The upcoming high-speed wireless communication systems will be hosted by millimeter and sub-millimeter-wave frequency bands. At these frequencies, electromagnetic waves suffer from severe propagation losses and non-line-of-sight (NLOS) scenarios. A new wireless communication paradigm has arrived to resolve this situation through the use of reconfigurable intelligent surfaces (RIS). These metadevices are designed to reconfigure the wireless environment in a smart way. Traditional RIS designs based on the implementation of 2-D configurations have been considered up to now. However, 3-D structures enable an extra degree of freedom in the design that can be taken as an advantage for the development of improved RIS structures with advanced functionalities. This article proposes the implementation of a novel electronically-reconfigurable RIS based on the use of 3-D graphene meta-atoms. The reconfigurability lies on the graphene conductivity, easily tunable with a biasing voltage. Different conductivity values vary the meta-atom electromagnetic response, modifying the RIS functionality. A multi-objective optimization framework determines the optimal phase state of each meta-atom to accomplish the desired RIS performance. The operation of the RIS as an efficient beam steerer/splitter, absorber and polarization selector is validated with full-wave results.

[Submitted in IEEE J-BHI] Retinopathy refers to any damage in the retina that causes visual impairments or even blindness. Identification of retinal lesions plays a vital role in accurately grading retinopathy and for its effective treatment. Optical coherence tomography (OCT) imaging is the most popular non-invasive technique used for the retinal examination due to its ability to screen abnormalities in early stages. Many researchers have presented studies on OCT based retinal image analysis over the past. However, to our best knowledge, there is no framework yet available which can extract retinal lesions from multi-vendor OCT scans and utilize them for the intuitive grading of the human retina. To cater this lack, we propose a deep retinal analysis and grading framework (RAG-FW). RAG-FW is a hybrid convolutional framework which extracts retinal lesions such as intra-retinal fluid, sub-retinal fluid, hard exudates, drusen and chorioretinal abnormalities (including fibrotic scars and choroidal neovascular membranes) from multi-vendor OCT scans. Furthermore, it utilizes them for the lesion-influenced grading of retinopathy as per the clinical standards. RAG-FW has been trained using 113,261 retinal OCT scans from which 112,261 scans were used for training and 1,000 scans were used for the validation purposes. Furthermore, it has been rigorously tested on 43,613 scans from five highly complex publicly available datasets where it achieved the mean intersection-over-union score of 0.8055 for extracting the retinal lesions and the F1 score of 99.52% for correctly classifying the retinopathy cases. The source code of RAG-FW is available at http://biomisa.org/index.php/downloads/.

The MEMS magnetometer determines the orientation for the MEMS inertial system. Because of the large noise of the MEMS magnetometer and the interference of soft and hard iron outside, the measurement error of the MEMS magnetometer is large. To reduce the effects of the random noises, the MEMS magnetometer arrays are designed in this paper. In our design, thirty-two MEMS magnetometers are welding on a printed circuit board (PCB), which area is 5×5 cm2. The forty general-purpose input-output (GPIO) ports, which are thirty-two data ports and eight clock ports, are used to collect the data of MEMS magnetometers. Then, averaging the thirty-two measurements of the MEMS magnetometers, the random noises of the measurements of the MEMS magnetometers can be reduced. Based on the averaging operation for the collected sensors’ data, a unified measurement model for the MEMS magnetometer arrays is constructed. Using the unified measurement model, an adaptive Kalman filter is developed to estimate the unknown parameters. To validate the performance of the MEMS magnetometer arrays, the simulation and experimental tests are designed. The test results show that, comparing with the single MEMS magnetometer, the random noises of the MEMS magnetometer arrays are reduced effectively.

With the development of robot technology and the arrival of industry 4.0 era, society pays more attention to col- laboration and interaction between human and robots. However, safety is still main concern in the development of human-robot collaboration. In this paper, a novel real-time collision avoidance approach for mobile manipulator is proposed by considering the motion status of the human, which includes the relative minimum distance and velocity (both magnitude and direction) between the robot and the human. The distance and velocity of the human hand are first estimated online using a vision sensor, and then defined as danger factors in the potential function of the potential field. The novel potential function proposed in this paper considers not only the safety problem, but also the efficient problem, i.e., the manipulator can make smart control decision to avoid the collision according to the relative velocity in case of the cross over. To overcome the local minimum problem and choose a best motion direction, we propose a motion sampling mechanism for motion planning. For each sample, the robot calculates the potential function to evaluate the safety and efficiency, and chooses a direction which is best for avoidance. We finally demonstrate our idea on a real mobile manipulator platform in a simulated co-worker environment.

As home energy management systems (HEMSs) are implemented in homes as ways of reducing customer costs and providing demand response (DR) to the electric utility, homeowner’s privacy can be compromised. As part of the HEMS framework, homeowners are required to send load forecasts to the distribution system operator (DSO) for power balancing purposes. Submitting forecasts allows a platform for attackers to gain knowledge on user patterns based on the load information provided. The attacker could, for example, enter the home to steal valuable possessions when the homeowner is away. In this paper, we propose a framework using a smart contract within a private blockchain to keep customer information private when communicating with the DSO. The results show the HEMS users’ privacy is maintained, while the benefits of data sharing are obtained. Blockchain and its associated smart contracts may be a viable solution to security concerns in DR applications where load forecasts are sent to a DSO.

The paper presents a mixed method approach for the analysis of power systems in augmented uncertainty scenarios, related to the increasing penetration of variable renewable energy and country specific constraints to be found in fragile states. In the formulated methodology, both deterministic and probabilistic load flow have their own specific, necessary and interactive role. To establish the soundness of the methodology, the analysis is conducted for a real case study, along with wind speed measurements (eleven-month duration), visual model validations, statistical and load flow analysis. The probabilistic simulations are based on Monte Carlo (MC) analysis. Synthetic data are created from probabilistic distribution functions (PDF) calculated on original measured samples, operational constraints, and load uncertainties. These data are processed by load flow simulations and the results consolidated and analyzed. To facilitate the implementation of the proposed methods, scripts developed in Python programming language have been created for the analysis of statistical data, sample generation, post processing, data visualization and the interaction with conventional software for load flow analysis. The scripts are made public and available for download. The proposed methodology of analysis, conceptualized for developing and fragile states, may also be used as a basis for all power system planning where the number of uncertainties is no longer negligible, and the use of deterministic methods alone would provide inadequate results.

The home automation system is indeed a system that offers a mobile application to monitor it through the smartphone or tablet. It can control home appliances like light, fans, air conditions, and smart security locks, etc. Bluetooth or Wi-Fi is used for remote control of things. Smart home is essentially how it looks: automating with such an easy touch of a button or a voice command the ability to manage things across the home. Several operations were both simple and extremely low-cost, such as setting up a light to turn on and off at the time of your moment. Others need much more money and resources, including advanced spy cameras. Some people thought that technology is taking a really huge part of our lives. It does! we’re living in a modern generation where smart and intelligent systems are necessary to be there wherever we are to make our lives easier and much better, for example, we can do many things faster, better, and more accurate.

Solar-powered photovoltaic (PV) system encounters a significant amount of losses due to different derating factors of PV modules throughout its lifespan. Thus, proper investigation is much needed to grasp the technical and economic impact of derating factors on the solar PV system, specially the one which is connected to the utility grid. This study performs a comprehensive discussion on various PV loss parameters followed by a techno-economic-environmental assessment of combined derating factor on an grid-connected and optimally tilted PV system located at Hatiya, Bangladesh using HOMER (Hybrid Optimization Model for Multiple Energy Resources) software. Some criteria linked with derating factor such as PV degradation and ambient temperature are further explored to analyze their impact on the aforementioned power system. Simulation results show that the system provides the best technical performance concerning annual PV production, power trade with grid, and renewable fraction with less emissions at a higher value of derating factor since it represents the lower impact of loss parameters. Similarly, financial performance in terms of net present cost (NPC), levelized cost of energy (LCOE), and grid power exchange cost is found lower when derating factor value is higher.

This paper carries out a comprehensive review on the existing radial distribution test systems available for the power system distribution research area. The information available from this review can be used to establish a database of cases suitable for distribution operation and planning studies. The importance of distribution system, the structure of the system in the grid and the various configurations of the distribution network are provided.

This is a short article, focusing on promoting more study on SEIR modeling by leveraging rich data and machine learning. We believe that this is extremely critical as many regions at the country or state/provincial levels have been struggling with their public health intervention policies on fighting the COVID-19 pandemic. Some recent published papers on mitigation measures show promising SEIR modeling results, which could shred the light for other policymakers at different community levels. We present our perspective on this research direction. Hopefully, we can stimulate more studies and help the world win this “war” against the invisible enemy “coronavirus” sooner rather than later.

This paper presents a quantum-assisted index modulation for next-generation IoT wireless networks. The NP-hard index selection problem is first formulated by a quadratic unconstrained binary optimization (QUBO) problem consisting of constraints of feasible solutions. To minimize the number of qubits required for a quantum circuit, this formulation is then simplified by a dictionary-based approach that partially exploits a classical computer. For both formulations, the numbers of required qubits and non-zero elements in QUBO matrices are analyzed algebraically, and found to be in close agreement with the actual measurement. It is observed that the Grover adaptive search can provide the quantum speedup for the index selection problem. This promising result implies that the on-off structure of index modulation is suitable for quantum computation, and future fault-tolerant quantum computers may be useful for obtaining high-performance index activation patterns. (c) IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Naoki Ishikawa, “Quantum speedup for index modulation,” IEEE Access, vol. 9, pp. 111114-111124, Aug. 2021. DOI: 10.1109/ACCESS.2021.3103207

We present an analysis of graphene loaded transmission line switches. Namely, we propose equivalent circuit models for graphene loaded coplanar waveguides and striplines and examine the switching performance under certain design parameters. As such, the models account for the distributed effects of electrically-large shunt switches in coplanar waveguides and we use the Babinet’s principle to derive the respective models for the coplanar stripline transmission lines. Using these models, we identify the optimum design of graphene switches based on transmission line characteristic impedance, scaling factor, graphene shape, and topology (series or shunt). We vary these parameters and obtain the insertion loss and ON/OFF ratio. Τhe extracted results can act as the design roadmap toward an optimum switch topology and emphasize the limitations with respect to fabrication challenges, parasitic effects, and radiation losses. In our models, we use measured graphene values (sheet impedance) instead of theoretical equations, to obtain the actual switching performance. Finally, the proposed equivalent models are crucial for this in-depth study; since, we simulated more than 2,000,000 configurations, a computationally challenging task with the use of full-wave solvers

In this paper, a large-area flexible tactile sensor for multi-touch and force detection based on EIT technology was developed. A novel design of a sensor material made of a porous elastic polymer and ionic liquid was proposed. The proposed conductive flexible materials combining elastic porous structures and conductive liquids provide continuous, linear changes in impedance with respect to touch forces. A deep learning scheme PSPNet based on MobileNet was adopted to postprocess the originally reconstructed images to improve the performance of tactile perception. By using this data-driven method, we can improve the spatial resolution of the tactile sensor to achieve a single-point position detection error of 7.5±4.5 mm without using internal electrodes.

This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind-source separation methods, and helps enable in the wild EEG recordings to be performed.

Abstract: Stock forecasting is challenging because of stock volatility and dependability on external factors, such as economic, social, and political factors. This motivates investors to seek tools to identify stock trends to reap profits. In this research, we compared several heterogeneous ensembles for financial forecasting, including averaging, weighted, stacking, and blending ensembles. In addition, we used a random forest regressor as the baseline. Regression was used to predict the next day’s closing stock price. We used classification to label closing stock value as HIGH or LOW by comparing with the opening stock value of a particular company. We used Long Short Term Memory (LSTM) models, Linear Regression, and Support Vector Machines (SVM) as individual models. Further, we analyzed 10 years of historical data of the most active 20 companies of the NASDAQ stock exchange for implementing ensemble models. In conclusion, experimental results depict blending ensembles perform the best out of compared ensembles in financial forecasting. Further, they reveal SVM is under-performing, LSTM outputs are satisfactory, while linear regression produced promising results. Data: Data for this research was gathered from online available sources from the NASDAQ American stock exchange. We gathered data for most active 20 companies and 10 years of historical data from 21st September 2019 backwards. We used 40044 data points in total.

Poynting theorem plays a very important role in analyzing electromagnetic phenomena. The electromagnetic power flux density is usually expressed with the Poynting vector. However, since Poynting theorem basically focuses on the power balance in a system, it is not so convenient in some situations to use it for evaluating the electromagnetic energies. The energy balance issue for time varying fields is revisited in this paper, and a set of energy balance equations are introduced, and a modified method for evaluating power flux is proposed.

Remote ice detection has emerged as an application of Radio Frequency (RF) sensors. While antenna-based “RFID” sensing can detect various measurands, antenna-based sensors are not currently designed based on a systematic methodology, and in most cases may have a low sensitivity requiring specialist hardware or broadband interrogation signals, incompatible with spectrum regulations. Here, we develop a systematic methodology for designing an antenna-based sensor, applicable to measurands inducing a dielectric change in the near-field of the antenna. The proposed methodology is applied to designing printable antennas as highly-sensitive sensors for detecting and measuring the thickness of ice, demonstrating best-in-class sensory response compared to more complex antenna designs. Antenna design is investigated systematically for wireless interrogation in the 2.4 GHz band, where it is found that a loop antenna outperforms a dipole owing to its more distributed capacitance. The antenna’s realized gain was identified as the optimum parameter-under-test, with “positive” sensing proposed as a method of improving linearity and immunity to interference. The developed loop antenna sensor exhibits resilience to interference and applicability to different real-world deployment environments, demonstrated through over 80% average ice thickness measurement accuracy and at least 5 dB real-time sensitivity to ice deposition.

In this paper, we propose a distributed demand side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach. We assume that each user selfishly formulates its grid optimization problem as a noncooperative game. The core challenge in this paper is defining an approach to cope with the uncertainty in wind power availability. We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework. In the latter case, we employ the sample average approximation technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability. Numerical simulations on a real dataset show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach. Postprint accepted for publication in IEEE Transactions on Control Systems Technology How to cite: P. Scarabaggio, S. Grammatico, R. Carli and M. Dotoli, (2021) “Distributed Demand Side Management With Stochastic Wind Power Forecasting, ”IEEE Transactions on Control Systems Technology, 2022. DOI: http://doi.org/10.1109/TCST.2021.3056751 © 2022 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.