Modeling Power Supply Noise in Delay Testing

Excessive power supply noise during test can cause overkill. This article discusses two models for supply noise in delay testing and their application to test compaction. The proposed noise models avoid complicated power network analysis, making them much faster than existing power noise analysis tools. can cause performance degradation and     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

Direction prediction for avoiding occlusion in visual surveillance

Occurrence of occlusion while providing visual surveillance leads to anarchy as the track of the subject under motion may be lost. This often results into the failure of the surveillance system. The approach of predicting motion of moving subjects and hence the chances of their mutual occlusion gives an upper hand to surveillance system to take in-time necessary action towards mitigation of loss of track during dynamic occlusion. Direction of motion of a moving subject plays a major role while studying its motion. Direction along with the velocity of a subject in a 3D plane completely describes the motion of any subject. This article proposes a model‘-based approach for direction prediction of a moving subject in a 3D global plane as acquired in a 2D camera plane. The proposed approach uses the eight discrete directions of motion as proposed in and models different directions. The proposed direction prediction method is experimentally verified with six different classifiers, i.e. regression analysis, simple logistic regression, MLP, k-NN, SVM and Bays classifier over existing as well as self-acquired databases. The initial simulation results are motivating as the overall accuracies achieved through different classifiers are of the range of 87–94 \(\%\), which advocates the suitability of the said approach.     

IIR system identification using cat swarm optimization

Conventional derivative based learning rule poses stability problem when used in adaptive identification of infinite impulse response (IIR) systems. In addition the performance of these methods substantially deteriorates when reduced order adaptive models are used for such identification. In this paper the IIR system identification task is formulated as an optimization problem and a recently introduced cat swarm optimization (CSO) is used to develop a new population based learning rule for the model. Both actual and reduced order identification of few benchmarked IIR plants is carried out through simulation study. The results demonstrate superior identification performance of the new method compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based identification.     

An integrated intelligent computing model for the interpretation of EMG based neuromuscular diseases

Intelligent computing system (ICS) and knowledge-based system (KBS) have been widely used in the detection and interpretation of EMG (electromyography) based diseases. Heuristic-based detection methods of EMG parameters for a particular disease have also been reported in the literature but little effort has been made by researchers to combine rule-based reasoning (RBR) and case-based reasoning of KBS, and ANN (artificial neural nets) of ICS. Integrating the methods in KBS and ICS improves the computational and reasoning efficiency of the problem-solving strategy. We have developed an integrated model of CBR and RBR for generating cases, and ANN for matching cases for the interpretation and diagnosis of neuromuscular diseases. We have hierarchically structured the neuromuscular diseases in terms of their physio-pyscho (muscular, cognitive and psychological) parameters and EMG based parameters (amplitude, duration, phase etc.). Cumulative confidence factor is computed at different node from lowest to highest level of hierarchal structure in the process of diagnosis of the neuromuscular diseases. The diseases considered are Duchenne muscular dystrophy, Polymyostits, Endocrine myopathy, Metabolic myopathy, Neuropathy, Poliomyletis and Myasthenia gravis. The basic objective of this work is to develop an integrated model of RBR, CBR and ANN in which RBR is used to hierarchically correlate the sign and symptom of the disease and also to compute cumulative confidence factor (CCF) of the diseases. CBR is used for diagnosing the neuromuscular diseases and to find the relative importance of sign and symptoms of a diseases to other diseases and ANN is used for matching process in CBR.     

Flow separation in a viscous parabolic shear past a sphere

Summary Axisymmetric parabolic shear flow past a spinning sphere in an unbounded viscous medium is considered using the matching technique and taking the non-uniform shear as the dominant feature, where the Reynolds number based on parabolic shearR _e 1 and the rotational Reynolds number based on the angular rotation of the sphereR ₀1 are such thatR ₀ ²/R _e =O(1). Forces on the body are calculated and flow separation from the boundary is studied. Coalescion of two wakes or detachment of a wake into two are observed, depending on the increase in magnitudeU of the free stream for a fixedR ₀ ²/R _e or increase inR ₀ ²/R _e for a fixedU respectively.With 8 Figures     

Distributed and robust parameter estimation of IIR systems using incremental particle swarm optimization

In recent years because of substantial use of wireless sensor network the distributed estimation has attracted the attention of many researchers. Two popular learning algorithms: incremental least mean square (ILMS) and diffusion least mean square (DLMS) have been reported for distributed estimation using the data collected from sensor nodes. But these algorithms, being derivative based, have a tendency of providing local minima solution particularly for minimization of multimodal cost function. Hence for problems like distributed parameters estimation of IIR systems, alternative distributed algorithms are required to be developed. Keeping this in view the present paper proposes two population based incremental particle swarm optimization (IPSO) algorithms for estimation of parameters of noisy IIR systems. But the proposed IPSO algorithms provide poor performance when the measured data is contaminated with outliers in the training samples. To alleviate this problem the paper has proposed a robust distributed algorithm (RDIPSO) for IIR system identification task. The simulation results of benchmark IIR systems demonstrate that the proposed algorithms provide excellent identification performance in all cases even when the training samples are contaminated with outliers.     

Differential Physio-Biochemical and Metabolic Responses of Peanut (Arachis hypogaea L.) under Multiple Abiotic Stress Conditions

The frequency and severity of extreme climatic conditions such as drought, salinity, cold, and heat are increasing due to climate change. Moreover, in the field, plants are affected by multiple abiotic stresses simultaneously or sequentially. Thus, it is imperative to compare the effects of stress combinations on crop plants relative to individual stresses. This study investigated the differential regulation of physio-biochemical and metabolomics parameters in peanut (Arachis hypogaea L.) under individual (salt, drought, cold, and heat) and combined stress treatments using multivariate correlation analysis. The results showed that combined heat, salt, and drought stress compounds the stress effect of individual stresses. Combined stresses that included heat had the highest electrolyte leakage and lowest relative water content. Lipid peroxidation and chlorophyll contents did not significantly change under combined stresses. Biochemical parameters, such as free amino acids, polyphenol, starch, and sugars, significantly changed under combined stresses compared to individual stresses. Free amino acids increased under combined stresses that included heat; starch, sugars, and polyphenols increased under combined stresses that included drought; proline concentration increased under combined stresses that included salt. Metabolomics data that were obtained under different individual and combined stresses can be used to identify molecular phenotypes that are involved in the acclimation response of plants under changing abiotic stress conditions. Peanut metabolomics identified 160 metabolites, including amino acids, sugars, sugar alcohols, organic acids, fatty acids, sugar acids, and other organic compounds. Pathway enrichment analysis revealed that abiotic stresses significantly affected amino acid, amino sugar, and sugar metabolism. The stress treatments affected the metabolites that were associated with the tricarboxylic acid (TCA) and urea cycles and associated amino acid biosynthesis pathway intermediates. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and heatmap analysis identified potential marker metabolites (pinitol, malic acid, and xylopyranose) that were associated with abiotic stress combinations, which could be used in breeding efforts to develop peanut cultivars that are resilient to climate change. The study will also facilitate researchers to explore different stress indicators to identify resistant cultivars for future crop improvement programs.     

A delay and energy aware coordination mechanism for WSAN

In this paper, a delay and energy aware coordination mechanism (DEACM) has been devised for wireless sensor–actor networks. In DEACM, a two‐level hierarchical K‐hop clustering mechanism is used to organize the sensors and actors for communication. In the first level, sensors form a K‐hop cluster using actors as cluster heads, and sink is made as the cluster head in the second level to form a cluster among actors. Sensor nodes, which are 1‐hop away from the actors, also called as relay nodes are elected as backup cluster head (BCH) based on the residual energy and node degree. BCH collects the data from sensors when an actor is away to perform actions in the affected area. The scheme is evaluated through exhaustive simulation in NS2 along with other existing schemes. Different parameters like average event waiting time, event reliability, and average energy dissipation are compared, varying the number of sensors, actors, and data transfer rate. In general, it is observed that the proposed DEACM outperforms other existing schemes. Copyright © 2016 John Wiley & Sons, Ltd.     

Design of a Stable DC Voltage Source and Computer Controlling of It Using an Indigenously Developed All-Digital Addressing-Cum-Control Hardware

We have developed a digitally operated addressing and control module (DACM) for addressing and controlling of equipment from a remote computer using a communication protocol developed in-house. This is useful for automation of an experiment that uses multiple equipment in a pre-decided synchronized manner. We also report design of a multipurpose high voltage direct current (DC) source that provides output of 0–100 V with an average stability of 1.90 (36) mV and has minimum step size of 3 mV. Operation of the DACM is examined by selecting the desired equipment, which in this case is the dc source, and remotely controlling its output from a computer. We also show that this can generate voltage with different waveforms within a 0–10 Hz frequency bandwidth. Such computer controlled ultra-stable high voltage sources tuneable to any arbitrary waveforms at low frequencies have many applications such as, driving a piezo for smooth scanning of laser frequencies, tuning length of a Fabry–Perot cavity, biasing of the electrodes in an ion trap and so on.