RSS 1.0Faculty Research and Publications
Recent Submissions
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Floating solar photovoltaic plants in India – A rapid transition to a green energy market and sustainable futureThe 18,000 square kilometers of water reservoirs in India can generate 280 GW of solar power through floating solar photovoltaic plants. The cumulative installed capacity of FSPV is 0.0027 GW, and the country plans to add 10 GW of FSPV to the 227 GW renewable energy target of 2022. The FSPV addition is small related to the entire market for solar energy, but each contribution is appreciated in the renewable energy market. FSPV could be a viable alternative for speeding up solar power deployment in the country and meeting its NDC targets. So far, the country has achieved the world's lowest investment cost for a floating solar installation. Despite the lower costs, generalizations are still premature because FSPV is still in its initial stages of market entry. Continuous innovation and timely adoption of innovative ideas and technology will support India in meeting its solar energy goals and progressing toward a more sustainable future. Governments must establish clear and enforceable policies to assist developers in reducing risks and increasing investor confidence in the sector. Economic and financial feasibility are examined, and various difficulties in technology, design, finances, environment, maintenance, and occupational health that impact the FSPV deployment are discussed. Based on the research, effective and comprehensive FSPV policy suggestions are included to support establishing an appropriate market, fostering competition and innovation, and attracting large-scale investment. This paper aims to stimulate interest among various policy developers, energy suppliers, industrial designers, ergonomists, project developers, manufacturers, health and safety professionals, executing agencies, training entities, and investment institutions of the FSPV plant to implement effective governance planning and help them to participate in their ways to assure sustainable growth.
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CFAR Different Schemes Behavior and Detection Performance of Moving TargetsIn this paper detection probability of different CFAR schemes CA, GO, SO, and OS-CFAR is computed via various values of false alarm probability. The CA-CFAR and GO-CFAR detection performance is superior over other schemes; OS-CFAR detection performance appears to be the quasi of CA-CFAR but needs more processing time than CA-CFAR. Moving target detection is investigated. Each of the CA, GO, OS-CFAR schemes gives acceptable detection probability at higher values of SNR and gives, as well, maximum performance in the range of 10-4 to 10-6 probability of false alarm. The SO-CFAR scheme doesn’t offer any advantage over other CFAR processors.
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Recommendations on Streaming Data: E-Tourism Event Stream Processing Recommender SystemThe Association for Computing Machinery ACM recommendation systems challenge (ACM RecSys) [1] released an e-tourism dataset for the first time in 2019. Challenge shared hotel booking sessions from trivago website asking to rank the hotels list for the users. Better ranking should achieve higher click out rate. In this context, Trivago dataset is very important for e-tourism recommendation systems domain research and industry as well. In this paper, description for dataset characteristics and proposal for a session-based recommender system in addition to a comparison of several baseline algorithms trained on the data. The developed model is personalized session-based recommender taking into consideration user search preferences. Technically, paper compare between six different models vary from learning to rank, nearest neighbor and popularity approaches and compared results with two benchmark accuracy. Taking into consideration the ability to deploy model into production environments and the accuracy evaluation based on mean reciprocal rate as per challenge guidelines. Our winning experiment is using one learning to rank model achieving 0.64 mean reciprocal rate compared to 37 model achieving 0.68 by ACM challenge winning team [2].
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Techno-Economic and Environmental Analysis of a Hybrid Renewable Energy System: Al Qurayyat City, KSAHybrid renewable energy systems (HRESs) are becoming more prevalent as they are viewed as economic off-grid sources of clean energy that could help reduce rural electrification and global warming problems. This paper aims to provide a techno-economic feasibility and environmental analysis of a HRES to be designed for meeting a daily load requirement of 389.4 kWh/day with a peak load of 82.71 kW, represented by the energy demand of thirty houses located in Al-Qurayyat city, Al Jouf Province, KSA. Thus, the aim of this paper coincides with the KSA’s “Vision 2030” and also with the “Net Zero Plan”, which promote sustainable energy solutions and net zero CO2 emissions, respectively. Moreover, the objective is achieved by designing a HRES consisting of PV, WT, a DG, converter and lead-acid BSS after taking into account the weather and operating conditions of Al Qurayyat city, which represents the novelty of this paper. Simulation of the system is achieved by HOMER to obtain the optimum configuration. After considering six arrangements, the results reveal that the ideal arrangement is indeed the PV/WT/DG//BSS with an optimized NPC and COE of $358,616, and $0.166/kWh while attaining a RF percentage of 92.8%. An alternative configuration, consisting of PV/WT//BSS would yield a 100% RF but with a NPC of $475,374 and COE of $0.22/kWh. The technical results show that the proposed HRES produces a total annual energy of 285,750 kWh/year with the PV, WT, and DG contributing 91.2%, 5.21%, and 3.58%, correspondingly. Regarding the environmental assessment, the optimized HRES annually saves a total of 206,678 kg of greenhouse gases.
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Video De-interlacing: From Spatial to Adaptive Based Motion DetectionWhile interlacing succeeds in reducing the transmission bandwidth, it introduces a number of high-frequency artifacts that can distract the human eye, such as flickers and thin vertical lines. Thus, a lot of de-interlacing algorithms are developed. Subjective and objective assessments of spatial and temporal de-interlacing techniques are given in this paper. Combining the benefits of the spatial and temporal algorithms, a motion detector is used to segment the frame into static and dynamic portions. The dynamic is segmented into slow- and high-motion pixels. Finally, an adaptive de-interlacing algorithm based on the results obtained from the motion detector is proposed and judged.
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Optimizing ADWIN for steady streamsWith the ever-growing data generation rates and stringent constraints on the latency of analyzing such data, stream analytics is overtaking. Learning from data streams, aka online machine learning, is no exception. However, online machine learning comes with many challenges for the different aspects of the learning process, starting from the algorithm design to the evaluation method. One of these challenges is the ability of a learning system to adapt to the change in data distribution, known as concept drift, to maintain the accuracy of the predictions. Over time, several drift detection approaches have been proposed. A prominent approach is adaptive windowing (ADWIN) which can detect changes in features data distribution without explicit feedback on the correctness of the prediction. Several variants for ADWIN have been proposed to enhance its runtime performance, w.r.t throughput, and latency. However, the drift detection accuracy of these variants was not compared with the original algorithm. Moreover, there is no study concerning the memory consumption of the variants and the original algorithm. Additionally, the evaluation was done on synthetic datasets with a considerable number of drifts not covering all types of drifts or steady streams, those that do not have drifts at all or almost negligible drifts. The contribution of this paper is two-fold. First, we compare the original Adaptive Window (ADWIN) and its variants: Serial, HalfCut, and Optimistic in terms of drift detection accuracy, detection speed, and memory consumption, represented in the internal window size. We compare them using synthetic data sets covering different types of concept drifts, namely: incremental, gradual, abrupt, and steady. We also use two real-life datasets whose drifts are unknown. Second, we present ADWIN++. We use an adaptive bucket dropping technique to control window size. We evaluate our technique on the same data sets above and new datasets with fewer drifts. Experiments show that our approach saves about 80% of memory consumption. Moreover, it takes less time to detect concept drift and maintains the drift detection accuracy.
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All-Optical Logic Circuits Based on the Non-linear Properties of the Semiconductor Optical AmplifierThis work shows the importance of utilising the nonlinear properties of the semiconductor optical amplifier SOA in constructing optical logic gates, half and full adders, flip-flops, counters and registers. Consequently, SOA may be considered as a promising component for building all-optical digital computer. By using the SOASIM software, This work shows how the optical buffer, inverter, unit-step pulse and falling/rising clock edges can be generated.
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Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus imagesIn this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.
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CMOS Implementation of Programmable Logic Gates and Pipelined Full Adders using Threshold Logic Gates Based on NDR DevicesThis paper presents a new prototyping technique, which allows efficient verification of circuit concepts based on negative differential resistance (NDR) devices. This prototype, which is called MOS-NDR, has been used to implement programmable logic gates and pipelined-ripple-carry full adders using linear threshold gates (LTGs).
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Real-time glove and android application for visual and audible Arabic sign language translationResearchers can develop new systems to capture, analyze, recognize, memorize and interpret hand gestures with machine learning and sensors. Acoustic communication is a way to convey human opinions, feelings, messages, and information. Deaf and mute individuals communicate using sign language that is not understandable by everyone. Unfortunately, they face extreme difficulty in conveying their messages to others. To facilitate the communication between deaf/mute individuals and normal people, we propose a real-time prototype using a customized glove equipped with five flex and one-accelerometer sensors. These sensors are able to detect the bindings of the fingers and the movements of the hand. In addition, we developed an android mobile application to recognize the captured Arabic Sign Language (ArSL) gestures and translate them into displayed texts and audible sounds. The developed prototype is accurate, low cost and fast in response.
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IoST: A Multi-CubeSat Cognitive Radio NetworkFifth- generation communication also known as internet of things is rolling out worldwide. It has provided higher network capacity and speed for mobile broadband communications; however, 5G network focuses only on terrestrial coverage. 6G and Cognitive Radio (CR) is expected to be the future solution to all heavy data traffic and globe coverage. Satellite communication is anticipated to cover rural areas, sea, spanning air, and space in what is known as Internet of Space Things (IoST). Low Earth Orbit (LEO) CubeSat orbits earth provide real time measurements with low transmission power and high data rate. Cognitive radio will focus on providing efficient spectrum use and resources allocations. In this paper, a multi CubeSat cognitive radio network is proposed to improve time delay, increase data exchange, and increase the Signal to Noise Ratio (SNR) of the communication system. simulation results demonstrate the convergence of the multi CubeSat system improving the signal to noise ratio for different number of CubeSats structure.
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Applications of Capacity Enlargement and Traffic Management Control in Hybrid Satellite-Terrestrial 5G NetworksThe significant volume of data traffic, ultimate data rates, reduced latency, more transmissions with efficient energy, spectrum consumption, and novel technological use cases must all be considered using the fifth generation (5G) mobile network. Future 5G networks will connect billions of objects, known as the Internet of Things (IoT) or massive Machine-T Communications (mMTC), in addition to standard Mobile Broadband (MBB) services. Non-Terrestrial Networks (NTNs) may serve connection requests anywhere and anytime by offering wide-area coverage and preserving service continuity, availability, and scalability. The satellite platform is a crucial NTN for integrating with the 5G network and delivering the necessary services. Soon, 5G network development will depend on satellite access, as stated in the 3Gpp Release 16 specification. This paper provides options for incorporating satellite systems into a 5G network design to increase capacity and extend coverage in 5G mobile networks. In addition, 5G networks must address a network model to resolve outage, congestion, and cell edge context issues. The last phase is the simulation of the problem formulation for the investigation of capacity and traffic in hybrid satellite-terrestrial 5G mobile backhauling networks.
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Medical image enhancement based on histogram algorithmsMedical images constitute important information that clinicians need to diagnose and make the suitable treatment decisions. The diagnostic process extremely involves the image visual perception. Unfortunately, the possibility of error existence in perception is not acceptable as it mainly affects the patients’ lives. Image enhancement improves the visual quality of image, helps the clinician in his decision and thus saves the patients’ lives. Histogram is a common tool for improving contrast in medical imaging. It recovers the lost contrast by redistributing the image brightness values that unfortunately may generate undesirable artifacts. Therefore, researchers developed the histogram-based algorithms to overcome this problem. This paper presents a comprehensive study of many histogram-based algorithms. We utilized the powerful MATLAB package to analyze the enhancement performance of these histogram-based algorithms. Moreover, this paper quantitatively compares the results and thus evaluates their performance by three metric parameters, which are the mean square error, standard deviation, and the peak signal to noise ratio
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Thermodynamics of the Bardeen Regular Black HoleWe deal with the thermodynamic properties of the Bardeen regular black hole with reference to their respective horizons. It is argued here that the expression of the heat capacity at horizons is positive in one parameter region and negative in the other, and between them the heat capacity diverges where the black hole undergoes the second-order phase transition.
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Application of Wavelet Decomposition and Machine Learning for the sEMG Signal Based Gesture RecognitionThe amputees throughout the world have limited access to the high-quality intelligent prostheses. The correct recognition of gestures is one of the most difficult tasks in the context of surface electromyography (sEMG) based prostheses development. This chapter shows a comparative examination of the several machine learning-based algorithms for the hand gestures identification. The first step in the process is the data extraction from the sEMG device, followed by the features extraction. Then, the two robust machine learning algorithms are applied to the extracted feature set to compare their prediction accuracy. The medium Gaussian Support Vector Machine (SVM) performs better under all conditions as compared to the K-nearest neighbor. Different parameters are used for the performance comparison which include F1 score, accuracy, precision and Kappa index. The proposed method of hand gesture recognition, based on sEMG, is thoroughly investigated and the results have shown a promising performance. In any case, the miscalculation during feature extraction can reduce the recognition precision. The profound learning technique are used to achieve a high precision. Therefore, the proposed design takes into account all aspects while processing the sEMG signal. The system secures a highest classification accuracy of 92.2% for the case of Gaussian SVM algorithm.
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Life Distribution of Commercial Concentrator III-V Triple-Junction Solar Cells in View of Inverse Power law and Arrhenius Life-stress RelationshipsThe paper shows that the life distributions of the commercial concentrator lattice match Triple-Junction III-V solar cells are significantly different when extrapolated using the inverse power law and Arrhenius life-stress relationships. This conclusion is drawn after applying the two relationships on real data of accelerated lifetimes of such solar cells that are assumed to follow Weibull distribution. The mathematical treatment for both cases is provided in more detail.
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Life Distribution of Commercial Concentrator III-V Triple-Junction Solar Cells in View of Inverse Power law and Arrhenius Life-stress RelationshipsThe paper shows that the life distributions of the commercial concentrator lattice match Triple-Junction III-V solar cells are significantly different when extrapolated using the inverse power law and Arrhenius life-stress relationships. This conclusion is drawn after applying the two relationships on real data of accelerated lifetimes of such solar cells that are assumed to follow Weibull distribution. The mathematical treatment for both cases is provided in more detail.
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Response of one-dimensional ionised layer to oscillatory electric fieldsTo provide fundamental insights into the response of laminar flames to alternating current (AC) electric fields, a simplified one-dimensional model using an ionised layer model is formulated with the conservation equations for the ion species with ionisation, recombination, and transport due to molecular diffusion and electric mobility. A parametric study is conducted to investigate the response of the ion layer at different voltages and oscillation frequencies, and the results are examined mainly in terms of the net current–voltage (I–V) characteristics. As the oscillation frequency is increased, a nonmonotonic response in the I–V curve is seen such that the current may exceed the saturation condition corresponding to the steady DC condition. In general the current reaches a peak as the unsteady time scale becomes comparable to the ion transport time scale, which is dictated by the mobility, and eventually becomes attenuated at higher frequencies to behave like a low-pass filter. The extent of the peak current rise and the cut-off frequency are found to depend on the characteristic time scales of the ion chemistry and mobility-induced transport. The simplified model serves as a framework to characterise the behaviour of complex flames in terms of the dominant ionisation and transport processes. The current overshoot behaviour may also imply that the overall effect of the electric field may be further magnified under the AC conditions, motivating further studies of multi-dimensional flames for the ionic wind effects.
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High-performance, energy-efficient, and memory-efficient FIR filter architecture utilizing 8x8 approximate multipliers for wireless sensor network in the Internet of ThingsIoT uses wireless sensor networks (WSN) to deploy many sensors to track environmental and physical parameters. The WSN measurements are frequently contaminated and altered by noise. The noise in the signal increases the sensor node’s computation and energy utilization, resulting in less longevity of the sensor node. The Finite Impulse Response (FIR) filter is commonly employed in WSN to pre-process sensed signals to remove noise from the sensed signals using delay elements, multipliers, and adders. Traditional multiplier-based FIR filter designs result in hardware-intensive multipliers that consume a lot of energy, and area and have low computation speed. These drawbacks make them unsuitable for IoT-based WSN systems with stringent power efficiency necessities. Approximate computing enhances the energy efficiency of an FIR filter. Arithmetic circuits utilizing approximate computing improve the hardware performance, with some loss of accuracy to save energy utilization and boost speed. A novel approximate multiplier architecture employing a fast and straightforward approximation adder is proposed in this study. Approximate multiplier M1 using OR gate and approximate multiplier M2 using proposed approximate adders are compared. The proposed approximate adder is suited for building an adder tree to accumulate partial product (PP) because it is less complicated than traditional adders. Compared to a one-bit-full adder, the critical path delay (CPD) is reduced significantly in the proposed methods. The accuracy comparison of M1. M2 and Wallace tree using the normalized mean error distance (NMED), the mean relative error distance (MRED), the maximum error (ME), and the error rate (ER) with the number of bits utilized for reducing error. For the area (delay) optimized circuit, when the bit used is 4, the delay is 0.4 ns for M1, 0.43 ns for M2, and 1.08 ns for the Wallace tree multiplier. For the delay (area) optimized circuit, when the bit used is 4, the delay is 0.16 ns for M1, 0.16 ns for M2, and 0.40 ns for the Wallace tree multiplier. To more accurately evaluate performance at the circuit level, the PDP and ADP are computed. The NMED, MRED, ME, and ER versus PDP and ADP are computed. The proposed multipliers M1 and M2 are compared with existing approximate multipliers. When an equivalent MRED, NMED, or ER is taken into account, M1 has the smallest ADP and PDP among other multipliers. The very low likelihood of a significant ED occurring is indicated by the small values of NMED and MRED in M1 and M2. The proposed solutions effectively reduce delay, area, and power while maintaining increased accuracy and performance.
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Advances and development of wind–solar hybrid renewable energy technologies for energy transition and sustainable future in IndiaWhile solar power projects are built on a continuous ground, wind power projects require scattered land, raising transmission costs and increasing the risk of land-related complications. Wind–solar hybrid (WSH) projects have been proposed to address these issues and accelerate installation. WSH power projects will create a well-defined area with sufficient infrastructure, including evacuation facilities, where the project’s risks can be reduced. The extensive coastline of India is endowed with high wind flow speed and plentiful solar power resources, creating an ideal environment for WSH projects to prosper while simultaneously improving grid stability and reliability. WSH plants guarantee higher transmission efficiency and cost-effectiveness than their stand-alone counterparts. As of 30.11.2021, 3.75 GW of WSH projects have been granted, with 0.148 GW of operational capacity and 1.7 GW of WSH projects in various bidding phases. In this paper, we discussed state-wise WSH potential, the key players in the WSH project, the National WSH, and the State WSH policy and amendments. Also, the WSH project’s physical progress and commercial details are covered. A feasibility study of the WSH plant is performed, and the primary design strategy for deploying WSH power facilities in India is discussed. It covers every step of this process, from design technique to choosing and evaluating potential locations for such hybrid projects, optimally placing wind turbines and solar panels, overall capacity mix for hybrid plants, and ultimately power evacuation optimization. Additionally, a brief study of the savings from these hybrid plants and the environmental, social, and governance standards which are necessary to implement these projects are provided. The potential challenges connected with WSH technologies are examined in depth, and potential solutions and mitigations for the challenges are provided. Designing a WSH for small-scale irrigation is provided along with the size and choice of wind and solar systems. Degradation of PV systems and carbon savings are included, along with some policy measures to boost the proportion of WSH in the entire power mix. In India, the development of large-scale WSH projects is still in its early stages, and more research is required to explore technical, commercial, and policy elements that influence project design. The policy suggestions for improvement of the WSH project are provided. The WSH project developers, potential investors, stakeholders, innovators, policymakers, manufacturers, designers, and researchers will benefit from the recommendations based on the review’s findings.
