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Waste heat recovery (WHR) is a promising technology that uses the thermal energy from the exhaust gases of an internal combustion engine (ICE) to aid propulsion in vehicles. This paper presents a model predictive control (MPC) framework to minimize the fuel consumption of an automotive ICE by integrating it with a WHR system. To this end, a control oriented model of a WHR system is developed and then integrated to a control oriented model of a turbocharged dual fuel diesel-natural gas ICE. The ICE model is derived based on experimental data collected from a 6.7 liter Cummins ISB engine modified for dual fuel operation. The designed MPC framework optimizes the ICE combustion, turbocharger, and organic Rankine cycle (ORC) system in the WHR to minimize fuel consumption of the ICE. The designed control framework also allows to meet time-varying exhaust gas temperature constraints for after treatment system light-off requirements and diesel particulate filter (DPF) regeneration. The results show that the optimal operation of the WHR and the ICE reduces the fuel consumption of the ICE by 5.9%.

One-cycle control (OCC) inverters are becoming a popular option for grid connected PV systems because of their simple, PLL free, and voltage sensor-less structure. Sensor-less inverters can increase the reliability of the system and reduces the hardware cost and mounting space. However, operating the inverter with bipolar modulation results in increased switching losses and filter size compared with other modulation schemes. To overcome this, voltage sensor-less OCC based H-bridge inverter which utilize unipolar or hybrid scheme to trigger the inverter has been proposed in this paper. The proposed unipolar scheme reduces the filter size and with the hybrid scheme, the switching losses and cost of the operating switches can be reduced. Detailed comparison studies of the three modulation schemes based on filter size, harmonic content and transformer-less inverter application are performed. Simulation studies have been performed to validate the proposed sensor-less OCC based inverters.

Internet of Things (IoT), a parasitic innovation and one of a kind revolutionizing the cyber networks far and wide over the globe have infiltrated several sectors of human lives. Ensuring and elevating the quality of human living, it continues to evolve with time. The much-evolved version of IoT, Narrow Band Internet of things (NB-IoT) has left the world astonished owing to its unique ability to digitally transform lives with much lesser frequency of operation. The ability to coexist with technologies such as Artificial Intelligence (AI) and Machine Learning (ML) having a contained asset wastage, provides NB-IoT a high ground over existing innovations. With the introduction of error-correcting codes such as BCH in the client and server-side of the network, NB-IoT prospers over Industrial sectors ensuring nearly absolute error-free data transfer. BCH codes incorporated alongside NB-IoT structures a favored arrangement in ensuring the quality of data and assuring the well-defined future of several industrial sectors

Falls are a leading cause of injury and death in older generations. An in-home fall monitoring system increases safety while allowing for continued independence. The WiFi-based In-home Fall-detection Utility monitors a user by harnessing WiFi Channel State Information, distinguishes falls from normal daily activities, and, if a fall occurs, automatically notifies the user's selected emergency contacts.

Establishing of parabolic reflector antennas is required for any satellite communications. The important parameters of antenna structure are Gain, Pointing accuracy and Natural frequency. This research intends to understand the free vibration characteristics of antenna structure with field test and numerical analysis. Antenna servo system band width depends upon antenna stiffness and structure fundamental frequency. Generally, String-Snap method is followed for finding the frequency physically. But it is a destructive test. Hence frequency obtained from numerical analysis is only considered for deciding the servo system. Nowadays, using microtremor (velocity sensor) frequency of antenna can be measured without any damage or destruction to antenna. In this paper, 7.2m diameter full coverage antenna is considered for field test and numerical analysis and the results are compared.

One cycle control (OCC) based single-phase grid connected inverters are becoming popular for photovoltaic (PV) distributed generator (DG) units, because of its simple structure, cost-effectiveness and phase-locked-loop free implementation. The grid connected DG unit should have a robust islanding detection mechanism incorporated into it. This paper proposes a novel hybrid islanding detection method (IDM), which is suitable for OCC based DG systems. The nature of the OCC based inverter, which drives the VPCC away from the normal operating voltage during an islanding event is utilized to incorporate a feedback based IDM. However, when the difference between the inverter output power and the load power is near to zero, this IDM may fail to detect the condition. To overcome this drawback, a hybrid IDM, which combines the feedback effect and injection of real power disturbance, is proposed. The proposed method, unlike other active methods, injects disturbance into the grid only when an islanding condition is suspected. The induced change in power delivered by the inverter is only for a small duration of time, and the inverter goes back to the normal operation under non-islanded condition. A simple voltage ride-through which meets the recent standards for grid integration is integrated with the proposed IDM. Simulation studies are performed to verify the effectiveness of the proposed IDM.

This paper presents a new control method to minimize the energy consumption of a micro-scale concentrated solar power (MicroCSP) system and building heating, ventilation, and air conditioning (HVAC) system. A new realtime optimal control method is proposed using the concept of “exergy” and model predictive control (MPC) techniques. To achieve this, first law of thermodynamics (FLT) and second law of thermodynamics (SLT) based mathematical models of MicroCSP are developed and integrated into a model of an office building located at Michigan Technological University. Then, an exergy-wise MPC framework is designed to optimize MicroCSP operation in accordance with the building HVAC needs. The new controller reduces exergy destruction by 28%, compared to a common rule-based controller (RBC). This leads to 23% energy saving, compared to the applied RBC.

As society is aging it is imperative to find suitable technologies that can enable seniors to live in their own homes longer to minimize the social costs associated with institutional care. While there are many apps and gadgets to deal with individual situations such as fall detection, smart medicine dispenser, remote doctor visit etc. there are no comprehensive holistic solutions that can deal with most of the challenges associated with aging. This is a complex task involving many disciplines. At West Virginia University (USA), we put together an interdisciplinary group including Electrical Engineers, Computer Scientists, Computer Engineers, Medical and Healthcare Professionals and Social Workers to create a realistic testbed to demonstrate integrated solutions that can be scaled-up by start-up companies using a wide variety of resources available at the University. This system known as RANIA (Residents Aware Network for Intelligent Assistance) is being developed by a group of nearly 100 students under the guidance of faculty from the College of Engineering, School of Health Sciences and the School of Social Work. In addition, we have also created a Collaboratory to enable participation from institutions around the world to create their own unique solutions suitable for specific environments. We believe this project can have profound impact on aging populations around the world by establishing the technical feasibility and economic viability of technologies that can enhance quality of living not only by delaying the need for institutional care but also reducing social costs associated with caring for seniors.

In the urban areas, such as developed cities, the citizens have all the basic amenities like Electricity supply with minimal number of power cuts, Food supply, comparatively proper roads and infrastructure of buildings, etc. But same is not the case with rural areas, where majority of villages suffer electricity shortage, agricultural issues, improper supply and distribution of water for various purposes, etc. Hence through our project, Now, there are numerous factors that can influence the formation of a smart village. In this main focus on Automatic Irrigation System, followed by an appropriate rainfall prediction Algorithm that can help us determine which crops are favorable to grow in a particular area. A new method which is Romyan’s method that crops need to be watered for plants and time required for motor to be ON is also introduced. Also by saving water and electricity which is normally wasted in watering crops, we can utilize it for other necessary purposes for the residents of villages.

Even though the advent of the Web coupled with powerful search engines has empowered the knowledge workers to quickly find the needed information, it still is a time-consuming operation. Presently there are no readily available tools that can create and maintain an up-to-date personal knowledge base that can be readily consulted when needed. While organizing the entire Web as a semantic network is a long-term goal, creation of a semantic network of personal knowledge sources that are continuously updated by crawlers and other devices is an attainable task. We created an app titled ExperTwin, that collects personally relevant knowledge units (known as JANs) from the Web, Email correspondence, and locally stored files, organize them as a semantic network that can be easily queried and visualized in many formats - just in time - when performing a knowledge-based task. The architecture of ExperTwin is based on the model of a “Society of Intelligent Agents”, where each agent is responsible for a specific task. Collection of JANs from multiple sources, establishing the relevancy, and creation of the personal semantic network are some of the many tasks performed by the individual agents. Tensorflow and Natural Language Processing (NLP) tools have been implemented to let ExperTwin learn from users. Document the design and deployment of ExperTwin as a “Knowledge Advantage Machine” able to search for relevant information while performing a knowledge-based task, is the main goal of the research presented in this paper.

A non-invasive blood glucose measurement method based on microwave transmission and applying machine learning technique to the data obtained, is proposed for monitoring the patients' blood glucose level. Using this technique, non-invasive measurement of the blood glucose concentration at the earlobe portion can be realized and put into use by analysing the reflected microwave signals. A third order Cole-Cole equation is derived to model the dielectric properties of human tissues. Particle swarm optimisation technique is used to determine the coefficients for the glucose concentration dependent equations. With these estimated dielectric values of human tissues, human earlobe portion is modelled and tested at a wide range of frequencies to analyse for the region of linearity. It was observed that frequency range from 6–8 GHz shows the linearity and calculating the complex permittivity values with the help of transmission parameters. Applying Machine-learning technique to the above process can facilitate a real-time processing which in turn is able to alert patients during hyperglycemia conditions, and can suggest a precise dose of insulin to intake.

Micro-scale concentrated solar power (MicroCSP) is a promising technology that uses solar energy to provide electrical energy and thermal energy for use in buildings. This paper presents a model predictive control (MPC) framework to minimize the energy consumption of the building heating, ventilation, and air-conditioning (HVAC) system by integrating it with a microCSP. To this end, a microCSP model is developed and then integrated to the building model of an office building in Michigan Technological University. The designed MPC framework optimizes thermal energy storage (TES) usage and thermal energy flows from the heat pumps to the building rooms. The optimal control results show that the integration of microCSP to the building HVAC system reduces the HVAC energy consumption by almost half (4752%) by optimally utilizing the solar energy. The designed MPC framework provides 46% energy saving, compared to a heuristically designed rule-based controller for the combined HVAC and microCSP systems.

With the advent of distributed generation, single-phase rooftop solar photovoltaic (PV) generators of small rating, feeding power to the utility grid has become popular. Cost and performance of the inverters used to integrate such systems to the grid depends upon the topology of the inverter used, modulation scheme employed, and the efficiency of auxiliary functions like maximum power point tracking (MPPT). The appropriate modulation scheme is generally decided by looking at the topology of inverter. For example, a transformerless H-bridge inverter is generally switched using bipolar modulation in order to avoid large leakage currents. However, selecting the appropriate technique for auxiliary functions like MPPT, is done independently and is generally not integrated with the control scheme. A large number of MPPT techniques were proposed in the literature, and choosing an appropriate technique suitable for the chosen inverter topology and the modulation scheme might be advantageous. This paper proposes the integration of one cycle control (OCC) modulation scheme for single-phase single-stage transformerless PV inverter with ripple correlation control (RCC) MPPT technique. Detailed numerical simulation studies are carried out to verify the effectiveness of the proposed integrated method. Real-time simulation studies are also carried out using the real-time digital simulator (RTDS) to verify the efficacy of the system further.

With the ever-increasing amount of data and associated knowledge required to perform many modern-day tasks, quickly finding context-sensitive information can contribute to significant gains in knowledge worker productivity. Here, we describe the design and construction of a system, called a Personal Knowledge Advantage Machine (pKaM), to assist users while they perform knowledge based tasks. In this paper, we illustrate this idea by using the computer programming domain, which undoubtedly is a knowledge-intensive task, as an example. We first describe the architecture of a pKaM followed by a description of how it may be used by a programmer new to the Python language. The pKaM system may be viewed as a collection of agents that discover, mark-up, organize, display and present contextually relevant pieces of knowledge to the worker during the entire life-cycle of the task. The design of pKaM emphasizes the use of the plug-and-play approach so that it can be adapted to any domain by merely plugging-in a new domain knowledge base. As this idea gains currency, it is our hope that open-source knowledge bases organized along the lines described here will become available for many domains.

This work investigates the effect of thin film graphene heat spreaders on the maximum temperature of integrated electric circuits. An electronic chip with a 4 × 3mm footprint featured a centrally located 0.5 × 0.5mm hot spot located on the top wall. The remainder of the top wall was exposed to a uniform background heat flux. The graphene heat spreaders were applied to the top wall of an optimized cooling array of micro pin-fins having airfoil cross-sections. A fully 3D conjugate heat transfer analysis was performed to investigate the effects of different heat spreader thicknesses on the temperature of the hot spot and the temperature distribution on the hot surface. Both isotropic and anisotropic thermal conductivities of heat spreaders were considered. The conjugate heat transfer analysis also incorporated thermal contact resistance.

Cochlear Implants (CI) are surgically installed biomedical gadgets that give hearing to significantly hard of hearing individuals by direct electrical incitement of the auditory nerve. It has been recognized that auditory simulation models in cochlear implants improve the perception of speech significantly. For music and hearing perception in noisy environments, phase cue plays a major role but it is not being conveyed by the present day auditory models. This paper describes the results of a study on the spectro-temporal effects on the perception of English phonemes comprising vowels and consonants. An auditory simulation model is developed to extract and process the spectral and temporal features using Hilbert algorithm. Six English phonemes are presented to normal hearing listeners under various signal-to-noise ratios (SNR) and their speech intelligibility scores are computed. Significant improvement is achieved and hence this model should be helpful for enhancing the speech intelligibility of cochlear implant subjects.

Metaheuristic algorithms are widely used to obtain optimal solutions for problems where only partial data is available or when computational capacity is limited. This feature of metaheuristic algorithms facilitates their introduction into channel decoding problem which is actually a search for the most likely transmitted codeword from a search space of 2n codewords in the case of binary codes and qn codewords in the case of q-ary codes. Genetic Algorithm (GA) — a metaheuristic technique-is used here for optimizing the search for the most likely transmitted codeword in channel decoding. Neural Network (NN) — an artificial intelligent algorithm is used for pattern classification in channel decoding. Combining the optimization capability of GA and pattern classification advantage of NN in the decoding process, Genetic-Neural network based Decoding (GND) algorithm with less decoding complexity has been proposed. The use of channel statistical information in decoding process ensures a significant improvement in performance. Exploiting this advantage a metaheuristic technique based Modified GND (Mo-GND) algorithm is proposed in this paper. The proposed Mo-GND algorithm promises better performance compared to all Soft Decision Decoding (SDD) algorithms while trading off complexity. Particle Swarm Optimization (PSO) another metaheuristic technique has an added advantage of faster convergence compared to GA. Applications which cannot afford the complexity trade off as in Mo-GND algorithm can make use of the fast converging decoding scheme based on PSO.

A ѱ-shaped micro-strip patch antenna is designed on a square ground plane with an inverted E slot is presented. ѱ patch is just below an inverted E slot on square ground plane and it is fed with a co-axial probe. The effect of size and shape of the slot on the ground plane and on patch are varied and are proposed as antennas A to E. The proposed antenna design parameters are varied to maximize the impedance bandwidth and it is giving a maximum bandwidth of 29.7% (4.5Ghz – 6Ghz) and a maximum gain of 5dBi. The proposed antenna is compared with square patch on square shaped ground plane results in 34% area reduction with improved bandwidth of 20%. All the proposed antennas are mainly compared with respect to Return loss, VSWR, Gain and Radiation efficiency. This antenna finds application in 'C' band and RFID.

GaN-based devices are of increasing importance for a wide range of system applications, such as power conversion and control, displays, and sensing in harsh environments. While dramatic progress in material quality and device performance has been achieved, several key impediments to the widespread adoption of GaN remain, including the high cost of native GaN substrates (needed to achieve low dislocation densities) and limited thermal conductance for through-substrate heat removal. Use of epitaxial lift-off to form devices on thin GaN-based films is one approach to circumvent these limitations, by enabling substrate re-use and close coupling of heatsinks to the active devices, while retaining the advantages of conventional epitaxial techniques. We report the first demonstration of single-crystal thin-film GaN Schottky diodes fabricated using large-area epitaxial lift-off as a step towards economical deployment of GaN-based electronics.

In this paper a novel dual band single layer single patch modified ψ-shaped slot micro-strip patch antenna is presented with CPW feed for dual band. By placing a single shorting post along zero potential field, a compact modified ?-shaped patch antenna is designed which is resonating at almost same frequencies with an area reduction of 50%. The designed antennas were compared with respect to gain, bandwidth, and radiation efficiency and radiation pattern. The results show the proposed antennas can be used for Wi-Max and RFID applications.

Wireless Sensor networks (WSNs) protocols and algorithms are tested nowadays with simulations. Future WSNs applications require real experimental measurements and analysis to capture the exact behavior of sensor environment. Path loss models are crucial in developing efficient localization framework and to solve coverage issues in different WSNs applications. In this paper, some radio propagation model is studied and investigated experimentally in a university campus with six storey buildings around the measurement scenario. The radio module works on ZigBee technology and in the results are presented for only two channels i.e channel 21 (2455 MHz) and channel 22 (2460 MHz). The free-space model and log-distance model is analyzed with measured path loss. The radio module used for experiment comprises of 32-bit RISC Jennic 5168 microcontroller with 2.4 GHz 802.15.4 compliant transceiver.

In this paper we describe the Discovery Agent of Vijjana, an umbrella project for creating a person-specific knowledge repository based on classification and analysis. Vijjana provides a framework designed for building collaborative knowledge networks that are domain specific and well bounded in search categories. Using the Vijjana model, we can envision creating an intelligent agent that presents context based information that is drawn from a personalized knowledge base. We call such an agent a Knowledge Advantage Machine (KaM) as it enables a user to take advantage of the personalized knowledge base to perform the task at hand more effectively — in much the same way mechanical advantage had been helping industrial workers. In this paper we discuss only the first part, namely the discovery agent. In this part the focus is on a conceptual way to bridge the gap between the presentation layer and the data access layer which is discussed here using the Object Relational Mapping through the Hibernate framework.

A slot loaded micro strip antenna with an aperture coupled feed is proposed. The basic radiator is a square patch with a 'dual loop' shaped slot cut into the patch area. The usable bandwidth of the slotted antenna is 840 MHz (2.37-3.21 GHz) which is appropriate for WLAN application in the 2.4-2.484 GHz band and WiMax application in 2.5-2.69 GHz band. The antenna is designed, simulated and optimized for wideband RF performance using HFSS. The simulated 2:1 VSWR bandwidth of the antenna is 30.10% with respect to the mid-band frequency of 2.79 GHz. The proposed antenna gain is greater than 6.5 dBi in the WLAN band and more than 7.3 dBi in the WiMax band. The antenna presents a patch size reduction of 44% when compared to a conventional square micro strip patch antenna. The antenna has an overall dimension of 70 x 70 x 11.58 mm3.

A new geometry of a square microstrip antenna embedded with a symmetrical n-shaped slot is presented here. The antenna resonates at a dual frequency band from 2.40–2.88 GHz and 3.42–3.58 GHz. This covers the WLAN 2.4–2.5 GHz band, WiMAx 2.5–2.69 GHz band and part of WiMax 3.5 GHz band. The proposed structure is fed with an aperture coupled feed and matched over a broad frequency band using a stub at the open end of the microstrip feed. The antenna gain is more than 7.8 dBi over the dual band. The antenna is suitable for wireless communication systems operating at 2.4/2.5/3.5 GHz band.

A novel structure of a slotted patch antenna loaded with a fan blade-shaped slot for wideband circular polarization is proposed. The proposed structure is aperture fed and matched over a broad frequency band. The antenna exhibits a simulated 3-dB axial-ratio bandwidth of 3.27% (2.41–2.49 GHz) with respect to the axial ratio resonant frequency of 2.45 GHz. The computed 1:2 VSWR bandwidth is 23.67% (2.17–2.75 GHz) with respect to the mid-band frequency of 2.45 GHz. The antenna gain is more than 8.0 dBi in the circularly polarized band.

When a sudden load is connected to the distribution system Voltage sag takes place. DVR (Dynamic Voltage Restorer) is one such custom power device which helps in mitigating voltage sag. The DVR injects required volt-ampere to the system in order to maintain nominal balanced sinusoidal voltage at the load. The operation of DVR is depends on the control technique used. In this paper four control techniques are used, the techniques used are pre sag compensation method, in phase compensation method, energy optimization method and in phase advanced compensation method for mitigation of voltage sag. The performance of all the four methods compared. It is observed that in phase advanced compensation method has given better results in magnitude of voltage sag and THD compared to other methods. The simulations are carried out by using MATLAB / SIMULINK software.

A workflow pattern could be defined as the methods by which a user typically utilizes a particular system. This paper presents a framework by which data mining techniques could be used to extract patterns from an individual's work flow data to exploit a type of architecture known as a Knowledge Advantage Machine (KAM). KAM is a type of semantic desktop and semantic web application that would assist people in constructing their own personal knowledge networks, as well as sharing that information in an efficient manner with colleagues using the same system. A KAM would be capable of automatically discovering new knowledge that is relevant to the user's personal ontology. Through experimentation, it is empirically demonstrated that a user's file usage patterns can be utilized by a software to automatically and seamlessly learn what is "important" as defined by the user. Further research is necessary to apply this principle to a more realizable KAM so that decisions can be fueled by work patterns as well as semantic or contextual information.

The performance of wireless sensor network heavily depends on efficiency with which the available energy resources are utilized and required QoS are ensured. Optimally placed multiple sinks play a significant role in enhancing network lifetime and reducing response time in wireless sensor networks. In this paper, we propose a computational intelligent method for the optimal placement of multiple sink nodes so that worst-case delay is minimized while keeping the energy dissipation during transmissions as low as possible. Our proposed method computes the optimal locations for sink nodes by identifying key players using Genetic Algorithm. Here Key players refer to the nodes that can be reached by as many remaining nodes as possible via direct links or short paths. The proposed method has been named KPP-MSP (key player problem based multiple sink positioning) and is simulated using Matlab. We also compare our method with Geographic Sink Placement (GSP) and Genetic Algorithm Sink Placement (GASP). The simulation results show that KPP-MSP has led to better network coverage, network lifetime and response time.

Clustering of nodes is one of the basic techniques used for data collection in wireless sensor networks. It has been observed that in most of these clustering techniques based upon the local attributes of sensor nodes, some of them are selected as cluster-heads. The nodes which are selected as cluster-head have to dissipate more energy as they have to receive and aggregate data of their respective member nodes. Moreover, Cluster-head nodes near to sink dissipate more energy as these nodes have to relay data of far off placed nodes. Due to this fast dissipation of energy, energy holes are created near to sink which in turn decreases network lifetime. In order to address these issues, we present energy efficient clustering method and propose to select sink as one of the cluster-heads. By selecting sink as cluster-head, the nodes placed near to it can be spared from performing duties of cluster-heads and thus the problem of energy hole creation near to sink can be avoided. The proposed method has been named as energy efficient clustering with sink as cluster-head - EEC-SCH. We compare EEC-SCH with LEACH-ERE, LEACH and WCA. Our simulation results show that EEC-SCH results into enhanced network lifetime, throughput and balanced energy consumption among sensor nodes.

Intelligent sensing, communication, and computing is leading the development of Wireless Sensor Networks (WSNs) and its applications. Artificial Neural Network (ANNs) is being extensively used to develop localization framework in WSN. In this paper, we present a comparative analysis of some conjugate gradient based Feedforward Artificial Neural Network (FFANNs) for developing localization framework in wireless sensor networks (WSNs). Localization is the process by which the sensor nodes in the network can identify their own location in the overall network. Localisation in WSN plays a crucial role in implementing myriad of applications such as healthcare management, disaster management, environment management, and agriculture management. Artificial neural network based localization framework are gaining importance due to faster speed of convergence and low cost of computation. We present analysis of conjugate gradient backpropagation with Powell-Beale restarts, conjugate gradient backpropagation with Fletcher-Reeves update, conjugate gradient backpropagation with Polak-Ribiere updates, one-step secant, and scaled conjugate gradient backpropagation training algorithms in this paper. Comprehensive evaluation of these training algorithms is done in perfect simulation scenario. The proposed method effectively demonstrates that conjugate gradient FFANNs based sensor motes can be designed for developing cost-effective localization framework.

Wireless Sensor Networks (WSNs) are nowadays extensively preferred for collecting data in the field of disaster management, military operations, habitat monitoring, medical monitoring, and environment monitoring. The location of the sensor which is sending this data is very important for developing efficient routing algorithms, energy efficient communication protocols, and other Quality of services (QoS). Localization is the process by which the sensor motes in the network can identify their own location in the overall network. In this paper, we analyse Radial Basis Function (RBF) Network for developing localization framework in WSNs. RBF based localization framework is to be analysed for faster speed of convergence and low cost of computation. We present analysis of RBF through probabilistic neural network and generalized regression neural network in this paper. The proposed method can be used for designing cost-effective localization framework.

The three-phase grid-connected converter is widely used in renewable and electric power system applications. In general, two power conversion stages are required when low voltage unregulated PV output is conditioned to generate AC power. This report presents a space vector algorithm for a 3 level diode clamped multilevel inverter for operation in over modulation range. This type of inverter has several advantages over standard two level voltage source inverter, such as greater number of levels in output voltage waveforms, lower dv/dt, less harmonic distortion in voltage and current waveforms and lower switching frequencies. The operation of multilevel inverter in both stand alone and grid connected mode is discussed. The stand-alone mode multilevel inverter is controlled to provide voltage of desired magnitude and frequency to local loads. In grid connected mode multilevel inverter operates in current controlled mode to supply pre-defined active power to the grid. This report presents a SVPWM algorithm with over modulation for three level based on standard two level SVPWM. The presented scheme is described clearly and hardware results are reported to demonstrate its effectiveness.

Research towards securing ad hoc networks has focused on numerous algorithms for detecting intrusions in specific kinds of attacks like black hole, sequence number, neighbor and packet forwarding attacks. In this attempt, impact of malicious node in ad hoc environment is discussed and implemented using ns2 in case of packet dropping attack. In this paper, MNI-AODV algorithm is proposed by incorporating malicious node isolation (MNI) method in EIDS-AODV [3] into an account. In this paper, methodology is proposed against malicious nodes by using MNI-AODV from an ad hoc environment. This paper also highlights the impact of malicious nodes in the presence of packet dropping attack. Further, Analytical model of proposed algorithm is presented using Rational Unified Process tool.

Clustering is one of the popular methods in wireless sensor networks for achieving energy efficiency, scalability and efficient routing. Residual energy and topological features that are related to a node with respect of its structural position in the network are used for electing cluster heads. However optimal numbers of nodes that may belong to a cluster are not taken into consideration while selecting cluster heads. The proposed paper aims to define a new centrality metric “cluster optimal degree centrality”. Our proposed centrality metric addresses the optimal numbers of member nodes as well as energy efficiency of a cluster. Finally based upon the defined centrality metric, a Fuzzy Inference System based cluster head selection method has been proposed. The experimental results have demonstrated that the method can effectively prolong the network lifetime and enhance cluster head selection and results in high throughput as compared to LEACH, CHEF and LEACH-ERE.

Mobile Ad Hoc Networks are the networks that have no base station for constructing the communication among devices. Such a communication network is built by the energy constraint hand held battery powered devices. In this paper, we are proposing a novel method for securing Mobile Ad Hoc Networks. There are many design issues and challenges for Mobile Ad Hoc Networks but the major challenge is the security. Security is a significant challenge that needs to be addressed as member or group authentication, confidentiality, data integrity and access control are strong necessities for such kind of networks. We use Dynamic Source Routing (DSR) as the principal protocol and present a method which selects the most trustworthy path between the source and destination that is free from malicious nodes. The proposed method identifies and punishes the malicious nodes inducing a variety of attacks like: malicious topology change behavior, dropping data packets, dropping control packets and packet modification.

Atmospheric turbulence degrades images by causing non-uniform geometric deformations and distortions due to the random fluctuations of refractive index over air media. In this paper, a new approach based on hierarchical layer parsing is developed. Each frame from a set of temporal frames is decomposed into a collection of isoplanatic space-time patches. Thereafter, each of temporal patches is parsed hierarchically to separate layers corresponding to, low rank background, sparse turbulence errors, and moving objects, using low rank matrix decomposition. In order to remove redundant blur that remains after local hierarchical parsing blind de-convolution procedure is applied. Finally, the stable background component and the one or more moving object components are fused to reconstruct the turbulence mitigated content.

Atmospheric turbulence degrades image with nonuniform geometric deformations and distortions, due to random fluctuations of refractive index over air media. Typical approaches to turbulence removal do not consider moving objects of interest. We propose a method that combines two independent approaches, non-rigid image registration and background subtraction using Gaussian mixture modeling (GMM), to detect moving objects in turbulent conditions. Nonrigid image registration removes geometric distortions and stabilizes overall scene. Then GMM based background subtraction technique is used to detect moving objects. We demonstrate robustness of our proposed approach under varying turbulence conditions using qualitative and quantitative comparisons with existing methods.

Filter Bank Multicarrier (FBMC) is emerging as a better alternative to the Orthogonal Frequency Division Multiplexing (OFDM) in next generation wireless multicarrier systems. For robust performance under doubly dispersive channels, the prototype filter in FBMC systems must be isotropic and orthogonal in time and frequency. The existing design methods for prototype filter design are the Extended Gaussian Function (EGF) design based on Zak transform and Weighted HermitePulse (WHP) design. In this paper, it is attempted to design the prototype filter by performing time frequency analysis on the Ambiguity function of isotropic Hermite pulses. The Filter coefficients are obtained as the weighted sum of the Hermite pulses and constrained optimization is performed to find out the weights. An alternative objective function and optimization procedure is used in the present study which yield better results compared to conventional WHP problem. The orthogonality and isotropic conditions of the designed filter is compared with the conventional filters using Ambiguity plots.

Protocols for wireless sensor network are based upon the application for which the network is deployed for. It is also the individual node characteristics which may lead to different network characteristics as well as protocols. Centrality measure defines the importance and role of a node within a network. It also helps in calculating its importance and relationship with its neighboring nodes. Hence centrality measures may help in resolving various inherent problems and developing efficient algorithms for wireless sensor networks. In this paper we present various graph centrality measures that are applicable to wireless sensor networks. The utility and importance of these graph centrality measures have also been discussed. Further we have proposed a new graph centrality measure named as cluster centrality for WSNs.

We present parallelization of single and two phase flow CFD solvers on a graphics processing unit (GPU) platform. Numerical simulations are done for some standard benchmark test cases for both single and two phase flow solvers. The formulation is based on finite volume method with SMAC algorithm on regular cartesian and collocated grid. Volume of fluid method in used for tracking the interface in multiphase flow solver. Pressure poisson equation is the most time consuming part of the solvers and hence this part is imported on to GPU. Pressure Poisson equation is solved by the conventional Gauss siedel method. Present day modern graphics hardware has several hundred cores which can be effectively used by CFD solvers to parallelize the computation. The results are validated against the reference solutions of the teat cases. A comparison is done between CPU and GPU simulations to estimate the computational acceleration and accuracy obtained.

Biomedical engineering technology research of late is focusing on the rapidly growing inter-disciplinary area of affordable healthcare, to promote evidence-based information and evaluate the low-cost healthcare technologies. With an ever increasing cost of medical diagnostics, and an increasing demand by individuals to access consumer-level products, there has been an explosion of low-end health technology, such as Micro Health applications on mobile phones. However, much of the technology still requires more scientific evidence for its suitability and efficacy. Moreover, technologies for resource-constrained locations often fail to take into account the social, economic and infrastructural issues such as, social acceptability, long-term compliance, available supply chains and economic sustainability plans. Against this backdrop presented a low cost EEG/EeG system suitable for real time monitoring and transmission of signals using the widely available GSM network.

The Vertical-Bell Laboratories Layered Space-Time algorithm is a multi layer symbol detection scheme used in MIMO system to obtain multiplexing gain. Spatial Multiplexing give rise to Multistream Interference, which makes the performance of the detectors more critical. This paper aims to investigate different V-BLAST detector algorithms like ZF, MMSE and ML with and without successive cancellation and optimum ordering, for different modulation schemes to overcome MSI. The work is also extended to study the effect of Rayleigh channel fading correlation and antenna cross polarization coupling on these V-BLAST detectors. Simulation results show the tradeoff between bit error rate performance and computation complexity in various detectors. It is also observed that lesser the channel fading correlation and cross polarization coupling values better is the performance of these detector schemes.

This paper presents, analysis of magnetizing inrush current for different voltage angles and harmonic analysis of that inrush current using different FFT windowing techniques. Time domain simulations were used to solve the inrush phenomenon. Square, Welch, Hanning and Parzen windows were used to truncate the obtained inrush current harmonic spectrum for investigation and with parzen windowing the harmonic magnitude is getting reduced due to its reduced side lobes. Inrush current harmonic spectrum obtained by using FFT algorithm in turn discrete fourier transform. Out of different solvers to solve state equation, ode23t solver is used. For an unloaded single phase transformer, inrush current in time domain and harmonic content of inrush current were presented. The results are helpful to estimate harmonics effects in the transformer inrush currents. The results establish a guide line for harmonic elimination due to inrush current and offer an important reference for system design.

With the increasing shortage in fossil fuels, and pollution problems renewable energy has become an important energy source. Comparing the all renewable energy sources wind energy is one of the most economical energy sources. In present generating units above 1 MW, variable speed concepts are usually applied that are either based on doubly fed induction generators (DFIG) or Permanent magnet Synchronous generator (PMSG) are mostly used in Wind Energy Conversion Systems (WECS). This paper discusses about the implementation of matrix converter for wind-energy conversion System employing permanent magnet synchronous generator (PMSG). The MATLAB/SIMULINK simulation software are used for simulation.

Cochlear Implants (CI) are surgically implanted biomedical devices that can provide hearing to severely deaf people by direct stimulation of the auditory nerve, electrically. Current CI speech processors do not convey phase information, which is important for perceiving music and for hearing in noisy environments. This paper discusses a new acoustic simulation model for CI, that extracts the ‘envelope’ by continuous-interleaved sampling (CIS) principle and ‘temporal fine structure’ cue (phase information) by Hilbert algorithm. The dominant channels are identified from the filter bank outputs and subjected to temporal analysis. The amplitude estimates are derived through rectification and low-pass filtering. By Hilbert transform, the temporal fine structure is extracted through formant peaks in the signal which contributes for speech intelligibility. After band pass filtering the speech signal, carrier signals that are specific to the band output, are constructed by placing square pulses in the extracted fine structure which has greater energy level. The envelopes are subjected to amplitude-modulation using the carrier signal and the synthesized speech is generated. 15 phonemes are analyzed for its envelope and phase information and the simulation results indicated that this model produced improvement in the vowels and consonants identification significantly. The proposed model should be helpful for developing advanced speech processing strategies and improving the speech perception of CI users.

We present a new framework for visualization and retrieval of knowledge organized as a personal semantic web. This framework is an extension of other semantic web technologies and aims to achieve a fast, elegant solution to carrying your knowledge with you. The architecture described has been developed on the Google Android platform and embedded OpenGL. We call this a Personal Knowledge Advantage Machine (pKaM) as it is designed to access a personalized semantic network using a user-specific ontology. This also includes a graphical browser which enables a user visualize the portion of the semantic network that is relevant to the specific user context.

A cyber-physical system is treated with a collaborative self-organizing knowledge network model, called Vijjana. While traditional communication between users of Vijjana forms a loosely coupled system, in cyber-physical system it is tightly-bound forming a policy-driven knowledge grid. If there is a compromised security in a cyber-physical system, the knowledge grid bootstraps to a subset grid, isolated from other nodes. A model for this bootstrap is proposed, which looks for carefully administered security policies in the cyber-physical system.