User Recognition Based on Human Body Impulse Response: A Feasibility Study

Human recognition technologies for security systems require high reliability and easy accessibility in the advent of the internet of things (IoT). While several biometric approaches have been studied for user recognition, there are demands for more convenient techniques suitable for the IoT devices. Recently, electrical frequency responses of the human body have been unveiled as one of promising biometric signals, but the pilot studies are inconclusive about the characteristics of human body as a transmission medium for electric signals. This paper provides a multi-domain analysis of human body impulse responses (HBIR) measured at the receiver when customized impulse signals are passed through the human body. We analyzed the impulse responses in the time, frequency, and wavelet domains and extracted representative feature vectors using a proposed accumulated difference metric in each domain. The classification performance was tested using the k-nearest neighbors (KNN) algorithm and the support vector machine (SVM) algorithm on 10-day data acquired from five subjects. The average classification accuracies of the simple classifier KNN for the time, frequency, and wavelet features reached 92.99%, 77.01%, and 94.55%, respectively. In addition, the kernel-based SVM slightly improved the accuracies of three features by 0.58%, 2.34%, and 0.42%, respectively. The result shows potential of the proposed approach for user recognition based on HBIR

Robust Distributed Clustering Algorithm Over Multitask Networks

We propose a new adaptive clustering algorithm that is robust to various multitask environments. Positional relationships among optimal vectors and a reference signal are determined by using the mean-square deviation relation derived from a one-step least-mean-square update. Clustering is performed by combining determinations on the positional relationships at several iterations. From this geometrical basis, unlike the conventional clustering algorithms using simple thresholding method, the proposed algorithm can perform clustering accurately in various multitask environments. Simulation results show that the proposed algorithm has more accurate estimation accuracy than the conventional algorithms and is insensitive to parameter selection.11Ysciescopu

Data-Reserved Periodic Diffusion LMS With Low Communication Cost Over Networks

In this paper, we analyze diffusion strategies in which all nodes attempt to estimate a common vector parameter for achieving distributed estimation in adaptive networks. Under diffusion strategies, each node essentially needs to share processed data with predefined neighbors. Although the use of internode communication has contributed significantly to improving convergence performance based on diffusion, such communications consume a huge quantity of power in data transmission. In developing low-power consumption diffusion strategies, it is very important to reduce the communication cost without significant degradation of convergence performance. For that purpose, we propose a data-reserved periodic diffusion least-mean-squares (LMS) algorithm in which each node updates and transmits an estimate periodically while reserving its measurement data even during non-update time. By applying these reserved data in an adaptation step at update time, the proposed algorithm mitigates the decline in convergence speed incurred by most conventional periodic schemes. For a period p, the total cost of communication is reduced to a factor of 1/p relative to the conventional adapt-then-combine (ATC) diffusion LMS algorithm. The loss of combination steps in this process leads naturally to a slight increase in the steady-state error as the period p increases, as is theoretically confirmed through mathematical analysis. We also prove an interesting property of the proposed algorithm, namely, that it suffers less degradation of the steady-state error than the conventional diffusion in a noisy communication environment. Experimental results show that the proposed algorithm outperforms related conventional algorithms and, in particular, outperforms ATC diffusion LMS over a network with noisy links.11Ysciescopu

P2X7 receptor regulates leukocyte infiltrations in rat frontoparietal cortex following status epilepticus

<p>Abstract</p> <p>Background</p> <p>In the present study, we investigated the roles of P2X7 receptor in recruitment and infiltration of neutrophil during epileptogenesis in rat epilepsy models.</p> <p>Methods</p> <p>Status epilepticus (SE) was induced by pilocarpine in rats that were intracerebroventricularly infused with either saline, 2',3'-O-(4-benzoylbenzoyl)-adenosine 5'-triphosphate (BzATP), adenosine 5'-triphosphate-2',3'-dialdehyde (OxATP), or IL-1Ra (interleukin 1 receptor antagonist) prior to SE induction. Thereafter, we performed immunohistochemical studies for myeloperoxidase (MPO), CD68, interleukin-1β (IL-1β), monocyte chemotactic protein-1 (MCP-1) and macrophage inflammatory protein-2 (MIP-2).</p> <p>Results</p> <p>In saline-infused animals, neutrophils and monocytes were observed in frontoparietal cortex (FPC) at 1 day and 2 days after SE, respectively. In BzATP-infused animals, infiltrations of neutrophils and monocytes into the FPC were detected at 12 hr and 1 day after SE, respectively. In OxATP-infused animals, neutrophils and monocytes infiltrated into the FPC at 1 day and 2 days after SE, respectively. However, the numbers of both classes of leukocytes were significantly lower than those observed in the saline-infused group. In piriform cortex (PC), massive leukocyte infiltration was detected in layers III/IV of saline-infused animals at 1-4 days after induction of SE. BzATP or OxATP infusion did not affect neutrophil infiltration in the PC. In addition, P2X7 receptor-mediated MCP-1 (released from microglia)/MIP-2 (released from astrocytes) regulation was related to SE-induced leukocyte infiltration in an IL-1β-independent manner.</p> <p>Conclusions</p> <p>Our findings suggest that selective regulation of P2X7 receptor-mediated neutrophil infiltration may provide new therapeutic approaches to SE or epilepsy.</p

Sericea lespedeza (Lespedeza cuneata) whole plant extract enhances rat muscle mass and sperm production by increasing the activity of NO-cGMP pathway and serum testosterone

Purpose: To analyze the effects of an aqueous extract of Sericea lespedeza (SL) on rat male menopause.Methods: Levels of nitric oxide (NO), endothelial nitric oxide synthase (NOS), cGMP, and prostaglandin E2 (PGE2) in the penile corpus cavernosum of the rats were evaluated using appropriate kits. Serum levels of dihydrotestosterone (DHT), testosterone, sex hormone-binding globulin (SHBG), and 17-beta hydroxysteroid dehydrogenases (17β-HSD) were measured with enzyme-linked immunosorbent assay kits. Total and motile sperms were counted on a hemocytometer. Histological changes in rat testis and epididymis were analyzed with hematoxylin and eosin staining.Results: The levels of NO, NOS, and cGMP (but not PGE2) increased in a dose-dependent manner (p&lt; 0.05) upon administration of an aqueous extract of SL (AESL), while levels of DHT, 17β-HSD, and testosterone increased in the group administered with 300 mg/kg of AESL. Epididymal sperm count increased by 24 % in such rats compared to controls (p &lt; 0.05).Conclusion: The aqueous extract of SL improves sperm count and muscle mass in rats by increasing the levels of NO, NOS, cGMP and testosterone. Thus, SL extract can potentially be developed as an alternative therapeutic agent for clinical management of TDS. Keywords: NO-cGMP, Testosterone, Hormones, Sperm count, Muscle mass, Sericea lespedeza, Lespedeza cuneat

SoEasy: A Software Framework for Easy Hardware Control Programming for Diverse IoT Platforms

Many Internet of Things (IoT) applications are emerging and evolving rapidly thanks to widespread open-source hardware platforms. Most of the high-end open-source IoT platforms include built-in peripherals, such as the universal asynchronous receiver and transmitter (UART), pulse width modulation (PWM), general purpose input output (GPIO) ports and timers, and have enough computation power to run embedded operating systems such as Linux. However, each IoT platform has its own way of configuring peripherals, and it is difficult for programmers or users to configure the same peripheral on a different platform. Although diverse open-source IoT platforms are widespread, the difficulty in programming those platforms hinders the growth of IoT applications. Therefore, we propose an easy and convenient way to program and configure the operation of each peripheral using a user-friendly Web-based software framework. Through the implementation of the software framework and the real mobile robot application development along with it, we show the feasibility of the proposed software framework, named SoEasy