Miracle-dz/UniFAR-FTUs huggingface dataset:
Contamination and comprehensive risk assessment of heavy metals in Liangtan River, Chongqing, China

Abstract Distribution and fraction of heavy metals (Pb, Cr, Zn, and Mn) in water and sediment in Liangtan River, Chongqing was analyzed using the atomic absorption spectrometry method. Pearson correlation coefficient, bioavailability index, geoaccumulation index and potential ecological risk index were introduced to classify the source and assess the environmental risk of the heavy metal. Pb, Cr, and Zn mainly existed in residual fraction and fraction bounded to Fe–Mn oxides while Mn mainly existed in the fraction bounded to carbonates and the residual fraction. Mn and Pb, Zn and Cr in the sediment were confirmed originating from, separate common sources. Igeo and were both in the order of Pb > Zn > Cr > Mn. Geoaccumulation indexes demonstrated that the Liangtan River has been heavily polluted by Pb and Zn with the class of 2–4 and 2–3, independently. From viewpoint of the whole Liangtan River, the mean RI (45.1) belonged to low risk level.
ReLoc 2.0: UHF-RFID Relative Localization for Drone-Based Inventory Management

Radio frequency identification (RFID) technology attracts extensive attention for industrial applications, especially supply chain businesses and inventory management. In this article, a passive RFID localization scheme based on unmanned aerial vehicles (UAVs) or drones is established for inventory management in warehouses. In case of the major challenge of the on-board antenna tracking errors in practical scenarios, the state-of-the-art (SOTA) numerical methods rely on accurate antenna positions, such as synthetic aperture radar (SAR)-based algorithms, which will introduce large positioning errors and become unreliable. To this end, we propose a new relative RFID localization method based on phase, received signal strength indicator (RSSI), and readability, which is little affected by antenna tracking errors. In the proposed method, the tagged items on the racks are located laterally through pinpointing the minimum of the unwrapped phases, which have been unwrapped based on the hybrid random forest (RF) model. RSSI differences and the read count of each patch antenna’s beam are utilized to distinguish the rack level of tagged assets via a boosting tree classifier. The experimental results in a real warehouse show that the proposed method achieves 27.1-cm mean lateral positioning accuracy and orders the tagged items horizontally and vertically with more than 96.7% and 98.0% accuracy, respectively.
Intelligent Crime Investigation Assistance Using Machine Learning Classifiers on Crime and Victim Information

In order to establish peace and justice in a society, it is essential to make proper and correct investigation of crime incidents. With the expansion of the utilization of computerized system to track crime and violence, computer applications can help law enforcement officers in a significant way. In most cases, crime incidents are kept in police database and these can be used for various helpful purpose. In this experiment, we have collected data of crime scenario from Bangladesh Police that had features such as area of crime, type of crime, number of victims and so on. Then we applied machine learning algorithms on the dataset for prediction of some attributes such as criminal age, sex, race, crime method etc. We used four different algorithms for our research: K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest Classifier (RFC), Decision Tree Classifier (DTC). Using the aforementioned algorithms with 10 fold cross validation, we achieved different accuracy from all four attribute labels ranging from an average of approximate 75% to an average of approximate 90%. Despite the clear need of further improvement, the results give clear implications that it is possible to achieve well performing automated system for suspect attribute prediction with further work. Finally, we ended the research by comparing and analyzing all the achieved results.
Research on the Strategy of Ballistic Missile Midcourse Recognition Based on Bayesian Network

Midcourse recognition of ballistic missile is one of the core problems in anti-missile early warning operations. Considering the variety of penetration means of ballistic missile, aiming at the attitude characteristics of missile in midcourse flight, this paper focuses on the analysis of five technologies suitable for the midcourse recognition of the warhead, such as the RCS sequence, establishes a Bayesian network model for midcourse recognition of warhead, and standardizes the conditional probability representation of the results of the five techniques according to the actual usage. Finally, the effectiveness of the model is verified by simulation experiments. Through the Bayesian network comprehensive recognition method, the warhead target can be recognized quickly and accurately, and the model has a certain fault tolerance. This model can provide a reference for the warhead recognition in the midcourse of anti-missile early warning combat.
A Synchronous Control Strategy for Dual-drive Feed Mechanism Considering the Change of Workbench’s Gravity Center

The double-drive feed system with high rigidity and fast response is widely used in high-precision NC machine tools and cartesian coordinate robots. However, the center of gravity of the workbench changes constantly in the process of movement. Therefore, this paper takes the independently-developed double-drive two-dimensional feeding test platform as the research object, analyzes the influence of the change of center of gravity on the double drive feed synchronization performance, takes into account the influence of the transmission chain stiffness of the feed system, and establishes the synchronization error mathematical model of the double-drive feed system. The synchronization performance of the system when different dual-drive synchronization control strategies were applied was analyzed by Simulink. Through experiments, the synchronization error of the dual-drive workbench at different positions and the error based on control strategy of fuzzy PID are obtained, which are compared with the simulation results to verify the accuracy of the simulation model and greatly improve the synchronization error.
Analysis of cluster characteristics in SHF band using ISTA and K-means++ algorithms

In the super high frequency (SHF) band used in the 5th-generation mobile communication system (5G system), the propagation characteristics are different from those in conventional propagation models, such as i.i. d. Rayleigh fading. Therefore, a new propagation model is necessary to evaluate 5G systems. We previously proposed a propagation model for the SHF band that regards the incoming radio wave mass as a cluster. However, the evaluation of parameters such as the number of clusters are found to be insufficient. Herein, we clarify the cluster characteristics of the SHF band by clustering simulation results and actual measurement results using the iterative shrinkage thresholding algorithm (ISTA) and k-means++ algorithm.
A Temperature-Aware Framework on gm/ID-Based Methodology Using 180 nm SOI From −40 °C to 200 °C

The advent of the Internet-of-Things brings new challenges in circuit design. The presence of circuits and sensors in harsh environments brought the need for methodologies that account for them. Since the beginning of the transistors, the temperature is known for having a significant impact on performance, and even though very low temperature sensitivity circuits have been proposed, no general methodology for designing them exists. This paper proposes a gm over Id technique for designing temperature-aware circuits that can be used either on measurement data, analytically, or based on simulation models. This model is validated using measurements up to 200°C of X-FAB XT018 transistors and later with a circuit design example.
Researches on Intelligent Traffic Signal Control Based on Deep Reinforcement Learning

The rapidly growing traffic flow exceeds the capacity of the existing infrastructure. It will cause traffic congestion and increase travel time and carbon emissions. Intelligent traffic signal control is a significant element in intelligent transportation system. In order to improve the efficiency of intelligent traffic signal control, the traffic information needs to be collected and processed in real-time. In this paper, we propose a deep reinforcement learning model for traffic signal control. In this model, intersections are divided into several grids of different sizes, which represents the complex traffic state. The switching of traffic signals are defined as actions, and the weighted sum of various indicators reflecting traffic conditions is defined as rewards. The whole process is modeled as Markov Decision Process (MDP), and Convolutional Neural Network (CNN) is used to map the states to rewards. We evaluated the efficiency of the model through Simulation of Urban Mobility (SUMO), and the simulation results proved the efficiency of the model.
Research on Synchronous Control of Gantry-type Dual-driving Feed System based on Fuzzy Single Neuron PID Cross-coupled Controller

The dual-driving feed system (DDFS) based on drive by center of gravity technology has been widely used because of its strong load capacity and better dynamic performance. For the influence of high feed speed and movement of moving parts on gantry-type DDFS, a new cross-coupled control algorithm was proposed. Based on the motion equation of DDFS, the differential equation of DDFS is obtained by considering the mechanical coupling of two axes and the change of friction on both sides of the beam caused by moving parts movement. The dynamic model of DDFS is obtained by Laplace transform. Use this dynamic model to design a single neuron PID cross-coupled controller (SNC) to optimize the synchronous performance and anti-interference ability between two axes. In addition, the fuzzy single neuron PID cross-coupled controller (FSNC) is designed to adjust the single neuron proportional coefficient K. The simulation and experiment show that the FSNC has the best synchronous performance and anti-interference ability when dealing with the movement of moving parts and the high-speed feed of beams.
A Hybird Learning-based Framework for Estimating Teaching Ability of Ideological and Political Courses in Modern Chinese University

This paper proposes a hybrid learning-based two-stage framework consisting of an RNNs-based architecture for subjective textual sentiment analysis and a deep decision forest for teaching ability estimation. According to the annotated self-collected dataset and SnowNLP corpus, the connectivity of the subjective items’ answers is enhanced to be fed into fixed-sized RNNs with LSTM units for distribution of soft labels. The outputted labels are merged with the objective items’ answers to form the learnable vectors for the deep global decision forest framework. We use the top-5 features' variance to weigh the random forests in each level of the cascade forest structure for a fitness voting result. On the self-collected dataset, the F1 score of 0.974 and the CPM score of 0.925 have reached, which are prior to those of the compared methods.
Bangla Missing Data Imputation using HexaGAN Framework

Missing values within data affect the performance of machine learning models. HexaGAN is a framework that addresses the problem of missing data along with the issues of class imbalance and missing labels via a technique called imputation. Frequently, real-world datasets encounter the aforementioned problem. In our work, we attend the missing data problem by introducing missing data into MNIST handwritten-numerical dataset and Bangla handwritten-numerical dataset named NumtaDB. Root mean square error (rmse) and F1 score are computed to evaluate the performance of missing data imputation and digitrecognition classification of imputed images. K-Nearest Neighbor is used for digit-recognition. Results indicated lower rmse and F1 scores which implies that the imputation performed by HexaGAN is reliable.
Brain-Computer Interface Based on Ear-EEG

Scalp-electroencephalography (EEG) has been traditionally used to develop a brain-computer interface (BCI) system. In recent years, EEGs recorded around or inside the ears, called ear-EEG, has been frequently used to develop a practical BCI system, and many previous studies have demonstrated its feasibility in terms of the performance and long-term use. In this invited talk, I will introduce recent advances in the development of ear-EEG-based BCIs, including several studies conducted in my laboratory. I will also discuss challenges and opportunities with the future BCI technology based on ear-EEG.
Sensor-Driven Achieving of Smart Living: A Review

This comprehensive review mainly analyzes and summarizes the recently published works on IEEExplore in sensor-driven smart living contexts. We have gathered over 150 research papers, especially in the past five years. We categorize them into four major research directions: activity tracker, affective computing, sleep monitoring, and ingestive behavior. We report each research direction’s summary by following our defined sensor types: biomedical sensors, mechanical sensors, non-contact sensors, and others. Furthermore, the review behaves as one-stop service literature appropriate for novices who intend to research the direction of sensor-driven applications towards smart living. In conclusion, the state-of-the-art works, the publicity available data sources, and the future challenge issues (sensor selection, algorithms, and privacy) are the major contributions of this proposed article.
On the Connection-Establishment Request Control Method to Improve Data Packet Delivery Ratio at Densely Populated Areas for Bluetooth MANETs

Bluetooth MANETs are Bluetooth-based communication methods between terminals, and various studies have been conducted for sharing data between mobile terminals such as smartphones in disasters. Recently, by using both Bluetooth Low Energy and Classic Bluetooth complementarily, a fast connection-establishment method have been pro-posed to mitigate the contention for the connection-establishment timing between terminals (hereinafter referred to as " the existing method"). Although the existing method improves the speed of connection between two terminals, the following problems occur in a densely populated area: a terminal receives too many connection requests from other terminals, and the number of successful connections decreases. As a result, the data packet delivery ratio of the network deteriorates. In this paper, we propose a method to establish a connection more reliably by controlling a terminal that sends a connection request based on the information of surrounding terminals (hereinafter referred to as “our proposed method”). Our proposed method utilizes Bluetooth Low Energy broadcast to collect information on surrounding terminals, and it controls the transmission of connection requests to each terminal. We have conducted experiments using an Android terminal and compared the results between our proposed method and the existing method. As a result, we have confirmed that the number of successful connections and the data packet delivery ratio of our proposed method are superior to those of the existing method.
Investigation of Hyperparameters for DOA Using Machine Learning

In estimating the direction of arrival (DOA) of a radio wave using array antennas, the correlation matrix calculated from the received signals at each antenna element is generally used. In recent years, applied researches such as DOA estimation and radio propagation estimation using machine learning have been conducted. In this paper, a method for estimating the DOA using machine learning is proposed, and clarify the influence of each hyperparameter on the estimation accuracy.
SDN-Enabled Secure IoT Architecture

The Internet of Things (IoT) is increasingly being used in applications ranging from precision agriculture to critical national infrastructure by deploying a large number of resource-constrained devices in hostile environments. These devices are being exploited to launch attacks in cyber systems. As a result, security has become a significant concern in the design of IoT-based applications. In this article, we present a security architecture for IoT networks by leveraging the underlying features supported by software-defined networks (SDNs). Our security architecture not only restricts network access to authenticated IoT devices but also enforces fine granular policies to secure the flows in the IoT network infrastructure. The authentication is achieved using a lightweight protocol to authenticate IoT devices. Authorization is achieved using a dynamic policy driven approach. Such an integrated security approach involving authentication of IoT devices and enables authorized flows to protect IoT networks from malicious IoT devices and attacks. We have implemented and validated our architecture using ONOS SDN Controller and Raspbian Virtual Machines, and demonstrated how the proposed security mechanisms can counteract malware packet injection, DDoS attacks using Mirai, spoofing/masquerading, and man-in-the-middle attacks. An analysis of the security and performance of the proposed security mechanisms and their applications is presented in this article.
Quad-Polarization-Reconfigurable Antenna With a Compact and Switchable Feed

A reconfigurable antenna with a compact size and quad-polarization reconfigurability is presented. By employing a switchable feed scheme, the antenna can operate at single-fed and dual-fed modes. At single-fed mode, the feed structure allows output ports switching to produce the excitations for either slant +45° or −45° linear polarization (LP). At dual-fed mode, it serves as a switchable phase-shifting power splitter for generating left-hand circular polarization (LHCP) or right-hand circular polarization (RHCP). To realize a simple and space-saving configuration, the antenna makes efficient use of the proposed feed structure, which is highly shared among the four polarization states. Measured results show that the antenna exhibits operating bandwidths of 230 and 260 MHz at circular polarization CP and LP states, respectively. Moreover, stable broadside radiation patterns as well as moderate gains could also be obtained.
Website Recommendation with Side Information Aided Variational Autoencoder

Recommender systems had been proposed to help people to find the interested items, such as recommending products to a buyer; identifying movies or music that a user will find interest, etc. However, the existing recommendation approaches mainly focus on capturing user-item interaction patterns for prediction, and ignore the user’s side information such as visit frequency and duration. In this paper, we study the side information aided website recommendation problem that using the browsing history of a set of users and their side information to predict the websites that will be of interest to a certain user. We propose a novel recommendation approach called SI-VAE that incorporates side information with the variational autoencoders (VAEs) model for top-k recommendation. The proposed method takes both user-website interaction information and side information as input, and adopts an encoder/decoder model to generate user’s interested websites from partial observations. The model of SI-VAE is implemented as a neural network, and trained with a multinomial likelihood objective function to form the ranking of user-website interaction probabilities. We conduct extensive experiments on two real-world datasets, which show that the proposed model outperforms the baselines in a number of performance metrics in website recommendation.
Accurate Recognition of Coronary Artery Disease by Applying Machine Learning Classifiers

Being one of the most common causes of death, coronary artery disease (CAD) has been an area of interest for many researchers throughout the decades. Recognition of CAD disease in the early stage can save a significant amount of lives. To achieve this goal, previously, many researchers have contributed significantly to the classification of CAD disease. Many of the studies achieved higher classification accuracy with a different number of features. However, in all the cases, either the number of selected features was too high or the accuracy was too low. In this study, we considered an extensive preprocessing approach followed by five feature selection algorithms (Boruta, lasso, minimum redundancy maximum relevance, recursive feature elimination based on random forest and chi-square test). We also considered the common features selected by all of the techniques. After that, seven classifiers (Naïve Bayes, decision tree, k-nearest neighbors, logistic regression, random forest, support vector machine, and neural network) were applied and the performances were measured in terms of accuracy, sensitivity, specificity, f1-score, MCC, and AUC values. With an overall accuracy of 92.31% and considering only 21 features, we showed that our combination of accuracy and selected features produced a better approach to classify CAD disease.
Aperture Efficiency Improvement by Reflectionless Metasurfaces for Large-Aperture Antennas

We design an impedance-matched reflectionless metasurface to manipulate the aperture phase of an X-band large-aperture slot array antenna. By proposing a design procedure for large-area metasurfaces in practical applications with preparing a database that relates the propagation characteristics of unit cells with their physical structures, we design a large-area non-periodic reflectionless metasurface consisting of 200×34 unit cells. A gain enhancement due to an aperture efficiency improvement by the metasurface is experimentally demonstrated at 9.41 GHz.
Extrinsic Calibration of Dual LiDARs Based on Plane Features and Uncertainty Analysis

Solutions equipped with two or more light detection and ranging (LiDAR) systems have been applied in several fields (navigation, mapping, target detection, and others) to obtain increasingly detailed 3D environmental information. Accurate calibration of multiple LiDAR systems is necessary for integrating and using their data. This study improves a convenient and reliable method for calibrating the relative poses between 3D LiDARs. The planar surfaces of a scene are measured from different poses; then, the planes are extracted and constraints are constructed to solve extrinsic parameters. The factors influencing calibration uncertainty are also investigated. It is found that the uncertainty is closely related to the geometric structure of the plane observations, and the rotation dilution of precision and translation dilution of precision are defined to denote this effect. The influence of different types of spatial plane geometries on calibration errors is discussed, and optimal observation conditions are derived. Experimental results for simulation and real data show that the proposed method can achieve an accurate extrinsic calibration of LiDARs, and the point cloud registration errors after calibration are less than 2 cm. The analysis of the calibration uncertainty provides a valuable reference and guide for experimental design and error evaluation.