Modelling, Simulation and Intelligent Computing

High power factor solutions are needed in Light Emitting Diode (LED) Television (TV) which otherwise results in the following problems: (i) Power is recycled from the backlight LED to the power source. (ii) Harmonics from backlight LEDs degrade the line which, in turn, affects the performance of other devices on the line. (iii) Additional losses are generated in the load which reduces efficiency. This paper proposes Cuckoo Search (CS) optimization algorithm based Interleaved DC-DC Single Ended Primary Inductance Converter (SEPIC) Converter for reducing the harmonics and thereby improving the efficiency of the power supply used for driving backlight LED. Cuckoo search (CS) algorithm using different fitness functions like Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Square Error (ITSE) and Integral Time Absolute Error (ITAE) are employed for finding the optimal controller parameters. Various characteristics like rise time, peak time, peak overshoot, settling time, input power factor, % THD and % efficiency are used to analyse the performance of the proposed scheme using MATLAB/Simulink software tool.

In India, 90% start-ups fail within the initial five years due to lack of huge capital and innovations. So, our idea is to develop a blockchain-based social network for start-ups, which will allow the collaboration between different start-ups and entrenched organizations who can provide some social and financial support to the start-ups. A token will be required to access this network, the user will earn the token through the Proof-of-Value protocol. In the proof-of-Value approach, the user needs to attach proof of their skills the subject matter expertise will verify it while stopping the spread of fake data. Thus, this will persuade the other member within the community and also the venture capitalist that the users are trustworthy and resourceful. In addition, the token will allow the users to get access to posting, commenting, and voting. The blockchain technology will ensure that the verification process to endorse skills is decentralized. The venture capitalist or a stranger will be able to trust the user more with this all-encompassing platform. And no second-guessing is required. Or delve into your social media profile to discover what sort of individual you are outside of work.

In this paper, thermal power plant is connected to a microgrid and frequency deviation is observed and the results of the fuzzy and PID controllers are compared with fuzzy and the best controller giving best results is considered. Also, the different parameters are taken care for maintaining stability of the system like area control error (ACE) of individual areas, load demand, etc. This paper focuses on reducing error in quick time that developed due to mismatch between generation and demand. Because if error is not minimized in quick time, then stability of the system is affected and also the power flow through the tie-lines is not uniform. Software used is MATLAB 2014b. Results are shown and compared using necessary graphs. The Simulink model is prepared by selecting the transfer function blocks from the library and assigning the values to them. PID controller is used to minimize the error in quick time. Also, PID controller is replaced with fuzzy PID controller.

Multiple strategic challenges are being faced by educational institutions across the globe which is of interest to both researchers and decision-makers. These challenges can be successfully addressed by analyzing the vast amount of data stored in multiple, unorganized, and unstructured operational databases in the educational institutes. Practitioners, researchers, and students would need data warehousing techniques to be able to utilize the knowledge stored in different archives. Data warehousing techniques include assimilating disparate sources of data, analysis of the requirements, designing the data, development, implementation, and deployment of the data. In this paper, a data warehouse (DW) for solving operational challenges of the center of higher education has been developed, which encompasses system design, ETL data processing, and online analytical processing analysis. The designing of this model is done using Mondrian and Pentaho business intelligence tool.

In this work, for the first time, the tri-layer sandwiched Van der Waals heterostructures of SnS2 and MoSe2 are investigated using first principle calculations. In such heterostructures, a monolayer of SnS2 (MoSe2) is sandwiched between two MoSe2 (SnS2) layers. Subsequently, such heterostructures are considered in two different stacking orders, i.e. ABA and AAA corresponding to the natural stacking of bulk MoSe2 and SnS2, respectively. The structural and electronic properties of such tri-layer heterostructures are extensively analyzed in comparison with the homogeneous SnS2 and MoSe2 tri-layers. In this context, emphasis has been given on the bond length and bond angles of metal and chalcogen atoms at the sandwiched layers of heterostructures. Finally, the influences of the homogenous and heterogeneous sandwiched layers on the energy band structures have been analyzed in detail from the orbital projections of electronic states at the conduction band and valence band.

Numerical and theoretical analysis of plasmonic sensors based on Graphene, Black Phosphorous, and Blue Phosphorous are accomplished in visible spectrum from 480 to 650 nm. Attenuated total reflection intensity is obtained using a transfer matrix method. Angular interrogation is used for the surface plasmon resonance curve. Performance defining parameters viz shift in resonace angle, minimum reflection intensity, and beam width are obtained in visible spectrum for sensors having monolayer Graphene, five layers Black Phosphorous, and five layers Blue Phosphorous. It is found that the sensor is best suited in a far visible region. Further, addition of nanolayers increases minimum reflection intensity and beamwidth. Black Phosphorous has the highest shift in resonance angle followed by Blue Phosphorous and Graphene but at the penalty of higher minimum reflection intensity and beamwidth.

Photovoltaic degradation rates play a vital role in visualizing and analyzing the performance of the PV modules over the long run. A site survey is conducted to calculate PV degradation rates. The results have shown that for the first three years since the initial installation, the degradation rates have remained in line with the manufacturer values (i.e., less than 0.6%), while the next two years the degradation rates have almost increased by 40%. This is due to discoloration of the encapsulant causing the reduction of the short circuit current (Isc). Mathematically, modeling such visual loss factors has not considered so far. The visual loss factor equation is developed and incorporated in the output current equation of the PV module. Further, the I-V curves are simulated and compared with the measured I-V curves. The results have shown an acceptable error percentage of around 0.3%.

Photovoltaic (PV) solar energy is growing rapidly in energy supplying for residential buildings. Since solar energy directly generates DC power, a DC microgrid is a better choice particularly in islanded mode of operation. The DC–DC converter is the most essential part of DC microgrid, and therefore, overall efficiency of microgrid largely depends upon the converter’s efficiency. The efficiency of converters depends upon the controller along with load conditions. The converter has typically lower efficiency in both the cases of heavy and light load conditions. This paper presents the analysis of converter efficiency improvement of DC microgrid using converter arrays at the place of centralized converters. The data of solar power generation and load demand have been used in the study, and it is found that converter array improves the efficiency by maximum 2.587% than centralized converter architecture.

Computational amplifiers are extremely useful for generating waveforms and solving numerous equations. In this work, memristive computational amplifier circuits were developed on spice simulator platform for the generation of step, pulse, exponential, and parabolic signals. On one side, decaying characteristics have been obtained based on the controlled decrement (or increment) in the memristance when the memristor is connected in the output (or input) loop of the amplifier. On the other side, rising characteristics were generated through exchanging the polarity of the input applied signal. These characteristics were further employed to solve exponential, linear, and parabolic equations. External voltage signals and internal circuit resistances were utilized to control the signal parameters such as rise time, fall time, delay, and amplitude. In the proposed circuits, the extension or reduction in the range of the generated signals was made possible through adjusting the external bias voltages. This work paves the way for futuristic low power, improved latency, and reduced on-chip area-based computational memristive amplifiers.

The Discrete Cosine Transform (DCT) plays a major role in many video coding standards such as High Efficiency Video Coding (HEVC). In this paper, a new algorithm that generates low complexity DCT approximation matrices with minimum number of low-frequency coefficients for all transform sizes 8, 16 and 32, is proposed. This algorithm accelerates the HEVC encoder in terms of total encoding time (ΔET) by 39.93% with a small (0.6104%) increase in bitrate and a small (0.5489 dB) decrease in Peak Signal to Noise Ratio.

This paper presents an area and power-efficient architecture for serial commutator real-valued fast Fourier transform (FFT) using recursive look-up table (LUT). FFT computation consists of butterfly operations and twiddles factor multiplications. The area and power performance of FFT architectures are mainly limited by the multipliers. To address this, a new multiplier is proposed which stores the partial products in LUT. Moreover, by adding the shifted version of twiddle coefficients, the stored partial products gain symmetry, and thus the size of LUT can be reduced to half. Further symmetry is achieved by adding another shifted version of twiddle coefficients and so on. This makes the proposed LUT multiplier recursive in nature. A new data management scheme is suggested for the proposed architecture. To validate the proposed architecture, application-specific integrated circuit (ASIC) synthesis and field-programmable gate array (FPGA) implementation are carried out for different symmetry factor. For instance, the proposed architecture for 1024-point with symmetry factor of two achieves 39.11% less area, 42.29% less power, 33.27% less sliced LUT (SLUT) and 29.18% less flip-flop (FF) as compared to the best existing design.

Induction machines have extensive demand in industries as they are used for large-scale production and therefore vulnerable to both electrical and mechanical faults. Over the past decade, online condition monitoring of industrial machinery has become one of the major research areas in fault detection and diagnosis. There are different types of stator winding insulation faults, of which the current work is focused on the identification of stator winding inter-turn fault as it accounts for 37% of the overall machine failures. Also, this fault, if identified at its incipient stage, can predominantly improvise machine downtime and maintenance cost. The proposed work uses acquired experimental data from both healthy and faulty three-phase induction motor to train the neuro-fuzzy classifier for fault severity evaluation. It has been observed that AI-based neuro-fuzzy classifier is capable of generating rules and membership functions on its own with a given set of experimental data whereas fuzzy classifier requires manual intervention for defining rules and membership functions. A comparison of fuzzy and neuro-fuzzy based fault identification is made, and the efficiency of both classifiers was compared.

A key issue to consider in the case of FinFET-based circuits is their susceptibility to multiple transients as a result of a neutron-induced particle strike. In this paper, we perform a device simulation-based characterization study on representative layouts of 14 nm bulk FinFETs to get insights into the charge collection efficiency and the extent to which multiple transistors are affected. We find that multiple transistors do get affected and the impact can last up to five transistors away (~200 nm). We show that the likelihood of two adjacent FinFETs getting affected (collecting maximum charge) is high, when their source/drain regions are biased high. This observation could be used as an indicator to identify vulnerable parts of the layout by looking at regions which have adjacent signal rails and possibly reduce such areas. In the case of two nearby multi-fin FinFETs, the charge collected per fin is seen to reduce as the number of fins increase. Thus, smaller FinFETs are more susceptible to high amounts of charge collection. A careful placement of small vulnerable gates may be necessary to reduce the likelihood of multiple transients.

This paper presents a comparative performance assessment for loss minimization of vector controlled induction motor (IM) drive based on two different algorithms, namely Genetic Algorithm (GA) and Golden Search (GS). Here the features of GA and GS both for estimation and recalculation of optimized flux component of current have been utilized for a better optimal efficiency operation of the IM drive. The GA- and GS-based algorithms greatly improve efficiency by reducing the core loss of the drive system. Moreover, both the approaches have no effect on parameter variation and also need no additional hardware for hardware implementation. However, GA-based loss minimization scheme proves its edge over GS-based scheme for the IM drive. The simulation results for different operating conditions are presented here. Stability study of the whole drive system is also carried out utilizing both the schemes. The performance of the proposed drive is validated experimentally on dSPACE-1104 based laboratory prototype.

This paper presents a study of space vector modulated single-phase to three-phase (1 × 3) direct matrix converter (DMC). In the direct matrix converters, DC link is not present; therefore, switching ripples get reflected in the output of matrix converter, and to attenuate these ripples, input as well as output filters are essential for better voltage regulation and performance of the converter. Filter design which is either of input source side or load side is based on some important operating parameters in the induction motor drive control. The design of filters considering source side as well as load side parameters is also described in the paper. MATLAB/Simulink environment is used to simulate the control strategy of the single-phase to three-phase direct matrix converter with a three-phase induction motor model; feasibility and validity of the converter are also discussed.

This work presents a compact and low-power bandgap voltage-reference design using self-biased current mirror circuit. This design eliminates the standard complementary-to-absolute-temperature (CTAT) bipolar device in the voltage-reference branch, reducing the bipolar area by 20%. Instead, the design shares the same bipolar device in the main CTAT branch for generating the reference voltage. An additional benefit of eliminating the voltage-reference branch is the reduction of total power consumption by approximately 30%. This novel topology reduces power and area of the core bandgap reference circuit without compromising temperature drift performance. Designed, fabricated and functionally tested in a 0.6um CMOS process. The simulation result shows the temperature coefficient of this design is 6.3 ppm/°C for a temperature range of −40 to 125 °C. This bandgap reference design occupies a silicon area of 0.018 mm2 and draws an average quiescent current of 2 µA from a supply voltage of 3.3–5 V. The simulated flicker voltage noise is 4.34 µV/√Hz at 10 Hz.

Cache compression improves the efficiency of a cache by increasing the effective cache size through compression and compaction of data blocks. In this paper, we propose a data compression technique which determines the base value of a cache line dynamically and stores the deltas with respect to this base, the base could be 2 bytes (B2), 4 bytes (B4) or 8 bytes (B8) in size. The dynamic base is chosen such that it maximizes the total number of compressed blocks in a cache line. We implement two types of dynamic base techniques which we call the B2B4 (combines B2 and B4) and B4 techniques. These dynamic base techniques are tested on image workloads and the results are compared against the fixed base compression technique. We see a 52.31% improvement in the number of compressed bytes over the fixed base method on an average, for B2B4 technique, which translates to an average improvement of 3.95% and a maximum improvement of 10.5% in compression factor. We also proposed a cache compaction scheme which utilizes the B2B4 compression technique and finds that such a scheme saves 8.2% of the cache area. We implemented the proposed scheme on an FPGA to analyze the performance and hardware overhead.

Hyperspectral images (HSIs) are contiguous bands captured beyond the visible spectrum. The evolution of deep learning techniques places a massive impact on hyperspectral image classification. Curse of dimensionality is one of the significant issues of hyperspectral image analysis. Therefore, most of the existing classification models perform principal component analysis (PCA) as the dimensionality reduction (DR) technique. Since hyperspectral images are nonlinear, linear DR techniques fail to reserve the nonlinear features. The usage of both spatial and spectral features together improves the classification accuracy of the model. 3D-convolutional neural networks (CNN) extract the spatiospectral features for classification, whereas it is not considering the dependencies in features. This research work proposes a new model for HSI classification using 3D-CNN and convolutional long short-term memory (ConvLSTM). The optimal band extraction is performed by a hybrid DR technique, which is the combination of Gaussian random projection (GRP) and Kernel PCA (KPCA). The proposed deep learning model extracts spatiospectral features using 3D-CNN and dependent spatial features using 2D-ConvLSTM in parallel. Combination of extracted features is fed into a fully connected network for classification. The experiment is performed on three widely used datasets, and the proposed model is compared against the various state-of-the-art techniques and found better classification accuracy.

Nowadays there is a boom in social network data streaming from various fields of interest related to finance, engineering, medicine, and general sciences. All these data are modeled as graphs for better analysis. Community detection is one such mechanism for the analysis of such massive data. Many community detection algorithms exist in literature. The existing algorithms are compared by using either real-world or artificial networks (modeled as graphs) but not both. This paper aims to make a comparative study of two popular existing community detection algorithms both on real-world and synthetic data and verify their performance. The approach in this paper makes good use of recent advances in graphical modeling of different social networks. We generated a random graph that represents most of the observed properties of a real-world dataset. The experimental results are tabulated and the computed metrics help in inferring the suitability or scalability of an algorithm for small or massive datasets.

The performance of a molecular communication system depends on various parameters like diffusion coefficients of the messenger molecules, the distance between the two communicating nanomachines, the volume of the nanomachines, the time taken for molecules to reach the receiver, and concentrations of molecules for different signals. Even after optimizing the adjustable parameters, sometimes nanomachines need to communicate with distances between them being comparably large than their optimal distances, making the communication to become unreliable. Hereby, in this paper, an intermediate nanomachine called relay is incorporated to assist the molecular communication process. Especially, network coding strategy is employed to improve the performance of the system by reducing the time taken by the signals and minimizing the error probability of detection. The numerical results will help to choose reliable parameters for the considered relay-based model of the molecular communication system.

In this article, the design of a compact Yagi–Uda-shaped patch antenna is presented for 5 GHz WLAN applications. The ground plane is maintained partial with slots etched symmetrical on both sides of the strip feed, the slots in ground plane are made, and the dimensions are tuned to achieve compact size. FR4 material is used as substrate with a compact size of 15 mm × 15 mm × 1.6 mm. Various dimensions of the radiator can be varied to tune the frequency. This tuning produces different channels around the 5 GHz to suite 802.11a/h/j/n/ac/ax protocols. The antenna performance is presented with the help of reflection coefficient, radiation pattern, and other antenna parameters.

In this paper, a new multiport, hybrid green-fed DC-DC bidirectional converter is designed and fed into the standalone system. This model is developed and integrated to study the concept of generating systems. In this model, a PV panel of 300 watts is designed and integrated to a bidirectional converter with a battery backup to extract power, while the wind power is harnessed with a transformer coupled a dual-half-bridge converter. The model proposed is designed with a meritorious objective of sustainable, cost-effective, less component count reduced losses, and good efficiency with a good reliability. The system works day and night to produce output with good efficiency. The simulation results are obtained using MATLAB software 2014a, and hence, the performance is analyzed.

Wear debris is produced in all machines containing moving parts. Wear debris or particles separate from these moving parts because of close contacts and friction and are contained in oil in an oil-wetted system. Analysis of wear debris provides important information about the condition of a machine. The produced particles come in different shapes, sizes, colors, and surface texture. This paper describes the morphological analysis of wear particles by using computer vision and image processing techniques. The aim is to classify these particles according to their shape attributes. Four particle shapes are classified by using Histogram of Oriented Gradients (HOG) and shape attributes including eccentricity, extent, major and minor axis length, equiv-diameter, and centroid distance. The shape classification can be used to identify origin of particle generation and thus predict wear failure modes in engines and other machinery. The objective of particle classification obviates reliance on visual inspection techniques and the need for specialists in the field.

Land mobile satellite (LMS) systems have become prominent in the fifth-generation broadband wireless communications by providing high quality-of-services to terrestrial mobile users at low cost. However, with the increasing smart technologies, wiretapping and security threats are becoming a major concern in such systems. In this paper, we investigate the secrecy performance of a downlink LMS system by employing a friendly jammer in the presence of an eavesdropper on the ground. Specifically, we derive the secrecy outage probability (SOP) and the probability of strictly positive secrecy capacity (SPSC) expressions of the considered LMS system under the pertinent heterogeneous fading models for the satellite channels and terrestrial jamming channels. We validate our analytical hypothesis through simulations and reveal the impact of jamming and key parameters on the secrecy performance of LMS systems.

The accurate prediction of power generation by the PV system is crucial during the designing stage and subsequently in the operation and maintenance phase. It provides a reference to evaluate the performance of the PV system. PVSYST is widely accepted simulation software for the PV system in the industry. In this study, a comparison of PVSYST simulation results and experimentally collected data for fix oriented and azimuth tracking solar system is analyzed. Five clear sunny days are selected for each case. The hourly average data has been used for comparison. The deviation of predicted values in case of fix oriented and azimuth tracking solar systems is 2.14% and 2.74%, respectively. This variation is primarily due to the mismatch of predicted weather data with real conditions. The results have concluded that PVSYST is reliable software to use for the prediction of PV energy generation with an acceptable margin. Further, the adaptation of azimuth tracking for the solar system is feasible and improves the average power production by 17.28% as compared to the fix-oriented solar system in the hot and humid environment of the UAE.

In the current era, since software automation is buzzing everywhere and it is creating huge opportunities to pay heed on generating rules and reasoning. In the same direction, our research work has been carried out. If any system to be context-aware its database should be in a position to provide few facilities to its system. So, this research work elucidates different contexts and its dimensions through the context dimension tree. The novelty of this research work is, it has used context data for generating the rules and reasoning using RETE rule-based algorithm, Rule assessment against facts/data and ordering of statement is been simplified using a particular algorithm.

Internet of things widens the scope of communication by connecting the physical objects to the Internet. These physical objects are vulnerable to various malicious activities, thus strong security features are required in IoT devices. Low power resource constrained-IoT devices limit the use of computational complex algorithm. In this paper, a lightweight authentication scheme has been proposed for fog-assisted IoT network to authenticate IoT devices at low computation cost. It uses three-way handshake with challenge response mechanism to verify the authenticity of the participating device. The performance is evaluated by using IFogSim tool kit and MATLAB, which shows that the proposed scheme is authenticating the user devices at low computational cost and storage utilization. It takes less handshake duration and average response time between the authenticating devices and the fog devices to improve the quality of service.

Biochemical oxygen demand (BOD) is the measurement of the amount of dissolved oxygen used by aerobic microbes for oxidizing organic matter in water bodies and used for analyzing the water quality. The actual BOD prediction method is cumbersome. Instead an automatic prediction model is required that is accurate, faster and less expensive. This paper presents a data-driven model for predicting BOD, in a lower-dimensional space obtained using dimensionality reduction techniques that help remove irrelevant properties of high-dimensional data. Machine learning algorithms, namely decision stump, SVM, MLP, linear regression (LR), and instance-based learner (IBK), were trained with the full dataset with 11 parameters. The training set was later transformed into a lower-dimensional space using principal component analysis (PCA) and correlation-based feature selection (CFS). The performance of the learners on the full training set and transformed dataset was analyzed using correlation coefficient, RMSE, and MAE. The algorithms are able to preserve their predictive accuracy on the lower-dimensional space.

Diabetic macular edema (DME) is one of the major causes for visual impairment and can even lead to permanent blindness if not treated early. Manual screening by ophthalmologists is time-consuming and error-prone which necessitates the need for automated detection and grading of DME. In this paper, a deep learning-based DME-grading model is proposed for automatic DME grading of retinal fundus images. The model consists of an autoencoder network and a DME-grading network. The autoencoder network learns features of retinal fundus images. The DME-grading network uses the learned features to detect and grade the risk of DME. The proposed method is evaluated using the IDRiD dataset. The class imbalance of IDRiD dataset is overcome by using image augmentation and class weights. The highest accuracy, precision, recall, and F1-score achieved by the proposed method are 68%, 66%, 68%, 65%, respectively.

This paper shall model the Stator Inter-turn Faults (SITF) in a Brushless DC (BLDC) motor using a novel fault modeling approach. In order to comprehensively analyze the effect of SITF on the machine performance, the proposed hybrid analytical-numerical approach is adopted for modeling the BLDC motor under SITF conditions. The hybrid modeling techniques take less computation time and are more accurate than the existing analytical methods. The behavior of the motor in terms of phase currents, back-EMF (EB), electromagnetic torque, and mechanical speed is studied to investigate the change in the characteristic performance of the machine during fault conditions. The significant change encountered in motor back-EMF is more realistic since the actual magnetic flux density (BM) profile obtained through numerical analysis is emulated in the analytically developed model of a motor. In addition, the outcomes obtained through hybrid modeling techniques, are further validated completely through Numerical Methods (NMs) like Finite Element Analysis (FEA) to validate the authenticity of the proposed methodology. The significant changes investigated in motor electromagnetic quantities draws an inference to the SITF in the BLDC motor.

Silicon Carbide (SiC) MOSFET devices exhibiting several advantages, including high blocking voltage, lower conduction losses, and lower switching losses, when compared to silicon-based devices have become commercially available, enabling their adoption into power supply products. This paper presents a novel approach to designing a cycloconverter using SiC MOSFETs as opposed to the conventional usage of IGBT. A comparative study is attempted between the two with respect to distortion and system efficiency. MATLAB/Simulink models and simulations are used to analyze the results for the above.

This paper proposes a demagnetization fault diagnostic method for a brushless direct current (BLDC) motor using Hall effect (HE) sensor sequence. The low-cost Hall effect sensors positioned 120° apart are used on the stator of a BLDC motor in order to monitor the change in the sequence during demagnetization fault conditions. Demagnetization effect in the permanent magnets (PMs) can be due to various causes; however, the broken PM defects are taken in our study. In contrary to the healthy operation of the motor, the significant change observed in the Hall sequence pattern during the demagnetization fault conditions is used as the fault signatures in this study. The change in machine quantities in terms of phase currents and magnetic flux density (BM) characteristics is investigated numerically through a developed co-simulation model of a BLDC motor with the Simplorer drive circuit in Maxwell 2D tool. The numerically obtained results are validated experimentally, and the change in Hall sequence signals is investigated in order to detect, diagnose and identify the demagnetization fault in the BLDC motor. The diagnosis of the broken PM demagnetization fault using the low-cost Hall sensor’s sequence is the novel contribution to this work.

An integral subsystem of a satellite is its Electrical Power System (EPS). Spacecraft power systems have undergone significant new developments in the last decade and will continue to do so even at a faster rate in the current decade. The EPS functions to supply continuous power during the satellite mission life, control and distribute power, support power requirements for peak and average electrical load, and protect payload operations against failures within the EPS. The design of the solar panels and batteries depends on the payload/s power demand and the mission lifetime. This paper studies the design, management and characteristics of the power subsystem of small satellites. The EPS for small satellites is required to have high efficiencies and low masses because of volume and weight constraints. Based on the power utilized by onboard equipment and devices, this paper proposes an efficient and durable power system.

This paper presents a way to realize three configurations of second-order band reject filters (BRFs). Only single operational transresistance amplifier (OTRA) and few passive components have been used in this work. CMOS realization of OTRA using 180 nm model has been implemented in PSPICE for simulation works. Simulated filter characteristics matches very well with the theoretical analysis for all the three configurations. Monte Carlo analysis and non-ideal analysis have been done. Analog design environment (ADE) tool of cadence virtuoso has been used to perform the layout of the proposed configurations.

Aiming at the problem of orthogonal frequency division multiplexing (OFDM) signal subcarrier number estimation, a subcarrier number estimation method based on Novel Test (NT) distance is proposed using the Gaussian nature of OFDM signal. The NT distance output at detection-end DFT module is smallest when DFT points match the transmitter. Theoretical analysis and simulation results show that this method can distinguish Gaussian distribution from non-Gaussian distribution and correctly estimate the number of subcarriers of OFDM signal.

In this paper, an optimization algorithm for cooperative spectrum sensing in cognitive radio (CR) is proposed, to maximize the spectrum sensing efficiency under the condition of limited interference, an optimization scheme of cooperative spectrum sensing mechanism for cognitive radio system. The system model is defined, and the cooperative spectrum sensing is used to jointly optimize the system targets, including sensing time, transmission time, and the number of sensing users participating in the collaboration. The simulation results show that the optimization scheme can maximize the spectrum sensing efficiency under the condition that the interference is limited.

With the advancement in the computational efficiency, there is also simultaneous increase in many efficient and secure biometric systems that are capable for the use of multiple sources of access authorization. Single biometric systems are inefficient and less secure which give rise to the advancement of multimodal biometric systems. Also, fusion of many biometric modalities is high area of interest, and here, many methods are deployed for the fusion of biometric data. Multimodal biometric system provides many evidences for the same person. In this paper, the design of multimodal biometrics based on face, ear, and iris modalities with multilevel fusion-based approach is preferred. In the presented work with multilevel multimodal fusion, 95.09% accuracy has been obtained which is better than highest unimodal accuracy; in this case, it is iris 94.06%. The obtained results are better than similar multimodal fusion-based model with single classifiers such as RNN with 90.58% accuracy and KNN classifier with 91.22% accuracy. So, in this work multilevel fusion of (i) different unimodal methods with (ii) feature level fusion of multiple traits has been proposed for person identification.

With the advent of technology, the modern warfare systems are becoming sophisticated. Such systems working as targets have very high acceleration capabilities. In future, it is expected that this acceleration capacity is going to increase many times. To chase such targets is a difficult task for the present-day missiles. Such an attempt would produce quite high demand of lateral acceleration. To address these issues, a novel guidance strategy is presented here which not only tracks such targets but also ensures that the required latex is contained.

In this paper, a proxy sliding mode control (PSMC) is designed for a DC–DC buck converter. The mathematical form of the controller combines the proportional–integral–derivative controller and a sliding mode controller in an algebraic way. The objective of the control is to regulate the output voltage of the DC–DC buck converter in the presence of line voltage and load uncertainty. Simulation and experimental results of the DC–DC buck converter are carried out to demonstrate the efficacy of the proposed controller.

Air, soil and water pollutions have the greatest risk factors for human health. There are different types of air pollutants which are emitted from human activities. One of these pollutants is nitrogen dioxide (NO2) which is produced from fossil fuel-based energy and use of motor vehicles. Since India is facing deteriorated air quality due to economic development, air quality management is becoming a real challenge. In 2015, an emission inventory (EI) was developed for India with 2015 as the base year. This EI is developed on an engineering model approach which is based on a technology-linked energy emission modeling approach. Accurate EI is important for future air quality modeling and air quality management. Since EI has uncertainties in data, some kind of estimation is essential. Estimation through extended fractional Kalman filter (EFKF) is considered in the present paper, and its performance is found to be superior as compared to a standard extended Kalman filter (EKF).

Recent years have seen an increased demand in negotiation technologies, seen as a key coordination mechanism for the interaction of providers and consumers that optimize the selling of different kind of goods in industries like real estate and used car marketplace. Suggested applications range from modeling interactions between customers and merchants in retail electronic commerce, to the online sale of information goods, or reducing operational procurement costs of large companies. A new tenant could use an AI agent to negotiate the final lease for his or her apartment. Usually tenants, landlords, and their respective brokers typically shed in a lot of time discussing and negotiating details of the lease terms, to achieve fair pricing. AI can help the negotiation process by grounding it in hard data and clear analysis. During this project, several laboratory-based focus group studies will be held to generate initial dataset and train a neural network-based AI model to negotiate the best deals.

An alternative computational model was developed to predict the level-dependent changes in the identification scores of both vowels for same and different fundamental frequency (F0) conditions. In this current study, the temporal-responses of the auditory-nerve model were the input layer to a multi-layer perceptron for predicting the identification scores of both vowels. The perceptron was trained to obtain the similar identification score (as observed in normal-hearing listeners) for different-F0 condition at 50 dB SPL. The training was done using the gradient descent with momentum and adaptive learning rate backpropagation algorithm, Finally, the perceptron was tested for same-and different-F0 conditions across various range of vowel levels. The model was successful qualitatively in predicting the level-dependent changes in concurrent vowel scores for same-and different-F0 conditions.

A dual-band planar rectenna, consisting of a modified quadrilateral square slot antenna with rectangular microstrip patch connect to a 50 Ω feed line to improve the impedance matching and a single-series diode configuration-based half-wave rectifying circuit for high conversion efficiency, operate in frequency bands of universal mobile telecommunication service UMTS (2.1 GHz) and higher WLAN/Wi-Fi (5 GHz), is proposed for RF energy harvesting and wireless power transmission. The inverted L-section transmission line is introduced between the diode and dc pass filter to eliminate the harmonics within the operating frequencies. The antenna is connected to the rectifying circuit by using a pair of 50 Ω SMA coaxial connectors. The peak measured conversion efficiency of proposed rectenna is 59.4% achieved at the input power of −9.8 dBm and optimized load resistance of 560 Ω, respectively.

In order to maximize the inductive link efficiency in wireless electric vehicle (EV) chargers, a ferrite layer is added to focus the magnetic field lines, improve the coupling performance, and reduce the leakage of flux to the surrounding ferrous materials. The geometry of ferrite directly affects the self and mutual inductances of the primary and secondary coils and accordingly their coupling factor. Three ferrite geometries are investigated in this work, and their coupling behavior is studied and compared to that of a ferrite sheet. Due to the inherent misalignment variations in wireless EV chargers, the simulation is conducted over a range of air gaps as well as lateral and longitudinal misalignments. Based on the simulation results, the geometry with long ferrite bars is recommended for dynamic EV charging scenarios in which large lateral misalignments are expected, whereas shorter bars are more recommended for static charging scenarios due to their smaller volume and cost-effectiveness despite their lesser tolerance for lateral misalignment in comparison to long ferrite bars.

Synchronous static compensator (STATCOM) can also be used to improve the dynamic performance of a power system apart from being used for reactive power compensation. This article performs a comparative study for robust STATCOM control designs based on graphical loop-shaping and simultaneous tuning using Particle Swarm Optimization (PSO) for a single machine infinite bus system (SMIB) equipped with STATCOM. The power system working at various operating conditions is considered as a finite set of plants. Fixed parameter robust controllers were designed considering the voltage magnitude of the voltage source converter (VSC), a part of the STATCOM system, as the input and speed deviation of the generator as the system output. Simulation studies are conducted on a simple power system which indicates that the designed robust controllers by the two methods provide very good damping properties over a wide range of operating conditions, but the simultaneous tuning method using particle swarm optimization is easy to implement compared to the cumbersome graphical loop-shaping technique.

Nowadays, wireless technology is developing at a great rate, as the process industries looking for the way to reduce the cost of the system, to improvise the performance of the system, and to comply with regulatory requirements. Especially in the short-range network, wireless technology addresses many operational challenges when compared with wired technology in oil, gas, and many other process industries. The wired network always needs real-time support for security, availability, and reliability in harsh industrial environmental conditions. These conditions will be overcome by introducing wireless technology in process industries, which in turn requires limited observation and maintenance. In this paper, a cylindrical tank is considered as single-input single-output (SISO) system, which is controlled wirelessly. Here, the plant is modeled as the first-order system by mathematical approach. After, IMC and direct synthesis tuning methods were used to tune the designed PID controller gain parameters.

A piezoelectric resonant pressure sensor fabricated with stainless steel with a modified design to improve its performance is proposed in this work. The sensor consists of a stainless steel diaphragm, inclined trusses, hinged vertical mounts, and a resonating doubly clamped beam. The deflection of the diaphragm with applied pressure is transferred to the resonating beam via a stress transmission mechanism comprising of inclined trusses and vertical mounts. The sensor is fabricated with SS 304 grade stainless steel using electrical discharge machining (EDM) and wire-cut EDM process. The sensor was tested for its characteristics for an input pressure of 0–25 bar. The experimental results demonstrate that the proposed sensor was found to have better linearity, higher sensitivity, and low hysteresis as compared to a similar pressure sensor existing in the literature. Sensor design is simple; fabrication involves well-known machining process, self-packed, and hence cost effective.

This paper explores source/drain (S/D) spacer technology-based reconfigurable field-effect transistors (RFETs) and a detailed physical insight toward the advantages of using spacer oxide in RFETs for applications involving rapid temperature fluctuations and reduction of circuit delay in contrast to conventional ambipolar FETs and other devices based on band-to-band tunneling (BTBT) such as TFETs. Temperature-based DC, analog and RF performance of gate-all-around (GAA), heterogeneous gate dielectric GAA, SiGe, and full silicon TFETs are compared. Moreover, it is also shown that the propagation delay in logic circuits is reduced for the proposed DG-RFET resulting in more robust and improved circuit performance.

Casualties of roadside accidents often die due to the delayed arrival of rescue groups. This is because there is an interval between the accident occurring and the authorities being notified. In some cases, the authorities are failed to be notified due to the absence of any bystanders and the incapability of the victim to call for help themselves. We propose a compact system called the RoadNurse, to provide location-based emergency service which locates an accident quickly and notifies the emergency services and the loved ones of the victim. It also provides the live location of the accident to accelerate the transfer of the victim to the medical centers. The system contains vibration sensors which detect a value greater than certain threshold, determining the possible severity of injury and then utilizes the GPS module to determine the precise location of the accident. This location is sent to the cloud server, which contains the details (name, location, contact, and severity-level capability of treatment) of hospitals in the city. The server processes the optimal hospital with respect to proximity to the accident and the severity of injury. The hospital and the victims loved ones are messaged the details of the accident by the GSM module, and a phone call is initiated with the hospital. This procedure serves as a lifeline to the victim and might be the difference between life and death in the future.

It is reported in this paper, a compact very low-loss onchip bandpass filter meeting the requirements of 5GnR N79 radio frequency front end (RFFE), using 0.18 μm CMOS on Si substrate IPD technology. A series LC onchip BPF structure is designed and simulated by combining a passive multilayer (ML) inductor and a spiral capacitor in high frequency structural simulator (HFSS) at component level. The filter exhibited a quality factor (Q) value of 9.28, with a fractional bandwidth of 10.85% (<20%). It had exhibited very good insertion loss of −0.8 dB and also excellent return loss of −32.89 dB, at a center frequency of 4.5 GHz. The physical dimensions of the inductor, capacitor, and bandpass filter are 380 × 240 μm2, 240 × 240 μm2, and 480 × 240 μm2, respectively. It had produced an excellent passband loss with a narrow passband characteristics, still occupying very small chip area. Hence, this proposed compact resonator filter definitely suits the 5G radio RFFE applications. We simulated the filter by focusing around 4.5 GHz, as this spectral band is being considered for the upcoming 5GnR N79 radio band trials and installations across several countries.

Individual condition monitoring (CM) strategies are capable to diagnose 30–40% of the defects, when they are performed individually. However, combining two or more individual CM strategies can provide more reliable information which will enhance the ability of fault detection. In this investigation, two intrusive CM strategies (vibration and lubrication oil analysis) and one non-intrusive CM strategy (acoustic signal analysis) are combined to form an integrated CM scheme. Experiments are performed on a miniature wind turbine gearbox bench top and the raw data is acquired and the defect sensitive features are extracted using discrete wavelet transform. Feature level fusion is accomplished to achieve integrated feature data set and the selection of optimal subset of significant features is done by various supervised featured selection methods. Finally, the obtained optimal feature subset is classified using SVM algorithm in order to diagnose the local defects of bearings as well as gears present in different stages of the wind turbine gearbox.

Next-generation wireless communication systems need antennas with multi-functionality, adaptability, and flexibility to provide efficient utilization of power and electromagnetic spectrum. Reconfigurable antenna can fulfill these demands by delivering multiple functionalities in a single antenna structure. These antennas can dynamically adapt to changing system requirements by altering their operating parameters. Reconfigurable antennas are classified as frequency, pattern, polarization, and compound reconfiguration. Compound reconfigurable antenna involves simultaneous reconfiguration of two or more parameters such as frequency and pattern, frequency and polarization, pattern and polarization and frequency, pattern, and polarization. This paper presents a comprehensive survey on reconfigurable antenna designs, realizing beamwidth reconfiguration with single or dual orthogonal polarization. Furthermore, this paper also investigates the performance comparison of multifunctional reconfigurable antennas achieving beam steering and beamwidth reconfiguration in a single antenna structure. The challenges and future research directions in beamwidth reconfigurable antennas are also discussed in detail.

This paper presents a new structure of a variable integrated noise source (VINS) implemented in a commercial CMOS technology. This VINS circuit is based on the new technique of dual-drain MOS transistor. It consists of one dual-drain NMOS transistor and one dual-drain PMOS transistor with a particular innovation; the two drains have different lengths. The VINS circuit has been simulated in a CMOS FDSOI 28-nm process. It can produce good quality bit streams without any post-processing. It has a typical low power dissipation of 100-µW. This novel circuit is a promising unit for LDPC decoders. The new VINS circuit can be used in a CMOS system-on-chip (SoC) for a variety of applications ranging from the data encryption and mathematical simulation to the built-in-self test (BIST) of RF receivers.

Extreme learning machine (ELM) is a single layer feedforward neural network algorithm used for classification problems due to its accuracy and speed. It provides a robust learning algorithm, free of local minima, suitable for high-speed computation along with fast learning speed. In this paper, ELM algorithm implementation on hardware and software is discussed. A low-cost hardware implementation of 16-bit H-matrix generation on FPGA is discussed in the paper. Hardware implementation is carried out on Nexys-4 board using MATLAB and hardware description language (HDL). Generation of H-matrix is carried out using two activation functions, piecewise log-sigmoid and piecewise tan-sigmoid. This paper aims at optimizing the hardware implementation of ELM algorithm by minimizing the utilized resources of the FPGA. Finally, the ELM algorithm accuracy and hardware utilization for both activation functions are compared.

Approximate computing is an emerging paradigm to create energy-efficient computing systems. Most of the image processing applications are inherently error-resilient and can tolerate the error up to a certain limit. In such applications, energy can be saved by pruning the data path modules such as a multiplier. In this paper, we propose a new truncation scheme and an error correction term which are applied to recursive multiplier architecture. Further, truncation method and correction term that compensates the error in the proposed approximate multiplier significantly reduce the area, delay and power. Finally, the proposed multiplier is validated on an image sharpening algorithm. Simulations carried out clearly prove that the proposed multiplier performs better compared to the existing multipliers.

Two-link robotic manipulator system with payload at tip is a highly complex and nonlinear system and faces a challenging task to control. Thus, a nonlinear proportional–integral–derivative (PID) controller is implemented in this paper using fuzzy logic where the parameters of the controller are optimized with a new metaheuristic algorithm based on the foraging behavior of the swarm of bees. The performance indices function to minimize the error between the reference signal, and the system’s output is taken as the integral of absolute error (IAE). The implemented controller is compared with the conventional PID controller. From the simulation studies, it is found that the implemented fuzzy PID controller works more efficiently than the PID controller in terms of the trajectory tracking, in the presence of parametric uncertainties as well as disturbance rejection and the noise suppression.

Neuromorphic circuits are becoming quite popular due to their ability to mimic the structure and behavior of human brain. Current research focuses on approximating spiking biological neuron behavior. Various neuron models have been proposed in the past that aid in investigating the behavior of neuronal systems mathematically. Mihalas–Niebur (MN) neuron model is one among them. In this paper log-domain based MN neuron model is implemented at 45 nm technology node. The paper studies the effects of process-temperature variations and also investigates the impact of Hot Carrier Injection (HCI), Bias Temperature Instability (BTI) on the performance of MN circuit. Average power consumption and spiking frequency are chosen as key performance measures to analyze the circuit performance before and after degradation.

In general, welding is a process in which two workpieces are joined together. The edge interface of the two halves are called weld seam. The main scope of this paper is to perform prediction analysis of 3D weld seam coordinates based on Non-Linear Auto Regressive with Exogeneous Input (NARX) Neural Network using various training functions and training ratios. Because developing a model for such complex processes using analytical techniques is time-consuming and prerequisite knowledge of the process is needed. Training NARXNN with the appropriate combination of learning rate, training-testing ratios, momentum coefficient and training function for the prediction of robot coordinates is a challenging task in Neural Networks. This work investigates Gradient Descent based Back Propagation, Scaled Conjugate Gradient method, Resilient Back Propagation, Levenberg–Marquardt algorithms in determining the 3D coordinates of weld seam. The proposed work compares the training algorithms based on Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE) for the real-time experimental data of weld shape. Experimental analysis is performed using the data obtained from real-time weld seam detection using 5 DOF robotic manipulators.

The aim of this research work is to demonstrate a standardized procedure for extracting subtle chaotic features to study the dynamics of aggressive and non-aggressive human muscle actions in the chaotic domain. The relevant features present in the electromyogram (EMG) signals are analyzed by exploiting the chaotic characteristics of the signal. Degree of Self-Similarity (DoSS), Largest Lyapunov Exponent (LLE), Correlation Dimension (CD), Approximate Entropy (ApEn) and Katz Fraction Dimension (KFD) are the features, extracted to study the chaotic aspects of normal and aggressive human upper arm muscles. This chaotic feature vector is utilized for signal characterization, which is fruitfully extended for classification of the EMG signals into aggressive and normal. The proposed extraction and classification technique was experimentally verified for validating the findings, using EMG signals available from the UCI machine learning repository database. The features are statistically categorized into three significant levels, applying ANOVA technique. The inferences lead us to conclude that the extracted chaotic features qualify as a distinguishing multi-feature set for EMG signals of different classes. Five different classifications approached were used for classification by using tenfold cross-validation. The maximum classification accuracy achieved was 97.5% with two of the most significant chaotic features.

In this paper, packed E-cell converter (MPEC) topology is investigated when integrated with three different PV sources of two different voltages and power ratings. Nine-level hybrid PWM with half parabola vertically shifted carriers is used which gives lesser THD when compared with triangular carrier waves. The advantage of the investigated topology is that the MPEC can continue a five-level operation even if a fault occurs on four-quadrant switch, without a change in topology. The modelling of the investigated system is done in MATLAB®/Simulink, and results obtained are presented and discussed in the paper.

In this paper, crowdsourcing has been used to obtain meaningful conclusions and insights about consumer behavior with respect to plant-based cheese. A fivefold rise in the number of people that follow strict, plant-based diets (and those who are lactose intolerant), increase in conscious consumerism due to environmental awareness and high costs of natural cheese production are factors driving research to make plant-based food products accessible to the masses, with respect to taste similarity and cost-effectiveness. Previous research focused on the sustainability and cheaper costs associated with the production process. However, taste and textural similarity to dairy counterparts were found to be lacking. This paper aims to tackle the barriers attached with organoleptic properties (taste and texture) of food products by making use of widely available data from the online vegan community who are immersed in preparing versions of famously non-vegan foods. These recipes are then tried by thousands of others who leave reviews on their experiences. The underlying objective of this research was to analyze sentiments behind the reviews and comments left on each recipe. This was useful to analyze which base ingredient was responsible for the most positive sentiment. To recognize sentiments, Natural Language Toolkit (NLTK) Valence Aware Dictionary and Sentiment Reasoner (VADER) was put to use, which was able to score each review from−1 to 1, a compounded score based on the negativity, neutrality and positivity of the statement. These scores aided in the decision of raw material selection.

This paper aims to tackle the problem of impaired visibility for drivers on the road due to fog, which is a safety concern. This novel approach is a unique comparative algorithm through integration with Google Maps and has several embedded functionalities to reduce noise caused by fog. Real-time input is collected in the form of continuous video frames, on which image processing is carried out. This is a two-step process, first using dark channel prior and second using histogram matching with ideal weather Google Street View images. In order to measure the fogginess of the image at each step, horizontal variance is used. The results obtained show a drastic increase in variance during the two-step process, which is in line with the theory that the higher the variance, the lesser the fogginess. The fog-free images are retrieved and put together to form continuous frames of a video, which is displayed on the driver’s screen in real time.

Active power filter (APF) is one of the effective means for harmonic current compensation in power grid. In this study, design and implementation of a shunt active power filter based on synchronous detection method is done. Harmonic and reactive current drawn by a nonlinear load are compensated by this filter. The proposed control technique has been simulated using MATLAB/Simulink and validated experimentally. Control algorithm is implemented in Real-Time Windows Target, along with a PCI 1711 card for data acquisition.

This paper proposes an option to harvest energy by using the Seebeck effect, which harvests energy through the temperature differences present. This energy harvesting tool/Peltier Module is fabricated using ceramic outer shell and the inner part made of bismuth telluride. The thermoelectric generator can supply low-power electronics and a combination of these TEGs can power much more than low-power electronics. The aim of this thermoelectric generator is to supply electricity of 5 V and 1 A to places where placing a Solar panel is not commercially viable. The devices that are aimed to be powered are the devices that are able to charge themselves using a USB port.

To overcome the challenges in MOSFET scaling, FinFETs have emerged as a probable candidate compatible with CMOS technology. Memory forms an integral part of almost all IC chips and contributes to the major share of power dissipated. Replacing MOSFET-based memory arrays with the quasi-planar FinFET helps to lower the leakage currents and thereby the power dissipation. The important criteria in the design of an SRAM cell are cell stability and cell area. The stability of the cell is determined by the static noise margin (SNM). This paper describes the modelling and simulation of a double-gate n-FinFET. It also discusses the effect of varying the gate material on the performance characteristics of the FinFET. The optimization of a 6T FinFET-based SRAM cell has also been presented. The cell optimization is in terms of the fin dimensions, namely fin width and fin pitch.

Continuous scaling of CMOS technology has led to reliability issues and process variability that affect the circuit performance of the SRAM cell. The dynamic behavior of SRAM cells are characterized by critical read-stability (Tread) and critical write-ability (Twrite) while the Static Noise Margins (SNMs) are deduced by the static metrics that are the key performance metrics. The work in this paper demonstrates the cumulative impact of process variability and Negative Bias Temperature Instability (NBTI) degradation on the dynamic metrics of the SRAM cell under varied temperature conditions. Degradation due to NBTI is incorporated by considering different activity factors (α) for the dynamic metrics. Time-zero or process variability is performed for fresh-case, symmetric and asymmetric degradation by Monte Carlo run simulations using foundry models in addition to examining the effect of correlation with their corresponding static metrics.

Quantum Cellular automata (QCA) is one of the promising next-generation technology which enables high performance and low energy Nano-electronic circuits. QCA presents a new dimension of ideas of designing the fundamental gates in digital electronics with minimum hardware. Here the logic level switching depends on the change in the polarization between the cells neglecting the current transfer which marks this viable technology as a promising candidate for upcoming generations. Moreover, the processing and transfer of information make use of quantum mechanics and cellular automata to deal with the disputes of CMOS transistor technology. In this manuscript, we have proposed the design of multi-logic gates using QCA architecture. The design layout has been simulated using QCA designer 2.0 and is in accordance with the desired logic. The results are compared with the existing design and found with less number of cells and less area.

The problem of features, objects, gestures, and face detection has been tackled using a numerous vision-based algorithms available in literature. Each of these algorithms requires a set of hyper-parameters, which need to be set on the basis of trial and error such that the results provide best performance to a situation. Mostly, researchers use trial and error approach to satisfactory result and solve the above problems. In this work, an approach has been suggested to determine an optimum set of hyper-parameters, which will provide a starting point for anyone using Viola Jones algorithm for hand gesture recognition or similar endeavors. This will reduce the time spent in searching for the best combination of hyper-parameters.