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Single Carrier Frequency Division Multiple Access (SC-FDMA) is an uplink communication standard for 3GPP (Third Generation Partnership Project) Long Term Evolution (LTE), which outperforms multicarrier systems due to its low Peak to Average Power Ratio (PAPR). The Volterra equalizer is a nonlinear advanced technique for improving data transmission system efficiency. It enhances noise at high frequencies and it is extremely sensitive to noise. This paper proposes a modified Volterra equalization technique to improve the performance of Localized Single Carrier Frequency Division Multiple Access (LFDMA). The modified Volterra equalizer is a combination of Volterra equalizer and Decision Feedback Equalizer (DFE) which reduces noise efficiently. Furthermore, the modified Volterra equalizer reduces computational complexity more effectively than the existing equalizers. Simulation results show that the modified Volterra equalizer provides improved Bit Error Rate (BER) performance compared to Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) equalizers. The simulation results yield BER of 10dB and 13 dB respectively for16QAM and 64QAM by using the proposed scheme.

This paper presents modelling of the parallel hybrid electric drivetrain and its performance assessment. It is identified with the help of Simulink platform using MATLAB environment. The objective of the paper is to implement a control logic in order to improve the performance efficiency. The aim of this paper is to understand the necessity of new vehicle system by comparing the efficiency between the conventional vehicle and the hybrid vehicle. Simulation is carried out with the help of environmental protection agency (EPA) certified drive cycle inputs, with efficient control strategies. Efficiency of the system is compared with conventional vehicle. The simulation proves that the parallel hybrid electric vehicle fuel economy is improved by 32.8% with all modes taken into consideration.

Blockchain Technology has certain inherent characteristics that can prove particularly useful to the field of IoT. It has the potential to add a layer of trust and transparency to the data reported by IoT based applications due to its decentralized and immutable nature. An application that utilizes these aspects of Blockchain has been explored in this paper. A smart inventory monitoring system that focuses on shipped goods was developed. The proposed system monitors the temperature and humidity of the goods' container. It also keeps track of whether the lid of the container was opened at a certain point or not and the duration for which it was left opened. These details are periodically uploaded to Ethereum Blockchain's Rinkeby Test Network along with a timestamp and location history. These details can then be retrieved from the Ethereum Blockchain and monitored for signs of tampering or undesirable conditions. The system was implemented on a Raspberry Pi 4. Details such as transaction construction and upload times have also been reported in the paper. The proposed system was able to provide an immutable and decentralized historical log of the shipped inventory as it passed through different stages of the delivery process.

Social experiences that use handheld mobile Augmented Reality with 6DOF tracking can potentially recreate the dynamics of in-person meetings, while still being more accessible than HMD based solutions. We describe a design space of possible avatars for such a platform along the dimensions of body part visibility and the type of movement. We propose a study that aims to measure the effect of five avatar types on social presence during group collaboration.

Water is one of the most precious resources on the Earth. It is shared by man, animals, and plant life. Up until the Flint Michigan water crisis in 2012, many Americans were ignorant to the legitimacy of their public water systems. As infrastructure continues to age, and with more advance mining techniques such as fracking continue to deliver unsavory elements into our water supply, it is imperative that we maintain a keen eye on our water systems. This paper looks at the lead contamination levels of schools in the state of New York. Over 43,000 pipes were flagged as having more than 15 ppb of lead contamination. 4,041 are flagged as out of service, and there exist 8,300 lead free buildings in the state. We extended the school level analysis to county level analysis to test the hypothesis to know whether there exists some factor that governed the distribution of lead levels across the counties. Results indicate that there is not any single factor, that governs the distribution of the lead across the counties. While the analysis shows improvement, however more steps need to be taken at a national level to secure the safety of our nation’s water supply. Results and future actions are described in this paper.

The electric vehicle is evolving as a futuristic technology vehicle to focus on the frequent serious energy and environment concerns. The Battery Management System (BMS) is the primary segment which has an essential job in controlling and securing the electric vehicle. The important functions of BMS include estimating battery state of charge (SoC) using various algorithms and propose features towards developing predictive intelligent BMS. Estimating SoC accurately is relevant for designing a predictive intelligent BMS. The accurate SoC estimation of a Li-ion battery is tough and involved task due to its excessive complexity, time-variant, and non-linear characteristics. This work intends to estimate and compare the SoC of thermal dependent Lithium Ion cell using different methods viz. Coulomb Counting (CC), Extended Kalman Filter (EKF) and Artificial Neural Network (ANN) algorithms.

Health is the major worrisome point whose impalpability increases with increase in the age. Thus, taking care of elders is very important responsibility. In such scenario, technology is helping people by providing living assistance. One of the major causes of health degradation or death of elders is ‘fall’. In this paper, a fall detection system is proposed based on machine learning. The system detects falls by classifying different activities into fall and non-fall actions and alert the relative or care taker of the elderly person in case of emergency. The dataset SisFall with variety of activities of multiple participants is used to calculate features. Machine learning algorithms SVM and decision tree are used to detect the falls on the basis of calculated features. The system acquires accuracy up to 96% by using decision tree algorithm.

Water resources are often polluted by human intervention. Water pollution can be defined in terms of its quality which is determined by various features like pH, turbidity, electrical conductivity dissolved oxygen (DO), nitrate, temperature and biochemical oxygen demand (BOD). This paper presents a comparison of water quality classification models employing machine learning algorithms viz., SVM, Decision Tree and Naive Bayes. The features considered for determining the water quality are: pH, DO, BOD and electrical conductivity. The classification models are trained based on the weighted arithmetic water quality index (WAWQI) calculated. After assessing the obtained results, the decision tree algorithm was found to be a better classification model with an accuracy of 98.50%.

This paper presents a miniaturized antenna for ultra wide band (UWB) applications. The geometrical structure of antenna mainly comprises a meander line notched structure, split ring resonator (SRR), and half-plane ground. The antenna is printed on an FR4 epoxy substrate. In this antenna, the microstrip line fed element has 50-H input impedance to achieve good impedance matching. The split ring structure is connected with meander line notches to obtain the UWB band of operations and high- frequency structure simulator (HFSS) software is used for design and simulation of the notched patch antenna.

The automotive industry is one of the fastest-growing sectors in terms of usage of technology. Most of the real time systems in automobiles are hard real time in nature due to the stringent timing requirements. As per the Automotive Standard Architecture (AUTOSAR), most of the safety critical applications are run using fixed priority scheduling techniques due to its highly deterministic and predictable nature. But, dynamic schedulers are preferred over static due to its increased flexibility and guarantees of time constraints. The latest AUTOSAR version supports dynamic scheduler for non-safety critical applications. This paper provides a performance comparison of fixed priority and dynamic priority scheduling algorithms for automotive subsystems.

Body association in VR is the extent to which users perceive a virtual body as their own. Prior research has studied the effect of tactile, visual and visuomotor stimuli on body association. Additionally, studies have been conducted to test the effect of olfactory stimuli on immersion, but how it affects body association hasn’t been explored. Through a systematic study, we compare the effect of tactile, visual, visuomotor and olfactory stimuli on body association in VR. This work paves the way towards understanding olfactory sensations and how they might affect experiences in VR.

The concepts of 2D geometry are often taught in classrooms without the analysis of the learners' understanding and interpretation of the existence of the concepts in natural surroundings. To bring in active participation of the students while they realize the practical application of the taught concepts, we have designed a module on Lines and Angles of an Augmented Reality (AR) intervention named ScholAR. Using ScholAR, the students can interact with an augmented 3D object. They can recall, visualize, identify the type of angle and then mark it by drawing on that 3D object. We performed a comparative study with 21 participants (6 dyads and 9 individuals) of 8th grade. In this paper, we report: 1) the perspectives of the students on their experience of performing the AR learning activities individually and in dyads, 2) the students' approaches while solving the AR learning activities, and 3) their motivation of using ScholAR. We found that 90.4% of the total participants preferred to perform the AR learning activities in collaboration. At α=0.05 (t=2.21, p=0.048), the performance of the dyads was significantly higher after using ScholAR. The usability and motivation level scores, however, were higher for the individuals (70.28; M=4.07) as compared to the dyads (65.23; M=3.94).

The raw data acquired from MR scanners are susceptible to noises resulting from truncation, inhomogeneity and patient motion. Truncation or Gibb's artifact is one of the most commonly seen artifact in MR images. Since the raw MR data are samples of Fourier transform, reconstructing images from this data will generate truncation artifacts. The application of apodization filters can reduce truncation artifacts in MR images. In this paper, we discuss and analyze the image quality improvements on applying two popular apodization filters called Fermi and Hamming in the image reconstruction pipeline.

In recent years, an observable trend in the automotive industry shows that more and more electronic features are getting added up. Hence the demand of computational resources in the vehicle is growing rapidly. A good scheduling strategy needed to utilize the limited resources of a multicore system to give a better performance in terms of preemption, migration, deadline-miss and dedicated resources for safety critical tasks. In this paper, a Dynamic Task Allocation and Scheduling for Multicore Electronic Control Unit is proposed. The focus is on engine control unit (ECU) where tasks execution changes dynamically as per engine speed. In the tri-core scheduling, one core is reserved as dedicated core for safety and critical task execution and remaining two cores are used for non-critical tasks. Proposed scheduling technique is a hybrid scheduler which is a combination of least laxity first (LLF) and earliest deadline first (EDF) schedulers. The extensive simulations carried out on hybrid scheduler shows better performance in terms of increased core utilization, less context switching and guarantee to meet deadline compared to the existing dynamic schedulers.

Intelligent crutches will be a breakthrough in the medical field for the patients undergoing therapy due to various ailments and accidents. In this paper we present a forearm crutch with sensor technology to monitor the proper usage of the crutch by these patients. The correct usage of the crutch will improve the recovery rate of the patients. So it is very necessary to monitor the usage of the crutch during the rehabilitation period. In this design only two external sensors are incorporated to avoid the complexity of using many sensors or optical motion capturing system or cameras etc. A force sensitive resistor (FSR) is used to measure the amount of force applied to the crutch and an accelerometer is used for determining the speed of movement of the patient. Based on the medical experts report the percentage of weight that a person can apply to the crutch is predetermined and is used as the reference value. The controller can check whether the applied weight is greater than the reference value and can take decision to warn the patient about the misuse of the crutch via speakers.

While testing an integrated circuit, large chip size, and excess power dissipation are the major issues. As compared to its working mode, the testing mode power dissipation is very high. In addition to this, the inefficiency of ATE and its time-consuming nature makes the external testing much more difficult. LFSR is used for testing ASIC chips. The pseudo-random variable generated by the LFSR is used for the testing process. The pseudo-random variable testing has some advantages such that it uses simple hardware for the on-chip test generating process. BIST is one of the most efficient low power testing methods. LFSR is used in the BIST for the generation of test patterns. This paper compares the various architecture of the LFSR for BIST and its associated power dissipation

Ambient intelligence is an evolving discipline which brings intelligence to our daily life through various domains comprising elderly assistance, preventive maintenance, video surveillance, etc. It refers to the concept of electronic surroundings that are subtle and reactive to the existence of people or things. In the up-to-date society, criminality reaches out to influence most aspects of people’s regular lives. Hence, individuals themselves started assuring against crime. This paper describes an ambient intelligence-based decision support system which provides home-based security. The proposed system mainly focuses on two techniques: face recognition to identify the house holders and anomaly detection to recognize the activities of the individual. Thus it can be used to send alerts to necessary authorities and family members if needed and enables real time monitoring of the captured anomaly through Internet of Things (IoT). The proposed framework produces an efficient face recognition and anomaly detection system with high performance rate.

Advances in modern medical imaging technologies such as X-Ray, Computed Tomography (CT), Ultra Sound (US) imaging, Magnetic Resonance Imaging (MRI), Positron emission tomography (PET) and Single Photon Emission Computed tomography (SPECT) enable better disease diagnoses and treatment assessment. This paper explains a layered architecture suitable for design and development of application software for medical imaging devices. The generic nature of medical imaging devices in the process of data acquisition, signals processing and image reconstruction is the major inspiration behind conceptualization of this architecture. Also the currently available medical imaging software follow a multi-stage interlinked processing workflow which can be directly mapped to the layered software approach. Layered software approach facilitates quick and easy customization, configuration and feature enhancements of the medical imaging software. The architecture facilitates the academic community or researchers to build end user solutions based on research outputs, which can be directly integrated and used in the main software workflow. This indeed provides opportunity for utilizing the technical expertise available for implementation of algorithms which can directly be used to interface with medical imaging devices. The architecture discussed in this paper, has been employed in the design of MRI imaging software as a case study to further illustrate its applicability in signal generation, data acquisition and image reconstruction.

Collaborative learning involves working in groups to solve a problem or perform a task. Collaborative learning is often encouraged in rural schools as due to lack of space and infrastructure, students are made to sit on floors and taught together within and between groups. Evidently, in rural schools, technologies have been introduced to support the existing teaching methods. Augmented Reality is one such technology that can provide a collaborative interactive experience. In our study, we provided an AR based application named 'ScholAR' to experimental group of 16 students of 7th grade as at that age they cultivate the ability to reason logically and develop conceptualizing skills. The application involved six tasks on the Mathematics topic of Introduction to 3D Solids targeted to enhance the spatial visualization skills of the students. We did a comparative study with the control group of 16 students who were taught the same topic using physical 3D models and the usual teaching method followed in their school. We report the results of this study, observations and analysis of the use of this AR application in a collaborative environment and the effect of collaboratively using the AR application on the students' performance.

The most popular technique used for high data rate services in 4G scenario is MIMO - OFDM. This paper proposes a novel adaptive companding method for Peak to Average Power Ratio reduction in IEEE 802.16 standards used in 4G wireless mobile communications with Orthogonal Space Time Block Coded MIMO-OFDM systems. This is obtained by fixing compander parameters independently for statistically unrelated symbols in OFDM waveform and according to the features of input symbols. The bit error rate performance analysis of the proposed system under Rayleigh fading MIMO channel conditions has also been presented. Simulation results showed that the proposed method performs better for a given PAPR preset value and observed a slight improvement in the BER performance over fixed compander.

In industrial plants or any critical utility plants, the ultimate goal is to maximize the production quantity and quality but at the same time keeping the production cost as low as possible. To achieve this, it is mandatory to keep plants in fully efficient condition so that the throughput of the system is maximum. In order to keep the system fully efficient it needs to be maintained properly. There are different maintenance strategies being used to maintain the efficiency of the plant. For any specific type of industry, maintenance affects the cost of goods produced. To avoid breakdown, the maintenance strategies should be planned in such a way that the maintenance tasks are executed at right time. Unnecessary maintenance tasks increase the maintenance costs and also the time required to execute them. Through this paper, the prospect of optimizing the plant operation i.e. to reduce the down time of the system using predictive maintenance (PdM) approach which will lead to reduced production cost has been explored.

Mixed-criticality system (MCS) is one of the upcoming and most challenging research areas in embedded systems. Real-time (RT) scheduling for MCS is a key aspect done during design time to analyze the schedulability and strictly guarantee the timing of various critical tasks. This paper tries to study if the existing non-MCS preemptive scheduling approach for periodic and aperiodic tasks can be extended to uniprocessor MCS. A comparison between the maximum response time for the tasks in a non-MCS and MCS is performed. It is inferred that the conventional preemptive scheduler is sufficient but does not give minimal response time for a high critical task. This provides the groundwork to further explore and contribute to the research area.

Wireless Sensor Networks (WSN) has become a part of our daily life with lots of future expectations, especially in the area like defense, health, agriculture and many more. This boundless expectation forces the network to become more reliable. The scope of a WSN lies mainly in monitoring and controlling a remote, unattended or hazardous area for a long time. Only a fault-tolerant system can assure a long time reliable service. In this paper, an overview of wireless sensor net-works is presented, with comprehensive analysis on fault tolerant WSN. This paper discusses the basic architecture of a wireless sensor network and examines the need of developing a fault tolerant wireless sensor and various strategies to attain the same.

As the need for energy is increasing these days, the energy generated by the conventional grid is not sufficient to meet the demand and this results in supply-demand mismatch. As a solution, in distributed storage, batteries are used as dispatchable loads in demand side for balancing the supply-demand mismatch. This article proposes design and development of a Battery Monitoring and Control Unit (BMACU) for the batteries used in distributed storage. The BMACU facilitates monitoring of State of Charge (SoC) and updating the computed value in the utility server. The control unit allows various modes of charging of battery based on SoC and the current scenario. BMACU enables fast charging if there is excess power generation and discharge the battery during deficit, thus improving system reliability.

One of the core functions of an operating system is scheduling applications such that overall system provides a satisfactory result. Real-time embedded systems have an additional requisite to provide correct output within deadline; failure could be catastrophic for safety critical systems. In this paper, the feasibility of Primary-Backup Fault Tolerant Scheduling (PB-FTS) to handle application level faults by means of switching between primary and secondary copy of task is examined. PB-FTS, which is the secondary scheduler, is evaluated over the different traditional scheduling algorithms such as Rate Monotonic (RM), Deadline Monotonic (DM) and Earliest Deadline First (EDF) under various fault scenarios. The fault handling capacity and performance of PB-FTS is measured by running MATLAB based simulations. Simulation results indicate a decreasing trend in fault handling capacity from 100% at 0.6 utilization factor for one fault to 53% at 1.0 utilization factor for three faults.

Mining is a risky activity, the risk factor increases exponentially when it comes to the underground mine. Proliferation in system intelligence, evolving infrastructure of the Internet of Things (IoT) is the next big and potential technology, which is revolutionizing the industrial world, and wireless communication technology has now become a necessity to develop and improve the safety measurements in systems; especially in underground mines. This paper brings out the role of Wireless Sensor Networks (WSN), Industrial Internet of Things (IIoT) and cloud server for optimizing safety and security in mining industries. In this work, a monitoring system consisting of sensor networks, which can be employed in the industrial environment to send the data to the cloud using IIoT concepts and to access the data remotely from anywhere to analyze the data and to generate a warning signal at an earlier time to avoid any catastrophe is presented.

Accurate estimation of channel state information in a time varying environment is a challenging problem in next generation high speed wireless communications. Adaptive Channel Estimation (ACE) techniques are used to estimate the channel coefficients of a time varying wireless channel. Normalized Least Mean Square (NLMS) algorithms are utilized to construct simple and stable ACE methods but the intrinsic sparsity of broadband MIMO wireless channel cannot be efficiently utilized by such methods. A Variable Step Size Sign Data Sign Error NLMS (VSS-SDSENLMS) algorithm is proposed in this paper as a method for adaptive sparse channel estimation in broadband MIMO-OFDM systems. $l_{0}$-norm sparse penalty was employed to the cost function of VSS-SDSENLMS algorithm to exploit the sparse information of time varying broadband wireless channel. Simulation results confirmed that the proposed algorithm improved the performance in terms of bit error rate with comparable computational complexity and better MSE performance at a faster convergence rate.

Wireless Sensor Networks has been an important area of research due to its wide and varied applications, especially in environmental and health monitoring. Most of the earlier work was focused on reducing the energy consumption in order to ensure long life of the network. Duty Cycling is one of the popular methods to achieve this. However, with the growing availability of energy harvesting solutions suitable for WSNs there is a renewed interest in optimizing performance of WSNs. In this work we analyze the performance of SMAC, one of the widely used MACs for WSNs with respect to the energy consumed. The energy consumption is compared with typical solar energy harvested. Based on the study we conclude that the protocol needs to be adaptable in terms of duty cycle based operation in order to effectively utilize the harvested energy.

In the 21st century dearth of energy and degradation of environment are among the major two problems. Thermoelectric devices provide a solution to such problems. Thermo electric devices are very small in size and therefore finds a wide range of application in areas like military, marine, space rovers, automobile industry, food and packaging industry etc. This paper begins with fundamental concepts of Thermoelectric followed by simplified modelling equations of a Thermo Electric Cooler (TEC). An easy to use interface of MATLAB/ Simulink model has been developed. The creditability of this paper lies in newly defined mathematical equations that results in a figure of merit and power consumption level that are much better as compared to outputs obtained by using existing methods of TEC Modelling. Using this concept as a base, an improved version of TEC, in practical terms, can be made in the future.

Accurate channel state information estimation in a time variant environment is a significant problem in Multi-Input Multi-Output (MIMO) — Orthogonal Frequency Division Multiplexing (OFDM) systems. Generally channel coefficients of a time variant wireless channel are obtained using Adaptive Channel Estimation (ACE) algorithms. One of the simple and stable ACE methods is based on the Normalized Least Mean Square (NLMS) algorithm. But, it cannot make use of the intrinsic sparsity of broadband MIMO wireless channel. This paper proposes a Variable Step Size-Sign Data Sign-Error Normalized Least Mean Square (VSS-SDSENLMS) algorithm for Adaptive Sparse Channel Estimation (ASCE) which is based on the application of sparse penalties to the cost function of SDSENLMS algorithm.

Multiple-input multiple-output (MIMO) system comprises of more than one antenna at transmitter and receiver which increases the capacity of the system. Spatial modulation (SM) is a new transmission method used in MIMO systems where symbol from signal constellation point and the location of transmit antenna in the antenna array conveys information. Spatial constellation point represents transmit antenna location in the array. Space-time block coded spatial modulation (STBC-SM) takes the advantages of space time block coding (STBC) and SM. It exploits multiplexing gain and transmit diversity. Another transmission scheme trellis coded spatial modulation (TCSM), mainly focused in correlated channel conditions, where available transmit antennas are grouped by set partitioning such that the distance between transmit antennas in each subset is maximum. In this paper, the performance of spatial modulation (SM), space-time block coded spatial modulation (STBC-SM), trellis coded spatial modulation (TCSM) in multicast MIMO system for different channel condition is investigated.