Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Signal Propagation Model Calibration on Localization Performance Limits for Wireless Sensor Networks

In this paper, we focus on wireless sensor network-based localization for user devices (UDs). Prior to UD localization, signal propagation model (SPM) is required to calibrate using training samples from a number of location grids. However, SPM usually suffers from measurement noise and inevitable error in calibration grid (CG) locations. This will significantly degrade UD localization performance. Nevertheless, the impact of CG location error, CG layout and the number of CGs on SPM calibration performance has not been characterized. Furthermore, the effect of SPM calibration error on UD localization performance has not been developed. In this paper, we aim to provide a unified framework for performance analysis of SPM calibration and UD localization. Firstly, we establish a closed-form Cramér-Rao lower bound on SPM calibration error and UD localization error, respectively. Secondly, the impact of measurement noise, CG location error and the number of CGs on SPM calibration performance is revealed. Thirdly, the influence of SPM calibration error, CG location error and measurement noise on UD localization performance is studied. The effect of modeling mismatch is also studied. The obtained analysis framework builds a theoretical basis for the design of efficient system optimization strategies, including resource allocation and CG deployment optimization, for UD localization performance enhancement.
Design of an IoT-based Flood Early Detection System using Machine Learning

Floods are a complex phenomenon that is difficult to predict because of their non-linear and dynamic nature. Gauging stations that transmit measured data to the server are often placed in very harsh and far environments that make the risk of missing data so high. The purpose of this study is to develop a real-time reliable flood monitoring and detection system using deep learning. This paper proposed an Internet of Things (IoT) approach for utilizing LoRaWAN as a reliable, low power, wide area communication technology by considering the effect of radius and transmission rate on packet loss. Besides, we evaluate an artificial neural network (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) neural network models for flood forecasting. The data from 2013 to 2019 were collected from four gauging stations at Brandywine-Christina watershed, Pennsylvania. Our results show that the deep learning models are more accurate than the physical and statistical models. These results can help to provide and implement flood detection systems that would be able to predict floods at rescue time and reduce financial, human, and infrastructural damage.
Joint Interference Alignment and Power Control for Dense Networks via Deep Reinforcement Learning

This letter proposes a joint interference suppression scheme in heterogeneous networks (HetNets) with dense small cells (SCs) and users. Different from the majority of existing studies, we adopt the co-tier intra-cell interference alignment (IA), while the co-tier inter-cell and cross-tier interference is suppressed by centralized power control in the macro base station (MBS). Specifically, the power control problem is modeled as a Markov Decision Process (MDP) with the aim of maximizing the sum spectrum efficiency. Considering the exponential growth of the output layer neurons faced by general deep reinforcement learning (DRL) algorithms, we propose a deep deterministic policy gradient (DDPG)-based algorithm to solve the problem. Simulation results demonstrate that the proposed algorithm is able to achieve better performance and wider application scope comparing with existing algorithms.
Deep Learning Short-Term Traffic Flow Prediction Based on Lane Changing Behavior Recognition

Short term traffic flow prediction is of great significance for reasonable traffic control and easing traffic congestion. Most of the existing methods are based on the traditional time-space parameters of traffic flow or feature extraction through deep neural network to predict short-term traffic flow. With the increase of road traffic volume, the influence of lane changing behavior on short-term traffic flow is greater. Combined with deep learning and image processing technology, a deep learning short-term traffic flow prediction method based on vehicle lane changing behavior recognition is proposed. The prediction results on real data sets show that the model has high prediction accuracy.
Distributed Diffusion-Oriented Cooperation for Power Sharing in Self-Expanding Microgrids

This paper proposes a multiagent-based distributed self-organizing control scheme that will enable the power sharing of massive renewable distributed generators (DG) in a low-voltage microgird. The proposed fully distributed self-organizing control strategy can achieve effective power sharing challenge among massive DGs by adopts a peer-to-peer diffusion protocol, in which DGs from the same neighborhood are allowed to communicate with each other at every iteration. Furthermore, learning adaptability via automatic agent discovery is proposed to alter topologies as a result of incoming or outgoing DGs in the microgird network. The resulting algorithm is distributed, cooperative and able to respond in real time to changes in the environment. Eventually, the proposed algorithm is superior over consensus algorithms in terms of convergence speed and utilizes reduced communication infrastructure compared to centralized controllers. The proposed approach can handle networks of different size and topology using the information about the number of nodes which is also evaluated in a distributed fashion. The effectiveness of the proposed control strategy is verified under various scenarios by a modified IEEE 34-bus test network.
Wide Input Power Range X-Band Rectifier With Dynamic Capacitive Self-Compensation

A microwave rectifier composed of a pair of Schottky diodes with dynamic capacitive self-compensation is proposed in this letter. The impedance of a Schottky diode usually varies with the input power. A method using paired diodes for capacitive self-compensation is presented to enhance the rectifier’s dynamic power range. The imaginary part of one diode’s impedance is compensated using another identical diode with a section of transmission line by impedance transforming. The proposed method reduces the effect of the input power on the diode impedance. An <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>-band rectifier using a pair of HSMS-286 Schottky diodes based on the proposed method is designed and fabricated. This rectifier, with a dc load of <inline-formula> <tex-math notation="LaTeX">$300~\Omega $ </tex-math></inline-formula>, achieves power conversion efficiency of 64.5% at 9.7 GHz and has a 15.3-dB input power dynamic range with efficiency exceeding 50%. The rectifier is expected to be used in wireless power transmission systems with flexible distance requirements.
Developing VR content for digital career guidance in the context of the pandemic

This paper presents our reflection on adapting our methodology for developing training content for young jobseekers to new influencing factors. The development process centers around small development teams working incrementally to expand a suit of VR applications being used for career guidance, so-called ‘immersive job taste’. COVID-19 measures have already impacted our ability to carry out user evaluations. Our process can be negatively affected by a headset vendor’s new policy. This paper ‘s contribution is a perspective on the lessons learned and challenges of creating content for a set of VR-based training and career guidance apps targeted at young job seekers, as a part of Virtual Internship project.
Implementing Predictive Model for Low Birth Weight in Afghanistan

Birth weight is a significant determinant of the likelihood of survival of an infant. Low Birth Weight (LBW) has become an increasingly common problem, especially in developing and underdeveloped countries. Therefore, an ability to predict the LBW is beneficial and a good preventive measure. It is also a good indicator of future health risks of that infant. This study is concerned about the implementation of predictive LBW models for rural and urban areas of Afghanistan based on the data obtained from the Afghanistan Demographic and Health Survey 2015. The main objective of the study is to identify the most suitable machine learning techniques (i.e. classifiers) among the five popular ones. These are K-Nearest Neighbor (K-NN), Naïve Bayes, Neural Network, Random Forest, and Support Vector Machine (SVM). Prior to implementing the predictive models, data preprocessing is carried out. This is done by means of data cleansing and feature selection. The well-known Correlation based Feature Selection algorithm (CFS) is employed to select the top ten attributes. The classifier in this study comprises two categories, Normal and LBW. The preparation of the dataset is carefully done to ensure well-balanced samples in each category. The study reveals that Random Forest is the best classifier with an accuracy of 84.70% and 85.2% and with the Area Under the Curve (AUC) of 91.0% and 92.1% for both rural and urban areas, respectively. The study has a direct benefit to the health and prevention policy making in Afghanistan.
Direction change of redirected walking via a single shoe height change

The authors explore the effect of proprioceptive system changes caused by foot height differences on reorientation walking by asking users to walk in a virtual environment with shoes of highly inconsistent heights. The results show that the height difference of the shoes has a great influence on the participants’ walking direction in the virtual environment, under normal circumstances, the participant will lean to one side when walking, but when the height difference between the two shoes increased, participants were more likely to lean in the opposite direction.
Energy-Efficient and Delay-Minimizing Charging Method With a Multiple Directional Mobile Charger

To prolong the battery lifetime of Internet-of-Things (IoT) devices, they can be charged by a mobile charger (MC) equipped with radio frequency (RF)-based wireless power transfer (WPT) capability. By concentrating power toward IoT devices, the energy efficiency of the MC increases when using a directional antenna instead of an omnidirectional counterpart. However, directional antennas have a narrow beamwidth, and thus, several IoT devices cannot be charged simultaneously. In this article, we propose a multiple-directional MC (MDMC) scheme that exploits multiple-directional beams to reduce the charging delay while maintaining the advantages of directional antenna with a higher charging efficiency. In our MDMC, an MC determines its charging points to visit and the directions of its beams at each charging point. The charging points can be determined in consideration of the distribution of the devices and the remaining energy. After selecting charging points, the travel paths are determined by considering the remaining energy of the IoT devices. In addition, to relax the problem complexity, we propose an efficient two-stage multiple-directional beam selection (MDBS) algorithm. In the first stage, the directions of beams are determined. The second stage calculates the charging time of each beam for minimizing delay. The simulation results show that the MDMC outperforms the existing single-directional antenna-based charging schemes in terms of the energy efficiency and charging delay approximately 15% and 25%, respectively.
A Novel Wireless Network-on-Chip Architecture for Multicore Systems

Using Wireless Network-on-Chip (WiNOC) for designing the multicore System-on-Chips can significantly decrease the latency and power dissipation of the network. This improvement is achieved by replacing the multi-hop paths between far apart cores with a wireless single-hop link. Due to space, power and cost limitations, it is crucial to determine both the optimum number of equipped wireless hubs and their proper positions. In this paper, we propose a novel approach to obtain the optimum configuration of a WiNOC by leveraging the Simulating Annealing algorithm. Simultaneous multiple communications in such a network can be achieved by using multiple access techniques such as Orthogonal Frequency Division Multiple Access (OFDMA) to create dedicated channels between a source and destination pair. This technique is more bandwidth efficient compared to previously used FDMA. Also, it can distribute the available bandwidth between wireless nodes according to the traffic demands. So, we introduce an adequate channel reallocation algorithm regards to the broadcasting nature of the OFDMA scheme. The introduced architecture shows better performance in comparison with the conventional WiNOCs. This improvement is especially observed for latency characteristics where an improvement of about 15 is obtained.
A High-Precision Ultra-Broadband Quadrature Generation at Millimeter-Wave Frequencies

In this paper, a high-precision ultra-broadband quadrature signal generator for millimeter-wave frequency band is proposed. The structure of cascade directional couplers is adopted, and the transmission line and equivalent lumped model are used to connect couplers. The proposed structure and the transmission line and lumped model are analyzed using the S-parameter matrix, and the corresponding theoretical formulas are provided. The simulation prototype is constructed using the 28 nm CMOS technology, so it can be integrated into a small area chip. In the frequency range of 50-150 GHz, the measured S11 and S22 of the proposed design are both less than -10 dB, while the gain mismatch is less than 1 dB, and the phase mismatch is fewer than five degrees. Compared to the traditional quarter wavelength directional coupler, the proposed generator can guarantee a high-precision quadrature output signal when the output load changes while reducing the gain and phase mismatches. In the same frequency range, the image rejection ratio is over 20 dB. The whole size of the proposed high-precision ultra-broadband quadrature coupler (HPUBC) is 260 um $\times 450$ um. The proposed designs are verified by 3D electromagnetic simulation.
Tunable Terahertz Perfect-Absorbers With Dual Peak Based on Reverse Graphene Patch Metamaterials

In this paper, we proposed a tunable graphene-based metamaterial absorber (MA) at dual terahertz band to achieve perfect absorption. The electric field intensity distributions of demonstrated structure is illustrated to explain the origin of two absorption peaks. Simulation result shows the proposed MA owning polarization-insensitive property and wide incident angle tolerance. An inversion algorithm is used to extract the equivalent impedance and explore the physical mechanism of multiple-peak absorption. The equivalent impedance is explored through algorithm when the Fermi energy level alters, consequently the variation of resonant frequency of corresponding absorption peak is determined. Simultaneously, the resonant frequencies of absorption peaks can be adjusted by changing the geometric parameters of the graphene. Furthermore, we adapt the proposed structure into reversely designed pattern, achieving similar characteristic of dual-band absorption peaks and tunability. Thus, our work provides new insight and useful design guidance to various tunable multiple band terahertz metamaterial perfect-absorbers.
StateReveal: Enabling Checkpointing of FPGA Designs with Buried State

The ability to save and restore the entire state of an FPGA design is essential for hardware checkpointing, which enables live migration, fault recovery, and novel debugging flows. However, hard blocks such as block RAMs and DSP blocks have state elements that cannot be saved or restored using conventional checkpointing techniques such as readback. This results in an incomplete checkpoint, thereby preventing checkpointing of most FPGA designs. In this paper, we propose general techniques that allow a complete checkpoint to be captured and loaded even when a design contains inaccessible state elements. The proposed techniques include using multi-cycle capture and inserting capture registers that add observability and controllability of this buried state. Moreover, we present StateReveal, a tool that detects the presence of inaccessible state elements in a design and automatically inserts the required capture registers. This enables checkpointing of designs that contain fully registered block RAMs and pipelined DSP blocks, which was not previously feasible. We verify the ability to capture and load a complete checkpoint on several designs using an enhanced checkpointing framework that supports multi-cycle capture. StateReveal currently supports Xilinx FPGAs, and the added circuitry has a low timing overhead of 5% and an acceptable area overhead that averages 19 LUTs and 35 registers per hard block.
On-Wafer Microrectangular Coaxial Delay Line by Using MEMS-Casting Process

In this work, a serpentine microrectangular coaxial delay line (MRCDL) operating from dc to Ka-band is realized based on MEMS-Casting (MEMS-C) process. As an emerging technology, the MEMS-C process provides a low-cost, rapid, and one-time prototyping approach for manufacturing the proposed MRCDL. Besides, the single-crystal quartz with low permittivity and low loss tangent is used to fill the MRCDL and supply high reliability. One prototype is fabricated for demonstration. The measured results show a maximum 2.42-dB loss in the 410-ps delay circumstance, with 23.5-ps delay flatness in the range of dc to Ka-band. Moreover, the loss characteristic is comparable to the Cu-based air-filled MRCDL in a similar electrical delay situation, due to the dramatically shortened physical length benefiting from the low-loss filled dielectric. Obviously, the proposed microrectangular coaxial line (MRCL) design indicates an interesting solution to microwave and millimeter-wave microdevices and systems.
Unknown Terrain modelling using 3D mapping

Exploration of unknown terrains to learn the geographical significance of the place plays a major role in making new discoveries that may lead to new inventions and better understanding about Earth. At times, it may be dangerous for humans to explore the terrains in person. The objective of this project is the creation of a 3D map using Unity platform dynamically and simulating it. This project uses a single robot which will help to reduce the dangers of cave explorers and archaeologists by creating a simulated map of the unknown terrain using 3D mapping in unity. The Master bot traverses and scans the area by moving periodically with an equal distance using an encoder, and then the values are transmitted wirelessly to a host machine. Thus, the values get parsed at the host machine and the data is retrieved. The 3D position and surface reflectance generated can be viewed as an image. The delay between scanning and mapping of the terrain is less than 2 secs. Thus, this bot can be used to explore unknown terrains cost efficiently and reliably
Adsorption Behavior of Lysozyme onto Carboxylated Chitosan Beads

Chitosan porous beads were prepared by coagulating chitosan acetate aqueous dope in basic alcohol. The N-succinylation and O-carboxymethylation were achieved by applying succinic anhydride and monochloroacetic acid, respectively, following the partial crosslinking to make acid durable chitosan beads. The degree of succinylation was regulated by applying acetic anhydride to block the amino group following the crosslinking. The NaOH concentration was a regulation factor for the carboxymethylation. The adsorption mechanisms of lysozyme on chemically modified beads were found to be different each other. The elution profile was affected by the pH shift, but not by ionic strength unlikely those of carboxymethyl-cellulose. N-acetylglucosamine (GlcNAc), saccharose and urea were found to inhibit the interaction of lysozyme with carboxylated chitosan beads in the presence of NaCl. The adsorption of lysozyme was achieved derectly from egg white and eluted fraction contained lysozyme of high purity.
Data Preparation for Reducing Computational Time with Transpose Stack Matrix for Action Recognition

Training model in deep learning on action recognition faces a problem with high computational cost and ineffective performance to deploy in common devices. The goal of this research is to reduce the computational cost using the data preparation technique called transpose stack matrix (TSM). TSM can be applied with convolution2D and extracts spatiotemporal features. We perform experiments using two datasets including KTH and UCF101(spilt-1). The results show that TSM requires less training time than I3D approach and has satisfactory performance. TSM has higher accuracy than I3D with 73.04% on UCF101 while TSM has accuracy approximate to I3D with 72.69% on KTH. When training on 5 epochs.
Wearable and Elastic Surface Coil for 1H Magnetic Resonance Imaging

Wearable and elastic surface coil using a conductive and stretchable textile for magnetic resonance imaging (MRI) of breast was designed. A high-conductive silver thread was woven with elastofiber to create two elastic textile coils. Three phantoms with varying sizes and a resolution plate were also designed to validate the coil performance. Signal-to-noise ratios (SNRs) were calculated for the phantoms with different planar cuts. Resolution plate hole diameter of 6 mm was clearly discernible on axial MR images. Despite its canonical circular geometry, the wearable and elastic textile coil with one channel only was successful. The elastic coil can improve clinical applications of MRI with larger filling factor and may provide better patient comfort.
Medical Text Entity Recognition Based on CRF and Joint Entity

Named entity recognition in Chinese electronic medical record is the core technology of key information mining in electronic medical record. The purpose of entity recognition is to identify the different categories of valuable entities in electronic medical records. However, there are some serious problems in Chinese electronic medical records, such as complicated disease names, clinical symptoms and drug names, different doctors’ use of professional terms, irregular writing, excessive professional terms, and unclear boundaries between different entities. At the same time, because it involves the privacy of patients, and unauthorized disclosure of such medical text information is not in line with national regulations. Therefore, at present, there is almost no public Chinese electronic medical record corpus, and all these problems increase the difficulty of entity identification in Chinese electronic medical record. In order to better identify entities in medical texts, this paper formulates a set of labeling rules for medical texts, and gives full play to the advantages of machine learning on top of the data set of this set of autonomous labeling rules. Conditional Random Field model CRF (Conditional Random Field) is used to obtain good experimental results. In the entity recognition experiment of a large number of medical records, the accuracy rate, recall rate and F value are improved to 87.92%, 82.33 and 85.03%, which are better than the evaluation indexes of existing studies and can meet the needs of practical application.
Research on Ignition Time Control of Initiating Explosive Device Based on Magnetoelectric Energy Converter

In order to meet the requirements of rapid reaction and high firing rate of ammunition in modern battlefield environment, the high instantaneous ignition design of magnetoelectric energy conversion initiating explosive device is carried out. By studying the influence of ammunition overload shock amplitude, charge discharge capacitance and circuit design on the induction ignition time, we can get three ways to reduce the ignition time of initiating explosive device based on magnetoelectric energy conversion: increasing ammunition overload shock amplitude, reducing charge discharge capacitance and using voltage limiter to design control circuit.