Miracle-dz/UniFAR-FTUs huggingface dataset:
CAR: The Clean Air Routing Algorithm for Path Navigation With Minimal PM2.5 Exposure on the Move

Transport related pollution is becoming a major issue as it adversely affects human health and one way to lower the personal exposure to air pollutants is to choose a health-optimal route to the destination. Current navigation systems include options for the quickest paths (distance, traffic) and least expensive paths (fuel costs, tolls). In this paper, we come up with the CAR (Clean Air Routing) algorithm and use it to build a health-optimal route recommendation system between the origin and the destination. We combine the open source PM2.5 (Fine Particulate Matter with diameter less than 2.5 micrometers) concentration data for Taiwan, with the road network graph obtained through OpenStreetMaps. In addition, spatio-temporal interpolation of PM2.5 is performed to get PM2.5 concentration for the road network intersections. Our algorithm introduces a weight function that assesses how much PM2.5 the user is exposed to at each intersection of the road network and uses it to navigate through intersections with the lowest PM2.5 exposures. The algorithm can help people reduce their overall PM2.5 exposure by offering a healthier alternative route which may be slightly longer than the shortest path in some cases. We evaluate our algorithm for different travel modes, including driving, cycling and walking. An analysis is done for more than 4,000 real-world travel scenarios. The results show that our approach can lead to an average exposure reduction of 17.1% with an average distance increase of 2.4%.
A New Cubature Kalman Filtering with M-method for Attitude Determination

The inertial navigation system (INS) aided by a magnetic compass (MCP) is designed for the underwater glider to obtain underwater navigation with higher accuracy, higher stability and faster tracking even though sudden change of attitudes. Even if the measurement becomes unobservable, the CKF still has the better determination precision. However, the computation of CKF is much larger than that of EKF. The paper proposes a novel algorithm integrating cubature Kalman filtering with M-method (CKF-M), which takes full advantage of efficient computation and anti-interference to acquire the navigation information with higher accuracy during long-term gliding. In the real glider experiments, the CKF-M has a good effect in the aspects of accuracy, stability and tracking even if the sudden change of attitude.
An Artificial Skin for Detecting Pressure and Shear Forces simultaneously based on Capacitive Sensor and Magnetic Sensor

Combining magnetic sensors and capacitive sensors, we have developed an artificial skin that not only can identify magnetic conductive metal, non-magnetic conductive metal and human being but also detect normal force and shear stress. The sensor is only 13 × 13 mm in size and is equipped with 2 × 2 capacitive pressure sensors and a three-axis magnetic type force sensor. We found that sensitive magnetic type force sensors are affected by some metals and capacitive sensors are affected by human body capacitance. With the magnetic type force sensor, the 3D distance between the magnet and the magnetic sensor can be measured, which is corresponding to 3D tactile force. Combining these two sensors, we can achieve a novel composite sensing for the tactile artificial skin. That is the device determines whether the contact is a human, magnetic conductive metal or non-magnetic, and simultaneously measures the normal force and shear stress change to the sensor.
A Feedback Force Controller Fusing Traditional Control and Reinforcement Learning Strategies

We report on a novel force controller that fused a traditional proportional–integral–derivative (PID) controller with a proximal policy optimization (PPO)-based deep reinforcement learning (RL) algorithm. The deep RL algorithm provides long-term predictive force compensation to the real-time PID controller. The controller was applied to a 2D motion task with the goal of maintaining constant normal force in both simulation and experimental environments. The training of the deep RL algorithm used state data including the contact force and the distance between the workpiece and the end-effector of the manipulator. The validations of the proposed controller revealed that the controller with the aided deep RL algorithm could decrease the root mean square error (RMSE) of the measured force by between 11% and 80% in the simulation environment and by between 7% and 45% in the experimental environment, as compared with the controller without the aided deep RL algorithm. In addition, the displacement profile of the end-effector had a better match to that of the workpieces when aided by the deep RL algorithm.
THz Waves Scattering by Multilayer System of Infinite Graphene Strip Gratings

Scattering and absorption characteristics of the multilayer graphene grating are studied at THz. Every layer consists of the infinite periodic graphene strip grating embedded into dielectric slab. The analysis and numerical results for the semi-infinite multilayer system is also presented. To solve the problem the combination of methods is used. The solution for a single layer is obtained with the use of the method of integral equations. Then the properties of the finite and semi-infinite system of layers are obtained from the set of matrix equations which connects the Fourier amplitudes of the scattered field. The equations are written in the operator form.
Identification of urgent posts in MOOC discussion forums using an improved RCNN

Although massive open online courses (MOOC) have successfully attracted a large number of learners, the low completion rate has severely hindered the development of MOOC. The complex and diverse information on the forum often makes many students' urgent problems unable to be solved in time. Therefore, how to identify the students' urgent posts from a large number of posts becomes a critical problem to be solved. This paper presents a model for identifying “urgent” posts that require immediate attention from the instructors. It is the first time deep learning methods have been applied to this task. We investigated different deep learning methods and proposed an improved recurrent convolutional neural networks to solve the problems of identifying posts. The experimental results show that our method outperforms state-of-the-art methods. The work has potential application across a range of platforms and can help teachers efficiently navigate their discussion forums so that timely intervention can support learning and potentially reduce dropout rates.
Design of a New Type Phase Shifting Array with Dual Radiating Modes

This paper introduces a novel approach that integrates the radiating behaviors of reflectarrays and transmitarrays together. A new type dual-mode phase shifting array is applied for validation, which consists of split-diagonal-cross unit cells and a polarizing grid. The horizontal and vertical polarized incident waves are separated into reflecting and transmitting modes due to the grid. Accordingly, one can easily switch the array between the forward and backward radiating modes individually. This simple solution could be potential as the increasing requirement of high integration in modern communications.
Adaptive Control of Flexible Joint Manipulators with Parameter Convergence

This paper is focused on the adaptive control design for flexible joint robots with parameter uncertainties. Based on the singular perturbation theory, the flexible joint robot is transformed into a fast subsystem and a slow subsystem. For the fast subsystem, the PD control strategy is used to suppress elastic oscillations by the feedback of the motor angular displacement error. For the slow subsystem, an adaptive control scheme is proposed to guarantee the parameter estimation error convergence. The closed-loop system is testified to be globally exponentially stable via the designed Lyapunov function. The comprehensive simulation results demonstrate great tracking precision and high robustness of the proposed control scheme.
A Dual-band MIMO Antenna for Bluetooth/WiMax/LTE Applications

A dual-band microstrip-fed planar MIMO antenna is proposed for application in Bluetooth (2.4 GHz), WiMax (2500MHz/ 3650MHz) and LTE (2570MHz-2620MHz/ 3600MHz-3800MHz) bands. A triangular ring resonator with a split at asymmetry position is used to realize dual-band response. The antenna provides two resonances at 2.6 GHz and 3.7 GHz. The structure is simulated and studied for S-parameters, envelop correlation coefficient (ECC), channel loss capacity (CCL), diversity gain (DG), radiation patterns and gain values, and the results are presented and discussed throughout the paper. The structure is fabricated and results are compared with simulated results to validate the simulation. The simple design of this proposed antenna and its miniaturized dimension reveals significance of this design.
Transfer learning for the classification of video-recorded crowd movements

The automatic recognition of a crowd movement captured by a CCTV camera can be of considerable help to security forces whose mission is to ensure the safety of people on the public area. In this context, we propose to fine-tune a model from the TwoStream Inflated 3D architecture, pre-trained on the ImageNet and the Kinetics source datasets, to classify video sequences of crowd movements from the Crowd-11 target dataset. The evaluation of our model demonstrates its superiority over the state-of-the-art in terms of classification accuracy.
A Framework for TSV Based 3D-IC to Analyze Aging and TSV Thermo-Mechanical Stress on Soft Errors

The CMOS aging, transient effects, and TSV thermomechanical stress degrade the resilience of 3D-ICs. The transients effects lead to soft errors and aggravated with the CMOS Bias temperature instability (BTI). In this paper, we analyze detrimental transient and BTI effect on soft error rate (SER) in 3D-ICs. However, TSV thermomechanical stress presents a considerable benefit by enhancing the critical charge (Qc) and reduce the SER due to decrease in the threshold voltage and increase in mobility of carriers in transistor present out of keep-out-zone and useful range. Therefore we propose a framework to evaluate the effect of transient, BTI, and TSV thermomechanical stress on critical charge and SER in 3D-ICs. Subsequently, through HSPICE simulation we show that for a lifetime of ten years and on the topmost layer of stacked 3D-IC, the reduction in SER of NAND gate by 5.12% – 9.05% and in 6T SRAM 2.51% – 4.76% and 3.77% – 5.64% decrease for storing 0 and 1 respectively.
Emergency Dispatch Optimization of Engineering Vehicles in Disaster Rescue Activities

This paper studies the emergency dispatch optimization problem of engineering vehicles in disaster rescue activities. We first analyze the composition of disaster rescue task, and study the situation where subtasks will compete for the same engineering vehicle. In order to properly allocate engineering vehicles to perform corresponding subtasks, we establish a nonlinear integer optimization model (P). Since there are fractional form equation and nonlinear equation in constraints of the model, we can not solve the model directly by the commercial solvers such as CPLEX. To solve the model efficiently, the Lagrangian dual and cone optimization algorithm (LDC) is proposed to iteratively obtain the optimal solution. Extensive computational experiments are implemented to demonstrate the effectiveness and efficiency of the proposed solution method.
Zero miss distance of 3D PPNG with time-varying speed

Using the exact nonlinear kinematics, the zero miss distance (ZMD) property of the three dimensional (3D) pure proportional navigation guidance (PPNG) law is examined for a missile with a time-varying speed to intercept an arbitrarily maneuvering target in the space. It is proven that, a navigation constant larger than 1 is the only condition required to guarantee the ZMD property of 3D PPNG, provided that 1) the missile is launched toward the target, and 2) the missile has a speed superiority over the target during flight. To this end, a new description of the 3D engagement geometry is proposed, in which the concept of plane angle is introduced to describe the cross-coupling in the nonlinear kinematics. Based on this, the approach for analyzing the ZMD property of PPNG in planar pursuit situations is extended successfully to general 3D cases: firstly, a certain time sequence and an asymptotic time function dependent on the maximum speed ratio are defined, based on which the upper bound of the relative range between the missile and the target is assured; then, it is proven by contradiction that there exists a subsequence such that the relative range is asymptotically converging to zero. Our methodology is expected to be useful for the design and analysis of other 3D missile guidance laws.
Retinal Image Multimodal and Multitemporal Registration for a Multispot Laser Photocoagulation Device

This paper aims to assist the physician during the treatment of retinal diseases by laser photocoagulation, aligning images obtained by different acquisition methods in preliminary examinations to the treatment. Thus, an algorithm with combinations and arrangements of related methods was proposed, in order to allow the multimodal recording of retinal images. The proposed algorithm performs the detection of points of interest using SURF, elaborates the characteristic descriptor of each point using a combination of SIFT and PIIFD, eliminates the outliers by means of a Robust Point Matching method via Vector Field Consensus (VFC) and finally performs the registration with the Geometric Transformation of the type Affine. The algorithm was tested in 40 pairs of multimodal retinal images and performed better when compared to existing algorithms in terms of precision and robustness.
Wideband Circularly Polarized 3D Wire Antenna for Wi-MAX Applications

In this paper, a crooked wire antenna is optimized to be functional in both the WLAN and the Wi-Max bands (2.4-2.48 and 3.3-3.7 GHz). The antenna presents a right-hand circular polarization (RHCP) with a 3 dB axial ratio (AR) bandwidth of 3.28 to 4.11 GHz. For the WLAN band (2.4-2.48 GHz), the polarization was linear. The antenna is fabricated using 3D printing techniques and occupy a total volume of 33.8 mm × 30.4 mm × 37.4 mm. The simulated and measured farfield results show good agreement with a radiation pattern that assures a near hemispherical coverage toward the upper hemisphere and a measured maximum gain value of approximately 6 dB. The radiation pattern of the antenna assures a near hemispherical coverage toward the upper hemisphere.
Experiment to educate ESD phenomena to engineers

The demonstration of electrostatic discharges with the experiment described in the IEEE EMC Education manual [1] is excellent for education of personnel in production and handling of electronic equipment. To educate design and test engineers a device was built with additional elements. It now demonstrates the effect of a fast rise-time transient, shows how the phenomenon can be transported by wires or cabling and an introduces the diversion of the discharge by designing the path of least impedance. A metal base-plate was added for portability and to serve as a defined target for discharges.
To Assess the Influence of Artifacts on Motor Imagery Based BCI

A major problem in brain-computer interface (BCI) is the presence of artifacts inside the electroencephalograph (EEG) data. In order to conduct the preprocessing of EEG more effectively, this paper focuses on the quantitative analysis of artifacts influence on the performance of motor imagery brain-computer interface (MIBCI). Independent component analysis (ICA) was applied firstly to separate the raw EEG signals into several independent components (ICs) so as to find artifact components. On this basis, the recognition accuracy before and after removing each artifact component were compared. Focusing on the influences of electro-oculogram (EOG), electromyography (EMG), power-line burst, and electrode artifacts, the comparison experiments were performed. The experimental results show that the accuracies obtained relative increments of 5.5%, 4.0%, 3.1%, and 1.7%, respectively, in terms of the order of EMG, power-line interference, electrode artifact, and EOG. The conclusion presented in this paper could provide a valuable reference for researches of MIBCI.
Coupled Inductors and their Applications

This article describes the topic of coupled inductors and their EMC advantages when used in switching power supplies. It is explained how a coupled inductor works, which advantages and disadvantages it has and how it can be applied in principle. Furthermore, various DC-DC converter topologies with coupled inductors are explained and a practical example is used to show how a coupled inductors can be used effectively. One way to reduce the costs and effort for the development of a power supply system is to use a distributed system where the power supply is distributed decentral within the system. The DC-DC converters can be selected in their topology according to the circuit requirements and the advantages and disadvantages, thus making the system as a whole in EMC design simpler and inherently better, since couplings due to long supply lines, complex high-current filters and inadequately adapted circuit topologies are eliminated.
A Robust Chinese License Plate Detection and Recognition Systemin Natural Scenes

In natural scenes, the license plates (LP) obtained typically have different angles and character lengths, while most LPDR systems are based on license plate front view and fixed length license plates. For the above problems of Chinese license plates, in this paper, we proposed a robust LPDR system, which detects and rectifies oblique license plates in the detect stage. In LP recognition stage, we designed a pre-trained recognition network based on CNN and RNN, which improves the accuracy of Chinese character recognition in license plates, while avoiding character segmentation and license plate length limitation. Our experiment results show that our LPDR system performs well in Chinese license plates dataset CCPD, especially CCPD-Rotate and CCPD-Tilt sub-dataset, with recognition rate of 92.7% and 93.5%, respectively.
Comparison between Machine Learning Algorithms in the Predicting the Onset of Diabetes

Diabetes is a standout amongst the most common illnesses on the planet today with high mortality rate, thus one of the greatest medical issues on the planet. There are numerous approaches to analyze the disease; one of these approaches is machine learning algorithms. This study will tell us how these machine learning methods outperform each other and which one is better to do this type of diagnosis. There are 768 records in our dataset, out of which 500 are healthy patients and 268 are diabetic. We have divided the data set into training data and test data. Using Multilayer Perceptron Artificial Neural Networks, we were able to achieve the highest accuracy of 96%.
Active Multiport Subarrays for 5G Communications

A novel hybrid beamforming architecture based on a phased array of active multiport subarrays is proposed for multi-user 5G applications. The subarrays have multiple simultaneous fixed beams which are smartly combined at the user locations with relatively large gains towards the intended co-frequency users and sufficiently low inter-user interference levels. The presented architecture has a wider angular coverage than the existing subarray-based hybrid beamforming schemes and reduces the signal processing complexity significantly as compared to the fully digital beamforming.
Detecting Negative Emotional Stress Based on Facial Expression in Real Time

Negative emotional stress can be seen as a physiological response to mental and physical challenges. Exposure to stressful situations with a long time can have adverse effects on people, such as depression, which finally results in suicide in severe case, so it is important to monitor stress in real time and treat it properly. In this paper, we propose a new framework for stress detection in real time. The framework detects stress by recognizing three stress related facial expressions, anger, fear and sadness. We also propose a connected convolutional network, which combines low-level features with high-level features to train the deep network to recognize facial expressions. If the number of stress related frames exceeds a threshold value, the framework will remind people to take a break to relax. The experiment results demonstrate that our proposed method has better performance on facial expression recognition and realizes high-performance stress detection.
C3SRAM: In-Memory-Computing SRAM Macro Based on Capacitive-Coupling Computing

This letter presents C3SRAM, an in-memory-computing SRAM macro, which utilizes analog-mixed-signal capacitive-coupling computing to perform XNOR-and-accumulate operations for binary deep neural networks. The <inline-formula> <tex-math notation="LaTeX">$256\times 64$ </tex-math></inline-formula> C3SRAM macro asserts all 256 rows simultaneously and equips one ADC per column, realizing fully parallel vector-matrix multiplication in one cycle. C3SRAM demonstrates 672 TOPS/W and 1638 GOPS, and achieves 98.3% accuracy for MNIST and 85.5% for CIFAR-10 dataset. It achieves <inline-formula> <tex-math notation="LaTeX">$3975\times $ </tex-math></inline-formula> smaller energy-delay product than conventional digital processors.
Ground Bridge Effect on Reduction of Conducted Emission from Three-Phase Motor Drive System

In electric vehicles, the pulse width modulation scheme is widely used in three-phase motor drive systems for its high performance. However, the fast switching of semiconductor devices may induce excessively high dv/dt and di/dt, which can cause electromagnetic interference (EMI) issues, making it difficult to satisfy electromagnetic compatibility standards. In this paper, the current conducted emission test for a three-phase motor drive system is performed based on CISPR 25 component-level testing setup for AM radio frequency band, which is from 530 kHz to 1.8 MHz. Under these conditions, common-mode (CM) noise generation mechanisms of the motor drive system are identified through measurements. It is found that the dominant mechanism is the mode conversion from differential mode (DM) switching current of the three-phase PWM inverter to CM current through the cable harness due to the imbalances in the PCB structure and EMI filter network which are at the dc input side of the three-phase inverter. Based on this finding, ground (GND) bridges are added between the digital signal and power GND nets to reduce the CM noise. The GND bridge effect on the reduction of CM noise in AM band is experimentally verified and further studied using circuit simulations.
The dynamics of food insecurity and overall health: evidence from the Canadian National Population Health Survey

The paper explores whether the responses to household food insecurity questions in cycles two and three of the Canadian National Population Health Survey help explain the links between socioeconomic status and health at the individual level. Short-term transitions in food insecurity status are correlated with changes in health status. There is some evidence for females but not for males that conditional on current health, current household food insecurity can lead to lower future health status, even in the short run. There is stronger evidence for both males and females that conditional on current household food insecurity status, lower current health status can lead to an increased probability of future household food insecurity.