later child weight and eating style. However due to the cross sectional nature of the study it cannot be ascertained whether baby-led weaning encourages a feeding style which is low in control to develop or whether mothers who are low in control choose to follow a baby-led weaning style.

Efficient Subspace Segmentation via Quadratic Programming <=SEP=> We explore in this paper efficient algorithmic solutions to robust subspace segmentation. We propose the SSQP, namely Subspace Segmentation via Quadratic Programming, to partition data drawn from multiple subspaces into multiple clusters. The basic idea of SSQP is to express each datum as the linear combination of other data regularized by an overall term targeting zero reconstruction coefficients over vectors from different subspaces. The derived coefficient matrix by solving a quadratic programming problem is taken as an affinity matrix, upon which spectral clustering is applied to obtain the ultimate segmentation result. Similar to sparse subspace clustering (SCC) and low-rank representation (LRR), SSQP is robust to data noises as validated by experiments on toy data. Experiments on Hopkins 155 database show that SSQP can achieve competitive accuracy as SCC and LRR in segmenting affine subspaces, while experimental results on the Extended Yale Face Database B demonstrate SSQP’s superiority over SCC and LRR. Beyond segmentation accuracy, all experiments show that SSQP is much faster than both SSC and LRR in the practice of subspace segmentation.

Agile software development in theory and practice <=SEP=> Kalermo, Jonna Maria Rissanen, Jenni Karoliina Agile software development in theory and practice / Jonna Kalermo and Jenni Rissanen Jyväskylä, University of Jyväskylä, 2002. 188 pp. Master's thesis In turn of the millennium, new software development ideas were presented in the form of Agile Manifesto as a counteraction to the traditional, rigorous development methods and process models. Agile Manifesto consists of four values and twelve principles of which the authors of this thesis formed a conceptual framework. It assists in analysing different aspects of agile software development one by one but also as a whole. Agile software development aims at fast, light and efficient development that supports customer's business without being chaotic or following any rigorous method. The aim of this study was to analyse Agile Manifesto and its applicability. This was done by conducting a literature review and an empirical case study. Agile Manifesto gives an ideological background for agile methods, such as Extreme Programming (XP). Such methods are most feasible for small organisations developing innovative software products. We analysed Agile Manifesto and found out that literature supported the ideas of it, although no revolutionary ideas were discovered. We used an empirical case study as an example of an agile in-house software development method in corporate venturing context. The case study supported principles and values of Agile Manifesto and confirmed the assumption that agility is heavily based on tacit knowledge, skilled and motivated employees and frequent communication. This sets several requirements for management and employees working in an agile mode: they have to be communicative, social, responsible and skilled.

Locality-sensitive hashing scheme based on dynamic collision counting <=SEP=> Locality-Sensitive Hashing (LSH) and its variants are well-known methods for solving the c-approximate NN Search problem in high-dimensional space. Traditionally, several LSH functions are concatenated to form a "static" compound hash function for building a hash table. In this paper, we propose to use a base of m single LSH functions to construct "dynamic" compound hash functions, and define a new LSH scheme called Collision Counting LSH (C2LSH). If the number of LSH functions under which a data object o collides with a query object q is greater than a pre-specified collision threhold l, then o can be regarded as a good candidate of c-approximate NN of q. This is the basic idea of C2LSH.
 Our theoretical studies show that, by appropriately choosing the size of LSH function base m and the collision threshold l, C2LSH can have a guarantee on query quality. Notably, the parameter m is not affected by dimensionality of data objects, which makes C2LSH especially good for high dimensional NN search. The experimental studies based on synthetic datasets and four real datasets have shown that C2LSH outperforms the state of the art method LSB-forest in high dimensional space.

Design of the Artificial: lessons from the biological roots of general intelligence <=SEP=> Our desire and fascination with intelligent machines dates back to the antiquity’s mythical automaton Talos, Aristotle’s mode of mechanical thought (syllogism) and Heron of Alexandria’s mechanical machines and automata. However, the quest for Artificial General Intelligence (AGI) is troubled with repeated failures of strategies and approaches throughout the history. This decade has seen a shift in interest towards bio-inspired software and hardware, with the assumption that such mimicry entails intelligence. Though these steps are fruitful in certain directions and have advanced automation, their singular design focus renders them highly inefficient in achieving AGI. Which set of requirements have to be met in the design of AGI? What are the limits in the design of the artificial? Here, a careful examination of computation in biological systems hints that evolutionary tinkering of contextual processing of information enabled by a hierarchical architecture is the key to build AGI.

Clustering Algorithms Applied in Educational Data Mining <=SEP=> Fifty years ago there were just a handful of universities across the globe that could provide for specialized educational courses. Today Universities are generating not only graduates but also massive amounts of data from their systems. So the question that arises is how can a higher educational institution harness the power of this didactic data for its strategic use? This review paper will serve to answer this question. To build an Information system that can learn from the data is a difficult task but it has been achieved successfully by using various data mining approaches like clustering, classification, prediction algorithms etc. However the use of these algorithms with educational dataset is quite low. This review paper focuses to consolidate the different types of clustering algorithms as applied in Educational Data Mining context.

Arbitrarily shaped formations of mobile robots: artificial potential fields and coordinate transformation <=SEP=> In this paper we describe a novel decentralized control strategy to realize formations of mobile robots. We first describe how to design artificial potential fields to obtain a formation with the shape of a regular polygon. We provide a formal proof of the asymptotic stability of the sys tem, based on the definition of a proper Lyapunov function. We also prove that our control strategy is not affected by the problem of local minima. Then, we exploit a bijective coordinate transformation to deform the polygonal formation, thus obtaining a completely arbitrarily shaped forma tion. Simulations and experimental tests are provided to va lidate the control strategy.

Advanced analytics: opportunities and challenges <=SEP=> Purpose – Advanced analytics-driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such analyses is then used to direct, optimize, and automate their decision making to successfully achieve their organizational goals. Data, text, and web mining technologies are some of the key contributors to making advanced analytics possible. This paper aims to investigate these three mining technologies in terms of how they are used and the issues that are related to their effective implementation and management within the broader context of predictive or advanced analytics. Design/methodology/approach – A range of recently published research literature on business intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their current state, issues and challenges learned from their practice. Findings – The findings are reported in two parts. The first part discusses a framework for BI using the data, text, and web mining technologies for advanced analytics; and the second part identifies and discusses the opportunities and challenges the business managers dealing with these technologies face for gaining competitive advantages for their businesses. Originality/value – The study findings are intended to assist business managers to effectively understand the issues and emerging technologies behind advanced analytics implementation.

On the Capacity Comparison Between MIMO-NOMA and MIMO-OMA <=SEP=> Non-orthogonal multiple access (NOMA) has been shown in the literature to have a better performance than OMA in terms of sum channel capacity; however, the capacity superiority of NOMA over OMA has been only proved for single antenna systems, and the proof for the capacity superiority of multiple-input multiple-output NOMA (MIMO-NOMA) over conventional MIMO-OMA has not been available yet. In this paper, we will provide our proof to demonstrate that the MIMO-NOMA is strictly better than MIMO-OMA in terms of sum channel capacity (except for the case where only one user is being communicated to), i.e., for any rate pair achieved by MIMO-OMA, there is a power split for which MIMO-NOMA can achieve rate pairs that are strictly larger. Based on this result, we prove that the MIMO-NOMA can also achieve a larger sum ergodic capacity than MIMO-OMA. Our analytical results are verified by simulations.

Predicting driver drowsiness using vehicle measures: recent insights and future challenges. <=SEP=> INTRODUCTION
Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs.


METHOD
The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time.


RESULTS
Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time.


CONCLUSIONS
Further research is necessary to examine more complex functions, as well as individual differences between drivers.


IMPACT ON INDUSTRY
A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures.

Curved double-layer strip folded dipole antenna for WBAN applications <=SEP=> This paper presents an optimal design of strip folded dipole antenna with a stacking technique to stabilize the resonance frequency. The designed double-layer strip folded dipole antenna worked well with the cylindrically curved surface on the human body. While being bent, the antenna will change the extended length in each layer within the stacking flexible copper-clad laminate. In this case, the properties of the antenna will be automatically adjusted. However, the extended length can occur at the contact point in each layer. So, we can choose between two points and four points by selecting different types of the designs. This reason, the performance of the extended length can be added by two and four when being compared to the previous multilayer design and the total layer of the new antenna was only two. The extended length of the antenna resulted in making antenna resonance frequency improvement become more stable. The contribution of proposed antenna was used only two layers of flexible copper-clad laminate that reduces a number of antenna's layers with a folded dipole antenna, which causes the designed less complicated, and the flexibility is advantageous for WBAN applications. The double-layer strip folded dipole antenna is designed for 2.45GHz WBAN application.

The Effect of Synonym Substitution on Search Results <=SEP=> Synonyms or other semantic associations can be used in web search in query substitution to improve or augment the query to retrieve more relevant search results. The value of substitution depends on how well the synonyms preserve semantic meaning, as any attrition in meaning can result in semantic drift of query results. Many synonyms are not synonyms in the traditional, thesaurus sense, but are semantic associations discovered automatically from online data, with the risk of semantic drift in substitution. This discovery of synonyms or other semantic associations arises from different methods applied over web search logs, and in this paper we review the candidate synonym pairs of words or phrases generated from three different methods applied over the same web search logs. The suitability of the candidate synonym pairs for the purpose of query substitution is evaluated in an experiment where 68 subjects assessed the search results generated by both the original query and the substituted query. It was found that two of the discovery methods returned significantly worse results with the substitution than were returned by the original query for the majority of queries, with only around 20-22% of substituted queries generating either improved or equally-relevant results. The third method however returned a very similar level of superior results as the original query, and saw over 71% of substituted queries generating either improved or equally-relevant results. These results indicate that even when candidate synonym pairs are confirmed as being semantically associated using other methods, they still may not be suitable for query substitution, depending on the method of synonym discovery.

Millimeter-wave mobile broadband with large scale spatial processing for 5G mobile communication <=SEP=> The phenomenal growth in mobile data traffic calls for a drastic increase in mobile network capacity beyond current 3G/4G networks. In this paper, we propose a millimeter wave mobile broadband (MMB) system for the next generation mobile communication system (5G). MMB taps into the vast spectrum in 3-300 GHz range to meet this growing demand. We reason why the millimeter wave spectrum is suitable for mobile broadband applications. We discuss the unique advantages of millimeter waves such as spectrum availability and large beamforming gain in small form factors. We also describe a practical MMB system design capable of providing Gb/s data rates at distances up to 500 meters and supports mobility up to 350 km/h. By means of system simulations, we show that a basic MMB system is capable of delivering an average cell throughput and cell-edge throughput performances that is 10-100 times better than the current 20MHz LTE-Advanced systems.

The Design of High-Level Features for Photo Quality Assessment <=SEP=> We propose a principled method for designing high level features forphoto quality assessment. Our resulting system can classify between high quality professional photos and low quality snapshots. Instead of using the bag of low-level features approach, we first determine the perceptual factors that distinguish between professional photos and snapshots. Then, we design high level semantic features to measure the perceptual differences. We test our features on a large and diverse dataset and our system is able to achieve a classification rate of 72% on this difficult task. Since our system is able to achieve a precision of over 90% in low recall scenarios, we show excellent results in a web image search application.

Emotion circuits in the brain. <=SEP=> The field of neuroscience has, after a long period of looking the other way, again embraced emotion as an important research area. Much of the progress has come from studies of fear, and especially fear conditioning. This work has pinpointed the amygdala as an important component of the system involved in the acquisition, storage, and expression of fear memory and has elucidated in detail how stimuli enter, travel through, and exit the amygdala. Some progress has also been made in understanding the cellular and molecular mechanisms that underlie fear conditioning, and recent studies have also shown that the findings from experimental animals apply to the human brain. It is important to remember why this work on emotion succeeded where past efforts failed. It focused on a psychologically well-defined aspect of emotion, avoided vague and poorly defined concepts such as "affect," "hedonic tone," or "emotional feelings," and used a simple and straightforward experimental approach. With so much research being done in this area today, it is important that the mistakes of the past not be made again. It is also time to expand from this foundation into broader aspects of mind and behavior.

A new computing environment for collective privacy protection from constrained healthcare devices to IoT cloud services <=SEP=> The Internet of healthcare things is essentially a new model that changes the way of the delivery and management of healthcare services. It utilizes digital sensors and cloud computing to present a quality healthcare service outside of the classical hospital environment. This resulted in the emergence of a new class of online web 4.0 services, which are termed “cloud healthcare services”. Cloud healthcare services offer a straightforward opportunity for patients to communicate with healthcare professionals and utilize their personal IoHT devices to obtain timely and accurate medical guidance and decisions. The personal IoHT devices integrate sensed health data at a central cloud healthcare service to extract useful health insights for wellness and preventive care strategies. However, the present practices for cloud healthcare services rely on a centralized approach, where patients’ health data are collected and stored on servers, located at remote locations, which might be functioning under data privacy laws somewhat different from the ones applied where the service is running. Promoting a privacy respecting cloud services encourages patients to actively participate in these healthcare services and to routinely provide an accurate and precious health data about themselves. With the emergence of fog computing paradigm, privacy protection can now be enforced at the edge of the patient’s network regardless of the location of service providers. In this paper, a framework for cloud healthcare recommender service is presented. We depicted the personal gateways at the patients’ side act as intermediate nodes (called fog nodes) between IoHT devices and cloud healthcare services. A fog-based middleware will be hosted on these fog nodes for an efficient aggregation of patients generated health data while maintaining the privacy and the confidentiality of their health profiles. The proposed middleware executes a two-stage concealment process that utilizes the hierarchical nature of IoHT devices. This will unburden the constrained IoHT devices from performing intensive privacy preserving processes. At that, the patients will be empowered with a tool to control the privacy of their health data by enabling them to release their health data in a concealed form. The further processing at the cloud healthcare service continues over the concealed data by applying the proposed protocols. The proposed solution was integrated into a scenario related to preserving the privacy of the patients’ health data when utilized by a cloud healthcare recommender service to generate health insights. Our approach induces a straightforward solution with accurate results, which are beneficial to both patients and service providers.

Unsupervised Histopathology Image Synthesis <=SEP=> Hematoxylin and Eosin stained histopathology image analysis is essential for the diagnosis and study of complicated diseases such as cancer. Existing state-of-the-art approaches demand extensive amount of supervised training data from trained pathologists. In this work we synthesize in an unsupervised manner, large histopathology image datasets, suitable for supervised training tasks. We propose a unified pipeline that: a) generates a set of initial synthetic histopathology images with paired information about the nuclei such as segmentation masks; b) refines the initial synthetic images through a Generative Adversarial Network (GAN) to reference styles; c) trains a task-specific CNN and boosts the performance of the task-specific CNN with onthe-fly generated adversarial examples. Our main contribution is that the synthetic images are not only realistic, but also representative (in reference styles) and relatively challenging for training task-specific CNNs. We test our method for nucleus segmentation using images from four cancer types. When no supervised data exists for a cancer type, our method without supervision cost significantly outperforms supervised methods which perform across-cancer generalization. Even when supervised data exists for all cancer types, our approach without supervision cost performs better than supervised methods.

Moving object surveillance using object proposals and background prior prediction <=SEP=> In this paper, a moving object detection algorithm is combined with a background estimate and a Bing (Binary Norm Gradient) object is proposed in video surveillance. A simple background estimation method is used to detect rough images of a group of moving foreground objects. The foreground setting in the foreground will estimate another set of candidate object windows, and the target (pedestrian / vehicle) from the intersection area comes from the first two steps. In addition, the time cost is reduced by the estimated area. Experiments on outdoor datasets show that the proposed method can not only achieve high detection rate (DR), but also reduce false alarm rate (FAR) and time cost.

Progressive Geometric Algorithms <=SEP=> Progressive algorithms are algorithms that, on the way to computing a complete solution to the problem at hand, output intermediate solutions that approximate the complete solution increasingly well. We present a framework for analyzing such algorithms, and develop efficient progressive algorithms for two geometric problems: computing the convex hull of a planar point set, and finding popular places in a set of trajectories.

Modeling and execution of event stream processing in business processes <=SEP=> The Internet of Things and Cyber-physical Systems provide enormous amounts of real-time data in form of streams of events. Businesses can benefit from the integration of these real-world data; new services can be provided to customers, or existing business processes can be improved. Events are a well-known concept in business processes. However, there is no appropriate abstraction mechanism to encapsulate event stream processing in units that represent business functions in a coherent manner across the process modeling, process execution, and IT infrastructure layer. In this paper we present Event Stream Processing Units (SPUs) as such an abstraction mechanism. SPUs encapsulate application logic for event stream processing and enable a seamless transition between process models, executable process representations, and components at the IT layer. We derive requirements for SPUs and introduce EPC and BPMN extensions to model SPUs at the abstract and at the technical process layer. We introduce a transformation from SPUs in EPCs to SPUs in BPMN and implement our modeling notation extensions in Software AG ARIS. We present a runtime infrastructure that executes SPUs and supports implicit invocation and completion semantics. We illustrate our approach using a logistics process as running example.

IoT smart parking system for reducing green house gas emission <=SEP=> Rapid climate change results natural calamity and severe economic impact and threats to the life. Burning fossil fuels by the medium of transportation contributes 1/3 of portion in increasing greenhouse emission and leading to raise surface temperate. Commuters in and around the developed cities faces difficulties in finding parking lot due to lack of notification process and autonomous parking systems. This causes commuters to take multiple rounds trips to get the parking slot which causes burning additional fuel and ultimately producing excessive CO2 emission. This paper describes solution to smart parking system using Internet of Things (IoT) to override parking hazards and explains how does it helps to minimize emitting greenhouse gases. IoT enables smart parking system using the system of interconnected Raspberry Pi, Distance Sensor, Pi Camera devices together. This hardware reacts to one another collects data and transmits to cloud storage.

A SURVEY OF DISTRIBUTED FILE SYSTEMS <=SEP=> This paper is a survey of the current state of the art in the design and implementation of distributed file systems. It consists of four major parts: an overview of background material, case studies of a number of contemporary file systems, identification of key design techniques, and an examination of current research issues. The systems surveyed are Sun NFS, Apollo Domain, Andrew, IBM AIX DS, AT&T RFS, and Sprite. The coverage of background material includes a taxonomy of file system issues, a brief history of distributed file systems, and a summary of empirical research on file properties. A comprehensive bibliography forms an important of the paper.