Miracle-dz/UniFAR-FTUs huggingface dataset:
A Low-Latency Object Detection Algorithm for the Edge Devices of IoV Systems

The emergence of edge computing (EC) and intelligent vision-based driver assistance system is of great significance for the prospective development of Internet of Vehicle (IoV). The additional computation capability and extensive network coverage provides energy-limited smart devices with more opportunities to enable IoV system for time-sensitive applications. However, when implemented in a vision-based driver assistance system, the transmission of a large amount of redundant data not only causes delay but also severely compromises the accuracy of object detection. In this paper, an improved object detection algorithm based on video key-frame for latency reduction on edge IoV system is proposed. It can significantly improve latency reduction performance at the expense of small detection accuracy. In our proposal, we adopt an important coefficient and frame similarity comparison algorithm to filter redundant frames and achieve key frames for object detection. Then an improved Haar-like feature based classification algorithm is used for object detection under the edge computation model. Finally, a scalable cluster object detection system is implemented as a practical EC case to verify our proposal, and extensive simulations confirm the superiority of the proposed scheme over regular schemes. It can speed up about 84 times with 40% of the similar frames filtered in comparison.
Rating Prediction Based on Merge-CNN and Concise Attention Review Mining

Online review websites provide an open platform for users to write reviews or give ratings on items (business services) as well as share their consumption experience. However, the volume of reviews is large, while the rating scores provide users with a quick picture of the items without reading all reviews. Recommendation systems can help users find items of interest by predicting user’s ratings on unrated items. Review contents contain more personalized preference features than simply user ratings. Therefore, it is important to consider both ratings and review contents when making rating predictions. This research proposes a novel approach that combines deep learning and review mining with attention mechanism for rating predictions. Review mining with attention mechanism is adopted to extract concise attention reviews with important words and sentences. A merge convolutional neural network (merge-CNN) model is proposed to consider both the target user’s preference features and performance features of target business for rating prediction. This method extracts quality business performance features from the quality reviews written by elite (credible) users. Moreover, the proposed method uses the concise attention reviews of target user’s neighbors to simulate target users’ reviews on unrated target business. Experiments were conducted on Yelp data sets to evaluate our proposed methods. The results show that the proposed method, i.e. considering concise attention reviews and quality reviews written by elite users, outperforms traditional methods in improving prediction accuracy. The experiment result also shows that our review simulation methods can well simulate target user’s reviews on unrated target business.
Detection of Arrhythmia in Real-time using ECG Signal Analysis and Convolutional Neural Networks

According to a study by the World Health Organization (WHO), cardiovascular diseases (CVD) are the leading cause of mortality across the world. Nearly 17.9 million deaths each year are due to CVDs, which equates to 31 % of all deaths worldwide. An estimated 85% of these deaths are caused by heart attack or stroke. Cardiac Arrhythmia is a major disorder humans' experience which can lead to fatal conditions such as Cardiac Arrest or Stroke. Unless it is tested by a physician, one cannot discern if he/she has Arrhythmia because of its asymptomatic nature in some cases. An Electrocardiogram (ECG) is commonly used to check a patient's heart rhythm. In this paper, our focus is to present a method through which Cardiac Arrhythmia can be detected and classified in real-time by a wearable ECG device. The MIT-BIH Database is used to train and evaluate our CNN model, resulting in an accuracy of 99.625%.
Context-based System for User-Centric Smart Environment

Internet of Things (IoT) solutions are becoming ubiquitous in various application domains. In homes and offices, IoT systems enable remote and scheduled operation of heating, ventilation and air conditioning (HVAC) systems, lighting control, control of kitchen and other home appliances, etc. Available solutions in the smart home domain mainly enable controlling systems and appliances remotely, e.g., by using a smartphone, or manually setting the times in which systems and appliances should operate. In this paper we propose a system for indoor lighting control which sets the colors and brightness of the lamps based on the context: time of the day, current lighting conditions in the room, and having users in the focus - their lighting preferences in the room. Feed-forward neural networks are used for learning about the most pleasing settings. The major advantage of this system is its user-centricity, i.e., taking into account users’ color and brightness preferences. The system is evaluated on twenty-two (22) users in a laboratory environment and demonstrates general user score rate of 68.18% as ”great” and 31.82% as ”good”.
Optimization Neural Network With PCA And PSO On Heart Disease Classification

Heart disease is disease caused by heart disease that causes abnormalities, forms of abnormalities such as heart rhythm, heart valves, minor or congenital abnormalities from birth and the resulting blockage of blood vessels is energy supply. Researchers here use classification to help predict heart disease. The results of data classification in the evaluation use the ROC curve and Confusion Matrix to determine the level of accuracy using Neural Network algorithms with PCA and PSO. PCA (Principal Component Analysisis) extraction fromvariable to reduce the variable without lossinformation contained in the original or original datawhile (Particle Swarm Optimization) is an optimization technique that is inspired by the behavior of flocks of birds or fish. Its characteristics are generally simple concepts, efficient calculations, and easy implementation. for optimization dimensions with the results of the AUC ROC curve of 98.05% of 0.977, it can be used according to the needs of the Neural Network algorithm with PCA and PSO optimization has good results in predicting heart disease patients.
Case Study of an Electric Vehicle Battery Thermal Runaway and Online Internal Short-Circuit Detection

A thermal runaway happened in a battery cell of an electric vehicle during driving, and the fire spreads to other batteries in a few minutes. Based on the recorded battery operation data, this letter analyzes the battery thermal runaway process, and identifies the critical time points of fault signals like cell voltages and temperatures, etc., which depict the start and spread of thermal runaway in the battery pack. By tracing back the clues, it is concluded that the cause of the fire is battery internal short circuit (ISC), and battery overcharge is a potential cause of ISC. Then, three different patterns of battery cell voltage abnormalities are compared, including battery external short circuit, ISC, and poor-signal wire connection. A cell voltage difference-based method is proposed to diagnose ISC online, which is simple and reliable to implement in engineering.
A Robust Data Reconciliation Method Based on Shape Parameters

Data reconciliation plays an important role in coordinating and meeting the law of conservation in industrial processes. Generally, the actual measured industrial data is easily contaminated by different overall errors. Therefore, it is important to build a robust data reconciliation method that minimizes the impact of severe errors on the data and improves its usability. This paper presents a robust estimator based on a new robust estimation function to improve the robustness of the data reconciliation method. First, for the newly proposed robust estimator, its main robustness and its objective and impact functions are analyzed. Then, the validity of the new robust estimator in data reconciliation is demonstrated in an example.
Characterization of Shorted Dipole Antennas for Low-Cost RFID Reader Applications

This article presents a comprehensive study on shorted dipole antennas aiming for low-cost radio frequency identification (RFID) reader applications. Starting from a low-profile shorted bowtie dipole antenna, it is found that by varying the shorting wall position, three different types of resonant antennas could be realized with different radiation features. A low-profile planar inverted F antenna (PIFA) -type dipole is obtained by moving the shorting wall toward the feeding, which exhibits a low resonance frequency without obvious back lobe radiation or sidelobes. When the shorting wall is placed in the middle of the radiating patch, a mushroom-type dipole antenna can be built. A zeroth-order mode is also observed. This structure differs from the magnetoelectric dipole due to a low-profile structure. Placing the wall toward the end of the dipole arm leads to a loop-type antenna with a broader beam and a reduced gain. It also shifts the resonance toward the higher frequency. Based on the PIFA- and mushroom-type structures, two circularly polarized antennas with crossed shorted dipoles are designed and investigated carefully. Finally, two optimized, compact, and low-cost RFID reader antennas based on simple metal stamping process and screws are designed, fabricated, and tested, showing an excellent RF reading range. This study demonstrates that for the shorted dipole antennas, miniaturization and bandwidth control can be achieved simultaneously. Particularly, for higher gain and smaller size, the shorting wall should be placed close to the feeding to suppress the loop mode. For omnidirectional radiation, the loop mode should be enhanced by moving the shorting wall close to the end of the dipole arm.
The rise of webinars: thousands of learners looking for professional development. A practical case study

In an unexpected situation caused by a pandemic in 2020, the population in the world had to spent several weeks at home. Institutions experimented with remote emergency methods for the teaching and learning processes. With more time and different motivations, people increased the enrollment to MOOC courses in more than 10 times than usual. There was a re-discovery of webinars to share valuable information and the authors explored on the effectiveness of their use to expose the content of MOOCs as a pre view for potential learners. Webinars are increasingly used in education to present information and teach skills with wide dissemination and high impact. This work explores on how a successful webinar is defined in many different ways. The important thing to remember is to use key metrics to be informed and then improve the online event strategy. The manuscript presents the experience of a series of webinars and constructs on previous success cases and best practices while organizing webinars. Learning from the data can enhance the experience, showcase the potential of a MOOC content and convert to an enrollment in a course or a course series.
Discrimination of Genuine and Acted Emotional Expressions Using EEG Signal and Machine Learning

We present here one of the first studies that attempt to differentiate between genuine and acted emotional expressions, using EEG data. We present the first EEG dataset (available here) with recordings of subjects with genuine and fake emotional expressions. We build our experimental paradigm for classification of smiles; genuine smiles, fake/acted smiles and neutral expression. We propose multiple methods to extract intrinsic features from three EEG emotional expressions; genuine, neutral, and fake/acted smile. We extracted EEG features using three time-frequency analysis methods: discrete wavelet transforms (DWT), empirical mode decomposition (EMD), and incorporating DWT into EMD (DWT-EMD) at three frequency bands. We then evaluated the proposed methods using several classifiers including, k-nearest neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN). We carried out an experimental paradigm on 28-subjects underwent three types of emotional expressions, genuine, neutral and fake/acted. The results showed that incorporating DWT into EMD extracted more hidden features than sole DWT or sole EMD method. The power spectral feature extracted by DWT, EMD, and DWT-EMD showed different neural patterns across the three emotional expressions at all the frequency bands. We performed binary classification experiments and achieved acceptable accuracy reaching a maximum of 84% in all type of emotions, classifiers and bands using sole DWT or EMD. Meanwhile, a combination of DWT-EMD achieved the highest classification accuracy with ANN in classifying true emotional expressions from fake expressions in the alpha and beta bands with an average accuracy of 94.3% and 84.1%, respectively. Our results suggest combining DWT-EMD for future emotion studies and highlight the association of alpha and beta frequency bands with emotions.
Achieving Fine-Grained Flow Management Through Hybrid Rule Placement in SDNs

Fine-grained flow management is useful in many practical applications, e.g., resource allocation, anomaly detection and traffic engineering. However, it is difficult to provide fine-grained management for a large number of flows in SDNs due to switches’ limited flow table capacity. While using wildcard rules can reduce the number of flow entries needed, it cannot fully ensure fine-grained management for all the flows without degrading application performance. In this article, we design and implement hybrid rule placement for fine-grained flow management (to be referred to as HiFi here after). HiFi achieves fine-grained management with a minimal number of flow entries through taking a two-step approach: wildcard entry installment and application-specific exact-match entry installment. How to optimally install wildcard and exact-match flow entries, however, is intractable. Therefore, we design approximation algorithms with bounded factors to solve these problems. We consider how to achieve network-wide load balancing via fine-grained flow management as a case study. Both experiment on a testbed built with open virtual switches and extensive simulation show that HiFi can reduce the number of required flow entries by about 45-69 percent and reduce the control overhead by about 28-50 percent compared with the state-of-the-art approaches for achieving fine-grained flow management.
Detection of Mental Illness Risk on Social Media through Multi-level SVMs

As shown by several previous studies, personal narratives through social media are often indicative of one's psychological state. In particular, mental illnesses such as depression were found to be associated with distinct linguistic patterns. However, many people with mental illness still do not receive full treatment. In this paper, we study mental illnesses through people's choice of words in expressing themselves on two popular social media platforms, Reddit and Twitter. Our goal is to develop an empirical model to detect and diagnose major mental disorders in individuals. We build a substantial dataset of posts made by people suffering from mental illnesses and the control ones, and in order to generate numerical feature from text we apply text cleaning and Word2Vec language modeling, and then for classification we used SVM machine which classifies posts and users with high accuracy. We achieve an accuracy of 95% on Twitter users and an accuracy of 73% on the Reddit challenge.
Design and Analysis for the Axe Foot of Bio-inspired Anchoring Robot

Mooring location has become increasingly important with the rapid development and utilization of marine resources. The mooring system of the underwater mobile robot is required to be efficient and intelligent. This paper proposes a novel anchoring robot by imitating the excellent inhabitation functions and physiological structure of the razor clam. This bio-inspired robot can autonomously dig holes, anchor, and detach to improve the efficiency of mooring location in marine operations. Specifically, the axe foot of the robot plays an important role in these movements. It contains a screw-cone drill and two soft parts. The screw-cone drill is the main forced part while the robot is in the digging state and the rationality of drill design is proved by mechanics analysis. Moreover, the soft part, which is connected with the upper end of the drill, is analyzed to show the capability of obstacle avoidance. The rationality of the structural design and a good submarine anchoring function of the axe foot are verified by the above analysis.
Trajectory-tracking and Coordinated Control of Six-wheel-drive Rocker-bogie Patrol Robot

This paper presents a method of hybrid trajectory tracking and coordinated control for a multi-drive patrol robot. In order to realize automatic inspection under complex terrain, a six-wheel-drive rocker-bogie suspension is used in this robot. Then, a hierarchical motion controller is designed with emphasis, where hybrid PD control and LQR optimal control strategies are used in top layer to achieve high-precision trajectory tracking, and a multi-drive coordinated control strategy based on robot position-orientation and rocker-bogie angles in its bottom. On this basis, simulations were carried out on self-designed robot. It shows that the motion control strategies can obtain high trajectory-tracking accuracy and effectively reduce the power loss of multi-drive system.
Time Derivativers of Zhang Dynamics with Han Tracking Differentiator Linked and Also Distinguished

In this paper, a series of time-derivative solvers, i.e., time derivativers, termed Zhang time derivativers (ZTD), is introduced on the basis of Zhang dynamics (ZD) to estimate the time derivatives of a time-varying signal, which is, e.g., a measured state variable of some unknown dynamic system. Equations of four types of ZTD are proposed in detail. Based on four low-order ZTD equations introduced by ZD with linear activation and equal parameter, the general form of the simplest type of ZTD is obtained through mathematical induction. More importantly, the links and differences between Han tracking differentiator (HTD) and ZTD are discovered and discussed.
Dynamic Vector Modeling of Three-Phase Mutually Coupled Switched Reluctance Machines With Single dq-Quadrant Look-up Tables

This paper presents a dynamic modeling method for a 3-phase mutually coupled switched reluctance machine (MCSRM) considering spatial harmonics, and saturation. The conventional modeling methods of MCSRMs are based on 3D look-up tables (LUTs), where the phase flux linkages are considered as state variables. These 3D LUTs describe the phase currents with respect to phase flux linkages, and rotor position. The 3D LUTs represent two <inline-formula><tex-math notation="LaTeX">$dq$</tex-math></inline-formula> quadrants, and are obtained from finite element analysis (FEA) by multi-phase excitation where the excitation currents cover two quadrants in the <inline-formula><tex-math notation="LaTeX">$dq$</tex-math></inline-formula> synchronous reference frame. The LUTs used in the proposed method represent the phase current, and electro-magnetic torque as vectors. The magnitude, and the angle of these vectors are represented by the sine, and cosine Fourier coefficients. Hence, rotor position is not an input to the LUTs, and the proposed method uses 2D LUTs. Additionally, the flux linkages in the four <inline-formula><tex-math notation="LaTeX">$dq$</tex-math></inline-formula> quadrants possess symmetry for MCSRMs. Therefore, LUTs corresponding to only one <inline-formula><tex-math notation="LaTeX">$dq$</tex-math></inline-formula> quadrant are required. The single-quadrant based LUTs reduces the number of FEA steps, and the size of the LUTs by 50% compared to the two-quadrant LUT based models. Finally, the proposed method is validated using FEA, and experiments for a 12/8 MCSRM.
Towards Industrial Scenario Lane Detection: Vision-Based AGV Navigation Methods

In this paper, we propose to apply lane detection to the automated guided vehicle (AGV) platform in an industrial environment to realize automatic driving of AGV based on visual navigation. Similar to the lane detection of urban roads or highways, we present two methods for AGV navigation band detecting. The first method is based on traditional image processing which combines an adaptive threshold method with Sobel operator to detect the navigation band. The other method is based on deep learning, which implements end-to-end edge detection by LaneNet and it overcomes the problem of uneven illumination that the first method is struggling with. The RANSAC algorithm is finally used to fit the edge lines from the detected edge pixels in order to generate the AGV control commands to achieve robust real-time AGV automated driving.
Design and Simulation of Adaptive Internal Model Control Algorithm

In the actual industrial control, many objects have the characteristics of large delay and parameter time-varying, which makes the traditional PID control difficult to obtain satisfactory control effect. The internal model control has many advantages, such as intuitive design, strong practicability, simple structure, few on-line adjusting parameters and no need of accurate object model. Especially for the improvement of robustness and anti-interference and the control of the system with large time delay, the control effect is particularly significant. But the system model of internal model control is not easy to get, and it is difficult to get a more accurate internal model. In order to obtain a more accurate internal model, improve the control effect of the internal model control system and improve the dynamic characteristics of the control system, it is necessary to identify the model parameters of the controlled object efficiently. In this paper, the improved particle swarm optimization (PSO) algorithm is used to identify the system online and combined with the internal model control method to control the controlled object.
A Modified Line-of-Sight Method for Path Tracking Applied to Robotic Fish

This paper proposes an improved line-of-sight method to navigate a two-joint-actuated tuna-like robotic fish. Based on the characteristics of thunniform swimmer, a simplified kinematic and dynamic model is constructed, and the intrinsic body swing is illustrated. To deal with the nonlinear and under-actuated dynamics of the robotic fish, the reference head angle is designed with cross-track error, which substantially simplifies the control of linear path tracking. Furthermore, with the physical constraint, co-simulation using Automatic Dynamic Analysis of Mechanical Systems (ADAMS) software and MATLAB simulink reveals the effectiveness. The obtained results show that the proposed method is able to make both the cross-track error and the heading error fluctuate in an acceptable range quickly.