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The sense of touch can be used for acquiring visual/auditory information through tactile stimuli. Sensory augmentation and substitution often require the extensive training of subjects, leading to exhaustion and frustration over time. In this paper, we present a vibrotactile device for delivering customizable spatiotemporal tactile patterns to be used as a promising method in relaying information through the skin. The proposed system is able to generate stimuli where both vibration frequency and amplitude can be modulated. The results prove that this assistive technology solution can be beneficial and efficient not only for delivering complex feedback, but also for people with some level of sensory impairment or total disability e.g. blind, color-blind or deaf people. [ABSTRACT FROM AUTHOR]

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]

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]

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]