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The electrical energy demand of the Kingdom of Saudi Arabia (KSA) has been observing a surging growth due to high population growth and urbanization rate. The generation mostly comes from the burning of petroleum products. The electricity sector emits more than one-third of the national greenhouse gas (GHG) of the country. Thus, it is essential to reduce the GHG from the electricity generation sector to maintain sustainability. This article mainly concentrates on the existing electricity generation and focuses on the Kingdom’s GHG emissions mitigation initiatives. Although the use of natural gas and the number of cogeneration plants have been increased in electricity generation, the country has also been focusing on other initiatives, including the adaptation of combined cycle power plants, carbon capture, utilization, and sequestration (CCUS), renewable and alternative energy sources, and various utility-scale energy efficiency measures. The ambitious and inspirational renewable energy (RE) targets of the Saudi Vision 2030 will significantly facilitate GHG emission avoidance, which will support the Kingdom to meet its Nationally Determined Contributions. This article recommends various mitigation measures, including the usage of the efficient internal combustion engine, poly-generation, combined heat and power (CHP) systems, deployment of renewable resources in the distribution and microgrid systems, bio-mass engine, and ocean thermal energy conversion plants. The decision-makers and the researchers will find the discussions and recommendations helpful in determining suitable and appropriate mitigation initiatives and technologies.

The utilization of renewable energy sources (RESs) has become significant throughout the world, especially over the last two decades. Although high-level RESs penetration reduces negative environmental impact compared to conventional fossil fuel-based energy generation, control issues become more complex as the system inertia is significantly decreased due to the absence of conventional synchronous generators. Some other technical issues, high uncertainties, low fault ride through capability, high fault current, low generation reserve, and low power quality, arise due to RESs integration. Renewable energy like solar and wind are highly uncertain due to the intermittent nature of wind and sunlight. Cutting edge technologies including different control strategies, optimization techniques, energy storage devices, and fault current limiters are employed to handle those issues. This paper summarizes several challenges in the integration process of high-level RESs to the existing grid. The respective solutions to each challenge are presented and discussed. A comprehensive list of challenges and solutions, for both wind and solar energy integration cases, are well documented. Finally, the future recommendations are provided to solve the several problems of renewable energy integration which could be key research areas for the industry personnel and researchers.

The equivalent electric circuit models reflect the electrical characteristics of the photovoltaic (PV) modules. Estimation of PV module parameters is considered as one of the challenging tasks while evaluating the performance. This article presents a new and useful approach to estimate the five-parameter PV module electrical circuit model. It translates the PV module parameter estimation process into an optimization problem using the information provided by the manufacturer on the rear side of the PV modules. It then employs an efficient metaheuristic technique, namely the backtracking search algorithm, to solve the developed optimization problem. The efficacy of the proposed approach is investigated by predicting the parameters of three PV module technologies: monocrystalline, poly-crystalline, and thin film. Finally, to check the feasibility of the proposed technique, this paper compares the approximate parameters of modeled I-V curves with experimental curves. The findings confirm the reliability of the estimated model parameters in simulating the near realistic characteristics of the PV modules.

This paper proposes an intelligent approach to detect and classify the power quality (PQ) events with the combination of machine learning and advanced signal processing techniques. It selects Stockwell transform, one of the efficient signal processing tools for feature extraction from the recorded signals. The extracted features are then fetched to one of the popular machine-learning tools, namely the artificial neural network (ANN), to develop the proposed intelligent PQ events detection and classification approach. This paper selects the hyper-parameters, e.g., number of hidden layer neurons, training algorithm, and activation functions through a systematic trial and error approach. To enhance the proposed approach performance, the weights and biases of the ANN are optimized using the grey wolf optimization (GWO) technique. Simulation results confirm the efficacy of the developed intelligent methodology in distinguishing PQ events from non-PQ events. Moreover, separates different PQ events, e.g., sag, swell, interruption, fluctuation, spike, notch, harmonics, from each other with reasonable accuracy. This research also investigates the efficacy of the proposed signal processing-based machine learning approach in the presence of measurement noises.

Some people are too ignorant of their surroundings. Most of them, usually, throw trash illegally in the streets or abandoned areas. In this paper, to get rid of such a problem, we have proposed a real-time monitoring mechanism to find the illegal trash throwing person for an intelligent surveillance system. The proposed mechanism of finding people and trash from the video frame can be done using subsequently applying the following several steps. At first, we have applied the shadow removal technique and then applied the background subtraction procedure. To reduce the noise levels, we have implemented labeling & filtering operations. For extracting feature vectors, a histogram of oriented gradients (HOG) is used. Finally, by applying a support vector machine classifier (SVM), we have identified both humans and trash from the video frames. The key contribution of this proposed framework is to gain better performance by applying the shadow removal technique before the background subtraction procedure. Besides, the temporary bounding box concept has been taken into consideration during the trash detection phase.

The coronavirus disease 2019 (COVID-19) caused a pandemic outbreak with affecting 213 nations worldwide. Global policymakers are imposing many measures to slow and reduce the rapid growth of the infections. On the other hand, the healthcare system is encountering significant challenges for a massive number of COVID-19 confirmed or suspected individuals seeking treatment. Therefore, estimating the number of confirmed cases is necessary to provide valuable insights into the growth of the outbreak and facilitate policy making process. In this study, we apply ARIMA models as well as LSTM-based recurrent neural network to forecast the daily cumulative confirmed cases. The LSTM architecture generates more precise forecasting by leveraging both short- and long-term temporal dependencies from the pandemic time series data. Due to the stochastic nature in optimization and random initialization of weights in neural network, the LSTM based model produce less reproducible outcome. In this paper, we propose a reproducible-LSTM (r-LSTM) framework that produces a reproducible and robust results leveraging z-score outlier detection method. We performed five round of nested cross validation to show the consistency in evaluating model performance. The experimental results demonstrate that r-LSTM outperformed the ARIMA model producing minimum MAPE, RMSE, and MAE.

Most of the time communication infrastructure is badly hampered by the natural disaster. Many people are trapped inside their house during disaster time and they cannot communicate with others as there is no network coverage. This paper aims to help those affected people by using Wi-Fi communication. We developed a real-time communication system for Android users where they can send a message and make an audio call through Wi-Fi and also develop an alarming system by which we can detect the appropriate location of the affected people. For audio call, we use UDP (User Datagram Protocol) and for messaging we use TCP/IP. When there is an audio call request it starts a peer to peer communication between the users. TCP/IP is used for messaging because this is a connection-oriented protocol which means there is a guarantee of message arrival. As our system is worked within the Wi-Fi hotspot range, the experiment shows that it can cover up to 32 meters (105 feet).

In this paper, the design and implementation of a low-cost myoelectric prosthetic hand prototype are presented. Our main contributions are - (i) proposing a new Computer Aided Design (CAD) suitable for laser cutter for prosthetic hand and (ii) a new feature, XCORR that calculates cross-correlation between two channels of EMG signals for same gesture, is introduced to classify Electromyography (EMG) signals. Individual parts are designed in a 2D plane and assembled to make a 3D structure in CAD software. Acrylic is used as the main material. The prosthetic hand can exhibit 6-degree of freedom (DOF), i.e., six gestures which are required to perform essential daily tasks and grabbing objects. Commonly used features, such as SMAT, HJORTH parameters are extracted from raw EMG signals and combined with newly introduced feature XCORR to classify 6 types of individual and combined finger movements. Using the classified EMG signal as input we have controlled the designed prosthetic hand in both simulation and hardware level. The controller is designed in such a way that the angular displacement of each finger meets a predefined angle value for each classified gesture. For classification of EMG signals, the highest accuracy is observed as 93.9% with a low computational load. The proposed hand will be affordable and suitable for amputees and help them regain their ability to work.

The research is about the feature extraction and recognition of Bangla (Bengali) phoneme, word and command. Power spectral analysis as a feature extraction technique used. Each frame of a speech signal (spectrum) divided into four parts to take the mean absolute value of each part as a pattern recognition technique. For pattern recognition and training purpose Neural network used for the recognition of Bangla phoneme, isolated word and command. The tools and techniques applied in the research provide good result in recognition for single male or female in Bangla phoneme, word and command. Speech recognition rate reduces when scale of experiment increases. Changing of window length during framing slightly influences recognition rate. Linear predictor coefficient analysis, Mel frequency cepstral coefficient could be taken into consideration for future research.

Within this research we investigates the tools and techniques how correctly recognize Bangla (Bengali) language. For feature extraction the power spectral analysis and linear predictor coefficient method considered. Speech samples taken from bangla phoneme, word, command and sentence. Each frame of a speech signal’s spectrum segmented into number of fragments to take the mean absolute value from each fragment for pattern classification. A two-layer feed forward neural network with maximum likelihood method used. Features extraction by power spectral analysis gives little more accuracy for bangla phoneme and word recognition comparing to linear predictor coefficient analysis. Both methods provide average result for bangla command and sentence recognition. The mel frequency cepstral coefficient could be another approach for future research.

With the increase of loading of long transmission line, transient stability of power system on the occurrence of a fault become one of the major concern for power engineers. Instability problem of power system on occurrence of the fault can be solved with the use of Static VAR Compensator (SVC) controller. Assessing the transient stability of a large inter connected power network very much nonlinear and tremendously complicated. To mitigate this complication and nonlinearity, a substantial research with SVC controller has been done in the power industry. To improve the system stability for dynamic and transient disturbances SVC plays a very important role, which is a shunt connected FACTS devices. In this work, genetic algorithm based optimal design of SVC controller to improve the transient performance of power system is proposed. The parameters of the damping controller by optimal selection of damping ratio of the critical eigenvalue employing genetic algorithm are tuned. The effectiveness of the developed optimal controller is investigated by simulating different abnormal conditions of a single machine infinite bus (SMIB) system using MATLAB based PST.

This paper presents a Neural Network-based Bangla phoneme recognition method for Automatic Speech Recognition (ASR). The method consists of three stages: at first stage, a multilayer neural network (MLN) converts acoustic features, mel frequency cepstral coefficients (MFCCs), into phoneme probabilities, where the second stage computes dynamic (velocity (Δ) and acceleration (ΔΔ)) parameters from the phoneme probabilities by using three point linear regression (LR). Finally, the phoneme probabilities, dynamic parameters, Δ and ΔΔ, and the input MFCCs, combined as hybrid features, are fed into a hidden Markov model (HMM) based classifier to obtain more accurate phoneme strings. From the experiments on Bangla speech corpus prepared by us, it is observed that the proposed method provides higher phoneme recognition performance than the existing method. Moreover, it requires a fewer mixture components in the HMMs.

Speaker-specific characteristics play an important role on the performance of Bangla (widely used as Bengali) automatic speech recognition (ASR). It is difficult to recognize speech affected by gender factors, especially when an ASR system contains only a single acoustic model. If there exists any suppression process that represses the decrease of differences in acoustic-likelihood among categories resulted from gender factors, a robust ASR system can be realized. In this paper, we have proposed a technique of gender effects suppression that composed of two hidden Markov model (HMM)-based classifiers and that focused on a gender factor. In an experiment on Bangla speech database prepared by us, the proposed system has provided a significant improvement of word correct rate, word accuracy and sentence correct rate in comparison with the method that incorporates only a single HMM-based classifier for both male and female speakers.

This paper presents a Neural Network-based Bangla phoneme recognition method for Automatic Speech Recognition (ASR). The method consists of three stages: at first stage, a multilayer neural network (MLN) converts acoustic features, mel frequency cepstral coefficients (MFCCs), into phoneme probabilities, where the second stage computes velocity (?) coefficients from the phoneme probabilities by using three point linear regression (LR). Finally, the phoneme probabilities, velocity (?) coefficients and the input MFCCs, combined as hybrid features, are fed into a hidden Markov model (HMM) based classifier to obtain more accurate phoneme strings. From the experiments on Bangla speech corpus prepared by us, it is observed that the proposed method provides higher phoneme recognition performance than the existing method. Moreover, it requires a fewer mixture components in the HMMs.

The transmission of multimedia streams with a particular and guaranteed quality of service (QoS) is a major challenge in multimedia communication over IP networks. In this paper, we analyze the distributed architecture of a video on demand (VoD) system to design global request handling and admission control strategies. We describe a highly available distributed VoD service using existing telephone network, which is inherently scalable and fault tolerant. The VoD service is provided by media server farms (MSF), which may reside in different local telephone exchange of a telephone network. We present the design and simulation results for a new distributed admission control and QoS adaptation controller architecture, called semi-distributed admission control (SD-AC) architecture for a set of MSFs