Due to the high penetration of renewable energy sources (RESs) into the power grids, future power networks will be more volatile to power oscillations, generation-demand imbalance, voltage fluctuations, and frequency oscillations caused by the intermittence nature of these sources and lack of system inertia. This paper introduces a multifunctional two-stage grid-connected PV power system based on detailed mathematical modeling and innovative control techniques to provide efficient ancillary services to meet the grid code requirements and to overcome the issues of photovoltaic generation. The simulation results show that the designed PV power system is capable of operating at MPPT and suboptimal points to ensure the ancillary services of ramp-rate control, active and reactive power control, frequency regulation, and ac voltage regulation, under varying operating and atmospheric conditions.
Convolutional Neural Networks (CNNs) revolutionized computer vision and reached the state-of-the-art performance for image processing, object recognition, and video classification. However, the intensive processing nature of CNN hinders its adaptation in the resources limited edge devices. Artificial Neural Networks are known to be error tolerant, hence, tradeoff between accuracy, performance, power, and latency to meet target application is applicable. This paper proposes the Spatial Locality Input Data method for computational reuse during the inference stage for a pre-trained network. The method exploits input data spatial locality via skipping partial processing of the multiply-and-accumulate (MAC) operations for adjacent data. The computational data reuse was evaluated on three well-known distinctive CNN structures and datasets: LeNet, CIFAR-10, and AlexNet. The computational data reuse method saves up to 34.9%, 49.84%, and 31.5% of MAC operations while reducing the accuracy by 8%, 3.7%, and 5.0% for the three models mentioned earlier, respectively.
With the advances of space exploration vehicles that consists of payloads and subsystems, the most crucial one is the Electrical Power Subsystem (EPS) as it is responsible to provide power to the payloads and loads. As the scientific community focuses on improving the existing satellites technologies in order to make them suitable for future and longer missions, the most promising technique is to model comprehensively the electrical power system so it has a better reliability, efficiency and stability. This paper will present a full model of the EPS of MYSAT-1, the first earth observation CubeSat built at Khalifa University-YAHSAT lab, including the primary power source, energy storage, energy management and loads using MATLAB/Simulink platform. Furthermore, the simulation results of the load dynamics will verify the EPS design along with its power management system.
Silicon photonics is an emerging technology in electronics computing. It consumes comparatively less power and can perform data movement and computations at a much faster rate than the conventional electronics approach. Electrons can travel up to a speed of 1000 m/s in silicon while photons can travel 100 thousand times faster - an immense increase in the current state of the art. Because of this, in the last few decades, Silicon photonics is also thought of as an approach to accelerate AI (Artificial Intelligence) hardware which is conventionally time and energy consuming. Silicon photonics can perform the computation of AI algorithms at a lightning speed. In the current state of the art, ANN requires considerable MAC (multiply-accumulate) operations, and this could be done by using optical interferometers and interconnects on silicon chips with the present semiconductor fabrication process.
Deep neural networks (DNN) can be trained to self-learn useful representative features of arrhythmias from raw ECG waveforms. The superior accuracy of DNNs is achieved at a cost of high computational complexity due to the large number of parameters. This limits its deployment to devices with high computing capabilities. In this paper, a lightweight CNN model based on ShuffleNet architecture is proposed as a solution to make the deployment of deep neural networks on small devices feasible. A novel encoding scheme for the label of the training and test set samples is employed, allowing the model to detect multiple classes in one sample. A loss function named Focal loss that proved to be effective when applied for DNN training on imbalanced dataset was also explored in this work. With 9x less number of trainable parameters, the proposed model has outperformed traditional CNN improving the F1-score by 2%.
Concrete is the most widely used construction material. Its properties are affected by the type and proportion of materials used in the mixing process. Inaccurate mixing affects the structure endurance and poses safety risks. Hence, it is critical to verify that the dried concrete is homogeneous, and consistent throughout the structure. In this work, a novel non-destructive material classification technique is proposed. It is based on passing Wi-Fi signals through several metallic and non-metallic samples, then analyzing the Channel State Information (CSI) amplitude and phase components, which are affected by the channel variations in the form of amplitude attenuation and phase shift. Placing different objects in the channel yields different CSI responses. The collected data are preprocessed using an averaging operation, which generates training examples for the 1-DCNN classifier. Accuracies ranging between 93.33%-100% were achieved. Therefore, the method is validated to be used for concrete classification later in the project.
Electrical Impedance Tomography (EIT) is a non-invasive, non-ionizing imaging technique that incorporates the internal conductivity changes to provide the spatial mapping of a region under test. The motivation to create better tactile sensors that have better skin-like properties has led to exploration of EIT-based tactile sensors that are thin, flexible and stretchable. An EIT-based tactile sensor can be realized by connecting electrodes around a conductive membrane boundary and using current excitation, it is possible to determine the internal conductivity distribution. In this paper, an experiment was conducted on an EIT-based tactile sensor to investigate its ability to detect thermal changes; where two aluminium cups at different temperatures (20.8?C and 65.4?C) were placed on the sensor at different times. The experiment showed that the proposed EIT system was able to recognize the 65.4C stimulus, while room temperature stimulus remained undetected; indicating the proposed EIT system is able to detect thermal changes.
In this paper, the Residue Number System (RNS) is employed for the design of an energy efficient, high throughput DNN accelerator. The proposed system is fully RNS-based, requiring no intermediate conversions to a binary representation. This is made possible due to the sign detection mechanism needed for the ReLU and MaxPooling operations that eliminate the need for converters after each CONV layer, while managing to maintain a small maximum word-length among the residue channels. Moreover, a method for reducing the complexity of the convolutional layers is introduced. By identifying common terms that occur frequently due to the small range of each RNS channel, inside each weight kernel, and by rearranging the order of computations, the number of multiplications required for each convolution is reduced up to 97%. The remaining multiplications are also simplified, as they are implemented through shift operations or fixed-operand multipliers.
The UAE has been a pioneer in regulating renewable energy in the middle east. The purpose of this paper is to define the main factors that affect customers' perception of photovoltaic solar projects for the residential sector as an alternative renewable source of electricity in the UAE. Reliably collected responses were used to build a hypothetical model using confirmatory factor analysis (CFA) and structural equation modeling (SEM). The findings showed that financial and environmental aspects are the main contributors to customers' perception in the UAE. Furthermore, it was apparent that different nationalities in the UAE have a similar average perception of PV projects. We suggest that perception may positively impact the intention towards adopting residential PV systems. The findings can be utilized to enhance the perception of customers and increase the tendency to install such projects in the residential sector.
Wear developments due to traffic volumes can negatively affect the slipperiness of flooring surfaces, hence, provide unsafe environments to users in terms of slip, trip and fall incidents. This study will review the current advancements in the literature associated with floors safety and investigate the latest technologies used for assessing the coefficient of friction of flooring surfaces. This study will contribute to enhancing the existing literature by exploring the development of a three-dimensional simulation model using a finite element method and highlight the benefits associated with such models in terms of safety level assessment of flooring tiles.
