Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cascade Learning Approach for Automated Detection of Locomotive Speed Sensor Using Imbalanced Data in ITS

Automatic and intelligent railway locomotive inspection and maintenance are fundamental issues in high-speed rail applications and intelligent transportation system (ITS). Traditional locomotive equipment inspection is carried out manually on-site by workers, and the task is exhausting, cumbersome, and unsafe. Based on computer vision and machine learning, this paper presents an approach to the automatic detection of the locomotive speed sensor equipment, an important device in locomotives. Challenges to the detection of speed sensor mainly concerns complex background, motion blur, muddy noise, and variable shapes. In this paper, a cascade learning framework is proposed, which includes two learning stages: target localization and speed sensor detection, to reduce the complexity of the research object and solve the imbalance of samples. In the first stage, histogram of oriented gradient feature and support vector machine (HOG-SVM) model is used for multi-scale detection. Then, an improved LeNet-5 model is adopted in the second stage. To solve the problem of the imbalance of positive and negative samples of speed sensor, a combination strategy which draws on four individual classifiers is designed to construct an ensemble of classifier for recognition, and the results of three different algorithms are compared. The experimental results demonstrate that our approach is effective and robust with respect to changes in speed sensor patterns for robust equipment identification.
Performance Evaluation of a "Switch-To-Target" Based Asynchronous SSVEP BCI Paradigm*

Because of the strong advantages such as anti-interference capability, less electrode montage, higher information transfer rate and no need of specific training for participants, steady-state visual evoked potentials (SSVEP) based brain-computer interface (BCI) has attracted more and more attentions. In order to achieve high accuracy and maintain considerable stability, this paper proposed an eye tracking technique based asynchronous SSVEP BCI method by directly localizing asynchronous eye-tracking-based switch to desired stimulation target to accelerate the BCI process. And real-time visual gaze feedback was also provided in necessity when participants could not focus their gaze to achieve satisfactory target identification results. By combining the heterogeneous signals of eye gaze position with a conventional asynchronous BCI paradigm, the proposed method reduced the trial duration while considerably high identification accuracy was maintained. Experiments were carried out on four participants with an average accuracy above 93% when the trial duration was 3s, while considerable performance can also be achieved when the trial length was shorten to 2s.
UAV Image High Fidelity Compression Algorithm Based on Generative Adversarial Networks Under Complex Disaster Conditions

This paper proposes an improved image high fidelity compression algorithm based on the generative adversarial networks (GANs) to deal with the problem that the UAV image has a large amount of data which is not conducive to post-processing. By adding an encoder in front of the generator, the disaster area image transmitted by UAV is compressed to meet the requirements of the generator. After the compressed image is trained together with the real image through the discriminator, the quality of the compressed image is constantly improved. This image compression algorithm can fully synthesize the codes of non-major areas such as trees and rivers in the image, and try to retain the codes of important areas such as houses and roads. The experimental results show that the proposed compression method in this paper has a higher compression ratio than the traditional compression method for the disaster area image, and can obtain images with strong sense of hierarchy.
Theroratical Study of Fully Printed Magnetically Tunable Reconfigurable Patch Antenna

Due to varying wireless standards in different regions, there is a big desire to have reconfigurable and tunable components which can be tuned to the frequency of interest. At present a single wireless device, such as a mobile phone contains multiple antennas, filters, all of them working at different frequencies. It will be great if an antenna or a filter can be tuned to work at different frequencies, thus the overall number of antennas or filters in a device can be reduced. This control is possible through a magnetic field, if these components are built on a magnetic substrate. Traditional magnetic substrates are expensive that involve complicated fabrication process and operate the material in saturated state making them inefficient. A better approach would be to operate the antenna or any other microwave device in the partially magnetized state. In this work, the authors present the theory and design of a polarization reconfigurable patch antenna with frequency tunablity in the unsaturated state. Operating away from the saturated state can improve the overall efficiency of the RF components. The analytical model is initially verified using a high frequency simulator to predict the antenna performance on a magnetic substrate with normal bias. A low-cost printing technique where the substrate could be realized using magnetic ink is used for the implementation of the antenna. A circular patch antenna working at 6 GHz is used for the validation of the theory. When triggered by an external magnetic field, the as-fabricated antenna shows a maximum tuning range of ∼16% and ∼5% for the two splitted frequency points. A good match is achieved between the theory, simulations and measurements of the antenna.
Global Based Deep Refineing Model For Person Retrieval

The performance of traditional part model in person retrieval is greatly affected by the quality of parts. The recent work[1] consider refining the hard partitioned part when training the network itself and got state-of-the-art performance, but during our experimentation, we found the masks which it generated contains the problems that misguide the networks with additional constraints, Targeting to solve above problem, we proposed a new networks called Global Refine Net. The backbone network focus on learning the local information which improve the ability of extract feature of details, Global Refine block introduce global information to adjust the hard-shaped part generated by the backbone network in an end-to-end manner. Also we modified the self-adversarial training mechanism in [1]. We employ an special loss function to prevent the incorrect convergence and adjust the degree of self-adversarial training, the new regularization term we added in the loss benefit both in stabilizing and speeding the training process. The performance of our model beat most previous soft partitioned works, improved about 2.3% rank-1 accuracy and 5.1% mAP to the PCB baseline on market-1501 dataset.
Design of a Linear Permanent Magnet Transverse Flux Motor for Needle-free Jet Injection

This paper proposes the work of analyzing and optimizing three types of linear transverse flux motors in order to build a compact handheld device for needle-free jet injection. The use of finite-element analysis, deep neural network models, and convex non-linear optimization in the study will be explored. The high performance computing infrastructure allowed for rapid creation of 534435 motor design data points via parallel 3-D finite-element analysis jobs. With the available data, a deep regression neural network was trained to make highly accurate predictions (96% accuracy) about any motor with design factors within the given ranges. The optimization process found a number motor configurations which satisfy the given specification but not yet found a practical design for the application. Future effort will focus on widening the search range, and more importantly tuning the optimizer, prototyping and experimental validation.
Performance Improvement of Monopole Loaded Hybrid Dielectric Resonator Antenna by using Sleeve-like Structure

In present work, one novel approach has been implemented to improve the performance of monopole loaded hybrid dielectric resonator antennas (DRAs). The antenna has been designed by incorporating two different kinds of conical dielectric resonator rings with conventional monopole. Further, the performance of the antenna has been improved by introducing sleeve-like structure. The proposed antenna operates over the frequency range 4–30 GHz which provides 120% impedance bandwidth ($\mathbf{S}_{11}\leq-10\ \mathbf{dB}$) with peak gain of about 5 dBi. It can be used as an electromagnetic sensor suitable for ultra-wide band applications.
Imitation of Human Upper-Body Motions by Humanoid Robots*

This paper presents a method for offline imitation of human upper-body motions. This approach is based on inverse kinematics with task classification which includes an end-effector tracking, joint limits avoidance and joint trajectories tracking, and the results are validated on the humanoid robot NAO. The whole process includes taking data from human using both markerless and marker-based motion capture systems, scaling down the human data to the robot size, calculating the joint angles, and iterating equations of inverse kinematics. After, the final values of joint angles are calculated, they are operated on the real robot using a publisher in Robot Operating System (ROS). On contrary to the studies in literature, we also focus on imitating human motions by a marker-based motion capture system, so we have additional joint orientation information and more accurate results.
A Wideband Slotted D-Shaped Monopole MIMO Antenna With Triple Branch Neutralization Line

A novel compact wideband multiple-input-multiple-output (MIMO) antenna is presented in this paper. The MIMO antenna comprises of a wideband neutralization line connected in between two symmetric slotted D-shaped monopoles and a defected ground structure which is cut into the top of the ground plane. With the introduction of wideband neutralization line as a decoupling technique and defected ground plane as an impedance matching element at the input ports of both the antennas, the proposed MIMO antenna achieves −10dB impedance bandwidth of 50.42% (8.90-14.90 GHz) with isolation better than 20 dB.
Compact Symmetric Quarter Mode Substrate Integrated Waveguide (QMSIW) Antenna

A compact symmetrical Quarter Mode Substrate Integrated Waveguide (QMSIW) based antenna with improved performance in terms of impedance matching, radiation efficiency, gain and the reduced cross-polarization level for C-band application is proposed in this paper. In this design, two circular ring slots are inserted to achieve circular polarization (CP) and four open circuit stubs in used at four corners to improve the impedance matching. The proposed antenna generates TE 202 mode and is designed for circular polarization. The measured results show that the impedance bandwidth ($\vert\boldsymbol{S}_{11}\vert$ < −10 dB) of 6.245-6.635 GHz is 6.07% and the 3-dB axial ratio bandwidth of 6.457-6.575 GHz for the CP. The measured maximum gain of the proposed antenna is 6.93 dBi. The antenna has achieved a radiation efficiency of 90.01% with respect to the resonate frequency at 6.425 GHz. The measured results are quite in agreement with the simulation results. The performance of the antenna is found to be much better than the similar kind of antenna reported in literature.
A Novel NAND Flash Memory Architecture for Maximally Exploiting Plane-Level Parallelism

Solid-state drive (SSD) has become one of the most dominant storage devices and is rapidly replacing conventional storage devices. The core component of SSD is NAND flash memory (NFM), where the actual data are stored. Cost pressure is the most critical factor limiting the further deployment of SSDs and past researches have focused on developing cost-effective high-density NFM. Although the cost-driven technology development increases per-chip capacity, it reduces channel-/way-level parallelisms for the given device capacity, resulting in the performance degradation. Such observation directs us to focus on a novel NFM architecture exploiting plane-level parallelism. The distinct features of this architecture are: 1) enabling a decoupled word-line (WL) selection for the mated planes and 2) segmenting each plane into subplanes for further maximizing the plane-level parallelism. The experimental results show that decoupled WL selection improves the throughput by up to 21.3% with a marginal overhead of less than 1%, compared to the conventional NFM architecture. In addition, adopting the plane segmentation improves the throughput by up to 43.9% with an additional overhead of 14%. Considering the tradeoff between performance and overhead, the proposed NFM architecture is a cost-efficient method to secure high performance under decreasing channel-/way-level parallelisms in high-density NFM.
Noise-Resistant Estimation Algorithm for TDOA and FDOA in Passive Emitter Localization

This paper addresses the joint time difference of arrival (TDOA) and frequency difference of arrival (FDOA) estimation problem in passive emitter localization, involving range migration during the long observation time. A noise-resistant estimation algorithm based on Keystone transform is proposed. This method can efficiently avoid the Doppler ambiguity problem of the conventional approach by constructing the phase compensation without any signal-to-noise ratio loss. Moreover, compared with the ideal maximum likelihood estimator via extensive numerical experiments, the proposed method can achieve comparable estimation performance and have gain a significant reduction to the computational cost. Extensive numerical experiments validate the effectiveness of the proposed algorithm.
Sentiment Analysis of User Entered Text

In this paper, classification algorithms (SVM and Naïve Bayes) are compared based on their classification accuracies on different datasets. They are then used to classify texts as positive or negative. For training and testing the algorithms, we will use the IMDb Large Movie Review Dataset, which contains 25,000 polar movie reviews each for training and testing and a movie reviews dataset provided by Cornell University containing 1,600 polar reviews each for training and testing.
A Configuration Approach for Convolutional Neural Networks Used for Defect Detection on Surfaces

The manufacturing industries must guarantee that the products delivered to clients do not have defects, such as irregularities on the surface. To that end, complex systems inspect the products on completion of their manufacturing to detect possible defects. But the design and configuration of these systems is cumbersome, specific for each system, and requires a lot of experience. Currently, there is a trend to build all these systems using Convolutional Neural Networks (CNN), due to the theoretical simplicity of this approach: images of the surface of the products are processed by a trained CNN, which detects defects in the images. But the generation of a well-trained CNN is also a complex process, generally not always properly documented in the literature, and strongly dependent on the application domain. To facilitate the use of CNNs, this paper proposes a configuration approach for CNNs whose objective is the detection of defects on the surface of manufactured products. As an example, the approach is used to configure a CNN to detect surface defects on manufactured rails.
Coordinated Power and Performance-Efficient Virtual Machines Scheduling in the Cloud

Cloud computing with live migration technique is considered as one of the most promising ways to cope with power consumption and performance management of a data center. Most prior works on performance and power management of the whole server farm are achieved in a separate way. To address this issue, in this paper we propose an efficient method for the whole server farm, which aims to dynamically consolidate virtual machines in a coordinated way that optimizes the energy and performance trade-off. Firstly, we focus on the virtual machine (VM) selection step. Then we consider the VM selection task as a Dynamic Decision-Making (DDM) problem and construct a coordinated cost function with power and performance. In this study, the Q-Learning strategy of Reinforcement Learning (RL) is adopted to solve this DDM problem. The proposed algorithm is simulated in CloudSim toolkit using real-world workload traces. Finally, experimental results indicate that our approach outperforms other algorithms in terms of energy consumption, the number of VM migrations, average SLA violation and the number of host shutdowns.
Fuzzy-adaptive Impedance Control of Upper Limb Rehabilitation Robot Based on sEMG*

In most rehabilitation robot control strategies, voluntary residual motor function of the stroke patients’ affected limb have not been involved enough. In this paper, we propose a novel fuzzy-adaptive impedance control method based on sEMG. First, muscle activation is calculated online to estimate the voluntary residual motor function of the human upper limb. Then, an impedance controller and a PID force regulator are constructed to control position and interactive force between human upper limb and robot. Moreover, the robot's impedance control parameters are regulated in real time according to the muscle activation by using fuzzy inference system. At last, we apply this control strategy to an upper limb rehabilitation robot. The experiment results show robot can regulate the actual assistive force and impedance in real time based on voluntary participation intention of the human, which indicates it is effective for the proposed impedance control strategy to be an "assist-as-needed" strategy.
Accelerator Design for Vector Quantized Convolutional Neural Network

In recent years, deep convolutional neural networks (CNNs) achieve ground-breaking success in many computer vision research fields. Due to the large model size and tremendous computation of CNNs, they cannot be efficiently executed in small devices like mobile phones. Although several hardware accelerator architectures have been developed, most of them can only efficient address one of the two major layers in CNN, convolutional (CONV) and fully connected (FC) layers. In this paper, based on algorithm-architecture-co-exploration, our architecture targets at executing both layers with high efficiency. Vector quantization technique is first selected to compress the parameters, reduce the computation, and unify the behaviors of both CONV and FC layers. To fully exploit the gain of vector quantization, we then propose an accelerator architecture for quantized CNN. Different DRAM access schemes are employed to reduce DRAM access. We also design a high-throughput processing element architecture to accelerate quantized layers. Compare to previous accelerators for CNN, the proposed architecture achieves 1.2–5x less DRAM access and 1.5–5x higher throughput for both CONV and FC layers.
Secure Formation Communication Method Based on the Opportunity Theory

In order to improve the security of formation communication system of aircraft, a new formation communication method based on opportunity theory is proposed. This paper assessed radiation power and topological information, the radiation energy and the communication topology of flight formation are optimized by means of opportunistic communication. Simulation results show that this method improves the communication security of flight formation while ensuring the performance of formation communication.
A Study of Effects of Metal Gate Composition on Performance in Advanced n-MOSFETs

This work investigates the effects of HfO<sub>2</sub> layer defects, formed by the Al atoms in the metal gate, on performance and reliability in advanced n-type metal–oxide–semiconductor field-effect transistors (n-MOSFETs) with different Al content in the gate stacks. Many groups have proposed theories (e.g., Si–H bond dissociation, trapped in high-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> defects and impact ionization) to explain the degradation mechanisms in MOSFETs, with most focusing on the dissociation of the Si-H bond at the Si/SiO<sub>2</sub> interface and the carrier trapping in the high-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> layer. However, for multi-<inline-formula> <tex-math notation="LaTeX">${V}_{{\text {th}}}$ </tex-math></inline-formula> devices, experimental results indicate that the formation of defects by Al atoms in the HfO<sub>2</sub> layer is the main reason for degradation in advanced n-MOSFETs. Therefore, we propose a mechanism to clarify the deterioration in advanced n-MOSFETs with different Al content in the gate stacks, and this mechanism is confirmed by positive bias temperature instability (PBTI), hot carrier instability (HCI), HCI power-law fitting and the gate-induced drain leakage (GIDL) measurement.
Thermal Spreading Resistance in Ballistic-Diffusive Regime for GaN HEMTs

To develop an efficient thermal design for gallium-nitride (GaN) high-electron-mobility transistors (HEMTs) that usually hold a super-high-power density, it is essential to accurately predict the junction temperature. In GaN HEMTs, the heat transfer process is dominated by thermal spreading resistance. Moreover, the phonon mean free paths (MFPs) of GaN are comparable with the channel layer thickness and the heat spot width. Thus, a ballistic effect emerges, resulting in the invalidity of Fourier’s heat conduction law. Therefore, Fourier’s law-based thermal resistance model should be reexamined and modified for this case. In this paper, we used the phonon Monte Carlo (MC) method to investigate the thermal spreading resistance in a ballistic-diffusive regime for GaN HEMTs. Our simulation results indicate that the ballistic effect significantly altered the temperature distributions within channel layers and resulted in a dramatic increase in the thermal resistance when compared with Fourier’s law-based predictions. Furthermore, a semiempirical thermal resistance model with a fitting parameter was derived. This model can accurately address the issues of thermal spreading and the ballistic effect. This paper can provide a more in-depth understanding of the thermal spreading resistance in a ballistic-diffusive regime, and it can be useful for the prediction of junction temperatures and for the thermal management of HEMTs.
SmartCampus Project

The aim of the SmartCampus project is to create an environment which enables high-quality, flexible and effective studying, both as traditional face-to-face and online. A focal result of the project is the CiNetCampus environment, developed in connection with the Master’s education in mathematical information technology. It increase flexibility in education with the help of video lectures and add interactivity to lecture videos. It was also possible to integrate other functionalities to the environment that are useful for teaching and studying. This paper focuses on the presentation of the CiNetCampus environment and the functionalities implemented for it. The paper also highlights the impacts introduced by the environment from the viewpoint of studying, as well as future development ideas.
Experimental Demonstration of Vehicle to Road Side Infrastructure Visible Light Communications

The increasing use of light emitting diodes in traffic lights offer excellent opportunities for implementation of visible light communications (VLC)based wireless technology as part of intelligent transport systems in smart environments. In this paper, we experimentally demonstrate vehicle to infrastructure (V2I)communications based on the VLC technology using a real traffic light and a camera over a link span of up to 80 m. We show a reduction in the modulation depth of the signal from 1000% to 50% in order to track the light source when sending ‘0’ symbols. Also presented is the effect of operating the camera in the focused and defocused modes. The results show transmission success rates of 100% and 90% over link spans of 70 m and 80 m, respectively under specific testconditions.