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The industrial internet of things (IoT) plays a major role in the growth of automation and increasing digital connectivity for machine-to-machine communication. The research community has extensively investigated the possibility of IoT and blockchain integration for the last couple of years. The major research is focused on the benefits of integrating blockchain with IoT. In this work, we first focus on the issue of integrating IoT nodes with blockchain networks, especially for non-real-time IoT nodes that do not have an in-built clock mechanism. As a result, they cannot establish communication with real-time blockchain networks. Another critical security issue is protecting data coming from IoT devices to blockchain networks. Blockchain is enough mature to protect the data in its ecosystem. However, information coming from outside of the world does not have any guarantee of data integrity and security. This paper first addresses the clock synchronization issue of IoT nodes with blockchain using a network time protocol and then proposes an IoT-blockchain light-weight cryptographic (IBLWC) approach to secure the entire IoT-blockchain ecosystem. This paper also presents the performance analysis of IBLWC as a suitable and cost-effective solution that incurs less processing overhead for IoT-blockchain-based applications.

Traditionally, low-frequency power supplies are used in dielectric barrier discharge (DBD) ozone generators. These generators require a very high output voltage. This may limit ozone production due to limitations imposed by the dielectric strength of the insulating material. Low-frequency generators also present low efficiency, large volumes, and difficulty in controlling ozone production. On the other hand, the advantages of high frequency DBD ozone generators are the increased power density applied to the chamber electrodes, and the voltage applied to the ozone chamber decreases, allowing for higher ozone production efficiency. From this point of view, in order to enhance and control the DBD ozone generator operating at high frequency, it is necessary to determine all parameter values and optimize the equivalent model for this type of generator. This work presents and proposes the practical methodologies used to extract all parameters of the high voltage high frequency (HVHF) transformer which can be used in these systems. Resonant frequency control techniques are presented in this paper. Elimination of the stray capacitance effect will also be implemented in this paper.

This paper presents a new ontology-based multicriteria spatial group decision support system (GDSS) dedicated to site selection problems. Site selection is one of the most complex problems in the construction of a new building. It presents a crucial problem in terms of selecting the appropriate site among a group of decision makers with multiple alternatives (sites); in addition, the site must satisfy several criteria. To deal with this, the present paper introduces an ontology based multicriteria analysis method to solve semantic heterogeneity in vocabulary used by participants in spatial group decision support systems. The advantages of using ontology in GDSS are many: i) it enables the integration of heterogeneous sources of data available on the web and ii) it enables to facilitate meaning and sharing of data used in GDSS by participants. In order to facilitate cooperation and collaboration between participants in GDSS, our work aims to apply ontology at the model's structuration phase. The proposed system has been successfully implemented and exploited for a personalized environment.

This paper reviews the factors affecting effective dashboards for urban water security monitoring and evaluation. Urban water security is a constantly evolving field influenced by several factors, including changes in climate, ecosystems, socio-economic status, and human beings. Although urban water security has been discussed in some parts of the literature, there has been minimal literature review that focused on the factors of urban water security and the effective dashboards for monitoring and evaluation. Using systematic literature review (SLR) and preferred reporting items for systematic reviews and meta-analysis (PRISMA), this paper reviewed 143 articles. The result shows growth in the environmental informatics landscape since the last ten years when the first article on the urban water management dashboard was published. The visual design was the most frequently discussed factor for dashboards, followed by user customization. It also shows that this topic can go deeper to integrate both factors and design an effective environmental dashboard. The discussion identified three potential opportunities for future research in water security and informatics: i) exploring other dimensions of effective dashboards, ii) considering more research on the environmental dashboard, and iii) investigating the real-life application of dashboards in urban water security.

Chronic disease (CD) such as kidney disease and causes severe challenging issues to the people all around the world. Chronic kidney disease (CKD) and diabetes mellitus (DM) are considered in this paper. Predicting the diseases in earlier stage, gives better preventive measures to the people. Healthcare domain leads to tremendous cost savings and improved health status of the society. The main objective of this paper is to develop an algorithm to predict CKD occurrence using machine learning (ML) technique. The commonly used classification algorithms namely logistic regression (LR), random forest (RF), conditional random forest (CRF), and recurrent neural networks (RNN) are considered to predict the disease at an earlier stage. The proposed algorithm in this paper uses medical code data to predict disease at an earlier stage.

This paper presents a study and an array design consisting of two microstrip patch antennas connected in series in a 2×1 form. This antenna provides better performance for the fifth-generation (5G) wireless communication system. The microstrip line feeding technique realizes the design of this antenna. This feed offers the best bandwidth, is easy to model, and has low spurious radiation. The distance between the feed line and the patch can adapt to the antenna’s impedance. In addition, the antenna array proposed in this paper is designed and simulated using the high frequency structure simulator (HFSS) simulation software at the operating frequency of 28 GHz for the 5G band. The support material used is Rogers RT/duroid® 5880, with relative permittivity of 2.2, a thickness of h=0.5 mm, and a loss tangent of 0.0009. The simulation results obtained in this research paper are as: reflection coefficient: -35.91 dB, standing wave ratio (SWR): 1.032, bandwidth: 1.43 GHz, gain: 9.42 dB, directivity: 9.47 dB, radiated power: 29.94 dBm, accepted the power: 29.99 dBm, radiation efficiency: 29.95, efficiency: 99.83%. This proposed antenna array has achieved better performance than other antenna arrays recently published in scientific journals regarding bandwidth, beam gain, reflection coefficient, SWR, radiated power, accepted power, and efficiency. Therefore, this antenna array will likely become an important competitor for many uses within the 5G wireless applications.

The evolvement of smart glass technology has gained a lot of interest through its energy-saving potential as one of the heating, ventilating, and air-conditioning (HVAC) system. This paper focuses on polymer dispersed liquid crystal (PDLC) film, a smart glazing film that changes its opacity in response to an electrical impulse. The power consumption of the smart film is considerably small. However, improper handling of the smart film such as not turning off the film after usage can lead to energy wastage. Hence, connecting the smart film to an internet of things (IoT) controller would be one of the possible solutions to ensure that the film is maintained properly. The objective of the work here is to develop a smart, low cost and efficient IoT-enabled smart controller for PDLC films with energy-saving features. In pursuance of materializing this concept, this paper delineates the design of a smart controller for the PDLC films. The implementation of the IoT features, NodeMCU, and environmental sensors enabled the smart film to be capable of switching automatically. In addition, voice-command features were also incorporated into the controller. With the successful development of the IoT smart controller, the PDLC films can operate autonomously and wirelessly.

This paper advances video analytics with a focus on crowd analysis for Hajj and Umrah pilgrimages. In recent years, there has been an increased interest in the advancement of video analytics and visible surveillance to improve the safety and security of pilgrims during their stay in Makkah. It is mainly because Hajj is an entirely special event that involve hundreds of thousands of people being clustered in a small area. This paper proposed a convolutional neural network (CNN) system for performing multitude analysis, in particular for crowd counting. In addition, it also proposes a new algorithm for applications in Hajj and Umrah. We create a new dataset based on the Hajj pilgrimage scenario in order to address this challenge. The proposed algorithm outperforms the state-of-the-art approach with a significant reduction of the mean absolute error (MAE) result: 240.0 (177.5 improvement) and the mean square error (MSE) result: 260.5 (280.1 improvement) when used with the latest dataset (HAJJ-Crowd dataset). We present density map and prediction of traditional approach in our novel HAJJ-crowd dataset for the purpose of evaluation with our proposed method.

In recent years, internet of things (IoT) represents a giant and a promoter area in innovation and engineering fields. IoT devices are spread in various fields and offer advanced services which assist their users to monitor and control objects remotely. IoT has a set of special characteristics such as dynamic, variety of data and huge scale which introduces a great challenge in the field of retrieval technologies, more precisely real-time retrieval. This paper addresses the issue of real-time retrieval of connected things and tries to propose an innovative solution which allows the retrieval of these things and their descriptive data. The paper proposes an on-line tool for real-time retrieval of connected things and their descriptive data based on network port scanning technique. The performance of this tool proves to be powerful under normal conditions, however more tests must be implemented in the aim to improve the proposed solution. The tool resulted from this work appears to be promising and can be used as a reference by network administrators and IT security managers for the development of new security mechanisms and security reinforcement.

The world is currently facing a strong epidemic and pandemic of coronavirus. This motivates establishing our paper, where this virus pushes researchers to study, investigate, observe, analyse and try solving its related issues. In this work, an artificial neural network (ANN) model of backpropagation neural network (BNN) with two hidden layers is proposed for expecting confirmed cases and death cases of coronavirus disease 2019 (covid-19). As a field of study, Iraq country has been considered in this paper. Covid-19 dataset from our world in data (OWID) is used here. Promising result is achieved where a very small error value of 0.0035 is reported in overall the evaluations. This paper may implicate establishing further researches that consider other parameters and other countries over the world. It is worth mentioning that the suggested ANN model may help decision maker people in taking quarantine movements against the strong epidemic and pandemic of covid-19.

The acute shortage of trained and experienced sonographers causes the detection of congenital heart defects (CHDs) extremely difficult. In order to minimize this difficulty, an accurate fetal heart segmentation to the early location of such structural heart abnormalities prior to delivery is essential. However, the segmentation process is not an easy task due to the small size of the fetal heart structure. Moreover, the manual task for identifying the standard cardiac planes, primarily based on a four-chamber view, requires a well-trained clinician and experience. In this paper, a CNN method using U-Net architecture was proposed to automate fetal cardiac standard planes segmentation from ultrasound images. A total of 519 fetal cardiac images was obtained from three videos. All data is divided into training and testing data. The testing data consist of 106 slices of the four-chamber segmentation tasks, i.e. atrial septal defect (ASD), ventricular septal defect (VSD), and normal. The segmentation of the post-processing method is needed to enhanced the segmentation result. In this paper, a combination technique with U-Net and Otsu thresholding gives the best performances with 99.48%-pixel accuracy, 96.73% mean accuracy, 94.92% mean intersection over union, and 0.21% segmentation error. In the future, the implementation of Deep Learning in the study of CHDs holds significant potential for identifying novel CHDs in heterogeneous fetal hearts.

The main goal of a battery management system (BMS) is to estimate parameters descriptive of the battery pack operating conditions in real-time. One of the most critical aspects of BMS systems is estimating the battery's state of charge (SOC). However, in the case of a lithium-ion battery, it is not easy to provide an accurate estimate of the state of charge. In the present paper we propose a mechanism based on an extended kalman filter (EKF) to improve the state-of-charge estimation accuracy on lithium-ion cells. The paper covers the cell modeling and the system parameters identification requirements, the experimental tests, and results analysis. We first established a mathematical model representing the dynamics of a cell. We adopted a model that comprehends terms that describe the dynamic parameters like SOC, open-circuit voltage, transfer resistance, ohmic loss, diffusion capacitance, and resistance. Then, we performed the appropriate battery discharge tests to identify the parameters of the model. Finally, the EKF filter applied to the cell test data has shown high precision in SOC estimation, even in a noisy system.

Class imbalance and overlapping on multi-class can reduce the performance and accuracy of the classification. Noise must also be considered because it can reduce the performance of classification. With a resampling algorithm and feature selection, this paper proposes a method for improving the performance of hybrid approach redefinition-multi class (HAR-MI). Resampling algorithm can overcome the problem of noise but cannot handle overlapping well. Feature selection is good at dealing with overlapping but can experience a decrease in quality if there is a noise. The HAR-MI approach is a way to deal with multi-class imbalance issues, but it has some drawbacks when dealing with overlapping. The contribution of this paper is to suggest a new approach for dealing with class imbalance, overlapping, and noise in multi-class. This is accomplished by employing minimizing overlapping selection (MOSS) as an ensemble learning algorithm and a preprocessing technique in HAR-MI, as well as employing multi-class combination cleaning and resampling (MC-CCR) as a resampling algorithm at the processing stage. When subjected to overlapping and classifier performance, it is discovered that the proposed method produces good results, as evidenced by higher augmented r-value, class average accuracy, class balance accuracy, multi class g-mean, and confusion entropy.

A survey on ultra-wideband complementary metal-oxide semiconductor (CMOS) power amplifiers for wireless body area network (WBAN) applications is presented in this paper. Formidable growth in the CMOS integrated circuits technology enhances the development in biomedical manufacture. WBAN is a promising mechanism that collects essential data from wearable sensors connected to the network and transmitted it wirelessly to a central patient monitoring station. The ultra-wideband (UWB) technology exploits the frequency band from 3.1 to 10.6 GHz and provides no interference to other communication systems, low power consumption, low-radiated power, and high data rate. These features permit it to be compatible with medical applications. The demand target is to have one transceiver integrated circuit (IC) for WBAN applications, consequently, UWB is utilized to decrease the hardware complexity. The power amplifier (PA) is the common electronic device that employing in the UWB transmitter to boost the input power to the desired output power and then feed it to the antenna of the transmitter. The advance in the design and implementation of ultra-wideband CMOS power amplifiers enhances the performance of the UWB-transceivers for WBAN applications. A review of recently published CMOS PA designs is reported in this paper with comparison tables listing wideband power amplifiers' performance.

This paper discusses neural network ANN controller applied for five phase shunt active power filter. Three-phase shunt active power filters are widely studied, however in this paper we investigate to extend their application to five phase embarked electrical network. Two-level five phase inverter is used as a shunt active power filter to reduce harmonics and compensate reactive power injected by a five-phase diode rectifier. An artificial neural network controller has been used to regulate the DC side of the inverter, thereby contributing to the calculation of the reference harmonic currents on the one side and controlling the injected harmonic currents on the other. Self-tuning filter (STF) is used to calculate the reference current. By using STF allow to extract the voltage and current fundamental components in the α-β-axis directly without the use of a phase-locked loop (PLL). Simulation results shows that the ANN controller presents appreciable performances in references tracking and the proposed five-phase shunt active power filter provides a sinusoidal supply current in phase with the line voltage with low harmonic distortion.

Wireless sensor network (WSN) has applications in military, health care, environmental monitoring, infrastructure, industrial and commercial applications. The WSN is expected to maintain data integrity in all its network operations. However, due to the nature of wireless connectivity, WSN is prone to various attacks that alter or steal the data exchanged between the nodes. These attacks can disrupt the network processes and also the accuracy of its results. In this survey paper, we have reviewed various attacks available in the literature till date. We have also listed existing methods that focus on data aggregation based security mechanisms in WSN to counter the attacks. We have classified and compared these methods owing to their encryption techniques. This paper intends to support researchers to understand the basic attacks prevalent in WSN and schemes to counter such attacks.

Facial recognition is a highly developed method of determining a person's identity just by looking at an image of their face, and it has been used in a wide range of contexts. However, facial recognition models of previous researchers typically have trouble identifying faces behind masks, glasses, or other obstructions. Therefore, this paper aims to efficiently recognise faces obscured with masks and glasses. This research therefore proposes a method to solve the issue of partially obscured faces in facial recognition. The collected datasets for this study include CelebA, MFR2, WiderFace, LFW, and MegaFace Challenge datasets; all of these contain photos of occluded faces. This paper analyses masked facial images using multi-task cascaded convolutional neural networks (MTCNN). FaceNet adds more embeddings and verifications to face recognition. Support vector classification (SVC) labels the datasets to produce a reliable prediction probability. This study achieved around 99.50% accuracy for the training set and 95% for the testing set. This model recognizes partially obscured digital camera faces using the same datasets. We compare our results to comparable dataset studies to show how our method is more effective and accurate.

Loss of the capability to talk or hear applies psychological and social effects on the affected individuals due to the absence of appropriate interaction. Sign Language is used by such individuals to assist them in communicating with each other. The paper aims to report details of various aspects of wearable healthcare technologies designed in recent years based on the aim of the study, the types of technologies being used, accuracy of the system designed, data collection and storage methods, technology used to accomplish the task, limitations and future research suggested for the study. The aim of the study is to compare the differences between the papers. There is also comparison of technology used to determine which wearable device is better, which is also done with the help of accuracy. The limitations and future research help in determining how the wearable devices can be improved. A systematic review was performed based on a search of the literature. A total of 23 articles were retrieved. The articles are study and design of various wearable devices, mainly the glove-based device, to help you learn the sign language.

Fast torque control is crucial for various applications, including diesel-electric locomotives, electric automobiles, and steel plants. The purpose of this paper is to utilize a microcomputer to control the torque of a separately-excited DC motor fed from a single-phase semi-controlled converter. There are numerous strategies to determine torque using direct or indirect methods. The greatest issue of direct strategy using torque meters is that the measuring device must be installed between the motor and the load. In this paper, we avoid this challenge by employing the indirect approach, specifically by monitoring several DC machine-specific quantities such as voltage, current, and speed, which are easier to determine experimentally. This technology is unquestionably helpful in real-world applications since it eliminates any mechanical impact on the installation. This paper describes the system configuration and provides an explanation of the architecture and system features. The simulation of the proposed system using TUSTSIM dynamic simulation program which is capable of simulating the control system with a digital controller in the loop is presented. An implementation of microcomputer-assisted torque control of DC separately excited motor with proportional integral (PI) controllers is presented. A typical oscillography of the driving characteristics is provided along with the experimental results.

A multi renewable source micro-grid system has many power quality issues when it is operated in grid islanding condition. During grid connected mode the voltage and frequency of the system will remain stable for small disturbances like load changes or environmental changes. During these sudden variations in the system, the grid supports the load maintaining the system stable. In this paper a droop control is introduced for the renewable source module 3-ph inverter during standalone mode supporting the load. For detection of grid islanding, an active islanding detection control is adopted which makes the 3-ph inverter to shift from synchronous reference frame (SRF) control to droop control. The performance of the droop control is improvised with fuzzy interference structure (FIS) module replacing the conventional proportional integral and derivative (PID) control. A comparative analysis is done in this paper with different controllers in droop control scheme using matrix laboratory (MATLAB) Simulink software. Stability analysis is carried out on the system determining the voltage and frequency fluctuations during grid disturbances. There is an improvement in load compensation, voltage ripple, voltage magnitude, frequency ripple and settling time with the introduced fuzzy droop controller.

The paper describes the development of a computer-based familial hypercholesterolemia (FH) screening application (FH CatScreen©). The application facilitates automatic scoring and categorisation of patients by medical practitioners based on four well-known FH diagnostic criteria. In the absence of a FH diagnostic criterion for Malaysian population, these four diagnostic criteria are commonly used criteria to classify patients FH severity levels to manage early interventions. We applied an adaptive software development approach comprising planning, development and validation phases to develop FH CatScreen©. A user study involving thirty medical practitioners was conducted to evaluate the effectiveness and usability of FH CatScreen©. The study showed that FH CatScreen© was able to provide a more correct, faster and better-informed assessment compared to the traditional paper-based method. The study further showed that FH CatScreen© has a good degree of performance and acceptance by the participants. The participants indicated that the simultaneous use of the four diagnostic criteria in FH CatScreen© has assisted them to compare the outcomes of each of the criterion side-by-side. It allowed them to decide on the severity of patient condition with high confidence. FH CatScreen© has demonstrated its expediency and efficacy in collecting the data on FH incidence and prevalence in Malaysia.

In recent years, the vehicular ad hoc network (VANET) has received great attention, as it is involved in the design of the intelligent transportation system (ITS). The VANET network includes message flows from vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) where the network is propped by wireless communication technology, such as IEEE 1609 WAVE and IEEE 802.11P. The VANET network implementation faces challenges, and one of these challenges is the design of routing protocols that transfer reliable and efficient packets from vehicle to vehicle. In VANET, steering is a challenging task in the highway and urban environment. Therefore, this paper presents an assessment of ad hoc on-demand distance vector protocol (AODV) performance in the highway and urban environment and to study the effect of vehicle density on protocol performance. The AODV protocol was simulated by MATLAB. In this study, the performance of AODV protocol was evaluated through four measures, namely, packet delivery ratio (PDR), overhead, end-to-end (E2E) delay, and dropped packets. The study in our paper showed that the best performance of the AODV protocol is in an environment where vehicle speed and vehicle density are low.

The climatic change issues can only be addressed by adding more and more renewable energy into the energy chart for any region. However, the criteria for the composition of the chart shall be based on availability, cost-effectiveness, complementary nature, and optimally fit with the load curve. Based on the geographical diversity and availability of renewable energy in India, the energy chart is different for different regions. In this paper, an attempt has been made to prepare a renewable energy chart based on the equality constraint optimization technique for economical load sharing between the locally available renewable energy sources. The HOMER-based software platform has been utilized for cost analysis purposes. Based on locally available renewable energy sources, micro-hydro, and solar-PV are considered for analysis. In this paper, the electrical load of an educational institution situated in the eastern Himalayan region is considered for the case study. The data received from the case study has been utilized for the preparation of an appropriate and economically viable renewable energy chart for the mentioned region. Even though the analysis model applies to a particular region, it can be suitably modified for other regions also.

In recent years, cryptocurrency technology has become an attractive area for investment due to its transparency, independence, and non-transactional nature. Many analysts and researchers talk daily on social media about the future of various cryptocurrencies. These ideas can significantly impact whether or not people are willing to invest. This paper provides a framework to help traders learn about the opinions of influential people and organizations in the field. Over the course of six months, the sentiment of more than 90 significant Twitter users was extracted for the proposed framework. In this study, we used the Vader open-source tool for sentiment analysis. This paper provides an excellent opportunity for investment through sentiment analysis of lesser-known or emerging cryptocurrencies. Also in this paper, we introduce the user importance factor to calculate the value of each tweet based on the number of retweets and comments. This factor shows the importance of their opinions instead of considering the number of followers of the authors. This factor causes a lower coefficient to be assigned to an author's opinion if it decreases in importance over time. The results show that in the short and long term, users' opinions are very effective in the market for cryptocurrencies and in predicting its price trend.

This manuscript deals with the various control strategies of storage system for an electrical vehicle. High demands in the electrical systems in the field of transportations leads to various challenges and more precise control and regulations techniques. Apart from the conventional grid system now a days the integration of renewable energy systems like solar, wind and fuel cell system leads to more complex system but these system shares the load from conventional generating system. This paper deals with the study and control aspects of the electrical vehicles associated with hybrid energy storage (HES) systems. In general, when systems are integrated with the main grid there are more distortions and ripples in the system. To reduce these distortions various control techniques are used. This paper proposes a neural network-based PI (NNPI) controller for HES system for electric vehicles for better distortion less outputs.

The use of the cloud by governments throughout the world is being aggressively investigated to increase efficiency and reduce costs. The majority of cloud computing risk management programs prioritize addressing cloud security issues that government organizations may face when they choose to adopt cloud computing systems, but these programs lack evidence of security risks, and problems with using cloud computing in developing nations are uncommon, so they called for more research in this area. The objective of this paper is to use quantitative models namely Spearman's Rank correlation coefficient, simple regression, and support vector machine regression (SVMR) for estimating cloud security issues based on cloud control factors for improving the mitigation of cloud computing security issues based on control factors using intelligent models in a government organization. Identify the proper cloud control factors for every cloud security issue from estimation errors using a standard for performance measurement like mean square error (MSE) and root mean square error (RMSE), performance measurement to evaluate and validate proposed models. SVMR is an approach to enhance practices for cloud security platforms to mitigate risks and infrastructure for cloud adoption in developing countries in this paper.

Condition-based monitoring (CBM) has been a vital engineering method to assess high voltage (HV) equipment and power cables conditions or health levels. One of the effective CBM methods is partial discharge (PD) measurement or detection. PD event is the phenomenon that always associated with insulation healthiness. PD has been measured and evaluated in this paper to discriminate PD signals from a good signal. A mixed-signal being fed at an AI technique with statistical modified input data to do fast classification (less than five seconds) with nearly zero error. In this paper, an unsupervised neural network is applied for PD classification. The methods combine the self-organizing maps (SOMs) and feature statistical transformation. By the combination of these methods, the ‘range’ normalization method produced the best classification outcomes. This development decided that PD information was effectively correlated and grouped by means of MATLAB’s SOM Toolbox and transformation device to discriminate the normal signal from the PD signal.

The computation of the optimal path is one of the critical problems in graph theory. It has been utilized in various practical ranges of real world applications including image processing, file carving and classification problem. Numerous techniques have been proposed in finding optimal path solutions including using ant colony optimization (ACO). This is a nature-inspired metaheuristic algorithm, which is inspired by the foraging behavior of ants in nature. Thus, this paper study the improvement made by many researchers on ACO in finding optimal path solution. Finally, this paper also identifies the recent trends and explores potential future research directions in file carving.

This paper studies the secondary network relying relay selection to transmit signal from the secondary source (base station) to two destinations. Especially, two destinations are required non-orthogonal multiple access (NOMA) scheme and it benefits to implementation of the Internet of Things (IoT) systems. However, eavesdropper over-hears signal related link from selected relay to destination. This paper measure secure performance via metric, namely secure outage probability (SOP). In particular, signal to noise ratio (SNR) criterion is used to evalute SOP to provide reliable transmission to the terminal node. Main results indicates that the considered scheme provides performance gap among two signals at destination. The exactness of derived expressions is confirmed via numerical simulation.

VANET is a branch of MANETS, where each vehicle is a node, and a wireless router will run. The vehicles are similar to each other will interact with a wide range of nodes or vehicles and establish a network. VANETs provide us with the infrastructure to build new solutions for improving safety and comfort for drivers and passengers. There are several routing protocols proposed and evaluated for improving VANET's performance. The simulator is preferred over external experience because it is easy, simple, and inexpensive. In this paper, we choose AODV protocol, DSDV protocol, and DSR protocol with five different nodes density. For each protocol, as regards specific parameters like (throughput, packet delivery ratio, and end- to- end delay). On simulators that allow users to build real-time navigation models of simulations using VANET. Tools (SUMO, MOVE, and NS-2) were used for this paper, then graphs were plotted for evaluation using Trace-graph. The results showed the DSR is much higher than AODV and DSDV, In terms of throughput. While DSDV is the best choice because of the low average end to end delay. From the above, we conclude that each strategy has its own negative and positive aspects that make it ideally suited to a particular scenario than other scenarios.

Smart grids are the cutting-edge electric power systems that make use of the latest digital communication technologies to supply end-user electricity, but with more effective control and can completely fill end user supply and demand. Advanced metering infrastructure (AMI), the backbone of smart grids, can be used to provide a range of power applications and services based on AMI data. The increased deployment of smart meters and AMI have attracted attackers to exploit smart grid vulnerabilities and try to take advantage of the AMI and smart meter’s weakness. One of the possible major attacks in the AMI environment is false data injection attack (FDIA). FDIA will try to manipulate the user’s electric consumption by falsified the data supplied by the smart meter value in a smart grid system using additive and deductive attack methods to cause loss to both customers and utility providers. This paper will explore two possible attacks, the additive and deductive data falsification attack and illustrate the taxonomy of attack behaviors that results in additive and deductive attacks. This paper contributes to real smart meter datasets in order to come up with a financial impact to both energy provider and end-user.

This paper presents a methodology for enhancement of panorama images environment by calculating high dynamic range. Panorama is constructing by merge of several photographs that are capturing by traditional cameras at different exposure times. Traditional cameras usually have much lower dynamic range compared to the high dynamic range in the real panorama environment, where the images are captured with traditional cameras will have regions that are too bright or too dark. A more details will be visible in bright regions with a lower exposure time and more details will be visible in dark regions with a higher exposure time. Since the details in both bright and dark regions cannot preserve in the images that are creating using traditional cameras, the proposed system have to calculate one using the images that traditional camera can actually produce. The proposed systems start by get LDR panorama image from multiple LDR images using SIFT features technology and then convert this LDR panorama image to the HDR panorama image using inverted local patterns. The results in this paper explained that the HDR panorama images that resulting from the proposed method is more realistic image and appears as it is a real panorama environment.

Researchers were able to correlate porn addiction based on electroencephalogram (EEG) signal analysis to the psychological instruments’ findings. In this paper we attempt to correlate the porn addiction to various cases of learning disorders through analyzing EEG signals. Since porn addiction involved the brainwave power at the frontal of the brain, which reflects the executive functions, this may have correlation to learning disorder. Only three types of learning disorder will be of interest in our study involving dyslexic, attention deficit and hyperactivity disorder (ADHD) and autistic children because they involved reduced intellectual ability observed from the lack of listening, speaking, reading, writing, reasoning, or mathematical proficiencies. Children with such disorder when expose to the internet unfiltered porn contents may have minimal understanding of the negative effects of the contents. Such unmonitored exposure to pornographic contents may result to porn addiction because it may trigger excitement and induced pleasure. Experimental results show strong correlation of learning disorders to porn addiction, which can be worthwhile for further analysis. In addition, this paper also indicates that analyzing brainwave patterns could provide a better insight into predicting and detecting children with learning disorders and addiction with direct analysis of the brain wave patterns.

Particle swarm optimization (PSO) is an optimization algorithm that is simple and reliable to complete optimization. The balance between exploration and exploitation of PSO searching characteristics is maintained by inertia weight. Since this parameter has been introduced, there have been several different strategies to determine the inertia weight during a train of the run. This paper describes the method of adjusting the inertia weights using fuzzy signatures called signature PSO. Some parameters were used as a fuzzy signature variable to represent the particle situation in a run. The implementation to solve the tuning problem of linear quadratic regulator (LQR) control parameters is also presented in this paper. Another weight adjustment strategy is also used as a comparison in performance evaluation using an integral time absolute error (ITAE). Experimental results show that signature PSO was able to give a good approximation to the optimum control parameters of LQR in this case.

The implementation of steganography in text domain is one the crutial issue that can hide an essential message to avoid the intruder. It is caused every personal information mostly in medium of text, and the steganography itself is expectedly as the solution to protect the information that is able to hide the hidden message that is unrecognized by human or machine vision. This paper concerns about one of the categories in steganography on medium of text called text steganography that specifically focus on feature-based method. This paper reviews some of previous research effort in last decade to discover the performance of technique in the development the feature-based on text steganography method. Then, ths paper also concern to discover some related performance that influences the technique and several issues in the development the feature-based on text steganography method.

The clustering network is a solution to improve data-rate transmission in small-cells. In this case, clustering small-cells (CSCs) adopt a multiple antennas concept. The multiple antennas are used to maximize the downlink data-rate transmission at the users, but it requires precoding techniques to minimize interference among CSC users. This paper proposes a block diagonalization (BD) as a precoding technique for minimizing interference among CSC users. The performance of the BD precoding implemented on the clustering network under various numbers of small-cells. The CSC also implements a water-filling power allocation (PA-CoopWF) to distribute the available transmission power along with the CSCs antennas. To show the performance, our paper simulates two types of precoding techniques; those are the proposed BD and minimum mean square error (MMSE) in CSCs. Based on the receiver user parts under the overlapping coordination of CSCs, our method based on the BD precoding achieves considerably higher data-rate transmission compared to the MMSE precoding, especially on larger clusters. The simulation also shows that by implementing CSC with the BD in short-range distances and higher numbers of antennas, it promotes better data-rate performances compared to the MMSE precoding by 2.75 times at distance 100m and 67% at 50 antennas.

This paper reports one best-practice in assessing the public speaking performance of advanced students at an Indonesian public university. The study involves an English course for an advanced class which was primarily related to public speaking skills. Considering that speaking is a productive skill that should be assessed through authentic assessment principles, the lecturers decided to assign the students with a mini-seminar project as part of their final examination. This project required the students to conduct a real-life contextualised seminar in which the organisers, speakers, and audience are composed of the students themselves. This paper discusses the rationale behind the planning and implementation of this successful project which involved a synthesis of assessment of, for, and as learning and critically evaluates the procedures of the assessment, the rubric developed therein, and the challenges experienced by the lecturers within the classroom. After the implementation, it can be concluded that this mini-seminar project as a doable alternative authentic assessment model that is applcable in a speaking class which focuses on the development of students’ public speaking skills. This mini-seminar project is recommended not only because it can be used as an alternative assessment model, but also it encourages students to work together in teams, and encourage them to work creatively, create something new in order to perform better.

A well-prepared abstract enables the reader to identify the basic content. This paper presents the solution of voltage fluctuations in urgent situations by providing voltage and reactive support from a distribution static synchronous compensator (DSTATCOM) in the grid. Also, it analyses the influences of DSTATCOM as a voltage controller and compares the system performance with and without DSTATCOM. The DSTATCOM is used in the study to maintain voltage in the microgrid (MG) to be around the rated value after Microgrid disturbance. A successful simulink model of the photovoltaic (PV) system and the proposed DSTATCOM are illustrated to work together as the Microgrid. Microgrids could provide unique resilience and reliability when the environment encountered with less water, higher temperatures, more frequent and harsh wildfires, and severe weather events. The proposed DSTATCOM was installed in different locations in the MG and the best location was chosen to achieve the goal of improved power quality and efficiency. In this paper, two scenarios are discussed with and without DSTATCOM. The simulation results show the difference between the MG with and without DSTATCOM and how the DSTATCOM can amplify power quality in the Microgrid. The proposed DSTATCOM has the capability to improve dampen power oscillations during transit events.

In this paper, three antennas with different designs, which are square, circle and edgy patch (flower) were simulated and fabricated. The experiment was carried out to study the effects of bending on these three different shapes of antennas. Two bending conditions which are H-plane and E-plane were used. The antenna was designed to resonate at 1.575 GHz for GPS application and to be incorporated with human arm. Thus, the bending angle was specified to be 135o which is about the size of a typical human arm. As the paper focusing on the bending effects only, the characteristic of the bending structure was set to be equal to the air, with Ɛr=1. The antenna characteristics such as gain, resonant frequency and radiation pattern were analyzed for these three shapes of antennas. As a result, the shape of the radiating patch has significant impact on the antenna performance under bending conditions. Based on the comparison of E-plane and H-plane data, the edgy shape was found to be more affected in term of gain performance as compared to other shapes. Bending on E-plane has shown severe degradation in antenna gain performance, whereby at H-plane, significant improvement in gain was observed.

Users and food marketers alike would share images of food in the hopes that viewers would be able to capture the emotion/sensation presented by the food. This is a form of food digitization, shifting away from the mechanical aspect of taste stimulation to the use of digitized visuals. This paper aim to evaluate the performance of taste perception made by users by just looking at food image. Using food images as a medium of taste stimulation, this paper reports international users’ perception of the taste of Malaysian local food through an online channel. By applying SPSS analysis, the author studied the patterns of similarity and dissimilarity of users’ perception of food images that have been compiled using an online survey. Interestingly observed, the results of the analysis enable the author to assess how close user perceptions were akin to the real taste. The author found that most user perceptions are closely matched with the food’s real taste which allows the author to conclude that visual media is possible in stimulating food taste experience. It is good news for food marketers and food tourism. Nevertheless, more samples of food images are required to further attest these findings.

The paper presents the electrochemical performance of supercapacitor with glass wool separator under organic electrolyte of tetraethylammonium tetrafluoroborate (TEABF4). The performance was evaluated using symmetrical two-electrode system and compared to an identical supercapacitor with commercially available cellulose paper separator under 1 M TEABF4. The application of glass wool separator reduces the bulk resistance of supercapacitor by 19.6%, promotes more efficient ions transfer across active surface of electrode and significantly improves specific capacitance by 19.1% compared to cellulose paper. The application of higher concentration TEABF4 (1.5 M) even improves the overall performance of glass wool-based supercapacitor by 32.2% reduction of bulk resistance and 61.9% increment in specific capacitance compared to 1 M TEABF4. In addition, the energy and power densities are significantly improved by 64% and 165%, respectively for the one with 1.5 M TEABF4. In general, the low-cost material glass wool material has great potential to replace commercially available cellulose paper as separator in developing much better supercapacitor.

In a world where the algorithm can control the lives of society, it is not surprising that specific complications in determining the fairness in the algorithmic decision will arise at some point. Machine learning has been the de facto tool to forecast a problem that humans cannot reliably predict without injecting some amount of subjectivity in it (i.e., eliminating the “irrational” nature of humans). In this paper, we proposed a framework for defining a fair algorithm metric by compiling information and propositions from various papers into a single summarized list of fairness requirements (guideline alike). The researcher can then adopt it as a foundation or reference to aid them in developing their interpretation of algorithmic fairness. Therefore, future work for this domain would have a more straightforward development process. We also found while structuring this framework that to develop a concept of fairness that everyone can accept, it would require collaboration with other domain expertise (e.g., social science, law, etc.) to avoid any misinformation or naivety that might occur from that particular subject. That is because this field of algorithmic fairness is far broader than one would think initially; various problems from the multiple points of view could come by unnoticed to the novice’s eye. In the real world, using active discriminator attributes such as religion, race, nation, tribe, religion, and gender become the problems, but in the algorithm, it becomes the fairness reason.