The integration of cloud computing and Wireless Sensor Networks (WSNs) to create Sensorcloud helps in extending the data processing capability and storage capability of WSNs. Knowing how weak WSNs are with regards to communication ability, how to collect and upload sensory data to the cloud in limited time has become an issue in Sensor-cloud. In the last decade, with increasing interest by researchers in the domain, a considerable amount of research works have been conducted and published in the research domain. The main objective of this study is to systematically review the current research on data collection in Sensor-cloud. Hence, the study also aims at identifying, categorizing, and synthesizing important studies in the field of study. Accordingly, an evidence-based methodology is utilized in this study. By doing so, 43 relevant studies were identified and retrieved to answer the formulated research questions. The systematic methodology offers a methodical and rigorous study selection and evaluation process that is repeatable and precise. The result shows that research on data collection in Sensor-cloud is relatively consistent with stable output in the last five years. Ten proposal contributions were identified with System, Framework, and Algorithm being the most used by the selected studies. In conclusion, key research challenges and future research directions were identified and discussed for researchers to propose effective solutions to the existing challenges. Although research on data collection in Sensor-cloud is gaining some traction in recent years, the works in the domain are not sufficient and concrete proposals are needed to improve data collection.

This paper uses distributional analysis to disentangle the effects of fitness landscape and stochasticity in Wagner's Gene Regulatory Network Based evolutionary scenario. Further advantages of the analysis include the ability to accurately identify global optima and potential extensions to approximate the distributional fitness more accurately than stochastic sampling at lower computational cost. This is an inital draft, and will be updated as further content is written.

It is known that the Central Limit Theorem (CLT) is not always the most appropriate tool for deriving closed-form expressions. We evaluate a Single-Input Single-Output (SISO) system performance in which the Large Intelligent Surface (LIS) acts as a scatterer. The direct link between the transmitting and receiving devices is negligible. Quantization phase errors are considered since the high precision configuration of the reflection phases is not always feasible. We derive exact closed-form expressions for the spectral efficiencies, outage probabilities, and average symbol error rate (SER) of different modulations. We assume a more comprehensive scenario in which $b$ bits are dedicated to the LIS elements’ phase adjustment. From Monte Carlo simulations, we prove the excellent accuracy of our approach and investigate the behavior of power scaling law and power required to reach a specific capacity, depending on the number of reflecting elements. We show that the LIS with approximately fifty elements and four dedicated bits for phase quantization outperforms the conventional system performance without LIS.

This paper deals with the optimal scheduling of public transport e-buses by considering charging rate, discharging rate of batteries, rest time between consecutive trips, and distance between charging stations & estimation of the minimum required charging time between consecutive trips considering given constraints of the journey, also explains how does it help in the optimized scheduling of electric buses.

The time-reversal technique has been developed to solve inverse problems and to reconstruct sources. However, much work on the topic so far has not considered the determination of source excitation times, which is a crucial step towards practical applications of the time-reversal method. In this paper, we introduce the concept of kurtosis into the time-reversal process to reconstruct not only spatial locations but also excitation times of the sources. Our method paves the way for extension and applications of the time-reversal method to real-world problems such as identifying radiation sources of different times in various environments. Numerical examples are presented to demonstrate the effectiveness of the proposed concept and method.

The Wave-Particle Duality is a basic property of microscopic particles. As a basic concept of quantum mechanics, the wave-particle duality theory from elementary particles to big molecules had been verified by lots of experiments. Different from electromagnetic wave, the matter wave’s propagation is not only fast but also adjustable. According to the special relativity theory, the group velocity with which the overall envelope shape of the wave, namely the related particle’s propagation and information convey speed is changeable with its energy and related wavelength, among which only the energy exceeds over the minimum value, the propagation can be starting and the velocity is not allowed to surpass the maximum value i.e. the light speed in vacuum. Take electron as an example, if the free electron beam gains energy higher than around 8.187×10ˉᴵ⁴J and the related wavelength is shorter than around 5.316×10ˉᴲnm, the matter wave can start to propagate.

RWPRRDAS is a wireless Photoplethysmography (PPG) and respiration measurement system which allows user to monitor the measured heart rate, blood pressure wave, respiration cycle and any other kind abnormality related to heart and its corresponding system. It uses a battery powered transducer to collect biomedical data and an android application to visualize the data in a proper manner and in a graphical way. This is a non-invasive type measurement procedure. This is a truly a mobile system which is easy to carry and operate. Since this device is not connected to power lines of AC applications and totally battery operated so there is no possibility of any electrical hazard in any case. Nowadays, many biomedical transducers come with a custom-made processor with an inbuild display, which offers very low processing power to visualize the biomedical data properly and with an ease of access to almost every age. RWPRRDAS collects the data using transducer and uses the processing power of a modern android mobile phone so that visualization and determination of heart disease and abnormality in respiration mechanisms become easy and reliable. And the measurement of data through a wireless media provides hassle-free & user friendly usable limit to almost every person.

The purpose of our study was to evaluate Microsoft Cognitive Service to detect COVID19 induced pneumonia and ordinary viral or bacterial infection in Lung using X-Ray and CT scan images. We have used Datasets from a recognized and trusted source to build our model. The primary objective is a Smartphone based on device real-time inference system. In this case, the model would run by a mobile device’s System on Chip (SoC) and will not require an internet connection for inference with zero latency. This system would be particularly suitable for rural areas of developing countries where internet connection is poor or not available. The secondary solution would be a web portal running the inference through REST API from Custom Vision. Now, given the nature of The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), which causes respiratory disease as a novel one, the majority of the radiologists are not acquainted enough to detect the virus-related changes from the X-Ray. Moreover, the morphology of COVID-19 and common Pneumonia are hard to differentiate from X-Ray alone without the patient’s symptoms by a radiologist. Here, AI comes into play with the role of an expert assistant. It is much faster and efficient to train a machine over thousands of labeled training data to observe and detect subtle differences between various X-Ray images to train its Artificial Neural Network and classify them quickly which is otherwise not possible by a human eye. A Radiologist can use the app to primarily identify the X-Ray in question and combine it with his/her medical expertise along with the patient’s case history before in conjunction with tests like RT PCR/Antibody.

The present paper proposes a novel device defined as an intelligent electronic fuse (iFuse) meant to be connected in series with any current-bidirectional voltage-unidirectional active switch present in a given converter. The iFuse duty is to isolate its series- associated switch from the rest of the converter circuit immediately after detecting that said switch has failed in short circuit. Nonetheless, it maintains the reverse (free- wheeling) current path originally offered by the failed switch. The failure detection is performed when the failed switch causes a shoot-through event. Therefore, the iFuse is designed to be able to block the elevated current occurring in such event. The iFuse allows increasing the fault-tolerant capability and the reliability of power converters where such qualities are hindered by switch short-circuit failures, as in converters featuring parallelized switches, neutral-point-clamped multilevel topologies, or redundant legs. The feasibility of the iFuse device is verified through experimental tests, proving that the device is able to detect the failure of its associated switch and isolate it from the rest of the converter circuit in 6 μs, while stopping short-circuit currents of up to 1 kA without incurring in harmful di/dt values.

Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.

Estimation of the clean speech short-time magnitude spectrum (MS) is key for speech enhancement and separation. Moreover, an automatic speech recognition (ASR) system that employs a front-end relies on clean speech MS estimation to remain robust. Training targets for deep learning approaches to clean speech MS estimation fall into three categories: computational auditory scene analysis (CASA), MS, and minimum mean-square error (MMSE) estimator training targets. The choice of training target can have a significant impact on speech enhancement/separation and robust ASR performance. Motivated by this, we find which training target produces enhanced/separated speech at the highest quality and intelligibility, and which is best for an ASR front-end. Three different deep neural network (DNN) types and two datasets that include real-world non-stationary and coloured noise sources at multiple SNR levels were used for evaluation. Ten objective measures were employed, including the word error rate (WER) of the Deep Speech ASR system. We find that training targets that estimate the a priori signal-to-noise ratio (SNR) for MMSE estimators produce the highest objective quality scores. Moreover, we find that the gain of MMSE estimators and the ideal amplitude mask (IAM) produce the highest objective intelligibility scores and are most suitable for an ASR front-end.

The aim of this paper is to design a modular, fault tolerant multi transmitter(Tx) -multi receiver(Rx) parallel connected common DC bus inductive power transfer(IPT) system to replace slip rings in wind turbines or brushless exciters. In parallel connected common DC bus systems, current unbalance is a major issue which results in thermal stresses and over current or voltages. In this paper, two different new current balancing methods are proposed: Cross-coupled Rx modules and intentional miss-tuning of Rx side resonant frequency. These methods are investigated both analytically and experimentally for a single Tx and two Rx system for a 500 W prototype. The proposed methods are tested independently, and then the combined current balancing method is also investigated. For the same missalignment case cross-coupled/de-tuned has a 55.8% current balancing improvement compared to de-coupled/fullytuned topology.

This short note addresses the question of why do all the reported incidents concerning electric shock drowning (ESD) appear to have happened in the United States. It was accepted for publication with title Discussion of “Examining the Risk of Electric Shock Drowning (ESD) As a Function of Water Conductivity” and available online in Transactions on Industry Applications website.

The PDF version of the manuscript is added. The abstract/manuscript is accepted to ICOPS 2020. The full paper version is added after the conference.

Slope deformation prediction is crucial for early warning of slope failure, which can prevent property damage and save human life. Existing predictive models focus on predicting the displacement of single monitoring points based on time series data, without considering spatial correlations among monitoring points, which makes it difficult to reveal the displacement changes in the entire monitoring system, and ignores the potential threats from nonselected points. To address the above problem, this paper presents a novel deep learning method for predicting the slope deformation, by considering the spatial correlations between all points in the entire displacement monitoring system. The essential idea behind the proposed method is to predict the slope deformation based on the global information (i.e., the correlated displacements of all points in the entire monitoring system), rather than based on the local information (i.e., the displacements of a specified single point in the monitoring system). In the proposed method, (1) a weighted adjacency matrix is built to interpret the spatial correlations between all points, (2) a feature matrix is assembled to store the time-series displacements of all points, and (3) one of the state-of-the-art deep learning models, i.e., T-GCN, is developed to process the above graph-structured data consisting of two matrices. The effectiveness of the proposed method is verified by performing predictions based on a real dataset. The proposed method can be applied to predict time-dependency information in other similar geohazard scenarios, based on time-series data collected from multiple monitoring points.

The Robotic Operating System (ROS) is the de-facto standard for the development of modular robotic systems. However, ROS is notorious for the absence of security mechanisms, only partially covered by recent advancements. Indeed, an attacker can easily break into ROS-enabled systems and hijacks arbitrary messages. We propose an integrated solution, ROS-Immunity, with small overhead that allows ROS users to harden their systems against attackers. The solution consists of three components: robustness assessment, automatic rule generation, and distributed defense with a firewall. ROSImmunity is also able to detect on-going attacks that exploit new vulnerabilities in ROS systems. We evaluated our solution against four use-cases: a self-driving car, a swarm robotic system, a centralized assembly line, and a real-world decentralized one. ROS-Immunity was found to have minimal overhead, with only an additional 7-18% extra system power per robot required to operate it. Furthermore, ROS-Immunity was able to prevent a wide variety of ROS system attacks with a worst-case false positive rate of only 17% and a typical false positive rate of 8%. Finally, ROS-Immunity was found to be able to react to and stop attackers after at most 2.4 seconds, when confronted with unknown vulnerabilities.

This paper presents a new power converter topology generated by the integration of the asymmetrical ZVS-PWM dcdc converter with a switched-capacitor ladder-type commutation cell. Circuit operation and theoretical analysis with emphasis on the soft-commutation process are included in the paper. The main advantage of the proposed converter with respect to the conventional asymmetrical half-bridge dc-dc converter is the reduction of the voltage stress across the power switches to the half of the input dc bus voltage, enabling the utilization of lower voltage rating components. Experiments conducted on a laboratory prototype with 1.4 kW power-rating, 800 V input voltage, 48 V output voltage and 100 kHz switching frequency are included, to verify the theoretical analysis and the design methodology. The maximum efficiency of the experimental nonoptimized prototype was 93.6%. Index Terms - Asymmetrical dc-dc converter, pulse-widthmodulation, switched-capacitor, zero voltage switching.

In this COVID-19 pandemic situation as we know Offices are partially opened and Schools and Colleges are about to open. So we have to face the situation with the possible measures to reduce the spreading of the COVID19. We have to move on by implementing strong protective measures while trying to keep the economy going. According to WHO Some of most common ways to protect ourselves from COVID19 are as follows: ● Take care in your workplaces. ● Take care of physical distancing. ● Take care to spread the word not the virus. ● What to consider for health before opening the workplace ● Take care of sanitization ● Take care of yourself. In these most common and preferable ways to protect ourselves is Proper Screening and if something went wrong in this then proper precautions. So while the time of screening the one who is checking the temperature of everyone can be more in danger, and this can also lead to more spread of virus. Because if while screening someone who is Positive, the one who is screening the positive patient can also get affected and after that he can affect more people by just screening them. So at this time we need to find the alternative for screening everyone contactlessly. For this we came up with a solution that is Contactless Thermal Detection which is made with all the safety measures for the Organization or any public places where we screening for temperature is needed for Covid. With this Employees, Workers, Students, Teachers can record their temperature while entering their respective workplaces contactlessly.