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Recent mobile telecommunication systems are using multiple-input multiple-output system (MIMO) collective with the orthogonal frequency division multiplexing (OFDM), which is well-known as MIMO-OFDM, to offer robustness and higher spectrum efficiency. The most important challenge in this scenario is to achieve an accurate channel estimation to identify the information symbols once the receiver must have the channel state information to balance and process the received signal. Hence, an effective technique is introduced by proposing the Taylor-least square error algorithm (TLSE) to improve the performance of the MIMO-OFDM system in multimedia applications. In addition, the user admission control is done in multi user-MIMO (MU-MIMO) system using the priority-based scheduling based on Dolphin-rider optimization (DRO) algorithm that is integrated within the space–time block code (STBC) STBC-MIMO-OFDM system for efficient power allocation to ensure the energy efficiency. The DRO is the integration of rider optimization algorithm (ROA) and Dolphin Echolocation (DE). Here, channel estimation is done using the novel optimization algorithm, termed TLSE, which is designed by modifying LSE with the Taylor series. Moreover, the fitness parameters, such as power, priority, throughput, and Proportionally Fair, are computed. The experimentation is conducted in different fading environments with three modulation schemes, binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), and quadrature amplitude modulation (QAM) with the performance metrics, namely bit error rate (BER) and throughput. The developed TLSE + DRO (QAM) outperformed other methods with minimal BER of 0.0001 based on channel-2 and maximal throughput of 0.9965 with respect to channel-1.

This article presents an experimental and theoretical investigation of acoustic noise and vibration (ANV) produced by permanent magnet synchronous motor (PMSM)-based electric drive. The principal s...

Time synchronization and node localization over a wireless sensor network (WSN) is a vital problem in applications such as Internet of Things (IoT) and context-aware pervasive systems. Addressing this problem leads to an efficient data fusion, power scheduling, bandwidth utilization, geographic routing, time-based channel sharing, and clustering among sensor nodes. In a WSN, sensor nodes utilize multi-hop data transmission model towards the data transfer. Utilization of large number of hops for the data transfer leads to poor time synchronization resulting in erroneous node location estimates. In this letter, we make use of a recent development in social networks termed as small world characteristics and propose a novel method of time synchronized node localization over a small world WSN (SW-WSN). The results are obtained via simulations over a medium scale WSN. The performance of the proposed method is evaluated by conducting detailed analysis of time synchronization and node localization errors over the SW-WSN. The results obtained illustrate that the proposed small world model yields improved localization results when compared to results obtained over a regular WSN.

A new technique for power transformer protection, that depends on the calculation of fault detection ratio (FDR) based on quartile of superimposed differential currents is presented. Internal faults are detected by comparing FDR with the predetermined threshold derived through analytical analysis and verified based on simulation results. The performance of the presented scheme is tested by modeling an existing Indian power transmission network using PSCAD/EMTDC software. Various types of abnormal conditions such as magnetizing inrush (including sympathetic and recovery) and over-excitation along with several types of internal (winding, inter-winding and turn-to-turn) faults have been investigated. Its validity has also been checked on the data of magnetizing inrush and simultaneous inrush with the internal fault in the transformer, which was obtained from the real network. The results reveal the effectiveness of the suggested technique in differentiating internal faults from other abnormal conditions. Further, it remains stable during heavy through fault with current transformer saturation. It is equally applicable for different ratings and winding connections of power transformers. Finally, comparative evaluation of the proposed scheme with the conventional and several existing techniques shows its superiority in terms of higher sensitivity during internal faults and improved stability in case of non-internal faults.

Development of energy-efficient wireless sensor networks is crucial in the deployment of IoT and IIoT for modern day applications like smart home, smart vehicles, and smart industries. Several methods like network clustering, mobile sink deployment and dynamic sensing rate have been used in improving the energy-efficiency of wireless sensor networks in IoT framework. However, these methods have been developed independently which can lead to certain network issues like reduced lifetime, network breakdown among others. In this work, an energy-efficient method that optimizes mobile sink scheduling while concurrently providing dynamic buffer management is proposed. A cooperative network model that incorporates node clustering and mobile sink deployment in variable node sensing rate scenario is first developed. However, in such cooperative network models, mobile sink scheduling and buffer overflow management which causes information loss become challenging. This is primarily due to limited buffer size, variable sensing rate of the nodes, and the unavailability of mobile sink at all times near a cluster. Therefore, a reinforcement Q-learning framework is developed for scheduling the mobile sink while minimizing the information loss caused by buffer overflow in each cluster of a clustered WSN. More specifically, the network behaviour is learnt in the context of buffer overflow using Q-learning approach. The proposed method computes the adaptive halt-times for the mobile sink based on information loss and buffer overflow in each cluster. Performance of the proposed joint mobile sink scheduling and dynamic buffer management method is evaluated on a medium scale WSN. A clustered wireless sensor network with a total of 600 sensor nodes is considered for performance evaluation. The proposed method is shown to learn the variable node sensing rate in a reasonable amount of time using convergence analysis. Numeric evaluations indicate that the proposed method minimizes the information loss in a medium scale wireless sensor network while improving the network lifetime simultaneously. The proposed cooperative network model also outperforms in terms of energy-efficiency when compared to conventional WSN. The results are motivating enough for the use of cooperative network model in practical WSNs for IoT applications.

This article presents a novel application of Pseudorandom Triangular Pulsewidth Modulation (PTPWM) technique on a permanent magnet synchronous motor (PMSM) drive to reduce acoustic noise and vibration (ANV). The proposed PTPWM method brings a significant reduction in torque ripples, acoustic noise, and vibrations in the motor, thereby enhancing the performance of the complete drive system under operation. The relationship between the stator current harmonics feed by drive and nonsinusoidal magnetic field flux distribution, with torque ripples is developed and analysed in this article. Furthermore, in-depth theoretical background and analysis about how PTPWM improves torque ripples, vibration, and acoustic noise of PMSM drives is presented. An extensive simulation and experimental investigations have been carried out for the validation of proposed control strategy.

Recent technology advancement has resulted in optimistic view toward the practicability of wireless sensor networks (WSNs) in the context of Internet of Things (IoT) and Cyber Physical Systems (CPS). However, to realize their full benefits in a broad range of commercial applications, there are still many technical hitches that need to be overcome. In this paper, we address three vital technical issues in a WSN: (1) distributed event detection, (2) distributed parameter estimation, and (3) network’s robustness. We make use of a recent development in social networks called small world characteristics and propose novel fault-resilient distributed detection and estimation methods over a small world WSN (SW-WSN). In particular, a small world WSN has been developed by mounting antenna arrays on sensor nodes for the purpose of beamforming. A low-complexity optimization problem for beamforming is formulated by introducing a new parameter Flow between node pairs. Additionally, a new beamforming algorithm is also proposed which optimizes this flow, leading to optimal beam parameters. The proposed method yields a lower average path length and a higher average clustering coefficient of the network. Experiments are conducted using simulations and real node deployments over a WSN testbed. Analysis and experimental results obtained demonstrate that the proposed SW-WSN model achieves faster convergence rates for both distributed detection and distributed estimation while being resilient to node failures when compared to results obtained using state-of-the-art methods.

This paper presents experimental investigation study of Acoustic Noise and Vibration (ANV) reduction in Permanent Magnet Synchronous Motor (PMSM) drives with a novel application of `harmonics spread spectrum technique'. In order to reduce the ANV of PMSM drives, the Random Pulse Width Modulation (RPWM) method is applied which distributes the power of harmonic noise in a wide range of discrete frequency spectrum. Furthermore, in-depth theoretical analysis is presented about the way RPWM improves torque ripples, vibration and acoustic noise in PMSM drives. The theoretical and experimental results obtained are used to draw the operational concept of the power electronics converters to reduce ANV; which is produced by PMSM drive. Time and frequency-domain analysis are carried out to examine the effects of change in the operating parameters such as pulse-width, etc. of the power electronics converters on the ANV. A small-scale laboratory set-up was developed for PMSM drive with low cost acoustic chamber for experimental investigations. Experimental results show significant reduction in ANV and torque ripples of the PMSM drive with the application of RPWM technique.

Mobility-tolerant data forwarding over time-varying IoT networks is a challenging problem. Selection of a relay node at every instant in time is crucial and calls for robust and distributed methods to improve the quality of services in such networks. In this paper, an optimal relay node selection method is proposed for robust data forwarding in time-varying networks. The proposed method selects a relay node based on joint optimization of two network parameters, namely data latency and link reliability. The Pareto front of this multi-objective optimization problem is used to illustrate the tradeoff between low data latency and high link reliability. The proposed optimal relay node selection method first identifies all possible relay nodes at every time instant. The weights of the bi-objective problem are updated at every hop and an optimal relay node is selected among all the possible relay nodes, by solving the joint optimization problem. During performance evaluation, random spatial patterns of Internet of Things (IoT) devices are modeled using the homogeneous Poisson point processes. The connectivity duration information of all the IoT devices with their neighbors is updated continuously and a relay node found is in an online manner. Simulation results indicate that the proposed method significantly improves data latency and the packet loss risk for data forwarding over time-varying IoT networks, when compared to conventional single parameter optimization methods. The proposed data forwarding method indicates additional gains in terms of transmission range, mobility tolerance, packet replication cost, and connectivity parameters of the time-varying IoT network.

Purpose Rapid tooling (RT) technique using rapid prototyping (RP) process has been looked upon as an approach which reduces time and cost of production. This study aims to produce electrode for electrical discharge machining (EDM) from acrylonitrile butadiene styrene (ABS) material using the fused deposition modeling (FDM) process of RP. The electrode is coated with copper to a depth of 1 mm by using electroplating to make it conductive. This electrode is termed as RP electrode. The performance of RP electrode having square shape is compared with that of solid electrode of copper having identical size. Design/methodology/approach In this study, the work piece material is chosen to be titanium Grade-V alloy (Ti-Al6-V4). The input parameters on the EDM machine such as discharge current, pulse on time and voltage are studied, and experiments are designed using the Taguchi method. Findings The results pertaining to the material removal rate (MRR), electrode wear rate (EWR) and surface roughness (Ra) are reported. It is found that the performance of a coated RP electrode is equally satisfactory when compared with that of a solid electrode. Originality/value This paper reports the machining performance of a square-shaped ABS electrode coated with copper. This technique, particularly when the electrode is of intricate shape, saves on cost and time of production of electrode to be used for EDM.

A series of eight novel donor‒acceptor based quinoxaline‒amine derivatives 2–9 were prepared by modulating donor species on quinoxaline core with Buchwald‒Hartwig coupling amination reaction. The synthesized molecules were fully characterized and studied for impact of D−A interaction on optoelectrochemical properties of derivatives. Absorption spectra of 2−9 display intramolecular charge transfer (ICT) transitions in the range of 377–456 nm. Dyes 2–9 show positive solvatochromism in emission and emit in blue‒orange region with emission maxima 472–592 nm on excitation at their respective ICT maxima in toluene, chloroform, DCM and neat solid film. Herein, dyes 2, 3 and 7 which possess intense emission in solid state were further studied for Aggregation Induced Emission (AIE) effect. The HOMO and LUMO energy level for compound 2–9 obtained by CV were found in the range of −5.23 to −5.83 eV and −3.55 to −3.74 eV. Theoretical studies of molecules were also carried out by using TD‒DFT calculations. The comparable HOMO and LUMO energy level of 2−9 with reported ambipolar materials and efficient solid state emission make synthesized compounds potential candidate for solid state emissive, ambipolar materials in organic electronics.

Improving the lifetime of a wireless sensor network is a challenging issue in the development of energy-efficient cyber-physical systems, especially in the design of sensor data aggregation protocols. Mobile sinks are typically used in this context to increase the lifetime of the sensor network in a cyber-physical system. In this work, a landmark-assisted mobile sink scheduling scheme is proposed for designing an energy-efficient data aggregation protocol. A landmark-node identification method along with network clustering is first developed using random walks over network graphs. Subsequently, using the locations of landmark-nodes, a mobile sink scheduling method is developed for sensor data aggregation in an extended Kalman filtering framework. This method is robust since location information of the landmark-nodes is also continuously updated in the Kalman filtering framework. Experiments on identifying landmark-nodes and mobile sink scheduling are conducted on the Intel Berkeley Research Lab dataset and over a real WSN test bed with a Kobuki–Yujin robot. Experimental results obtained for network lifetime analysis and energy-efficiency for various network configurations indicate an improvement over standard methods and are motivating enough for use in practical cyber-physical system applications.

This investigation reveal as the Physico-chemical parameters of Girna project were monitored over period of one year July 2007 to June 2008. The fresh water of the Girna project is mostly used for the drinking and Agricultural purposes. The study of different parameters like Temperature, Rain fall, pH, Turbidity, T.D.S., Dissolved Oxygen, Dissolved carbon dioxide, Total hardness, calcium, Magnesium, and Chloride were well studied and suggest that the quality of water is not polluted.

In this study, a series of bipolar, donor–acceptor–donor (D–A–D)-based quinoxaline diarylamine/heterocyclic amine derivatives were synthesized in good yield by the Buchwald–Hartwig amination reaction and fully characterized by different spectroscopic methods. The optoelectronic properties of the dyes were scrutinised using electronic absorption and fluorescence spectroscopy, cyclic voltammetry, thermogravimetric analysis, and differential scanning calorimetry and further by the DFT and TDDFT study. The photophysical properties of dyes are influenced by their peripheral amines and the nature of solvents used. The presence of intramolecular charge transfer (ICT) transitions in the absorption spectra of amine derivatives is attributed to the existence of the D–A building units, which induces blue to yellow emission in both the solution and the neat solid film. Strikingly intense solid-state emission in some of the dyes has moved the area of study toward aggregation induced emission (AIE) phenomenon and showed the formation of nanoaggregates at above 50% water fraction (fw) of THF/H2O solvent mixtures, which was further confirmed by the FEG-SEM technique. The linkage of different amines with the quinoxaline core moiety tunes the electrochemical properties with lower band gaps and comparable HOMO–LUMO energy levels in reported ambipolar materials. Thus, the existence of a strong solid-state emission with AIE activity, ambipolar nature and good thermal properties of dyes attribute their use as solid-state emitters and ambipolar materials in optoelectronic devices.

A series of donor–acceptor-based 6H-indolo[2,3-b]quinoxaline amine derivatives were synthesized with different donor amines. The impact of the different donor moieties on 6H-indolo[2,3-b]quinoxaline was systematically examined by absorption-emission spectroscopy, cyclic voltammetry and theoretical studies. The photophysical properties of these molecules, such as intramolecular charge transfer transitions (400 to 462 nm) and emission (491 to 600 nm), were influenced by the nature of the peripheral amines. Further, the solid-state emission demonstrated by some of the dyes aroused our interest in performing photophysical studies on the aggregation-induced emission phenomenon; these dyes showed nanoparticle formation in THF/H2O solvent mixtures. Varying the strength of the peripheral amines in the derivatives also tuned the electrochemical data, with lower band gaps (1.56 to 2.21 eV) and comparable HOMO–LUMO energy levels with reported ambipolar materials. The donor–acceptor architectures and HOMO–LUMO energies were further rationalized using DFT/TDDFT calculations. Thus, the opto-electrochemical studies and high thermal stability of these molecules indicate their potential application as solid-state emissive, ambipolar materials in optoelectronic devices.

Improving the energy-efficiency of remotely deployed sensor nodes in agriculture wireless networks is very challenging due to a lack of access to energy grid. Network clustering and limiting the amount of sensor data are among the various methods to improve the lifetime of these sensor nodes. In this work, an optimal sensor-selection scheme is proposed to improve the Quality of Service in clustered agriculture networks. the proposed method selects a limited number of sensor nodes to be active in each cluster. It considers the estimator performance while selecting limited sensor nodes for environmental monitoring. the optimal sensor-selection process considers the information of remaining energy of sensor nodes in agriculture wireless networks. the proposed method selects a limited number of nodes in each cluster while maximizing the estimator performance at the receiver. Our cluster based sensor-selection scheme optimizes the energy-efficiency in a distributed manner. Network clustering also ensures a uniform sensor-selection over a network area. Extensive experiments are conducted to analyse the performance of the proposed optimal sensor-selection method over different network scenarios. Our experimental results indicate significant improvements in energy-efficiency of agriculture wireless sensor networks and motivate the proposed method in practice.

This paper describes the sensorless operation of the rotor position of a brushless permanent magnet synchronous motor (PMSM) drive. In order to estimate the rotor position of the brushless PMSM drive, the current slope detection technique is used. The concept involves the use of instantaneous three-phase stator currents and voltages to provide the instantaneous rotor position and speed of the PMSM drive. In-depth analysis and mathematical models include the analysis of the current slope detection technology, under the steady state and dynamic speed response of the permanent magnet synchronous motor drive’s rotor position sensorless operation. Initially use voltage frequency (V/F) control technology to start the brushless PMSM drives. When the PMSM drives reaches a few percent of the synchronous speed, the V/F control method will switch to the rotor position sensorless control algorithm. The sensorless control technique is implemented in MATLAB/Simulink environmental tools and further validated over a small-scale experimental set-up.

In this paper a sparse optimization problem is formulated for reproduction of higher order ambisonics (HOA) encoded signals. The problem is solved using the least absolute shrinkage and selection operator (Lasso). The proposed solution is efficient enough to reproduce the desired sound field with reduced number of loudspeakers. Furthermore, this paper highlights the amplification in the ambisonics components due to the near field effect of the secondary source. The near field compensation(NFC) of HOA encoded signals is carried out to reduce the curvature distortion in the reproduced sound field. Experiments on sound field reproduction using the proposed method are conducted using finite distance loudspeaker arrays. Performance improvements are noted when compared to the standard HOA reproduction techniques in terms of sound field reproduction accuracy and normalized error.

Development of angular superresolution methods for resolving targets using multifunction phased array radar is challenging. Angular superresolution of closely spaced coherent targets with strong interferences in the context of phased array radar has hitherto not been addressed. In this paper a novel beamforming method with angular superresolution is proposed for resolving closely spaced coherent targets in the presence of interferences. A dynamic subarray beamforming framework is first developed based on the knowledge of the number of interferences. The output obtained from the dynamic subarray beamformer is then smoothed using an augmented covariance method to account for the coherence of targets. Superresolution method is then used to obtain robust DOA estimates even at low SNR. Experiments on DOA estimation are conducted in typical target detection scenarios and the results are evaluated using several performance metrics to illustrate the significance of the proposed method.

In this paper, a novel method for spatial Head Related Transfer Function (HRTF) interpolation is proposed. The property of linearity of HRTF spectral phase with frequency is first illustrated. An optimization problem is then formulated to compute the interpolated HRTF phase by imposing linearity constraints on the spectral phase of adjacent spatial angles. A weighted error minimization is carried out in an alternating optimization framework to obtain the solution to this problem. Phase-only reconstruction is used to obtain the HRIR for subsequent rendering of binaural audio. Interpolated HRTFs obtained by using the CIPIC and SYMARE database are evaluated using statistical analysis. Subjective evaluations are also performed to evaluate the quality of the binaural audio rendered using the proposed method.

Lower order ambisonics suffers from low spatial resolution, where hardware complexity is high for direct recording the higher order ambisonics (HOA). This problem can be solved by upscaling the order-l ambisonics (B-format signals). In this paper, a sparse plane-wave decomposition method using sequential matching pursuit is developed for upscaling the order of ambisonics. The proposed method maintains the same sparsity level across multiple measurements and is computationally efficient. The performance of the proposed method is evaluated based on the error in encoded signal and reconstructed sound field, and compared with the state-of-art upscaling techniques. Perceptual evaluations are also conducted, which indicates a significant improvement in spatial resolution.

This paper presents a rotor position estimation scheme for high performance Permanent Magnet Synchronous Motor (PMSM) drive. The rotor position of PMSM drive is estimated by measuring stator voltage and current quantity of PMSM drive. Initially the PMSM drive run by V/F control technique. Once the motor reaches up-to 10% of synchronous speed, then V/F control technique switch over to sensorless control algorithm. Sinusoidal Pulse Width Modulation (SPWM) technique has been used to generate three phase voltages from the dc link source. The closed-loop speed control has been shown to be effective from standstill to rated speed. Computer simulation and experimental results are presented to demonstrate the effectiveness of the scheme.

Keyword spotting in a continuous speech is a challenging problem and has relevance in applications like audio indexing and music retrieval. In this work, the problem of keyword spotting is addressed by utilizing the complementary information present in spectral and prosodic features of the speech signal. A thorough analysis of the complementary information is performed on a large Hindi language database developed for this purpose. Phonetic and prosodic distribution analysis is performed toward this end, using canonical correlation and Student T-distance function. Motivated by these analyses, novel methods for spectral and prosodic information fusion that optimize a combined error function is proposed. The fusion methods are developed both at the feature and the model level. Improved syllable sequence prediction and keyword spotting performance are obtained using these methods when compared to conventional methods of keyword spotting. Additionally, in order to enable comparison with the state-of-the-art deep learning-based methods, a novel method for improved syllable sequence prediction using deep denoising autoencoders is proposed. The performance of the methods proposed in this work is evaluated for keyword spotting using a syllable sliding protocol over a large Hindi database. Reasonable performance improvements are noted from the experimental results on syllable sequence prediction, keyword spotting, and audio retrieval.

This paper presents a novel method to distinguish internal fault from magnetizing inrush of a power transformer. In this method, the fundamental frequency positive and negative sequence components have been utilized to calculate internal fault detection (IFD) ratio that effectively classify the internal (LG, LL, and turn-to-turn) faults from magnetizing inrushes such as residual and sympathetic inrush. Moreover, this method remains stable against over-excitation and CT saturation during external fault events. Evaluation of the proposed scheme has been carried by simulating various types of power transformer operating conditions on a small part of Indian power system network that is precisely modeled in PSCAD/EMTDC. At the end, a comparative evaluation with recently developed method reveals superiority of the proposed method.

Clustering of devices plays a significant role in the development of energy-efficient wireless sensor networks for IoT applications. In this paper, a novel clustering method is proposed which utilizes the multiple sensor modalities of devices to develop an energy-efficient data aggregation protocol. It groups the devices of a network into different clusters using a fused resemblance matrix. The proposed method fuses different sensor modalities like radio, acoustic and light to obtain robust resemblance coefficients. The weights for fusion of different modalities are computed using the modelling error and within a confidence interval. The proposed clustering method is developed using the hierarchical agglomerative clustering framework. Performance of the proposed method in cluster-based data aggregation is evaluated for various sensor networks. Experimental results illustrate the significance of the proposed multi-sensor data based clustering scheme in energy-efficient data aggregation over IoT networks.

In this paper, a pulse width modulation based two loop PI control technique is proposed to control the load bus voltage magnitude in the presence of constant power load in standalone PV/Battery dc power system. Inner loop controller is used to control the inductor current and outer loop controller is used to control the load bus voltage. Both controllers are tuned and their stability is observed by frequency domain measures. Instantaneous maximum power is drawn from photovoltaic array by Incremental Conductance algorithm using a DC-DC boost converter. The effectiveness of the proposed method is validated using MATLAB/Simulink software for different solar irradiation and varying load conditions.

Energy balancing and faster data transfer over a wireless sensor network (WSN) is an important problem in applications like cyber-physical systems, Internet of things, and context-aware pervasive systems. Addressing this problem leads to increased network lifetime and improved network feasibility for real time applications. In WSNs, sensor nodes transfer the data using multihop data transmission model. The large number of hops required for data transmission leads to poor energy balancing and large data latency across the network. In this paper, we utilize a recent development in social networks called small world characteristics for proposing a novel method of low-latency and energy-balanced data transmission over WSN. Small world WSN (SW-WSN) exhibits low average path length and high average clustering coefficient. A cognitive SW-WSN is developed by adding new links between a selected fraction of nodes and the sink. A new data routing method is also proposed by optimizing energy cost of the links. This method yields uniform energy consumption and faster data transfer. Experiments are conducted using simulations and real node deployments over a WSN testbed. The performance of the proposed method is evaluated by conducting exhaustive analysis of network lifetime, residual energy, and data latency over the WSN. Experimental results obtained indicate that the proposed cognitive small world model achieves energy balancing, increases network lifetime, improves energy efficiency, and reduces data latency when compared to results obtained using various state-of-the-art approaches. The results are motivating enough for the proposed method to be used in large and medium scale network applications.

Localization of sensor nodes and efficient data gathering over a wireless sensor network (WSN) is vital in applications such as cyber-physical systems, Internet of Things, and context-aware pervasive systems. In WSNs, sensor nodes transfer the data cooperatively using multiple hops over a network. The large number of hops required for data transmission leads to erroneous distance estimation between node pairs, resulting in a large localization error. In this paper, we utilize a recent development in social networks called small-world characteristics for proposing a novel method of joint localization and data gathering over a WSN. A small-world WSN is developed by introducing data mobile ubiquitous LAN extensions (MULEs) into a conventional WSN. A small-world WSN exhibits low average path length and high average clustering coefficient. Such a small-world WSN when designed with novel routing strategies leads to reduced hop counts in sensor data transmission. Additionally, a method for optimal data MULE allocation is also developed. This method minimizes an objective function, which is a normalized weighted sum of network parameters such as bandwidth requirement and localization error. The optimal data MULE allocation method computes both the optimal number of data MULEs and their placement in the network. On the other hand, the joint localization and data gathering method that utilizes a multidimensional-scaling-based cooperative localization method is also developed for this purpose. Experiments are conducted using simulations and real node deployments over a WSN testbed. The performance of the proposed method is evaluated by conducting exhaustive analysis of power consumption, bandwidth required, localization error, data gathering efficiency, and throughput. The obtained experimental results indicate a significant improvement on several evaluation parameters when compared to results obtained on the conventional WSN. The obtained results also illustrate reasonable improvement when compared to results obtained using conventional small-world characteristic introduction and localization methods. The results are motivating enough for the proposed method to be used in large- and medium-scale network applications.

This study presents a new power transformer protection scheme based on extraction of positive and negative sequence components of superimposed differential currents. Different types of internal faults, magnetising inrush, and over-excitation conditions have been generated by modelling in service power transformer of power transmission system of India using PSCAD/EMTDC software package. The simulation results clearly indicate the efficacy of the proposed scheme in detecting different types of internal faults like turn-to-turn, inter-winding, line-to-ground, line-to-line, and line-to-line-to-ground. Together, the proposed scheme is competent to achieve effective discrimination between various external events (over-excitation condition and various magnetising inrushes) and internal faults. Moreover, it provides equally promising results during current transformer (CT) saturation condition as well as for different connection and rating of the power transformer. Further, it remains stable during errors in the CT and tap change condition. Additionally, authenticity of the proposed scheme has been evaluated using internal fault and magnetising inrush data recorded in the real field. At the end, comparative assessment of the proposed scheme with the existing and conventional techniques clearly indicate its superiority in terms of better sensitivity for internal faults and higher stability during external events.

Speaker Recognition is a vital application of speech processing. Speaker Recognition performs a task of authenticating or recognizing a speaker based on the unique features captured which characterize the speaker. Characteristics or features which are unique to an individual such as fundamental frequency, speaking style, pitch, and duration are used as distinguishing components of the human speech signal. Exploring these characteristics for various applications with an attempt to implement a robust speaker recognition system has been the impetus behind the research in this domain. This paper makes an attempt to present the available Feature Extraction and Recognition techniques with their merits and demerits. It also discusses the pre-emphasis stage of the speaker recognition system. The standard databases available for speaker recognition along with the criterion for their selection are also reviewed. The paper presents an overview of various toolkits and performance parameters of Automatic Speaker Recognition System.

Small world characteristics have been observed and studied in social networks in the context of information dissemination in important applications like disease spread among communities. However, the significance of small world characteristics in wireless sensor network (WSN) applications has hitherto not been investigated. This work investigates the significance of introducing small world characteristics in a conventional WSN for improving the node localization accuracy. In this context, a novel constrained iterative average path length reduction algorithm is proposed to introduce small world characteristics into a conventional WSN. This method utilizes a novel frequency selective approach for the introduction of small world phenomena. The frequency selective approach utilizes two frequency bands for link addition and pruning to create an optimized small world WSN. Subsequently, a constrained least squares node localization method over the small world WSN is developed using both model-based and model-free signal propagation approaches. The proposed constrained iterative average path length reduction method provides reduced average path length and a high average clustering coefficient when compared to conventional WSN. Constrained least squares node localization over the small world WSN yields improved localization performance when compared to node localization over conventional WSN. Performance analysis in terms of localization error, power consumption, bandwidth and anchors required for node localization is also discussed. Cramer-Rao lower bound analysis for node location parameters is also used to provide insights into the error bounds obtained using the proposed method. The experimental results obtained are motivating enough for the introduction of small world phenomena in conventional WSN for applications like node localization and related applications.

It has been determined that variations in vibroarthrographic (VAG) signal characteristics have a direct association with various diseases of the knee joints including degenerative and traumatic etiology. Several studies have proposed temporal and time-frequency parameters for the analysis and classification of VAG signals; however, very few statistical modelling methods have been explored to analyze distinctions in the spectral domain characteristics of VAG signals. The aim of our work is to classify VAG signals into normal and abnormal thus portraying knee health condition by proposing a novel set of features. Spectral domain transformations of VAG signals were derived using the short-time Fourier transform (STFT) approach to extract statistical characteristic features from VAG signals. Several time-domain statistical features; the mean and variance values of spectral domain features namely – spectral mean, peak, skewness, kurtosis, flux, and slope were utilized to express VAG signal fluctuations. Further, mutual information test, ANOVA F-test, and Chi-square test were used to select significant features and assess their effectiveness. Random forest, Gaussian Naive Bayes, and support vector machine (SVM) algorithms were used to perform signal pattern classification. The results showed that subjects with knee joint abnormalities possess higher values of spectral flux, spectral slope, and shape factor than healthy subjects. An overall classification accuracy of 89.23%, sensitivity of 87.09% and specificity of 91.18% were obtained using the random forest classifier trained with features selected by the mutual information test. The proposed system could effectively screen almost 94% of subjects accurately based on area under the ROC curve.

In this work, we have synthesized a series of donor-acceptor type molecules involving electron donor diarylamines/heterocyclic amines and electron acceptor 9,10-anthraquinone by employing palladium catalyzed C−N bond forming amination reaction in good yield and fully characterized. Absorption spectra of all the synthesized molecules showed an intramolecular charge transfer (ICT) transitions in the range of 417−554 nm. Electrochemical properties of the compounds were studied by cyclic voltammetry (CV). The HOMO and LUMO energy levels of synthesized compounds were found in the range of −5.18 to −5.79 eV and −3.22 to −3.53 eV respectively as calculated by CV. Theoretical studies were also carried out by using DFT and TD-DFT calculation. On the basis of HOMO and LUMO energy levels, synthesized compounds could be used as ambipolar materials in organic electronics.

In this paper, a novel method to localize indoor wireless sensor nodes using visible light in non-line of sight (NLOS) condition is proposed. The proposed method is able to identify NLOS condition in a sensor network and subsequently localize the sensor nodes. Since visible light is used for localization, the reflection points are first localized using time difference of arrival in a maximum likelihood framework. The location of the sensor nodes is then estimated using range and reflection angles which are themselves computed using novel geometric methods. Simulations and real field wireless sensor node deployments are then used to evaluate the performance of the proposed method. Experimental results indicate that the proposed method is able to localize sensor nodes with a high degree of accuracy when compared to conventional methods under NLOS conditions. The method also demonstrates reasonable robustness under sensor and ambient noise conditions.

Imidazoanthraquinone derivatives with extended π conjugation were synthesized and characterized. Palladium-catalyzed Suzuki–Miyaura and Sonogashira couplings were employed to synthesize these derivatives. Photophysical and electrochemical studies of the new imidazoanthraquinone derivatives were performed, and their absorption and emission maxima were observed in a wide visible range. The photophysical studies revealed the formation of charge-transfer complexes. Cyclic voltammetry provided good estimates of the reduction and oxidation potentials of these compounds and eventually the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels. Electrochemical studies revealed low-lying ELUMO values of ca. –3.7 eV for these compounds. The donor–acceptor architecture and HOMO–LUMO energies were further studied through DFT calculations. On the basis of the ELUMO values of these compounds, photoquenching experiments were performed in the presence of the electron donor poly(3-hexylthiophene) (P3HT). These experiments suggest that these compounds could be used as electron acceptors in heterojunction solar cells.

This work explores the use of phoneme level information in cohort selection to improve the performance of a speaker verification system. In speaker verification, cohort is used in score normalization to get a better performance. Score normalization is a technique to reduce the undesirable variation arising from acoustically mismatched conditions. Proper selection of cohort significantly improves speaker verification performance. In this paper, we investigate cohort selection based on a speaker model cluster under the i-vector framework that we call the i-vector model cluster (IMC). Two approaches for cohort selection are proposed. First approach utilizes speaker specific properties and called speaker specific cohort selection (SSCS). In this approach, speaker level information is used for cohort selection. The second approach is phoneme specific cohort selection (PSCS). This method improves cohort set selection by using phoneme level information. Phoneme level information is further employed in a late fusion approach that uses a majority voting method on normalized scores to improve the performance of the speaker verification system. Speaker verification experiments were conducted using the TIMIT, HINDI and YOHO databases. An equal error rate improvement of 19.01%, 14.61% and 19.4%is obtained for the proposed method compared to the standard ZT-Norm method for TIMIT, HINDI and YOHO datasets. Reasonable improvements in performance are also obtained in terms of minimum decision cost function (min DCF) and detection error trade-off (DET) curves.

A series of five novel dyes based on indolo[2,3-b]quinoxaline skeleton, derived from anthraquinone, have been synthesized through cyclo-condensation reaction in good yield. The photophysical, electrochemical and thermal properties along with computed HOMO-LUMO energy levels were studied for the synthesized compounds. Their absorption and photoluminescence properties were investigated in various solvents and in neat solid film and found to possess characteristic electronic absorption and emission spectra which strongly depend on the nature of solvents used. Compounds show intramolecular charge transfer transitions (ICT) in the range of 501–561 nm with high molar absorption coefficient (e). These indoloquinoxaline derivatives emit in the range of 580–648 nm in solutions and 672–700 nm (red region) in neat solid films. Electrochemical data indicate that the dyes possess relatively low–lying LUMO levels in the range −3.29 to −3.43 eV. The thermal stability observed for these compounds suggests their use under ambient conditions. The in–built donor–acceptor architecture and HOMO–LUMO energies were further rationalized using DFT calculations. This study suggests that these compounds have potential to be used as n–type materials for optoelectronic devices.

A series of novel donor–acceptor type of molecules (2–6) based on dibenzo[a,c]phenazine were synthesized by employing a palladium = catalyzed C–N bond forming amination reaction in a good yield and were then fully characterized, whereby the optical properties of 2–6 were determined by UV-vis absorption, fluorescence spectroscopy, and the electrochemical properties by cyclic voltammetry. The absorption spectra of 2–6 showed intramolecular charge transfer (ICT) transitions in the range of 447–513 nm in solution. The HOMO and LUMO energy levels of 2–6 were in the range from −5.03 to −5.29 eV and −2.75 to −2.87 eV, respectively, with an electrochemical band gap within 2.26–2.45 eV. The HOMO energy levels of 2–6 are comparable with those of the most commonly used hole-transporting materials, which makes them potential candidates for hole-transporting materials in organic electronics.

Magnetic binary oxide particles (MBOP) were prepared by template method using chitosan in the laboratory for the removal of As(III) from water. The prepared MBOP has super paramagnetic property which is sufficient for magnetic separation. Column study was performed at two different flow rates of 2.0ml/min and 5.0ml/min and comparison was made with regenerated MBOP, commercial activated carbon and commercial activated alumina. It is observed that fresh MBOP has higher breakthrough time and capacity than regenerated MBOP by a factor of 1.25 and 1.37 respectively. In Logit method, the values of K (adsorption rate constant) and N (adsorption capacity coefficient) were obtained as 0.2066 (L/mgh) and 1014(mg/L) for 5.0ml/min flow rate. All the drinking water parameters are within the limit of BIS 10500-2012. Toxicity characteristic leaching procedure (TCLP) and semi dynamic tests were performed for the mix ratios of 01:02:01, 01:02:05 and 01:02:10 and were found safe for the disposal.

This paper presents a novel application of Pseudorandom triangular Pulse Width Modulation (PWM) technique for a Permanent Magnet Synchronous Motor (PMSM) drive to reduce acoustic noise and vibration. Among various available spread spectrum techniques, pseudorandom technique has recently applied for power electronics converters to reduce harmonics in voltage and currents, with promising results. The pseudorandom carrier signal produced through the random synthesis of two triangular carriers, each of the same frequency but opposite in phases. The random selection of the two triangular carriers is decided by “0” or “1” states of the pseudorandom binary sequence random bits. A suitable simulation and experimental investigation has been carried out. Performance result shows the significant reduction of torque ripple in the PMSM drive with proposed pseudorandom modulation technique.

Mobile sinks are generally used in wireless sensor networks for energy-efficient data collection. Scheduling of mobile sink in a large scale network is a challenging problem. Tracing of some nodes in the network as landmark-nodes, via clustering can lead to efficient mobile sink scheduling. In this paper, a novel method for landmark-node tracing based on multi-agent random walks on network graphs is proposed. This method ensures low complexity while maintaining the clustering efficiency, especially over large WSN. Additionally, it is energy-efficient and improves the lifetime of a network. Low complexity of the proposed method is illustrated by analysing the cover time and hitting time of multi-agent random walks. Extensive experimental results obtained on Intel Berkeley Research Lab data indicate large improvements in energy-efficiency and computational complexity when the proposed method is used for landmark-node tracing.

Robotic calibration allows for the fusion of data from multiple sensors such as odometers, cameras, etc., by providing appropriate relationships between the corresponding reference frames. For wheeled robots equipped with camera/lidar along with wheel encoders, calibration entails learning the motion model of the sensor or the robot in terms of the data from the encoders and generally carried out before performing tasks such as simultaneous localization and mapping (SLAM). This work puts forward a novel Gaussian Process-based non-parametric approach for calibrating wheeled robots with arbitrary or unknown drive configurations. The procedure is more general as it learns the entire sensor/robot motion model in terms of odometry measurements. Different from existing non-parametric approaches, our method relies on measurements from the onboard sensors and hence does not require the ground truth information from external motion capture systems. Alternatively, we propose a computationally efficient approach that relies on the linear approximation of the sensor motion model. Finally, we perform experiments to calibrate robots with un-modelled effects to demonstrate the accuracy, usefulness, and flexibility of the proposed approach., Comment: To be published in International Conference on Intelligent Robots and Systems (IROS), 2019

This paper discusses the design and performance analysis of low-cost acoustic chamber for measurement of acoustic noise and vibration of electric machines. The purpose of this paper is to find the parameters that allow the characterization of acoustic chamber thereby making an attempt in deriving and obtaining the data of acoustic chamber. The parameters that are being calculated are acoustic transfer function, reverberation time, sound power level and cut-off frequency of acoustic chamber. To obtain the transfer function of acoustic chamber, a pair of microphones and amplifier as a source of sound have been used. Based on the results of transfer functions obtained between the pair of microphones, the cut-off frequency of acoustic chamber is determined and validated through Inverse Square Law. Noise measurements are conducted in both an ordinary environment (laboratory) and acoustic chamber. The acoustic chamber provides the free-field conditions, which satisfy the standard parameters required for the design and test for acoustic chamber. The results obtained both in an ordinary environment and acoustic chamber are compared to produce useful practical information about the noise level change caused by sound fields. The results obtained are found to be helpful in understanding the physical features and characteristics of acoustic chamber.

We herein report the design and synthesis of novel donor‒acceptor (D‒A) type 2,3-di(thiophene-2-yl)pyrido[2,3-b]pyrazine and 2,3-di(furan-2-yl)pyrido[2,3-b]pyrazine amine derivatives by employing palladium catalyzed Buchwald‒Hartwig coupling reaction. The synthesized compounds have been characterized by spectroscopic, electrochemical and thermal methods. Absorption spectra of all dyes showed an intramolecular charge transfer (ICT) transitions (λmax = 438–473 nm) in solution and positive solvatochromism with emission maxima in blue‒orange region (λemm = 489–617 nm). Further, solid state emission was studied for aggregation‒induced emission (AIE) effect in THF–water system due to the nanoparticles formation, as confirmed by FEG‒SEM technique. The HOMO (−5.22 to −5.75 eV) and LUMO (−3.35 to −3.45 eV) energy level of these molecules were measured by cyclic voltammetry and correlated by DFT calculations. On the basis of comparable HOMO and LUMO energy level with reported ambipolar materials and efficient solid state emission warrants the application of synthesized compounds as emitter and ambipolar charge transport materials in organic electronics.

In this paper, Gaussian process regression (GPR) for fingerprinting based localization is presented. In contrast to general regression techniques, the GPR not only infers the posterior received signal strength (RSS) mean but also the variance at each fingerprint location. The GPR does take into account the variance of input i.e., noisy RSS data. The hyper-parameters of GPR are estimated using trust-region-reflective algorithm. The Cramer-Rao bound is analysed to highlight the performance of the parameter estimator. The posterior mean and variance of RSS data is utilized in fingerprinting based localization. The principal component analysis is employed to choose the k strongest wi-fi access points (APs). The performance of the proposed algorithm is validated using using real field deployments. Accuracy improvements of 10% and 30% are observed in two sites compared to the Horus fingerprinting approach.

Sensor node localization in mobile ad-hoc sensor networks is a challenging problem. Often, the anchor nodes tend to line up in a linear fashion in a mobile sensor network when nodes are deployed in an ad-hoc manner. This paper discusses novel node localization methods under the conditions of collinear ambiguity of the anchors. Additionally, the work presented herein also describes a methodology to fuse data available from multiple sensors for improved localization performance under conditions of collinear ambiguity. In this context, data is first acquired from multiple sensors sensing different modalities. The data acquired from each sensor is used to compute attenuation models for each sensor. Subsequently, a combined multi-sensor attenuation model is developed. The fusion methodology uses a joint error optimization approach on the multi-sensor data. The distance between each sensor node and anchor is itself computed using the differential power principle. These distances are used in the localization of sensor nodes under the condition of collinear ambiguity of anchors. Localization error analysis is also carried out in indoor conditions and compared with the Cramer-Rao lower bound. Experimental results on node localization using simulations and real field deployments indicate reasonable improvements in terms of localization accuracy when compared to methods likes MLAR and MGLR.

This work presents simulation, analysis and experimental verification of Fast Fourier Transform (FFT) algorithm for shunt active power filter based on three-level inverter. Different types of filters can be used for elimination of harmonics in the power system. In this work, FFT algorithm for reference current generation is discussed. FFT control algorithm is verified using PSIM simulation results with DLL block and C-code. Simulation results are compared with experimental results for FFT algorithm using DSP TMS320F28335 for shunt active power filter application.

In this paper, we address the issue of near-field source localization using spherical microphone array. The spherical array has been widely used for far-field source localization due to ease of array processing in spherical harmonics domain. Various methods for far-field source localization has been reformulated in spherical harmonics domain. However, near-field source localization that involves joint estimation of range and bearing of the sources has hitherto not been investigated. In this paper, the near-field data model is developed in spherical harmonics domain. In particular, three methods that jointly estimate the range and bearing of multiple sources in the spherical array framework are proposed. Two subspace-based methods called the Spherical Harmonic MUltiple SIgnal Classification (SH-MUSIC) and the Spherical Harmonics MUSIC-Group Delay (SH-MGD) for near-field source localization are first presented. In addition, a method for near-field source localization and beamforming using Spherical Harmonic MVDR (SH-MVDR) is also formulated. Formulation and analysis of Cramer–Rao bound for near-field sources is presented in spherical harmonics domain. Various source localization experiments were conducted on simulated and signal acquired over spherical microphone array in an anechoic chamber. Root-mean-square error and probability of resolution are utilized as measures to evaluate the proposed methods. The significance and practical application of the proposed methods is discussed using experiment on interference suppression. The near-field SH-MVDR beamforming is utilized in this context.

In this work, five novel 3,6-disubstituted-tribenzo[a,c,i]phenazine (2–6) derivatives were synthesized in good yield by employing a palladium catalyzed C–N bond forming amination reaction and fully characterized. Photophysical properties of 2–6 were studied both in solvent and neat solid film by UV-vis absorption, fluorescence spectroscopy and electrochemical properties by cyclic voltammetry. Absorption spectra of 2–6 showed intramolecular charge transfer (ICT) transitions in the range of 460–512 nm in solution. The HOMO and LUMO energy levels of 2–6 are in the range of −5.18 to −5.35 eV and −3.06 to −3.17 eV respectively with electrochemical bandgap within the range of 2.12–2.29 eV. HOMO energy levels of 2–6 are comparable with most commonly used hole transporting materials such as TPD, α-NPD and spiro-OMe-TAD etc. and thus make them potential candidates for the hole transporting material in organic electronics.