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Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results. [ABSTRACT FROM AUTHOR]

This paper introduces a novel multi-classification technique, which improves two conflicting main objectives of classification problems, i.e., classification accuracy and worst case sensitivity. Global performance measures such as overall accuracy might not be enough to evaluate classifiers and alternative measurements are essentially required. This paper addresses a new model selection problem to construct a tree-based hierarchical classification model based on ensemble of six different classifiers. In our proposed approach, the model selection is tackled as a multi-objective optimization, which not only considers the accuracy of the classification, but also tries to maximize the worst case sensitivity of the multi-class problem. The proposed technique is applied on nine different classes corresponding to various breathing disorders for designing a wearable remote monitoring system. This model correctly classified the respiratory patterns of ten subjects with an accuracy of 99.25% and a sensitivity of 97.78% with detecting the changes in the anterior-posterior diameter of the chest wall during breathing function by means of two accelerometer sensors worn on subject’s rib cage and abdomen. The effects of the number of sensors, sensor placement, as well as feature selection on the classification performance are also discussed. [ABSTRACT FROM PUBLISHER]

Respiratory disorder is a highly prevalent condition associated with many adverse health problems. As the current means of diagnosis are obtrusive and ill-suited for real-time m-health applications, we explore a convenient and low-cost automatic approach that uses wearable microelectromechanical system sensor technology. The proposed system introduces the use of motion sensors to detect the changes in the anterior–posterior diameter of the chest wall during breathing function as well as extracting the informative respiratory features to be used for breathing disorders classification. Extensive evaluations are provided on six well-known classifiers with novel feature extraction techniques to distinguish among eight different pathological breathing patterns. The effects of the number of sensors, sensor placement, as well as feature selection on the classification performance are discussed. The experimental results conducted with ten subjects show the best accuracy rates of 97.50% by support vector machine and 97.37% with decision tree bagging (DTB) with all features and after feature selection, correspondingly. Furthermore, a binary classification is proposed for distinguishing between healthy people and patients with breath problems. The different assessments of classification parameters are provided by measuring the accuracy, sensitivity, specificity, F1-score and Mathew correlation coefficient. The accuracy rates above 98% suggest superior performance of DTB in binary recognition supported by the suggested new features. [ABSTRACT FROM PUBLISHER]

The measurement of human respiratory signals is crucial in cyberbiological systems. A disordered breathing pattern can be the first symptom of different physiological, mechanical, or psychological dysfunctions. Therefore, a real-time monitoring of the respiration patterns, as well as respiration rate is a critical need in medical applications. There are several methods for respiration rate measurement. However, despite their accuracy, these methods are expensive and could not be integrated in a body sensor network. In this work, we present a real-time cloud-based platform for both monitoring the respiration rate and breath pattern classification, remotely. The proposed system is designed particularly for patients with breathing problems (e.g., respiratory complications after surgery) or sleep disorders. Our system includes calibrated accelerometer sensor, Bluetooth Low Energy (BLE) and cloud-computing model. We also suggest a procedure to improve the accuracy of respiration rate for patients at rest positions. The overall error in the respiration rate calculation is obtained 0.53% considering SPR-BTA spirometer as the reference. Five types of respiration disorders, Bradapnea, Tachypnea, Cheyn-stokes, Kaussmal, and Biot's breathing are classified based on hierarchical Support Vector Machine (SVM) with seven different features. We have evaluated the performance of the proposed classification while it is individualized to every subject (case 1) as well as considering all subjects (case 2). Since the selection of kernel function is a key factor to decide SVM's performance, in this paper three different kernel functions are evaluated. The experiments are conducted with 11 subjects and the average accuracy of 94.52% for case 1 and the accuracy of 81.29% for case 2 are achieved based on Radial Basis Function (RBF). Finally, a performance evaluation has been done for normal and impaired subjects considering sensitivity, specificity and G-mean parameters of different kernel functions. [ABSTRACT FROM AUTHOR]

Network‐on‐chip (NoC) performance largely depends on the underlying deadlock‐free and efficient routing algorithm. The effectiveness of any adaptive routing algorithm strongly depends on the underlying selection strategy. When the routing function returns a set of admissible output channels with cardinality greater than one, a selection function is used to select the output channel to which the packet will be forwarded. In this study a novel selection strategy, LATEX, is proposed that can be used with any adaptive routing algorithm for specified applications. The objective of the proposed selection strategy is to efficiently balance traffic load and reach better performance results. Performance evaluation is carried out by using a flit‐accurate simulator under two real traffic scenarios. Result experiments show that the proposed selection strategy applied to several routing algorithms significantly improves average delay, max delay and power consumption. [ABSTRACT FROM AUTHOR]

Defect in manufacturing of integrated circuits is almost inevitable, and fast scaling in technology has caused the components of a Network-on-Chip (NoC) to be more susceptible to faults. Therefore, it is crucial to sustain chip production yield and reliable operation in the presence of defects. A fault-tolerant application-specific NoC should be able to detect a fault and recover the system to correctly operate the mapped application. In this paper, a fault-tolerant NoC architecture designed in VHDL and synthesized using Xilinx ISE is presented which not only is able to recover from single permanent router failure, but also improves the average response time of the system in the different traffic loads. As hardware overhead is a major issue while considering fault tolerance, a new component, called Link Interface (LI) is also developed to reduce cost overhead. The Video Object Plan Decoder (VOPD) core graph is used as a real application in this study. [ABSTRACT FROM AUTHOR]

Mapping algorithm, which is an important phase of an NoC design tries to map most frequent and most critical communications in such a way that minimize the physical distance between the source and destination nodes. The objective of this paper is to achieve an application-specific NoC design that minimizes the communication cost and improves the fault tolerant properties. First, a heuristic mapping algorithm that produces a set of different mappings in a reasonable time is presented. Although this mapping does not explore the design space thoroughly, it considers a part of design space, which in general minimizes the communication costs of solutions while yielding optimum communication costs in some cases. Comparison of the communication cost results makes it obvious that final solutions found by our proposed approach outperform the results of other methods, which proposed in literature. Then, the used routing algorithm and the concept, vulnerability index, which is considered as a criterion for estimating the fault-tolerance of mapped application, are presented in details. Lower communication cost leads to an NoC with better metrics such as energy consumption and latency; and reducing the vulnerability index optimizes fault tolerant properties of NoC. In order to yield a mapping which considers trade-offs between these two parameters, a linear function is defined and introduced. It is also observed that more flexibility to prioritize solutions within the design space is possible by adjusting a set of if-then rules in fuzzy logic. [ABSTRACT FROM AUTHOR]

Abstract: As the integration of transistors on today’s embedded systems scales, so does the shrinking size of chips, thus making the on-chip communication a challenging issue on the VLSI designs. However, network on chips have emerged as a promising technology to tackle the on-chip communication constraints. Likewise, the reliability issues have become the salient problem, since regarding to the inaccessible failures of on-chip elements, there must be some levels of embedded fault tolerance techniques. In this paper, an innovated technique is revealed providing fault tolerance in the on-chip networks over single and multiple permanent switch failures. The experimental results achieved by the system simulation in SystemC TLM environment are validated with the mathematical analysis modeled for system reliability that we extend in this paper, which demonstrate the extensive reliability enhancement of this paradigm. Along with the system improvement, silicon area overhead is calculated utilizing VHDL low level simulation and Orion synthesis. [Copyright &y& Elsevier]