Intelligent Computing and Applications

The objective of this paper was to deal with the imprecise data involved in different kinds of searching techniques in a more efficient way and thus to characterize an innovative version of search under the uncertainty. This paper is one of the outcomes of a rigorously reviewed experimental work carried out on the synthesis of constraint satisfaction problems through tree searching algorithms where it was found that the traditional tree search algorithms fail to search in uncertain problem domain.

In this paper, a modified radial basis function network (RBFN) based on self-organization mapping (SOM) has been designed and developed for rotation invariant clear as well as occluded fingerprint recognition. The SOM-based RBFN learns different fingerprint images and performs subsequent rotation invariant recognition of clear and occluded images. The system is efficient, effective, and fast. Also, the performance evaluation of the system is substantially moderate.

We present an automatic face identification system using an unsupervised optimal clustering algorithm (OCA)-based RBF network. In this present system, we propose a completely unsupervised clustering algorithm for training of the RBF network in which the system automatically searches for suitable threshold to perform natural clustering. This system performs the identity of a person irrespective of different facial expressions, poses, and partial occlusions. Experimental results show that the performance of the system in terms of accuracy, precision, recall, and F-score is moderately high. At the same time, the total learning time as well as performance evaluation time is moderately low.

Influenced by the human learning, neural network promises to provide computer science discipline a breakthrough that rivals anything it has yet witnessed. Once neural networks are trained properly, they can replace many human functions in targeted areas. This study deals with an important step in that journey by analysing the impact of training data set drawn from the actual learning process of a human being, on artificial neural network learning. Use of kid’s handwriting samples as training samples has been used to demonstrate its effect on neural network performance as it has maximum variance that helps neural network to exhaustively explore input space. The effect of training samples obtained from kid’s handwriting has been analysed on handwritten character identification and compared with the training samples obtained from young subjects. From the study, it is concluded that neural networks trained by kid’s writing data sample are able to recognize young subject writing test sample inputs with an increased average recognition rate of 13 % as compared to neural networks trained from young subject writing data. Similarly, neural networks trained by kid’s writing are able to recognize kid’s writing test sample inputs with an increased average recognition rate of 38.7 % as compared to neural networks trained from young subject samples.

RC4 has proved itself as robust enough and is trusted by many organizations. A number of researchers claimed that though this stream cipher is simple, fast, easy to implement, it has some weakness and bias in its internal states. Some researchers argued that the

swap

function of RC4 in key-scheduling algorithm (KSA) and pseudo-random generation algorithm (PRGA) is the main reason of weakness. The authors of this paper eliminated the KSA and used a mathematical process to generate the internal state array(s) of RC4. Also, the PRGA has been modified to handle two S-boxes to generate two keystream bytes in one loop. Both the algorithms, original and modified, are tested with the NIST Statistical Test Suite. It has been found that the modified RC4 is giving a better randomness in the ciphertexts, hence giving a better security.

This paper presents a distributed algorithm for gathering of swarm mobile robots in the presence of horizontal line obstacles. A swarm of mobile robots with unlimited visibility are randomly located within bounded area. Each robot performs a deterministic algorithm, and as a result, they will finally gather at some point without exactly reaching that point. We have followed

asynchronous

timing model and

full

-

compass

models.

The paper presents the design of sensor node for wireless sensor network (WSN) dedicated for structural health monitoring (SHM) application. The sensor node, based on Cortex M3 ARM controller, integrates software and hardware for measuring vibration, temperature and humidity data in a compact miniaturized unit. The sensor node is designed for acquiring low frequency vibration with 50 Hz bandwidth. It uses ZigBee protocol based wireless transceiver. A Laptop works as base station to receive information from sensor node. Performance of the sensor node is evaluated by measuring vibration of a 3 phase AC motor and its frequency response is compared with standard vibration data logger Slams Stick

TM

and a single axis mobile industrial accelerometer.

In this article, a predator–prey optimization (PPO) is comprehensively developed and successfully applied for solving a single-objective non-linear economic load dispatch (ELD) problem to optimize the generation cost of large-scale power system, considering the non-linearities, such as valve points, and prohibited operation zones (POZs) in the generating units. The effectiveness of the proposed method is examined and validated by carrying out extensive tests on two different test systems, and its results are compared with other those obtained by other techniques available in the recent literature.

Sparse signal processing appears to be an emerging technology having certain application areas like denoising, deblurring, inpainting, etc. The dictionaries used in sparse signal processing are of much importance as they hold the basic patterns to retrieve the original image. A wide range of complete and overcomplete dictionaries are used for reconstruction of signals in the presence of noise. But no comparative study of these dictionaries is available in the literature till now for indexing their performance. Present work is devoted to carry out such a comparative study which would help in indexing the performance and effectiveness of the dictionaries in sparse signal reconstruction to reduce speckle noise. The results have been compared and analyzed with a set of standard test images.

Reed–Solomon codes are commonly used to detect and correct errors in digital data during transmission and storage. In this paper, a new optimization algorithm has been proposed which is very simple and efficient for reducing the complexity of the Galois field constant multipliers in terms of XOR2 gates, and hence, the area overhead of RS(32, 28) encoder decreases. RS(32, 28) encoder has been implemented using four optimized constant field multipliers. Using proposed algorithm, the number of XOR2 gates can be reduced by 34.95 and 50.49 %, respectively, for local and global optimization over non-optimized design without affecting its delay. The number of slices and LUT required for FPGA-based design of RS(32, 28) encoder is also reduced compared to unoptimized design.

Graphene and silicene are currently attracting the interest of many researchers in the field of material science due to some unique material properties [

1

]. Recently Reddy et al. [

2

] have studied the use of using graphene in cloud computing and found graphene is very effective for this purpose, but zero bandgap for graphene is really a problem to use it here. In this paper, we study to improve this property of graphene by preparing a composite of it with silicene and found if graphene is used as an n-type dopant in silicene, then bandgap is induced in graphene. Due to this property, the composite can be used to improve the area of performance, size, speed, and cost in cloud computing services.

E-learning system involves an open participation of student, teacher, and administrator among different regions in the world. Due to the use of Internet as electronic communication media, there are several types of risks and threats such as alternation or deletion of student’s marks given by a teacher may hamper the E-learning environment in different ways. To implement privacy and confidentiality of the information, we must use suitable encryption technique. In this paper, we have proposed an object-oriented modeling of international data encryption algorithm (IDEA) for implementation of privacy and confidentiality of information which would be communicated between teacher and student at the time of viewing marks for a subject. For an efficient design, we use UML-based approach.

The tiny, battery-powered sensor nodes of the wireless sensor networks (WSNs) sense and send reports to a processing center called sink or base station. The sensor nodes require more energy while gathering information for longer durations. This study proposes a protocol heterogeneous in energy which analyzes basic distributed clustering routing protocol low-energy adaptive clustering hierarchy (LEACH) with BAT optimization algorithm to be used for cluster formation and cluster-head (CH) selection. Pipelining is used for packet scheduling. Simulations show that the energy consumption gets reduced significantly.

The field of wireless networks is an important and challenging area. In this paper, routing in mobile adhoc networks (MANETs) using ant algorithm has been described. ANTHOCNET algorithm makes use of ant-like mobile agents which sample the nodes between source and destination. In MANET, each and every node has an additional task by which it can forward packets between two or more nodes. The routing protocol in MANET should be capable of adjusting between two or more nodes. The routing protocol should be capable of adjusting between high mobility, low bandwidth to low mobility, and high bandwidth scenario. AODV protocol is being estimated for higher throughput. By varying the time cycle, throughput and packet efficiency can be increased. When throughput is increased, efficiency is also high. This also improves the quality of services.

There is currently lot of work in ambient intelligence particularly in context awareness. Context awareness enables service discovery and adaptation of computing devices for ambient intelligence application. There is a common agreement of the fact that context-aware systems should be responsive to multi-agents, covering a large number of devices, assisting a large number of people and serving a large number of purposes. In an attempt to achieve such context-aware systems with scalable scenario implementations, we propose an adaptive and autonomous context-aware middleware for multi-agents with Type 2 fuzzy rough context ontology. Our model provides a meta-model for context description that includes context collection, context processing and applications reactions to significant context changes.

Multiple images have to be sent some time for different purposes. Those images encrypt individually before their sending through non-secure channel. This process is painful and also time-consuming. In this paper, multiple images are encrypted using genetic algorithm. Here, a set of crossover points and a set of values are used to diffuse and encrypt the images. These two sets are computed from a key sequence. The original images have been diffused by genetic algorithm. Bitwise XOR operation has been applied between key set and diffuse images to get encrypted images. The effectiveness of the algorithm has been tested by number of statistical tests like histogram analysis, correlation, and entropy test. A comparison is made between the proposed algorithm and other genetic-based encryption algorithm. Finally, it has been observed that the proposed algorithm is given better result in all tests with less execution time.

Case-based reasoning (CBR) is an appropriate methodology that applies logical reasoning using similarity measure to relate a current problem case with past similar cases. It has been applied successfully in medical diagnosis and has been experimented in different domains of application in diagnosis and detection. In this paper, we have proposed knowledge-based decision support system which uses the concept of CBR to detect cholera disease. CBR is problem solving method which is derived from artificial intelligence and is based on some base cases which can be revised in order to determine homogeneous cases for new problem. Experimental results show that the proposed model Cholera Easy Detection System (CEDS) assists the doctors to make a consistent decision. Through this work, we are intending to provide facility to the medical research scholars as well as medical unit in order to help them identify cholera when the patient is infected with correspondence symptoms of that disease. Moreover, the CEDS also assists in minimizing errors of deviation that have been found to be noticeable cause of medical errors.

Some of the application of WSNs such as disaster management and battlefield control demand fast data delivery, adaptable to network dynamism, and recovery from node failure. The proposed work aims overcoming the energy issues, recovers from node failure, and balances the power consumption of nodes while transferring data from source to sink. Optimal route is selected on the basis of minimum distance and minimum transmission energy required. Multi-hop technique is preferred for routing as it consumes less power. Comparing the results with dynamic source routing protocol (DSR), it could be inferred that proposed model is efficient in terms of power consumption; it has better network lifetime and throughput.

Cataract is a clouding of the lens inside the eye which leads to a decrease in the vision. It is the most common cause of blindness and is conventionally treated with surgery. The main objective of this paper is to develop a system which helps to detect cataract in affected eyes. The system consists of selection of area of interest from an image of an image and extraction of certain features of images. The proposed work consists of selection of the area of interest from an eye image and formation of membership values and their mean values from the textual properties of the image with an objective to differentiate between the normal and affected eye for the purpose of detection of degree of sickness of the eye. The said work is confirmed from the result as obtained from the irregular frequency distribution of the input image and the value of irregular deviation of the frequency of the image. A set of images consisting of normal and affected eyes has been collected for carrying out this work.

MOEA/D is a popular decomposition based on multiobjective evolutionary algorithm. This work considers a triobjective concentric hexagonal array geometry optimization problem where the three objectives are to obtain low sidelobe radiation pattern with high directivity and with as low number of active elements as possible. For simplicity, the optimization problem is modified from min-max-min search to min-min-min search using the duality principle. An unconstrained search utilizing MOEA/D based on Tchebysheff decomposition method is conducted to obtain the optimal front. Results reflect that the obtained optimal geometries perform much better than several regularly spaced larger concentric regular hexagonal antenna arrays.

In this research paper, quantum particle swarm optimization (QPSO) algorithm, an algorithm founded on the basic theory of particle cluster and properties of quantum mechanics, has been introduced for design of uniformly spaced scanned linear array antennas with definite value of sidelobe level (SLL), first null beam width (FNBW), and broad null depth. An effort is made such that these parameters are made equal to their related specific values. This is usually done by changing excitation current amplitude of the elements. The expression of array factor is derived by using the property of linear array antennas. One example has been presented with 35 isotropic antennas. Generated Pattern is scanned to an angle of 30°. The obtained results have shown that this technique is able to find required value of SLL, FNBW, and wide null depth. Although, the proposed method is developed and applied to a linear array of isotropic antennas; however, the principle can easily be applied to other types of arrays.

This paper deals with the implementation of decision feedback equalization-based receivers for MIMO systems in correlated Nakagami-m channel. Moreover, suboptimal, pre-equalization combining decision feedback receiver (DFE) architectures are examined and performance is compared with its linear counterparts.

In today’s world, people use Internet services and access applications on their mobile phones anywhere in the world. Wireless communication networks such as Third Generation Partnership Project’s (3GPP’s) Long Term Evolution (LTE) assist people to access Internet on their mobiles at high speed and in a seamless manner. When mobile devices move from a network from time to time, they need to be handed off to another network in order to provide users with same quality of service (QoS). In this paper, a FL-based handover scheme in LTE network is presented by considering parameters like received signal strength (RSS), data rate, and network coverage area. The handover scheme is implemented through simulation in NS-2. The results show improved packet delivery ratio (PDR) and decreased packet loss.

This paper presents a novel discrete phase-locked loop (DPLL) based on FPGA. In this DPLL, phase offset of reference sinusoidal signal is followed at the output of NCO with no dead zone (i.e., full phase lock-in-range). All the signals and components used in this DPLL system are realized as discrete digital time components. Hilbert transform is used to generate analytic signal, and then CORDIC algorithm in vector mode is used for instantaneous phase detection. A novel simple algorithm is used to compute the phase offset difference between reference sinusoidal signal and NCO output’s discrete sinusoidal values. Either LUT-based sine wave generator or CORDIC-based sine wave generator is used in NCO. The total system is implemented using Xilinx ISE and compared with existing DPLL block. Also, performance of phase lock time and phase lock-in-range is analyzed.

In this paper, a new scale length theory for tri-material surrounding gate tunnel field-effect transistor (TMSG-TFET) has been proposed and derived. The scale length accounts for the effective conduction path of subthreshold leakage and thereby captures the short-channel effects (SCEs) and subthreshold factor. In order to derive the subthreshold swing in terms of scaling factor, the effective conducting path effect (ECPE) must be considered. Compared to conventional scaling theory, scaling of TMSG-TFET with ECPE has shown a much lower subthreshold slope (SS) of

S

< 60 mV/dec. The simulations of the proposed work are performed using 2D TCAD Sentaurus device simulator. The analytical results are compared and verified with the TCAD simulation results. Finally, results of the proposed work are compared with the scaling theory for MOSFETs with ECPE.

Image segmentation is the process of subdividing a digital image into its constitute regions such that pixels belong to the same region will be same based on some image property (such as grayscale value, color, texture) and pixels in the different group will be different based on the same image property. Till date, different researchers have taken image segmentation problem from a different point of view and developed several image segmentation algorithms. This paper is going to address an optimization-based approach in color image segmentation where optimized threshold value is chosen by maximizing the Kapur’s Entropy Function.

Large-scale global optimization is one of the most challenging problems in the domain of stochastic optimization. Due to high dimensionality in the entire optimization process, different types of problems may occur for finding the global optima, e.g., solution space increases exponentially, problem complexity increases, and candidate search direction also increases exponentially. So, deterministic optimization algorithms cannot perform well for this kind of problems. Differential evolutionary algorithm is a population-based, stochastic search and optimization algorithm which can be used for global optimization problems. In this paper, we present self-adaptive dynamic population-based differential evolutionary algorithm which automatically adapts its parameters including population size.

Human Face recognition is one of the widely used biometric techniques for face identification and verification. It includes several subproblems like illumination variation, expression changes, aging, occlusion, and rotation of face images. Varying illumination is one of the well-known and challenging problems in human face recognition applications. In this paper, we proposed a novel approach to solve varying illumination problems in face images. The different stages include adaptive histogram equalization (AHE), Gaussian filtering, Log transform, difference of AHE+Gaussian filtering+Log image, and AHE+Log image, and then, we perform normalization. We are using principle component analysis (PCA) method for face recognition. The experimental results of proposed approach are compared with existing approaches, and it shows that our approach improves the performance of recognition under varying illumination conditions on Yale Face Database B.

In this study, performance of an analytical control strategy for semi-active suspension system called ‘jerk-driven damper’ (JDD) is analyzed. The analysis composes modeling of JDD control and MATLAB simulation considering a very realistic road input. Three continuous ‘positive amplitude half sinusoidal’ severe bumps are considered as input to the vehicle. For the ease of simulation, a semi-active damper is assumed to be a single degree of freedom (1DOF) incorporated into quarter-car model which is a subject of base excitation. The optimality in terms of vertical acceleration (comfort objective) of sprung mass for JDD is examined over ‘passive,’ ‘skyhook’ (SH), and ‘acceleration-driven-damper’ (ADD) control. The performance of JDD is found to be better on comparison.

In this paper, a voltage-controlled-based ring oscillator is proposed to generate different frequencies. Frequency of a ring oscillator depends on two factors: number of delay stages (length) in the ring and number of delay stages of the inverters, which can be controlled by a set of control voltages. In the proposed design of voltage-controlled ring oscillator, multiplexers are employed to control either length of the chain (

N

) or control voltage of the inverters or both to vary the delay hence output frequency. Three different types of pseudo NMOS/PMOS/NMOS with PMOS inverters-based ring oscillators are studied to generate harmonic such as frequencies. Simulation carried out in UMC 180 nm CMOS technology shows promising results.

A simple and realistic method is introduced to design an analog amplifier, having some design criteria, using Alpha-Power MOS law model, which becomes notable in short-channel MOSFETs (SCMs). Estimation of

α

,

V

th

, and

k

in Alpha-Power MOS law from simulation is the fundamental job to design an analog circuit using SCM, so that the simulated drain current should fit the Alpha-Power-based drain current equation. Work is done by simulation in UMC 180-nm technology. Design starts by extracting I–V value from the characteristics curve of a device NMOS using simulator. This paper also includes the variation of

α

,

k

with respect to gate voltage to minimize the design errors. Device dimension setup using the estimated value to meet the design criteria is described. Design procedures and analysis of simulated data using proposed method are briefly described and verified by designing an amplifier with resistive load. Proposed method is much more efficient, fully technology independent and free from complex mathematical expressions associated with the short-channel devices. Proposed method shows design performance quite closer and acceptable also very much suitable for initial design based on hand calculation.

In this work, an attempt has been made to design an intelligence technique-based expert system using adaptive neuro-fuzzy inference system (ANFIS) for predicting tool wear in milling operation. An artificial neural network is used for designing an optimized fuzzy logic system, so that the tool wear can be modeled for a set of input cutting parameters, namely feed rate, depth of cut, and cutting force. The proposed method uses two different learning approaches, namely back-propagation gradient descent method alone and hybrid method (i.e., combination of the least squares method and back-propagation algorithm) for training of first-order Sugeno-type fuzzy system. The predicted tool wear values derived from proposed ANFIS were compared with the experimental data.

In this paper, an elliptic curve crypto processor (ECCP) is proposed using an improved quad Itoh–Tsujii algorithm, as primitive, on field-programmable gate arrays (FPGAs) platform for binary fields generated by irreducible trinomials. Efficiency is obtained by eliminating the precomputation steps required in conventional quad Itoh–Tsujii algorithm scheme. Experimental results show that the proposed ECCP architecture has better area-time product compared to existing techniques .

The detection of weapons hidden underneath a person’s clothes is of prime importance for security of the public and also safety of public areas such as railway stations, malls, and airports. The screening procedures done manually give inaccurate results when the object is far away from the security personnel and when in a crowded area. The aim was to use a RGB visual image and a corresponding IR image and make a new algorithm for such an application with the help of fusion techniques. This involves not only detecting the weapon correctly, but also retaining the information of the original visual image as much as possible. Thus, using multi-exposure, multi-focus, and DWT fusion techniques, we propose an algorithm to retain the visual as well as the information regarding the weapon in our final output. We may also add that this will also help strengthen evidences against the ‘accused’ since he/she can be indisputably recognized with the help of the obtained output.

In this paper, we have formulated a two-dimensional (2D) analytical model for surface potential and electric field of a p-type tri-material gate (TMG) gate-all-around (GAA) nanowire tunneling field effect transistor (TFET). The proposed model includes the effect of voltage at the drain end, work functions of the gate metal, oxide thickness, and radius of the silicon nanowire. Incorporating the advantages of surrounding gate with three different gate material work functions, this nanostructure of TMG GAA TFET is proposed and shows improved electrical characteristics. Also a step change of potential along the channel is obtained, which screens the region R1 from the drain potential variations, thus reducing the drain control over the channel. This results in appreciable diminishing of short channel effects and hot carrier effects, which are investigated by developing a 2D analytical model. The analytical results are compared and verified with the simulation results.

In this paper, particle swarm optimization (PSO) method, which represents a recent approach for optimization problems in electromagnetic, is applied for array pattern synthesis of hexagonal array (HA) and concentric hexagonal array (CHA) of uniformly excited isotropic antennas which can generate directive beam with minimum relative side lobe level (SLL). Two examples has been presented and solved. In first example, the PSO is used to determine an optimal set of ‘ON-OFF’ elements in a 12-element thinned HA and 24-element thinned CHA, and in second example, PSO is used to determine an optimal set of amplitude distributions in a 12-element HA and a 24-element CHA that provide a radiation pattern with maximum SLL reduction. Optimization is done without significant change in the value of first null beam width (FNBW). Simulation results show that, in first example, the number of effective antenna elements can be brought down from 12 to 6 totals with simultaneous reduction in SLL by −23.85 dB for HA and from 24 to 11 totals with simultaneous reduction in SLL by −20.71 dB for CHA. In second example, SLL is reduced to less than −31 and −27 dB for HA and CHA, respectively, by optimizing inter-element spacing and inter-ring spacing along with amplitude using PSO.

In this paper, performance of sparse-grid Gauss–Hermite filter (SGHF) in bearings-only tracking (BOT) problem has been studied and compared with the performance of unscented Kalman filter (UKF), cubature Kalman filter (CKF), and Gauss–Hermite filter (GHF). The performance has been compared in terms of estimation accuracy and percentage of track loss, subjected to high initial uncertainty. It has been found that track loss of SGHF is less than all other quadrature filters with comparable estimation accuracy.

In the present-day scenario, industries are being completely automated in order to achieve better products at a faster rate. Automation also includes efficient monitoring system to enhance the safety standards. This paper presents how passive infrared (PIR) sensor, when implemented in combination with GSM technology, would smoothen the manufacturing process. Also, as these industries deal with very sophisticated machineries on a large scale, there should be no compromise, when it comes to the safety of working personnel. We have also worked out how PIR sensor along with a suitable optical filter can be implemented to achieve the desired safety levels in a steel industry.

Sentiment analysis has become a leading research domain with the advent of Web 2.0 where Web users express their opinions in user forums, blogs, discussion boards, and review sites. The online information is considered to be a valuable source for decision making, improving the quality of service, and helping the service providers to enhance their competitiveness. Since the processing of high-dimensional text data is not scalable, different feature selection mechanisms are being used to confine the study to only most informative features. These features are then used to train the classifier to improve the accuracy of sentiment-based classification. This paper explores six feature selection mechanisms (IG, GR, CHI, OneR, Relief-F, and SAE) with five different machine learning classifiers (SVM, NB, DT, K-NN, and ME) thereby providing Accuracy, on the movie review data set for each. Comparative results show that Naive Bayes (NB) outperforms other classifiers and works better for Gain Ratio (GR) and Significance Attribute Evaluation (SAE) feature selection method.

This paper deals with the enhancement of transmission system loadability with single contingency by optimal location and settings of multi-type Fexible AC Transmission System (FACTS) devices such as Thyristor-Controlled Series Compensator (TCSC) and Static Var Compensator (SVC). Contingency Severity Index (CSI) is used for the optimal location of FACTS devices. To enhance the socioeconomical benefits, the solution to the problem is optimized using Particle Swarm Optimization (PSO) technique thereby minimizing the Installation Cost (IC) of FACTS devices and Severity of OverLoading (SOL).

Interruptions in any process industry due to machinery problem induce a serious financial loss. And as we know that induction motors occupy a major area in machinery and process industry, detection of faults beforehand is a key to avoid the state of financial or production crisis in future. The present work proposes a novel algorithm for the detection of broken rotor bars in induction motor. Stator current in addition to rotor vibration in an induction motor was measured and employed for fault detection of broken rotor bar. Multiscale entropy (MSE) is used as statistic-based approach in order to tackle the nonlinear behavior existing in rotor bar using vibration and current as the diagnostic media, as both cumulatively considered describe the regularity in the diagnostic information. The proposed work presents an approach to analyze features that distinguish the rotor vibration and stator current samples of normal induction motor from those of the broken rotor bar. Further, backpropagation neural network classifier is applied over the resultant feature set which distinguishes the faulty data set from the healthy with an accuracy level of 15.5 % for vibration and 14 % for current.

Potentiostat is a circuit arrangement that maintains the electrochemical stability and also buffers the output signal. It senses the signal applied to it and thus generates the output proportional to the electrochemical current. This paper proposes an efficient design of a low-amplitude signal sensor with very low power consumption. The proposed design proves its significance as a low-amplitude signal sensor, which finds its applications in the field of biomedical. Different types of low-amplitude signals are processed through the proposed potentiostat design to analyze the sensed output levels. Further, this paper also makes the variability analysis of proposed design to demonstrate its immunity against the process parameter variation. The modeling of proposed potentiostat is done in SPICE, and the simulation results have been extensively verified using the same.

A ball beam mechanism is found to be highly unstable system. PID control has been mostly used for stabilizing the same. However, tuning of PID controller gains is a difficult issue. In this work, attempt has been made to develop a fuzzy model-based ball and beam mechanism. Initially, an analytical model of the system is presented in the nonlinear state space form. The feasibility of the proposed scheme for ball and beam system is demonstrated using MATLAB/SIMULINK. Computer simulations are exploited to demonstrate the validity and feasibility of the developed control schemes.

Health informatics has been witnessing a tremendous modernization by leveraging the information technology and networking. Big Data tools offer a platform for organizing huge volume of data generated out of the medical informatics systems. They offer mechanism to store data in a distributed manner and offer parallel processing environment to process the large amount of data. Even though such platforms offer scalable way of managing large volume of data, those tools do not provide mechanism to get value from the large volume of data. Healthcare data is peculiar in nature because it contains many links to within themselves, such as symptoms, practitioners, and medication. Processing such data using traditional RDBMS, Big Data tools to get the hidden value from it, is cumbersome. In this paper, we propose a framework based on graph database to connect various elements of healthcare data to get more value/insight from the healthcare data.

We investigate the ergodic capacity of multiple-input multiple-output (MIMO) system in Nakagami-m fading channels with linear receivers. Particularly in this paper, zero-forcing (ZF) receiver is considered. In this paper, we were able to demonstrate the impact of the channel impairment on the channel eigenvalues of the MIMO system and also represent the useful insights into the impact of the correlated Nakagami-m channel parameters (e.g., correlation coefficient and Nakagami fading parameter

m

).

Antenna arrays are capable of reducing co-channel interference and multipath fading. Beamwidth parameters have key role in the performance of an antenna array. Estimation of the first null beamwidth (FNBW) is important for the design of the array. Artificial neural networks (ANNs), also known as neural networks (NNs), use simple mathematical tools. The ability of trained ANNs to predict results for the unseen inputs makes them suitable for real-time applications. They can map the nonlinear behaviour of antenna arrays easily. This paper presents the neural estimations of the FNBW parameter for the broadside and end-fire uniform linear antenna arrays (ULAs), using radial basis function neural networks (RBF-NNs). Precise estimation of FNBW helps in achieving desired accuracy in array design and operation.

In this paper, the extended

H

∞

filter (EHF) has been logically reformulated as a modification of the extended Kalman filter (EKF). Thereafter, a robustness metric and a sensitivity metric have been defined, analogous to that in Saha et al. (IEEE Trans Instrum Meas 63(4):964–971, 2014 [

1

]), for the EHF which is used to determine a suitable combination of the filter tuning parameters. The major objective of this paper is to predict the usable range of the robustness bound

γ

for obtaining reliable filter performances. For this purpose, the same problem as in Saha et al. (IEEE Trans Instrum Meas 63(4):964–971, 2014 [

1

]) of tracking a 2D ballistic target has been studied and the desired states of the EHF have been chosen to be the same as the measurements. The plots of the metrics have been obtained for various choices of

γ

. The corresponding RMSE performances have then been plotted for each of these choices of

γ,

while the filter process noise covariance has been varied over the range obtained from the metric plot. It is observed that the metrics are useful in determining the usable range of

γ

for which the designer is able to obtain the desired trade-off between robustness and sensitivity.

This paper considers the use of Luby transform (LT) codes as a forward error correction (FEC) scheme for wireless body area networks (WBANs). We evaluate the bit error rate (BER) and packet error rate (PER) performance of LT codes for the following communication scenarios in WBAN: (i) in-body communication between invasive devices and hub and (ii) on-body communication among noninvasive devices and hub with line of sight (LOS) and non-LOS (NLOS) channels. A WBAN in which sensor nodes monitor the physiological parameters of human body and sends the data to a common hub regularly is considered. We also evaluate the performance of Bose–Chaudhuri–Hocquengham (BCH) codes, the FEC scheme specified by IEEE 802.15.6 for WBANs.

Neural circuits and their applications for retrainable circuits have created new avenues in circuit design and fault correction. Electric systems and electronic circuits can experience a number of faults due to faulty connection of wires, misfiring of switches, wear-out of certain components and so on. Unlike a conventional circuit, a neural circuit is not affected by a single-component failure and thus can self-correct the fault. Once a fault occurs, the circuit can be retrained almost instantly to obtain the required output with the remaining healthy circuit once again. In fact, the fidelity is even better sometimes as overfitting is reduced. In this paper, a filter and an inverter were subjected to faults with more than half the transistors removed. The simulated circuits retrained in seconds to obtain the desired characteristics with almost the same accuracy. This paper delineates the various aspects of such a self-recovering electric system and the magnitude of the fault that can be recovered from.

Scenario-based approaches have become attractive for designs through testing of embedded systems (ES). For verification, the system under development should be analyzed during its operation. Modeling of

dynamic scenarios

provides advantages of combining runtime verification with test generation and supports dynamic analysis of systems. This paper presents application of

dynamic scenarios

for test generation of a practical embedded signal processing system as case study.

It has been descriptively shown in the past that the boundary conditions in particle swarm optimizers are an important algorithmic part which restricts the particles within the solution space, thereby increasing their scope of participation in solution finding process. Usually, velocities of the particles are regulated to bring them back to solution space; here, a different fundamental approach of regulating the position of particles is taken; particles which go out of solution place are relocated inside by maintaining symmetry about the boundary. A three concentric ring circular antenna array is taken as the optimization target for the reduction of side lobes in the radiation pattern. Results show better performance of this boundary condition over other established ones in terms of quicker convergence and obtaining better optimization solution.

In this paper, we propose a novel method of human identification by ear images using collaborative representation-based classification (CRC) with single training sample per class. The system first employs force field transform on each sample ear image that enhances ear structure from a redundant background, and then, the test sample is reconstructed collaboratively using training samples of all classes and eventually classified to that particular class which gives minimum reconstruction error. The presented technique achieved an encouraging recognition rate of 97.71 % with single training sample per class on a database of 330 images developed at our laboratory.

Problems in computational mechanics involve higher order nonlinear differential equations with complex boundary conditions, which are difficult to solve analytically and need numerical methods to predict the approximate solution. A large number of mesh points are utilized for better accuracy of the numerical technique which results in storage and operations of a large amount of data. It is of utmost importance that the time taken to perform these calculations is reduced to a realizable scale. General purpose graphical processing units (GPGPUs) provide high number of floating point operations per second (FLOPS) and potentially offer the most efficient architecture to carry out large-scale calculations in computational mechanics. In the present work, an attempt has been made to reduce the computational time for obtaining numerical solution of heat transfer by conduction, laminar flow in a rectangular channel, lid-driven cavity, and flow past square cylinder by programming GPGPUs using compute unified device architecture (CUDA), while maintaining overall second-order accuracy.

This paper presents an efficient receiver design for on-chip current mode signaling (CMS) interconnects. The CMS interconnects using proposed receiver have 34 % lesser delay for interconnect length of 20 mm and around 3 times higher throughput at room temperature than that of conventional voltage mode signaling (VMS) interconnects. The analysis is performed for single-ended interconnects. The present work is useful for analyzing the effectiveness of voltage and current mode signaling techniques for on-chip interconnects. The simulations are performed for 180-nm technology node using Tanner EDA tool.

This paper demonstrates design of analog circuits using

g

m

/

I

D

method. It explains the effectiveness of

g

m

−

I

D

approach and then generates required plots using any simulator. Other than complex method of generating

g

m

−

I

D

plots, which requires advanced simulators, it is shown that a plot of

I

D

/

g

m

can be easily generated directly using simulator or through a simple program capable to manipulate device current–voltage data. The success of

g

m

/

I

D

technique lies on the fact that it employs a simple rule of scaling device dimension (

w

) and scales the current as well as transconductance (

g

m

) equally, when other parameters are constant. Therefore, when a reference device with required

g

m

and

I

D

is found, it can be scaled up to generate desired

g

m

at the given bias current (power)

I

D

. Proposed method is not only technology independent, it is also free from complex mathematical expressions associated with the device as it employs data generated from simulators. As it is not based on analytical methods or models, its accuracy (independent of BSIM or ACM; and their parameter values) is much better specially for analog design. Most importantly incorporating simulator in the design process, analysis of the designed circuit, using the same simulator, is expected to match the desired performance closely. Using this simple approach, design time becomes shorter and a workable design can be made very quickly. Two basic amplifier circuits are designed using the proposed method, and simulation results are discussed.

This paper presents an evolutionary-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs opposition-based gravitational search algorithm (OGSA) for optimal settings of OPF control variables. The proposed approach is tested on the standard 26-bus test system with different objective functions those reflect minimization of fuel cost, that of transmission loss, that of sum of total voltage deviation while maintaining an acceptable system performance in terms of limits on generators’ real and reactive powers, line flow limits, outputs of various compensating devices, etc. The simulation results of the proposed approach are compared with those reported in the literature. The obtained results demonstrate the potential of the proposed approach and show its effectiveness and robustness to solve the OPF problem of power system considered.

In this paper, the main objective is to show how to obtain precise concrete road design using an information systems and artificial intelligence (ISAI) applied in the numerical design stage of a plate on elastic solid system. In this system, finite difference solution for very large plate on elastic solid is used. In this solution, computations take only few minutes using computers with very low capacity. In this paper, the logic of solution is determined according to the principles of finite difference method using an electronic interior control subsystem. In this deterministic approach, plate is divided into small parts. One of these parts, which is named intelligent object (IO), is solved under traffic loadings by ISAI technology. Then, the plate surface is scanned by IO, and precise solution for plate thickness is determined by the system. The system incorporates ISAI technology.

The video data that are used in the multimedia applications are not completely secure because of multiple unsecured hops present between the sender and the receiver. Any intermediate node may behave malicious and copy of the data packets illegally. This paper introduces a novel method to send video data through dual channel using dual data path. The frames’ pixels are scrambled. The video frames are divided into two frame streams. At the receiver side, video is reconstructed and played for a limited time period. As soon as small chunk of merged video is played, it is deleted from video buffer. The approach has been tried to formalize, and initial simulation has been done over MATLAB. Preliminary results are optimistic, and a refined approach may lead to a formal designing of network layer routing protocol.

Static program analysis has shown tremendous surge from basic compiler optimization technique to becoming a major role player in correctness and verification of software. Because of its rich theoretical background, static analysis is in a good position to help produce quality software. This paper provides an overview of the existing static analysis techniques and tools. Further, it gives a critique of static analysis approach over six attributes, namely precision, efficiency, coverage, modularity, scalability, and automation.

Suspension design is always been a challenging task for automobile designers in view of multiple input parameters, complex objectives, and disturbances which are stochastic in nature. The conflicting nature of ride comfort (RC) and road holding (RH) compels a judicial compromise between these two. In the present work, full factorial design of experiment (DoE) has been used successfully for the purpose of multi-objective optimization of RC and RH with input variables spring stiffness (

K

), damping coefficient (

C

), sprung mass (

M

), and speed. For experiments with combination of various input variables, a quarter car test rig is developed with proper data acquisition system by NI-hardware and Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) to collect real-time data. A regression model of RC and RH with

R

2

value 95.98 and 95.78 %, respectively, can be effectively used to evaluate optimal settings of various input parameters using response optimization with a high desirability value.

Computational and microbial molecular-level participation of sox operon and its repressor protein (SoxR) in sulphur oxidation from

Pseudaminobacter salicylatoxidans

(KCT001) was investigated. Documentation reveals that

P. salicylatoxidans

(KCT001) has

sox TRSVWXYZABCD

operon that is regulated by a repressor protein (SoxR). Previously, various experimental procedures such as DMS-mediated DNA methylations and hydroxyl radical footprinting have disclosed that SoxR interacts first with an operator region-

sv

(present in between soxS and soxV). Detailed computational studies were accomplished in the present study. 3D models of repressor protein and the DNA sequence from operon’s promoter region were demonstrated using molecular modelling techniques. Molecular docking simulation was performed to predict DNA–protein interaction. Amino acid residues and nucleotide bases responsible for interaction were identified by PyMOL and Discovery Studio software suite. This novel residue-level study is paramount for initiating transcription in the operon, thereby leading to sulphur oxidation.

Authorship attribution (AA) is the task of identifying authors of anonymous texts. It is represented as multi-class text classification task. It is concerned with writing style rather than topic matter. The scalability issue in traditional AA studies concerns with the effect of data size, the amount of data per candidate author. Most stylometry researches tend to focus on long texts per author, but it is not probed in much depth in short texts. This paper investigates the task of AA on Telugu texts written by 12 different authors. Several experiments were conducted on these texts by extracting various lexical and character features of the writing style of each author, using word n-grams and character n-grams as a text representation. The support vector machine (SVM) classifier is employed in order to classify the texts to their authors. AA performance in terms of

F

1

measure and accuracy deteriorates as the number of candidate author’s increases and size of training data decreases.