Industry Interactive Innovations in Science, Engineering and Technology

The Sun and the Jupiter both astronomical objects emit radiation in radio frequencies and have much importance in the Earth’s atmosphere. We have recorded data of both Solar and Jovian signal on NASA’s Radio Jove instrument at 20.1 MHz. The simultaneous visibility of the two objects—the Sun and the Jupiter—is of much importance in this study. In this paper, we have done the comparison using the Jove receiving system on those dates especially when the Jupiter rises at nighttime. It shows that the intensity of solar radio emission is well above Jovian emission intensity. It has been observed that solar radio bursts are stronger than Jovian bursts. They occur usually when there are sunspots on the visible corona while Jupiter noise storms are more likely to occur at nighttime and so they are much prominently recorded.

In recent time due to diminishing fossil fuel reserves and increasing social pressure in terms of environmental pollution, complex power system has no other option except to seek for another possibility of alternative energy to eliminate the environmental pollution. Solar energy accounts for the most of the renewable energy on earth. PV module is a particular procedure to accomplish the generation of electric power from solar radiation using semiconductor devices which exhibit photovoltaic effects. The concepts of a PV module and its characteristics have been studied. The P–V and V–I curves have been obtained at changing solar irradiation levels and temperatures.

Biogas has globally remained a renewable energy source derived from anaerobic digestion of organic matter. This process treats the organic matter efficiently producing a effluent whose residue is rich in inorganic elements like nitrogen and phosphorous needed for effective plant growth. In our investigation determination of yield coefficients of methane and carbon dioxide in methanogenesis was done considering volatile fatty acid and long chain fatty acids as the principal substrates obtained after the acidogenic degradation of vegetable market waste. The purpose of considering these two substrates is that these are the primary substrates for the methanogens needed to produce biogas. Besides, a pure chemical source of volatile fatty acid and long chain fatty acids has been used to conveniently determine yield coefficients of the products of methanogenesis which matched well with the biogas generated from the digester.

Economic load dispatch is one of fundamental issues in optimal power system operation. Several classical and modern heuristic techniques have been used to solve the problem. A novel algorithm is presented in this paper for solving economic load dispatch problem using a recently introduced simple yet powerful optimization technique called Jaya algorithm (JA). Most of the modern heuristic techniques require setting of control parameters which are usually problem specific. Moreover, there is no specific rule for the selection of these user-defined control parameters for most of the heuristic techniques. A wrong parameter may even lead to premature convergence. One of the major advantages in the proposed algorithm is that it does not require any problem-dependent control parameters. The economic load dispatch (ELD) problem is formulated by considering prohibited zones, ramp rate limits, and transmission losses satisfying a set of equality and inequality constraints. The proposed algorithm is tested on a six-unit test system in order to verify its effectiveness and efficiency. The simulation results are compared with those obtained by modern heuristic techniques. The simulation results show that the proposed technique has the capability of producing comparable results.

This paper presents utilization of particle swarm optimization in solving combined economic emission dispatch (EED) problem. The economic emission dispatch is an important problem in power sector as it combines two major objectives viz., cost minimization and emission minimization while maintaining operational constraints. Several meta-heuristic techniques have been developed in recent times and have been applied on power dispatch problems. PSO is such a meta-heuristic technique where time-varying acceleration coefficients (TVAC) are incorporated and used in the EED problem in this work. Thus it addresses the techno-economic-environmental aspect of power system operation. Economic emission dispatch problem is first resolved using weighted sum method, and second trade-off curve between two objectives has been found, referred to as pareto front which traces solutions obtained by non-dominated approach of the problem. The formulation is implemented on IEEE 30 bus test system and outcome obtained validates effectiveness of this research work.

This paper presents an application of rule-based proportional integral (PI) controller for the load frequency control (LFC) taking into account the effect of power transmission network. The analysis is carried out considering a six-bus system and genetic algorithm (GA) is used to optimize the gains of conventional as well as rule-based PI controller. The results show that dynamic response of frequency deviation improved significantly using rule-based PI controller compared to conventional PI controller.

In this paper an efficient technique is described for managing the congestion in electric transmission network based on rescheduling the nearby generators and/or shedding some of the loads. To incorporate the uncertainty in the system objectives and parameters, fuzzy environment is considered for the formulation of the problem. In the solution process bio-inspired computational technique, genetic algorithm (GA) is used. The approach is illustrated by standard IEEE 30-bus 6-generator test system.

Eigenenergies of lowest three quantum states in semiconductor quantum ring is analytically determined as a function of surface-to-volume ratio subject to the application of external electric field applied along the axis of the ring, which is perpendicular to ring plane. n-GaAs material is considered for simulation, and the results are compared with that obtained in the absence of field. Result shows that with increasing S/V ratio, energy increases almost linearly in the presence of field, whereas the behavior is nonlinear when field is absent. Critical finding in this respect is that intersubband transition remains constant with increasing S/V ratio, which speaks about its candidature as optical emitter/detector within dimensional variation over nanorange. Variation is also calculated for different surface areas and volume of nanodevice. Result speaks about possible tuning of the eigenenergy by external field in IR wavelength region.

Dependence of conductance on equivalent circuit parameters in single electron transistor is analytically computed for electrical performance estimation. Distorted conductance profiles are obtained when a few passive components exceeds threshold limit, and negative spikes are also possible, as revealed from simulation. Steady-state master equation is solved with appropriate boundary conditions when source and drains are connected via quantum dot, which ensures tunneling process. Fermi Golden Rule is applied to calculate probabilistic values of all stochastic processes and effect of source and drain resistances and capacitances as well as gate capacitance are considered for determining conductance. Simulated findings are important for practical application of SET as infrared detector and charge sensor.

Perovskite-type La-doped Yttrium Chromite (Y_1–XLa_xCrO₃) nanoparticles are prepared via sol–gel method. The formation of the compound is confirmed by X-ray diffraction (XRD) technique. Particle sizes are calculated by Transmission Electron Microscopy (TEM) measurements. Electrical DC conductivity is studied. DC conductivity analysis shows that all the samples have semiconducting behavior. Dc activation energy decreases when the lanthanum doping content is increased. The dielectric properties of all the samples are studied within the temperature range 298–523 K and in the frequency range 20–1 MHz. Impedance (AC) analysis suggests that the assistance of grain boundary governs over the grain.

Generic Object detection technique plays an important role in the traffic surveillance and security-related issues. Research has been done over the past several years and accomplished great progresses via convolutional neural network (CNN) which has greatly enhanced the performance in image classification and object detection. This proposal is similar to the notion of R-CNN [1], presents a novel method that combines the spatially pooled features (sp-Cov) as a part of aggregated channel (ACF) and CNN for object detection. The proposed technique uses sp-Cova and ACF to select the possible object on interest regions and then trains a CNN model to filter out non-face candidates. Merging the results of sp-Cov + ACF and CNN to get the final detection window(s). The proposed framework achieves the good performance with state-of-the-art methods on numerous benchmark data sets.

Grid provides a clear, coordinated, consistent and reliable computing medium to solve complex sequential and parallel applications through the use of idle CPU cycles. Scheduling optimizes the objective function(s) by mapping the parallel jobs to the available resources. Owing to the heterogeneity of resources in grid, scheduling associates with class of NP-hard problems due to which reaching optimal solution surpasses the time constraint. Metaheuristic algorithms take polynomial time to reach the near-optimal solutions for NP-hard problems. Major research issues in metaheuristic algorithms are solution quality and convergence speed that have been revised by using consolidation approach. This paper proposes a hybrid PSACGA algorithm that consolidate the features of Particle Swarm Optimization (PSO), Ant Colony optimization (ACO) and operators of Genetic algorithm to solve parallel job scheduling problem. Experimental results of the proposed technique are compared with existing deterministic and metaheuristic job scheduling algorithms. Experimental results have indicated that the proposed hybrid PSAGA algorithm provides better performance than existing contemporary algorithms.

Mobile Ad hoc Networks (MANETs) is rapidly gaining popularity due to its infrastructureless and self-configured capabilities. In MANETs, a group of nodes make a network where every node, individually can play sender, receiver or router and the communication is either single hop or multi-hops. So, scheduling is an important part to this kind of network for either route discovery or data packet transfer within this network. All scheduling algorithms in MANETs are either packet scheduling in term of QoS or MAC access. Packet scheduling algorithm is more important than MAC access. Packets are also two types, control packet which are used for route establishment and data packet. Scheduling algorithms can be priority based or non-priority based. We can broadly classify the entire scheduling schemes into two types: Non-real-time scheduling or Real-time scheduling. This paper makes a survey of all kind of scheduling algorithms in Mobile Ad hoc Networks in chronological order with classification of Non-real-time and Real-time scheduling schemes.

Data Warehouse is an incorporated database premeditated to expand while crafting decision and crisis resolving, espousing exceedingly condensed fact. The amalgamation of security modus operandi with data warehouse is a unanimously established premise for contemporary researchers. The crucial tip of the projected effort through the formulated work emphasizes on the design and implementation of the novel Double Ended Bottom-Up Approach architecture and the integration of the cryptographic modus operandi amid it having precise intent of augmenting safety and performance therein. The proposed cryptographic algorithm has been engendered based on the concept of employing Country ISO Code within. Devising the inventive algorithm ensures considerable diminish in admittance time, considering apt retrieval of the entire indispensable information. Related appliance of the proposed work is in a diversity of organization, where accrual of plausible record is of extreme insinuation. The collection of organization embraces an amalgamation of scholastic organizations, business houses, curative medicinal enterprises, defense and security agencies with sensitive data, classified enterprises and forwards.

The basic meaning of crime against women is direct or indirect mental or physical torture or cruelty towards women. Crime against women is increasing every year and as per the research they have doubled over the past ten years, according to latest data released by the NCRB (National Crime Records Bureau). As many as 2.24 million approx. crimes were reported against women over the past decade. On an average 25 crime per hour against women are reported, at least a complaint every two minutes. To control crime, the eyes have to be set on the factors which are influencing the crime against women. For this consideration there are various factors affecting the crime against women. In this paper factors are identified for crime against women. The impact of the individual factor has been checked for the overall crime rate in Delhi on the basis of regression analysis using SPSS tool and thereafter K-means clustering technique has been applied to classify the respondents or cases into clusters on the basis of degree of crime rate for various factors influencing the crime against women.

Data gathering from inhospitable terrains such as volcanic area, dense forest, sea bed are a major application area of wireless sensor network (WSN). The replacements of sensor node batteries are not feasible and as a result all the protocols in WSN should be energy efficient to elongate network lifetime. In hierarchical routing protocol (HRP) nodes are assigned different tasks of varying energy intensity as per their role which are interchanged across rounds. It leads to load balancing and energy preservation. We propose in this paper an energy efficient load balanced data gathering method based on rough fuzzy c-means (RFCM) and ant colony optimization (ACO) and coin it as RFCM-ACO. The deployed are partitioned into clusters by RFCM followed by ACO-based lower and upper chain formation. The chain leader (CL) for lower chain and super leader (SL) for upper chain are elected using a fuzzy inference system (FIS). Simulation results indicate that RFCM-ACO outperforms LEACH, PEGASIS and Hybrid_FCM in terms of network lifetime and load balance.

In recent decades, artificial intelligence (AI) has found numerous applications in the field of medicine. In this paper, the key idea is that doctors are not available at emergency or doctors may do some mistakes by coincidence. To overcome or eliminate these problems, here we consider some diseases and design a prototype. In this work, we investigate different types of diseases according to various infectious agents (bacteria, virus, and parasite) and the respective diseases caused by them. Afterward, we make a formulation so that it deals with history of the disease and prescribing the proper medications according to the disease detected.

Twitter is one of the most popular online social networking and micro-blogging service that enables its users to post and share text-based messages called Tweets. The data generated daily in terms of tweets are enormous and represents a rich source of information. To elicit actionable intelligence, various natural language processing (NLP) and text mining techniques are applied. Detecting of trends from twitters represents an important set of problems with a wide variety of applications and has huge appeal to diverse communities. In this paper, a simple trend detection technique based on term frequency has been proposed. In the first step, term document matrix of the tweet stream is created and top words are identified. The top word list is dynamically updated based on new streams. Time series is generated for the top words. Trends of the words are detected using Mann–Kendall non-parametric test. The method has been applied on few topical twitter datasets and proved to be quite effective.

This article presents a brief review on full-reference (FR) image quality assessment (IQA) techniques. The discussion starts with traditional Peak Signal-to-Noise Ratio (PSNR) and then gradually moves to complex IQA models based on wavelets (or its various variants) and human visual system (HVS). The techniques are discussed from mathematical perspective to theoretical viewpoint. These discussions will be very useful for relevant researchers to have an apparent understanding about the status of recent FR-IQA. Few research problems are also discussed as an outcome of the article.

This paper aims to provide a better accuracy for face recognition procedure. This new algorithm is based on accurate feature extraction and proper classification. In this paper feature coordinate-based ICA is used for feature extraction. Pixel values of invariable coordinates (containing decisive data) for every training set are considered for analyzing through ICA. After feature extraction, these values are used for fuzzy minimal structure oscillation-based classification. Proposed face recognition procedure accentuates improved classification considering the feature vectors, which is the outcome of independent component analysis of the face image.

Critical performance of three-layer surface plasmon-based sensor is computed at visible spectra. Reflectance is calculated at wider range of incidence angle and also for different thicknesses of gold layer. A sharp dip is observed at reflectance profile when propagation constant of incident wave and that of plasmon surface becomes nearly equal. Field enhancement is also obtained for different structural parameters. Findings will play important role in designing optical sensor at visible range for bio applications.

In this paper, we accomplish a comprehensive study of optical waveguide modeling incorporating modal index concept in refractive index profile for two specific rib waveguide structures using fourth-order finite difference method in combination with Newton–Krylov subspace algorithm. The numerical results verify the behavior of this higher order compact (HOC) approximations for stability and convergence with least computational time. Obtained results for normalized indices and modal indices are compared with other methods to verify the accuracy and efficiency of the simple scheme used. Also variations of these indices with waveguide structure parameters help to identify their optimized values for efficient wave propagation which are found to be material dependent.

Internal model control (IMC) technique is one of the well-accepted model-based controller designing methodologies which is widely used in process industries due to their simplicity and ease of tuning. Most of the IMC tuning provides good set point response but unsatisfactory load rejection behavior. To overcome this limitation for industrial processes SIMC technique is reported in the literature. In this technique, to derive the SIMC controller expression, higher order processes are approximated as first-order plus time delay model. Hence, uncertainty is always there in process modeling and as a result SIMC controller may fail to provide the satisfactory performance with conventional fixed tuning. A fuzzy-tuned SIMC controller is reported here to surmount this drawback and its efficacy is established through real-life experimentation on a laboratory-based level control loop.

An electromagnetic sensing device, MagStar, has been developed by authors, measure both magnetic hysteresis loop and magnetic Barkhausen emission. The device explains its credibility for characterizing different types steels, SA213T22 and IF steel with varying microstructural phases treated at different heat treatment conditions. MagStar is also very versatile to be applicable for both industries and educational research due to its portability and low cost.

The current work reports of an obstacle avoidance and object tracking algorithm integrated and tested on robot. The system is designed with high torque geared DC motors and it has a payload capacity of up to 3 kg. The robot is fully autonomous and it is designed to patrol high security/hazardous zones and dynamically report any suspicious activity which when observed initiates an alarm to the security for further action whiles it continues to track and report the suspect. This has a major advantage over conventional CCTV systems in terms of cost and memory requirements and would not require constant human supervision. The robot proposed hereof uses wheel-encoders, ICbased Gyroscope, IR Line Laser, and spy camera as the basic sensing elements. It also has a smart charging feature which makes it energy efficient.

In this work, we report of a noncontact level measurement based on machine vision techniques. Data has been acquired with a high-definition webcam @ 30 frames per second. Image-processing algorithms have been developed and optimized using LabVIEW. The measurement may then automate a servo actuated hand-valve. On testing, the system was found to be linear, repeatable, and accurate to ±0.5% of the measured value for 50 measurements carried out. In the scheme are proposed hereof, the levels may just be remotely observed on a LabVIEW SCADA panel and used as a set point to a regulatory control-actuator system. The system is rugged and can be calibrated to 0.05% precision for all rotameter types used in any environment from a simple DM plant to a petrochemical industry for a fixed camera distance.

The Internet of things contains billions of interconnected things such as physical objects, animals, or human beings that give the power to transfer information over a network without human interaction. The huge amount of data continuously generated by these sensing devices raises the challenge of searching for the most relevant sensor data according to user’s query. Also, the queries specified by users are in natural human language which cannot be processed by sensors. To handle these challenges, devices in an IoT environment are grouped together to form clusters which reduces the search space. Every device is given a unique ipv6 address to identify it within the network. Then, the user’s abstract query is transformed into low level form which can be recognized by sensors. Experimental results show that the search time and response time is improved using this approach in large scale IoT environments.

This paper describes a method for assessing risk of the workers at construction sites using interval type-2 fuzzy analytic hierarchy process. Historical accident data, subjective judgments by the experts and relative importance of the risk factors are combined together to determine the current risk level of construction sites. The linguistic terms associated with the model are represented by interval type-2 trapezoidal fuzzy numbers. The proposed method can identify the risk factors which are most important in improving worker safety and, therefore, determines the areas on which the management should emphasize in order to improve the safety of the workers. The application potentiality of this model has been demonstrated by a case example of risk analysis of a construction industry.

In present scenario, MIMO is an attractive technology to enhance the system capacity using its antenna configuration. This capacity analysis is possible through the probability distribution of elements used in MIMO. In this work, a comparative analysis of probability distribution of elements has been done in a scattered environment of MIMO system. Different iterative values have been considered to perform this comparison. This time-based iterative value is useful to achieve the probability distribution with respect to different elements. Altogether, capacity comparison is also done through different antenna patterns. From the graphical output it is shown that, better level of distribution occurs at higher levels of iterative value.

The rise in demand for cloud computing services has thrown sharply into focus the subject of energy efficiency and cooling methods. The words “green” and “computing” can often translate into commercial and production successes, vendors and consumers alike are keen to optimize the services offered through cloud data centers as much as possible. While various existing methods help in bringing down rising temperatures of servers operating in cloud data center infrastructure, most authors would agree that pushing in cold air requires energy to be fed to cooling equipment and the associated infrastructure. Based upon existing research conducted, we approached the problem in a new light—concentrating on server utilization to regulate the temperature. We introduce Mean Utilization Factor concept that allows detecting and regulating the amount of cool air that is to be channeled in and around the servers within a cloud data center to bring down the operating temperature.

The target of this study is to build up a VISSIM simulation model and align it to find out volume-to-capacity proportions of turning movements at uncontrolled intersections under Indian mixed traffic conditions. Driver and traffic behavior related parameters are adjusted after examination of the field information. The microscopic simulation outputs of the calibrated VISSIM model are contrasted with the field capacity values assessed from gap acceptance. Classified Movement volumes gathered at a four-legged uncontrolled intersection in the state of Maharashtra is utilized. Driver Behavior parameters (car-following, lane changing and lateral driving) are initially decided for homogeneous movement having one of the six classifications of vehicles considered in the stream and after that the outcomes are accumulated to get the estimations of these parameters for a mixed stream. Every single other factors and movement activities on every methodology are kept as steady. The calibrated VISSIM model is then used to decide capacities (volumes) by flooding a section at once for non-priority movements. Simulated volumes were obtained for each movements and turns after the 7th run. Gap acceptance capacity is calculated using HCM 2010 formula for the intersection. Volume-to-capacity ratio is used as the critical measure for assessing traffic flow operations within the intersection. After assessment through calibration it was found that the uncontrolled intersection is operating under low to moderate congestion (volume-to-capacity ratio less than 0.85) thus experiencing lesser service delays.

In this paper, transfer matrix technique is used to compute the First Brillouin zone of DNG/air material composition for the application in photonic crystal. Three different types of physically realizable metamaterials are considered as the constituent of the periodic arrangement. Tuning of the Brillouin zone is made by suitable changing the structural parameters and coupling coefficient between forward and backward propagating waves for all three structures (paired nanorod, nano-fishnet with rectangular void, nano-fishnet with elliptical void). Results obtained from the first principle show the possibility of transition between perfect and quasi electromagnetic bandgap which is important for the possible application as photonic filter. Result is compared with the obtained band structure of conventional SiO₂/air composition.

To provide good quality of services to Mobile Users (MU) is main aim of every Cellular network. Radio bandwidth is critical resources which should be used optimally. More bandwidth is consumed due to frequent Location Update and paging. So, if we know the location of Mobile users in advance Location update and more paging can be reduced. In this paper, we implemented HMM method to predict current location of mobile user in Cellular network based on their previous mobility pattern and behavior. By the implementation, results shows that based on previous state of mobile user we can able to predict its current location. If previous state history is more than chance of getting accurate location of mobile user is higher.

In Wireless Sensor Networks (WSN), the problems of sinkhole detection and mitigation have been studied through various methods, but suffer with the problems of high complex detection and overhead introduced by detection process. The Quality of Service (QoS) has been degraded greatly by the impact of sinkhole attack because the malicious node can read much information about the source or the communications; this helps malicious nodes to perform various kinds of attacks. In order to overcome such drawbacks of network security, in this paper propose a Neighbor Constraint Traffic Centric (NCTC) Method for sinkhole detection to support QoS development of WSN. The problem of multiple identifications is resolved by verifying the transmission performed through other neighbors of the malicious node. From the traffic incurred in the neighbor of malicious node is used to verify the location or traffic sent by the malicious node. The method produces efficient results in sinkhole detection and increases the throughput and also the packet delivery ratio and reduces the frequency of sinkhole detection.

It has been several years Background subtraction techniques were put into use in vision and image applications for motion detection. However, most of the methods fall short of providing fine results due to dynamic backgrounds, illumination variation, noise, etc. Uniqueness of the proposal is construction of a steady background from a video sequel. In the editorial, proposal is to develop a steady background representation from a certain video sequel. The background is updated on arrival of each frame. For detecting moving objects, the constructed background has been compared with diverse frames of the video sequel. For this, the background model is developed using combination of Local Binary Pattern (LBP) and Gaussian averaging. Gaussian averaging employs different forms that occur with time to confines the underlying opulence of the background. Likewise, a spatial region of hold is used by LBP. The projected proposal depends on spatio-temporal forms occurring with time to fabricate a suitable model background. Efficacy of the projected proposal is established by comparing the outcomes with some of the existing avant-garde background subtraction methods on open standard records.

In this research paper, a design of CMOS integrator circuit for sigma delta ADC has been carried out. The proposed integrator claims a key role in low-power CMOS sigma delta ADC. The low-power CMOS sigma delta ADC is used to design smart temperature sensor for aerospace application which would sense a temperature range of −50 to 150 °C. The analog output of temperature sensor is digitized using low power CMOS sigma delta ADC. The integrator behaves as a low-pass filter for input signal and high-pass filter for quantization noise. Here, the input and output are measured across the capacitor. This circuit is designed using Cadence Virtuoso UMC90 nm CMOS technology. For the proper operation of the circuit, power supply is used in the range of +1.3 to −1.3 V. As the input square wave is applied, during the positive half cycle, the voltage across capacitor increases from zero to the maximum (peak value of applied voltage). During the negative half cycle, the capacitor starts to discharge and comes to zero.

VLSI and nanocomputing has become the most desirable feature of any integrated chip. Computers are also becoming more portable. ICs are being introduced everywhere. This huge implementation of IC has also opened a scope of research. Size of IC has become an issue to think about. Power dissipation is also another important consideration as performance of VLSI chip design. Low-power high-speed CMOS circuit design methodologies will be elaborated in this paper.

Networking technology is playing a major role in daily life for communicating each other. Security is the most important issue in wireless networks. Recently, trust and reputation mechanisms are used for providing security through monitoring the behaviour. However, the existing works lack in providing reliable security to wireless networks. In this paper, we propose an intelligent beta reputation and dynamic trust model (IBRDT) for providing security in wireless networks. This model is the combination of dynamic trust and beta reputation trust for secure routing in wireless networks.

Optical bandwidth of ternary photonic crystal based Butterworth filter is computed for polarized incidence of electromagnetic wave; and effect of incidence angle and structural parameters are studied within lower range on the filter performance. Result is compared with that obtained for normal incidence. Transfer matrix technique is adopted for calculation; and SiO₂/air/TiO₂ material system is considered for simulation purpose. Simulated findings in favor of p-polarized wave incidence for varying incidence angle owing to higher bandwidth and less ripple in passband for filter application.

Wireless sensor networks (WSNs) employ numerous sensor nodes possessing sensing, processing, and wireless communication abilities to monitor a specified sensing field. As sensor nodes are mostly battery operated and are highly constrained regarding energy resources, it is essential to explore energy optimization methods to prolong WSN lifetime. Target tracking is a very conventional WSN application that demands both useful and coherent energy management. This paper proposes a distributed shortest path data collection algorithm for connected target coverage to maximize WSN lifetime pertaining to both static and mobile multi-hop WSNs. The performance is evaluated in TinyOS employing the TOSSIM simulator based on the parameters like percentage of alive nodes, load distribution of nodes, and network lifetime.

In traditional wireless sensor networks (WSNs), a single sink or base station is used to gather data from the whole network which suffers some serious performance issues, like latency, congestion, network failure, etc. Deploying multiple sinks in WSNs can improve the network performance, but it increases sink deployment cost. In this paper, we propose distributed algorithms to find out the minimum number of sinks to be deployed and their optimal positions in the deployment region, while ensuring a certain latency and fault tolerance level. We have considered both deterministic and random sensor node deployment strategies. Simulations of the algorithms are carried out in ns-3 network simulator, which shows number of sinks varies inversely with transmission range of nodes and network latency; and it is linearly proportional to the fault tolerance level.

Performance of flash ADCs is beset in presence of offsets in comparators. Offsets present in comparators of a flash ADC give rise to bubble or sparkle error. There are several methods to eliminate this error—both first order or higher ones. In this paper, performance evaluation of flash ADCs will be carried out using hot code generator and bit swap logic (BSL) in presence of such offsets. It is well known that while hot code generators can take care of only first-order error in the thermometric code, BSL method can take care of any order of error. Simulation for a 3-bit flash ADC has been carried out in the presence of offsets and it has been shown that while the hot code generator can get rid of only first-order error, the BSL method overcomes any order of error.

In this paper, the performance of throughput analysis is done in an energy efficient amplify and forward (AF) relay assisted cognitive radio network. An effective optimization problem is formulated to observe the effect of two relays in proposed scenario. To achieve the desired goal, a constrained optimization problem is studied under the constraints of probability of detection threshold and maximum limitation of fixed power budget. On the basis of mathematical formulation and effective searching technique, an optimal sensing time is found to achieve high throughput of the proposed system.

Bayesian Decision Theoretic Rough Set (BDTRS) model is a significant advancement in the field of attribute reduction of an information system. However, a lack of the related software for implementing this model can be observed preventing their use in practice. In this paper, the BDTRS model for attribute reduction is further studied and implemented using R programming language as functions. These new R functions are further compared with few existing sophisticated rough set based attribute reduction methods (R functions), available within “RoughSets” package of R. For comparative analysis, secondary data sets from UCI Machine Learning repository has been used. Improved results have been achieved by the implemented BDTRS-based R functions compared to other existing functions. The implemented BDTRS model may now perform attribute reduction for high-dimensional large size practical field data sets which was not possible earlier.

In this paper, we propose symbol table driven DNA compression algorithm aimed to use as a cloud service. Bioinformatics requires a huge amount of genomic data to analysis, so optimal storage and compression is a great challenge to this field. We categorized the DNA sequence into three different parts according to the occurrence of A T C and G and use two different symbols tables to map the DNA sequences into a compressed sequence. We are intended to deploy our proposed compression algorithm in cloud, so that the user of this field can access this Software as a Service over the cloud. Through our proposed method of compression, we claim to achieve a compression rate of 1.82.

Electrohydrodynamic (EHD) printing is a micro- and nano-manufacturing process of printing high-resolution functional material on a substrate. It is a very exciting alternative to the conventional inkjet printing technology for micro-droplet generation. In this work, an approach has been made to tune the process control parameters to achieve better functioning of the printing process. The droplet size and the printing frequency have been taken as performance measure of the printing process whereas applied voltage, back pressure, and nozzle standoff height have been selected as the process parameters which are to be tuned through optimization. Desirability function analysis have been employed to optimize the process parameters for multiple output variables simultaneously. Composite desirability values have been computed and based on these values; the optimal process parameters which leads to better printing performance have been proposed.

There are several factors that affect the performance of a wireless sensor network (WSN) namely storage capacity, energy loss, change in topology, network congestion, deployed environment, intermediate medium used for communication. Out of all these factors of WSN, this paper addresses two major challenges to be refined to decrease the depletion of energy and data loss due to congestion across the network. Initially, node degree and topology of sensor nodes are adjusted periodically at a regular time interval. This ends up in saving the battery power consumption of sensor nodes. The latter factor is a check done for congestion avoidance, by performing rate change using fuzzy logic to balance data flow. Moreover, once when congestion has occurred the search for the best path to reach; sink node is done using LRTA* (Learning Real-Time A Star) heuristic algorithm. Simulations were done to compare the network lifetime of the proposed congestion control mechanism with existing methods. Results show that the heuristic approach for congestion check performs better to the traditional methods.

Viral sequence classification has widespread applications in structural and functional categorization, clinical and epidemiological studies. Most approaches of subtyping and classification depends on an initial alignment step to asses similarity score followed by distance-based phylogenetic or statistical algorithms. We observe that interval distributions of nucleotide(s) over the sequence possess the potential for sequence comparison and devise an algorithm that determines the similarity/dissimilarity score among pairs of sequences. Classification of HIV virus subtyping by the method obtains exact tally with its biological taxonomy.

Glaucoma is one of the eye diseases that can lead to the blindness if not detected and treated at proper time. This paper presents a novel technique to diagnose glaucoma using digital fundus images. In this proposed method, the objective is to apply image processing and machine-learning techniques on the digital fundus images of the eye for separating glaucomatous eye from normal eye. Image preprocessing, techniques such as noise removal and contrast enhancement are used for improving the quality of image thus making it suitable for further processing. Statistical feature extraction methods such as Gray-Level Run Length Matrix (GLRLM) and Gray-Level Co-occurrence Matrix (GLCM) are used for extracting texture features from preprocessed fundus images. Support Vector Machine (SVM) classification method is used for distinguishing glaucomatous eye fundus images from normal, unaffected eye fundus images. The performance of the trained SVM classifier is also tested on a test set of eye fundus images and comparison is done with other existing recent methods of Glaucoma detection.

This paper develops a drowsiness detecting system by resolving the Electroencephalogram (EEG) signals. The acquired EEG signals are subjected to noise and are removed by subtracting the artifacts from the original EEG recording using Biorthogonal Wavelet Filter. Features are extracted using Discrete Wavelet Transform in particular Daubechies’ wavelet with five-level decomposition is utilized to segregate the signal into five sub-bands, namely, delta, theta, alpha, beta and gamma. The statistical moments such as mean, variance, standard deviation and power of the signal are calculated and stored. These moments serve as an input to the next stage, i.e., system classification. Unsupervised learning using K-means clustering is employed as the classification of the signals are not known. Following this, Support Vector Machine and Pattern Recognition Network are employed for supervised classification. This system provides strong decision making during a real-time sleep detection scenario.

In the present study, an analysis of a mathematical model for blood flow through an overlapping stenosed artery is presented by treating the blood as a Newtonian fluid and taking the pressure gradient as a periodic function of time. Perturbation technique is applied to obtain the analytical expressions for velocity profile, volumetric flow rate and wall shear stress by assuming the Womersely parameter to be very small. Effects of overlapping stenosis and hematocrit of red cells on these flow variables are discussed graphically for better understanding of the model.

In this paper, a routing algorithm for human body area network has been proposed. This algorithm is energy efficient and further, avoids congestions to some extent. One of the main challenges of WBAN is the node life time, i.e. energy level of the nodes. The nodes are selected in this algorithm for communication depending upon three factors that are energy level, number of hops the packet needs to travel while opting that node, and finally the request queue length which is actually the total number of packets traversed through the node for last t time interval. The first factor is for the shortest path to the destination as human body area network consisting of critical data that is needed to be sent fast, however, annexed by other factors as well,namely life time of nodes and congestion-free communication since the shortest path may be congested sometime because of over burden of data. The second factor actually considers the energy level of nodes and the third factor tries to avoid the path which may already been followed by huge numbers of packets.

Diabetic retinopathy is a retinal abnormality found among diabetes patients which may lead to blindness. Hard exudates (HE) is one of the most significant symptoms of DR, which are lipoprotein and cellular debris leaked out of the damaged blood vessels of retina. Detection of HE during early stages helps the patients from loss of vision. This paper proposes a Tabu search-based dynamic thresholding approach to detect HE in retinal images. The proposed method uses Hough transform, median filtering, image thresholding, etc., with a goal to detect HE. The proposed method proves the effectiveness of Tabu search algorithm-based image thresholding technique for the detection of diabetic retinopathy. The proposed method is tested using DIARETDB0 and DIARETDB1 image database and it exhibits a sensitivity of 97.45% and specificity of 96.85%.

In this paper, we use 2D tri-nucleotide representation based on chaos game theory. We extend the representation from 2D to 3D by taking the third coordinate as the multiple of the first two ones. Complete coding sequences of β globin genes of 10 species are now compared using four types of descriptors—1. Mean of the components of the represented sequences, 2. Standard deviation of the components of the represented sequence, 3. Highest eigen value of M/M matrix and 4. Highest eigen value of J/J matrix. The results in the four cases are critically examined. It is found that the use of J/J matrix with highest eigen value as the descriptor is the best one among the others.

With the emergence of multidrug resistant mycobacterium tuberculosis, it is an immediate necessity to design new potent drugs. One step toward it is to understand the mechanism of inhibition of the enzyme InhA (trans-2-enoyl-ACP-reductase), which has an important function in the building of micobacterial cell wall. An attempt through molecular modeling and simulation methods is presented here to explain binding affinities of different derivatives of drugs and also the role of some important amino acid residues in the active site of the InhA.

In a developing country like India with a majority of the population living in the rural areas sudden and fatal heart attacks are very common amongst the age group of 30–60 years. Studies have established that subdued cardiovascular abnormalities often go unnoticed in the ECG in the early days and clinical intervention is sought only during emergency conditions. Till date, the recordings of ECG along with heart rate, heart sounds and PPG are still the ‘only’ confirmatory diagnostic tools for cardiovascular abnormalities. This problem increases many folds especially in the rural areas where lack of awareness towards health issues gets multiplied with cost factors and lack of adequate medical facilities for the populace. Prior to design and implementation of CHMS our work was committed to the hardware unit CHMS (Cardiac Health monitoring System in Real time) but further advancements and coupling with the handheld user-friendly mobile phone has prompted us name our indigenously designed Mobile ECG Acquisition Device as CHMS. CHMS is a low-cost predictive tool which enables the subject to record and store electrical signals from the heart in a simple user-friendly and readable format for future reference. In this paper, we have analyzed the data collected by the CHMS in real time from consenting adult volunteers. The objective was to establish the accuracy of data acquired by the CHMS. A comparative analysis with the “standard” commercial Recorder has also been represented. Standard Deviation has been calculated to be within limits.

Outcome-Based Education or OBE as in short we know, means organizing for results: based on what we do instructionally on the outcomes we want to achieve. Outcome-based practitioners start by determining the knowledge, competencies, and qualities they want students to be able to demonstrate when they finish school and face the challenges and opportunities of the adult world. OBE, therefore, is not a “program” but a way of designing, delivering, and documenting instruction in terms of its intended goals and outcomes. During the 1980s Americans concluded that their schools were in serious trouble and that many children were not learning. In fact, children learn all the time; they are wonderful learners. They learn how to talk and walk, ride bicycles, and handle their parents and teachers. Schools take on the responsibility for planning the learning of children through the curriculum. As numerous educational reports and studies have recently shown, schools do not do this very successfully. One of the panaceas offered as a solution to this problem is an optimistic philosophy rooted in a “success for all” ideology of educational restructuring, a philosophy that may be broadly called “Outcome-Based Education” (OBE). The following research paper contains the limitations of Outcome-Based Education.

Activity-based learning is becoming an urge for modern day’s learner centric, outcome-based education system. These techniques have been adopted within the Faculties of Engineering degree. This paper defines the adoption and application of these techniques. The paper reports on the design, development, and implementation of subject and discusses how it uses activity-based learning to ensure that students become more aware of design and team processes.

Although Outcome-Based Education has not been affluently realized and implemented in public education amid plentiful countries, it has been effectively espoused in the technical fields in India. Outcome-Based Education has become a substance of conversation in facade of public media, encompassing a tinge of squabble. Critics differentiate this modus operandi of edification in unenthusiastic terms. In the formulated paper, the authors have proposed an implementation modus operandi of Outcome-Based Education in the field of engineering and higher studies. The authors have embodied a specific correlation amid Institute vision, mission, Program Educational Objective, Program Outcome, and Course Objective and Course Outcome. Based on the Program Outcome, how curriculum and syllabus design and implementation would be adopted is represented in detail. Based on several assessments along with feedback mechanism, the entire procedure refinement would be accomplished that would lead to successful realization of Outcome-Based Education.