The use of solar panels has some advantages over other conventional electrical generating methods, as there is no sound pollution in collecting solar energy using solar panels, and also it has a minimum need for maintenance. In addition, it helps in the greenhouse effect which does not contribute to any CO2 pollution, as the conversion of light to electricity does not contain any chemical reactions. Using photovoltaic (PV) systems that are connected to a load will require a Maximum Power Point Tracker (MPPT) to maintain the highest possible efficiency of power generated. The resistance of the PV panels is different from the load resistance, the MPPT will control the duty cycle of the Insulated Gate Bipolar Transistor (IGBT) in the DC-DC converter to match the PV and load resistance for best efficacy. However, the use of MPPT with the connection to a controller collecting the maximum power generated from the PV system. In this paper, we design and implement a Recurrent Neural Network (RNN) based MPPT method to improve the efficiency of the power observation for the PV system for any value of irradiation (G) and temperature (T). Mainly, we compare two controller methods, using 104 sets of data for an ANN controller that was designed and tested in the past, with the same 104 sets of data to train the proposed RNN controller, as ANN used prediction in its calculations to find the best output efficiency, RNN will use a recurrent connection in the hidden layers that allow information to flow from one input to another.
This paper deals with the development of a proximity-coupled-fed S-band array antenna intended for microwave imaging application. The array antenna which is designed to work at the center frequency of 3 GHz is employed as a sensing device to transmit and receive emitted electromagnetic waves. The proposed array antenna is deployed on two layers of FR4 epoxy dielectric substrate and composed of four square patches arranging in a 2 × 2 configuration. After hardware realization, the fabricated array antenna is experimentally characterized and measured in an anechoic chamber. The characterization result demonstrate that the fabricated array antenna has the reflection coefficient of −17.29 dB at its center frequency, with the fractional bandwidth of 9.3% in the frequency range from 2.92 GHz to 3.2 GHz. These results are comparable to the simulated ones and adequate for the desired application.
Industrial networks interconnect devices to support industrial processes and manufacturing systems. Industrial applications results in time-critical and/or mission-critical data traffic, which require high reliability, guarantees on the delay bounds and robust time synchronization with high precision. Time Sensitive Networking (TSN)is considered as a suitable multi-service network technology for industrial systems and applications. TSN provides greater timing accuracy, enhanced performance in reducing packet delay variation, and adds more networking determinism. However, the configurations of TSN switches presents a challenge (e.g. time-aware traffic shaping schedules). Each output port of a TSN switch has to follow timed schedule to achieve the desired performance. The schedules have to be distributed at the network initialization and have to be maintained upon new devices joining or obsolete device being removed. In this paper, we use the Software Defined Networking (SDN) paradigms to overcome the aforementioned challenges. We propose the Software-Defined Time Sensitive Networks (SD-TSN), where end devices sign contracts with the network controller, to check the availability of the network resources and to select an optimized path for the data transmission. Therefore, It achieves overall improved network resources management, while meeting QoS requirement with high reliability. Our performance analysis show that SD-TSN accurately schedule the critical time traffic resulting in a bounded end-to-end latency with very low jitter. Even in the presence of different types of data traffic, the critical data traffic meets the quality of service requirements with a maximum jitter of $\mathbf{6}\ \mu\mathbf{s}$ . Moreover, SD-TSN takes 10 seconds run-time, compared to 2 minutes run-time when traditional TSN scheduling is used, to calculate the schedules of 100 TSN critical flows.
A new Diffusion Artificial Bee Colony (DABC) heuristic algorithm is developed to estimate system parameters in Wireless Sensor Networks (WSNs). The main contribution is the incorporation of ABC algorithm in the traditional Diffusion Least Mean Square (DLMS) algorithm which leads to a better Mean Square Error (MSE) performance. The DABC algorithm shows excellent convergence beyond the noise variance boundary. In the diffusion stage, each node shares the local best cost function and corresponding local best particle position to immediate neighboring nodes. The extensive simulations show that the proposed DABC approach achieves excellent MSE improvement (MSD deterioration) in comparison to existing DLMS algorithms.
Companies throughout the world are making an innovative switch from oil and gas to renewable energy sources, such as wind and solar power. As the world transitions to renewable energy, the demand for electric vehicles (EVs) has increased significantly. EVs mainly use Lithium-ion batteries because of their durability and efficiency. However, as the number of Lithium-ion batteries increases with the goal of reduction of emission and low energy cost, it comes with a major drawback of safety which affects efficiency. To address these challenges, this study investigates ways on how to improve the storage management system in an EV. In this research, different Lithium-ion battery states of an EV were monitored to effectively improve the battery management system (BMS). Two different drive cycles, federal test procedure (FTP) 75 and wide-open throttle (WOT) simulation time of 2474 seconds are used to obtain the results. The implementation of State of charge (SOC) technique has been applied to evaluate the energy remaining in the battery as well as the aging effects/dynamic load. The results obtained show a rate that gradually slows down in a linear manner. In addition, EVs have a less likely chance of experiencing power loss due to a very sophisticated gear system and it is very similar to a hybrid electric vehicle or internal combustion engine.
The proposal of a facial recognition system to increase security, through facial recognition with multiple utilities such as facilitating the access of people with adequate protection measures in times of Covid-19, as well as security when seeking to hide their identity. The methodology considers the use of tools such as Python and OpenCV, as well as models such as Eigen Faces, Fisher Faces, and LBPH Faces, as units of analysis are considered photographs and portions of the video that capture facial expressions that then their patterns are trained with facial recognition algorithms. The results obtained show that the LBPH Faces obtained confidence values lower than 70, with a 95% certainty of recognition and a shorter recognition time, improving the accuracy of facial recognition, also with the increase of the data was achieved to improve the accuracy of recognition as well as improve confidence regarding the safety of people.
Propagation delay in blockchain networks is a major impairment of message transmission and validation in the bitcoin network. The transaction delay caused by message propagation across long network chains can cause significant threats to the bitcoin network integrity by allowing miners to find blocks during the message consensus process. Potential threats of slow transaction dissemination include double-spending, partitions, and eclipse attacks. In this paper, we propose a method for minimizing propagation delay by reducing non-compulsory message broadcasts during transaction dissemination in the underlying blockchain network. Our method will decrease the propagation delay in the bitcoin network and consequently mitigate the security threats based on message dissemination delay. Our results show improvement in the delay time with more effect on networks with a large number of nodes.
Load frequency control is a vital control problem in power system operation and control. The primary purpose of load frequency control is to keep power system frequency at a nominal value and control net power interchange between power system tie-lines at predetermined limits during load variations or disturbances. One common proposed control method for load frequency control is the linear quadratic regulator (LQR) feedback controller. However, all the state variables may not always be available and measurable, limiting the application of the LQR controller. Therefore, A disturbance observer-based controller is proposed to estimate the state variables using the available measurements. The observer-controller gains are optimized by particle swarm optimization. The proposed controller has been validated in a well-known and widely used power system test case, Kundur's two-area 4-machine 11-bus power system and 10-machine IEEE 39-bus power system implemented in Matlab® and Simulink ® , under different faulted conditions, and load disturbances. Furthermore, simulation results produced faster frequency control loop stabilization with minimal frequency nadir, which proved the effectiveness of the proposed controller in an interconnected power system.
In this study, we propose a predictive model for forecasting future ransomware and malware attacks based on the previous time series data from 2005–2021. We use a time-series regression technique that relies on the neural network algorithm to estimate the forecasting of ransomware and malware attacks in future years over time. Our experiment has applied two hidden layers with the optimal parameter (weight and biases). We modify our model in terms of building time series to predict short-term future values up to 2026. To reach the minimum potential training error, we train our model on 60 epochs to achieve Mean Square Error (MSE) at minimum values. We have achieved the highest accuracy of 99% for forecasting malicious activities (ransomware and malware). The predictive model shows a massive increase in ransomware risk. The current lines of defense cannot keep up with the evolution of ransomware to prevent them.
Recently there have been a lot of improvements in the field of transportation and machine optimization. With the increase in global data, capturing and modifying it to functionalities that can perform human-needed functions efficiently is essential. As humans evolve so do the technologies, hence our expectations for technologies to work automatically as they understand the environment and human nature. This paper discusses the key concept of Intelligent Networks and Intelligent connected devices. It shows the emerging application of intelligent networks in the field of transportation systems and industrial development. Topics included are service creation, Intelligent transportation systems, industry 4.0, various applications based on technologies V2X, and concepts of improved traffic systems. Automation is the key that will lead to an advanced lifestyle.
The RSA algorithm is an asymmetric encryption algorithm used to ensure the confidentiality and integrity of data as it travels across networks. Security has grown in importance over time, resulting into more data requiring encryption. Parallelization represents an ideal solution to speed up the encryption and decryption processes. An advance implementation of RSA using parallelization concept leads to improve security and performance. In this paper, we represent a parallelized version of Multi-Keys RSA algorithm implemented using OpenMP library. Furthermore, we provide parallel implementation of Multi-Keys RSA under both static and dynamic scheduling with different chunk sizes, and our experimental results show that static scheduling is more optimum for RSA cryptography as compared to dynamic. As a final result, we have achieved an average speed up of 4.4 and efficiency of 0.7.
Particularly amid Covid-19, enterprises' digital transformation has rapidly accelerated, making cybersecurity an even bigger challenge. Financial institutions adopt FinTech technologies to advance their service and achieve an enhanced customer experience that creates a competitive edge in the market. FinTech products utilise open banking API services to allow communication between a financial institution and a FinTech provider. However, such an integration introduces significant security concerns. Therefore, financial firms must ensure that a robust API service to protect the bank's infrastructure and its customers' information. To address this concern, we propose a Framework for Open Banking API security that utilises STRIDE model to identify security threats in FinTech integration via Open Banking API and Bayesian Attack Graphs to automate predictions of the most exploitable attack paths.
The Internet of Things (IoT) is a promising and emerging technology in which many devices communicate, process, and exchange information with other devices, servers, or applications. The communication link could be between people to devices, or machine to machine, resulting in an extensive network of connected devices. Due to the increase in the range of applications, the attack surface has also increased. Usually, data transfer through an IoT network is critical, so confidentiality is a significant concern. In an IoT network, confidentiality can be achieved using Secure Shell (SSH) and Transport Layer Security (TLS) protocols. However, they have overheads that resource-constrained sensor devices cannot handle. Therefore, lightweight security mechanisms must be incorporated into various protocol stack layers. In this work, we have implemented AES128, Elliptic Curve Cryptography (ECC), ChaCha20, and Corrected Block Tiny Encryption Algorithm (XXTEA) in the MQTT protocol on ESP8266 and ATmega328p IoT devices. We present the performance evaluation of these encryption algorithms on a hardware testbed and a Contiki OS-based cooja simulator using the Tmote Sky module. The hardware experiments show that the ESP8266 chip with XXTEA encryption algorithm embedded with MQTT protocol is more efficient. At the same time, the simulation results show a rise in power consumption if we choose a robust encryption algorithm and increase the number of nodes.
This report highlight the process of the nano-technology for the nano-robots which includes the literature review, molecular machines, how the sensors work in nano-robotics, its applications and the future scope. As the nano-robots also helps in the field of nano-medicine like cancer treatment and several tools for the treatment of human body. The carbon atom is used in a diamond structure for the exterior of the nano-robots because of its properties and strength and it also helps in the drug delivery inside the human body. As far as the scientist predict, the current study of the nano-technology involves the great achievement in the field of the nano-robots.
Electronic mails are a popular communication tool in the digital era. However, communications across the untrusted Internet pose major security threats to e-mail communications. This work aimed to examine the cryptographic security concepts behind the e-PGP and S/MIME email protocols, present the mathematical concepts, and highlight the applications for improving e-mail security. The work presents a review of the security implications of PGP and S/MIME in protecting the relevant principles of email security, such as confidentiality, integrity, availability, authenticity, and non-repudiation.
The rapid growth of wireless devices as societies adapt to the Internet of Everything (IoE) has led to saturation of spectrum resources. Dynamic spectrum access has been considered a promising solution to alleviate congested channels by allowing unlicensed users to access licensed channels when the licensed users are idle. Various coexistence challenges arise as unlicensed users compete over a limited amount of channel resources. In this article, we build on a previously defined bio-social inspired dynamic spectrum access coexistence scheme where unlicensed users achieve fair sharing of resources by choosing to defer to nodes with more urgent transmission needs. To prevent selfish nodes from taking advantage of the deference mechanism, we propose a decentralized rogue node detection behavioral model. While foraging for resources, each node performs rogue node detection using hardware fingerprinting. We show that we can achieve 99% rogue node detection accuracy with fast detection convergence time and low communication/coordination overhead.
One of the biggest new trends in artificial intelligence is the ability to recognise people's movements and take their actions into account. It can be used in a variety of ways, including for surveillance, security, human-computer interaction, and content-based video retrieval. Vision-based approaches for recognising human action have been introduced by numerous researchers. Several challenges need to be addressed in the creation of a vision-based human activity recognition system, including illumination variations in human activity recognition, interclass similarity between scenes, the environment and recording setting, and temporal variation. Many works have been reported on CNN that use only a few layersto fix HAR issues. Feature extraction from high-resolution images using standard shallow CNNs with simple learning schemes. In thiswork, we have developed HAR-11 Net model for identification and recognition of human activities. The experimental findings demonstrate that the proposed system is capable of identifying human actions, and that it performs significantly better than existing systems.
Seismic facies can be used as novel features to classify different classes of seismic structures. Classification of seismic structure is beneficial for mineralogy, grain size approximation, the permeability of deposition units, and the identification of areas of interest. To extract features of seismic images, the following extraction methods were used: Discrete Wavelet Transform Features, Discrete Cosine Transform Features, Discrete Fourier Transform Features, and Gabor Features. The classification methods being considered are Support Vector Machine (SVM), Random Forest (RF), Fast Decision Trees (FDT), and Naïve Bayes (NB). The proposed study uses the LANDMASS database, composed of two datasets, LANDMASS-1, with 17,667 images, and LANDMASS-2, with 4,000 images. The datasets contain seismic images of four different classes of seismic structures; Chaotic, Fault, Horizon, and Salt Dome. The outcome of this study proves that the combination of Forest Tree classification method and the Discrete Cosine Transform Features extraction method achieved the highest accuracy, which was around 94.17% - higher than that achieved considering similar methods reported in the extant literature.
The robotic welding process is widely used in many industry sectors, and its use in production lines is becoming more common day by day. Obtaining a smooth weld seam in robot welding depends on the geometric structure of the welding path and the stability of the control loop. However, the weld path and the weld gap are usually not fixed, and their change negatively affects the automatic control. Programming the complex welding path by the operator may take more time than executing the task for some welding jobs. In addition, the variable weld gap negatively affects the weld quality in the constant control loop. This study proposes a system that provides a real-time definition of the weld path and its geometry on the embedded system to address this issue. The weld path image is captured using a camera, and the weld path is determined by image processing techniques using the embedded Linux operating system running on system-on-chip (SoC) hardware. The images captured through the Hard Processor System (HPS) unit are stored in memory, processed in the FPGA unit, and output by the HPS unit. Unprocessed SoC images and measurement images of weld pieces are presented with their values. When the values obtained from the processed weld path image are compared to manually measured path values, it is seen that the proposed system produces successful results.
A novel scheme to enhance the spectral efficiency (SE) for an underlay cognitive radio system (UCRS) is proposed. A cross-layer optimization (CLO) of both the physical layer (PHY) and the data link control layer (DLC) of a secondary user (SU) transmission is considered. The transceiver uses discrete Fourier transform modulated filter banks. An optimum scheme for joint bit and power loading (JBPL) as well as coding adaptation (CA) using punctured convolutional codes is employed at the PHY. At the DLC, a truncated automated repeat request (TARQ) is implemented to ensure reliable transmission. A lower bound on the SE for coded transmission is derived analytically and maximized subject to different quality-of-service (QoS) and stochastic chance-based constraints as well as third-party colored Gaussian interference at the SU receiver. The system performance is analyzed in terms of the number of retransmissions and the achievable SU SE. It turns out that the bound tightens for increasing values of the average energy-per-bit-to-noise-ratio.
