Miracle-dz/UniFAR-FTUs huggingface dataset:
X-Ray Image Classification for Parcel Inspection in High-Speed Sorting Line

In recent years, some object detection algorithms have been proposed for X-ray image testing. Most of them are developed for baggage inspection to detect target objects automatically. However, these automated detection algorithms are still very limited to meet the need of practical application and X-ray screening systems are still being manipulated by human inspectors mainly. This situation of inspection is very unsuitable for parcel inspection in logistics industry owing to the huge amount of parcels. In this paper, we propose a classification method for X-ray image, which is a new method for automated inspection and can promote the inspection efficiency of logistics industry effectively.
Speech denoising based on Group Sparse Representation in the case of Gaussian Noise

This work addresses speech denoising problem in the presence of Gaussian noise based on group sparse representation. In this paper, the short-time Fourier transform (STFT) coefficients are viewed as an image in the time-frequency domain. In doing so, the group sparse representation denoising method is successfully applied to perform speech enhancement. To efficiently solve the optimization problem, the low-rank approximation model is developed. Numerical studies based on NOIZEUS dataset demonstrate the superior performance of the proposed estimation algorithm.
Short-Term Demand Prediction Method for Online Car-Hailing Services Based on a Least Squares Support Vector Machine

The purpose of this paper is to study the short-term demand prediction for online car-hailing services problem. Prediction of short-term network car demand can provide many benefits such as an increase in the income of network car drivers. In addition, demand prediction is an important resource for recommendation systems, carpooling systems, and network car scheduling; and therefore, predicting the demand for network cars has great significance. In the last few decades, scholars have studied various problems related to short-term demand prediction for online car-hailing services based on clustering algorithms and regression algorithms. However, these studies are still problematic because the accuracy of demand prediction is not high enough. Therefore, this paper studies a method of improving the accuracy and the efficiency of demand prediction. Due to the high prediction accuracy and the fast training efficiency of least squares support vector machine (LS-SVM), a short-term demand prediction method for online car-hailing services based on LS-SVM is proposed. The modeling process involves selecting the dependent and independent variables, the basic principles of the LS-SVM, the kernel function and superparameter, model training, and prediction. In a numerical experiment, we use network car order data as the network car demand data to test the model. We show that the experimental results with the LS-SVM method implemented in this paper and then compare the model with lasso linear regression, nearest neighbor regression, decision tree regression, and neural network. The experimental results show that the short-term demand prediction model for online car-hailing services based on LS-SVM performs better than the other methods.
Sparse Representation for Waveforms Classification

Waveforms classification is an important task in many applications such as disease diagnosis, earthquake prediction and speech recognition. In this paper, a sparse representation based method is proposed for waveforms classification. Firstly, K singular value decomposition (K-SVD) method is applied to each class of training samples to obtain a corresponding dictionary. Then, for a test sample, it is sparsely represented and reconstructed by each dictionary respectively, and assign it to the class with the smallest reconstruction error. To verify the classification ability of the proposed method, two experiments on both simulated and real-world data sets are conducted. The final experimental results demonstrate that our proposed method can obtain a good performance in terms of the classification accuracy and noise tolerance.
Millimeter Wave Channel Characterization for a Large Waiting Hall by Measurements and Simulations

In this paper, based on 28 GHz indoor MIMO channel measurements, a cluster model based on Euclidean distance is analyzed. Moreover, according to QuaDRiGa (Quasi-Deterministic Radio Channel Generator) simulation platform, large-scale parameters like delay spread, ASA (Azimuth Spread of Arrival), Ricean K-factor and time evolution characteristics are simulated and validated. Results show that the cluster model describes the multipath environment physically. The QuaDRiGa simulation results fit quite well with measurement data. This verifies the applicability of the QuaDRiGa simulation model in millimeter wave bands. Received power and delay spread modeled as a function of distance in both LOS and NLOS cases, and the QuaDRiGa has better space-time continuity. The provided results are useful for design of 5G millimeter wave communication systems.
Feature Fusing of Feature Pyramid Network for Multi-Scale Pedestrian Detection

Pedestrian detection is a fundamental component in many real-world applications such as automatic driving, intelligent surveillance, person re-identification and robotics. Therefore, it has attracted massive attention in the last decades. However, pedestrians in an images always exhibit different scales, which constitutes a significant mode of intra-class variability and affect the performance of pedestrian detection algorithm. To address this problem, we apply FPN (Feature Pyramid Network)for pedestrian detection. FPN exploits the inherent multi-scale structure of a deep convolutional network to construct a feature pyramid that has rich semantics at all levels and facilitates the detection of objects at different scales. To leverage the information from different levels of the feature pyramid, we extend the FPN-based pedestrian detection by fusing the feature of each level with adaptive feature pooling. Furthermore, we also integrate a Squeeze and Excitation module to the ROI pooled features from each level before the feature fusion. The experiment result on Caltech dataset shows that our approach outperforms the basic FPN-based pedestrian detection and robust towards to various scale of pedestrian.
Two Dimensional (2D) Convolutional Neural Network for Nepali Sign Language Recognition

Sign language is a basic mode of communication between people who have difficulties in speech and hearing. If computers can detect and distinguish these signs, communication would be easier and dependency on a translator reduces. This paper provides the structure of the system which translates Nepali signs from Nepali Sign Language (NSL) into their respective meaningful words. It captures the static hand gestures and translates the pictures into their corresponding meanings using 2D Convolutional Neural Network. Red glove is used for segmentation purpose. Data set is obtained by manually capturing images using a front camera of a laptop. The system got higher accuracy for the model that recognizes 5 signs than the 7 and the 9 signs model. It also facilitates the users to search for the signs using their corresponding English words. Its core objective is to make easy communication between differently-abled people and who do not understand sign language without involvement of a translator.
N400 Extraction from Fewer-Trial EEG Data Using a Supervised Signal-to-Noise Ratio Maximizer Method

N400 is a kind of event-related potential (ERP), which is related to language processing of brain and can be used for the evaluation of clinical psychological diseases. There still remain some problems in the accurate N400 waveform extraction from fewer-trial EEG data under the low signal-to-noise ratio (SNR)level. In this study, a supervised signal-to-noise ratio maximizer (SSM)method to obtain N400 waveform from multi-channel EEG data is proposed. The SSM algorithm designs a spatial filter for low-rank ERP component and extracts the N400 by 40-trial EEG datasets of each subject. The algorithm has more excellent performance in estimating the accurate N400 waveform from simulation data and real EEG data, compared to SIM and the regularized SOBI algorithms. The results show that the proposed method can effectively achieve the N400 extraction from fewer-trial EEG data.
Comparison of Heat Assisted Lap Joints of High-Temperature Superconducting Tapes With Inserted Indium Foils

Easy and simple fabrication of a lap joint of high-temperature superconducting (HTS) tapes is desirable for various HTS applications such as power cables and current feeders. We have developed mechanical joints with a low-temperature heat treatment at around 370 K where indium foils are inserted between joint surfaces. Though the joint showed a good performance, it took over 10 min to fabricate one joint and ideally the fabrication time should be further shortened. In this study, we proposed to use ultrasonic welding (UW) to assist a heat generation at the joint for lap joints with indium foil, which can be completed within 1 s. We prepared various lap joint samples of HTS tapes: REBCO (REBa<sub>2</sub>Cu<sub>3</sub>O<sub>7</sub>, RE: rare-earth) or BSCCO (Bi<sub>2</sub>Sr<sub>2</sub>Ca<sub>2</sub>Cu<sub>3</sub>O<sub>10</sub>) tapes were pressed together with an indium foil using a UW machine or pressing machine with cartridge heaters. Then, the current–voltage curve was evaluated at 77 K and the joint resistance characteristics were discussed depending on the joint methods and HTS tapes. The samples with copper-stabilized REBCO tapes all showed joint resistivities of 31.6–35.6 nΩcm<sup>2</sup> without critical current (<italic>I</italic><sub>C</sub>) degradation for both joint methods. The samples with silver-sheathed BSCCO tapes showed joint resistivities of 18.6–46.5 nΩcm<sup>2</sup> for both joint methods; however, some of those fabricated by the UW showed <italic>I</italic><sub>C</sub> degradation. The degradation was able to be prevented by optimizing UW conditions.
OPEN CHALLENGES IN BUILDING COMBAT SIMULATION SYSTEMS TO SUPPORT TEST, ANALYSIS AND TRAINING

High expectations to leverage Modeling and Simulation (M&S) capabilities to support operational testing, systems analysis and user/operator training is motivated by a number of factors. As the complexities of new operations, such as multi-domain operations and operations in highly contested area operations, can no longer be tested and trained in live environments, leveraging M&S capabilities efficiently is paramount. In this paper, we use the differentiation of combat simulation requirements in terms of functional (e.g., model fidelity) and non-functional (e.g., real-time, distributed) characteristics to define a trade space to highlight challenges in relation to simulation purpose. 5th generation weapon systems require lots of data with high fidelity, depending on the purpose of the simulation. As functional and non-functional characteristics are interdependent, the design of supporting simulation systems must be well aligned with the purpose when developed, and is constraining possible purposes to be supported once it is developed.
Image Edge Detection Base on Conditional Generative Adversarial Nets

We propose a method to prepare for developing the quality of the reconstructing objects from edge detection. We know that some conditional generative adversarial networks like pix2pix, learn a loss function to train the mapping from input image and output image. In case of using single mapping, we cannot guarantee that all samples in X and all samples in the Y are reasonably corresponding. So, we suppose to utilize bijection and we make the optimization for the pix2pix'U-net, which can develop our model to reconstruct objects from edge detection needing to be repaired. These can let our image generated by edge detection with our method get less probability of mode collapse and ensure the image style more similar to samples.
Closed-Loop Position and Cadence Tracking Control for FES-Cycling Exploiting Pedal Force Direction With Antagonistic Biarticular Muscles

A functional electrical stimulation (FES)-based position and cadence tracking controller is developed to enable cycling by exploiting antagonistic biarticular muscles. A model of a stationary cycle and a rider is developed as a closed-chain mechanism. A strategy is then developed to switch between muscle groups based on the force direction of each muscle group. Stability of the developed controller is analyzed through Lyapunov-based methods. Experiments were conducted in seven healthy individuals and one individual with Parkinson’s disease to illustrate the performance of the developed method. Specifically, the developed method was compared with voluntary tracking in terms of the position and velocity tracking errors. From the experimental results, we conclude that the proposed method can realize FES-cycling close to voluntary tracking.
SUBSAMPLING VARIANCE FOR INPUT UNCERTAINTY QUANTIFICATION

In stochastic simulation, input uncertainty refers to the output variability arising from the statistical noise in specifying the input models. This uncertainty can be measured by a variance contribution in the output, which is estimated commonly via the bootstrap. However, due to the convolution of the simulation noise and the input noise, the computation effort required in the existing bootstrap schemes are typically substantial. This paper investigates a subsampling framework as a computation saver. We demonstrate the strengths of our subsampled bootstrap in terms of theoretical computation requirements, and substantiate them with numerical illustrations.
Adaptive 2-D DOA Estimation using Subspace Fitting

Direction-of-arrival (DOA) estimation is a ubiquitous task in array processing. In this paper, we propose an adaptive 2-dimensional direction finding framework to track multiple moving targets by using the subspace fitting method. First, we expand the steering vectors of the current snapshot in a Taylor series around the DOAs of the previous snapshot. Then we transform the subspace fitting problem into a set of linear equations. As a result, the DOAs of each snapshot can be updated by solving a set of linear equations and we no longer need to search the 2-D spatial spectrum. In comparison with the traditional 2-D MUSIC, the proposed method not only reduces the computational complexity considerably but also has better estimation performance.
Nonlinear Modeling and Docking Tests of a Soft Modular Robot

Soft modular robots have the advantages of both bionic continuum robots and modular self-reconfigurable robots. They have potential application for working in narrow space and uneven terrain. The outstanding abilities of the soft modular robots are infinite degrees of freedom and a changeable configuration. However, these advantages also incur difficulty in mathematical modeling and continuous motion control, especially for docking, which is the key to realizing a changeable configuration. Therefore, the development of a soft modular robot requires a modeling method to guide the module’s motions accurately. In this paper, we design and manufacture a pneumatic soft modular robot with a novel connecting mechanism to achieve docking. A nonlinear dynamic model of our soft module is established. In particular, the analytic solutions of the module plane docking are presented. Based on the nonlinear dynamic modeling, three experiments are performed under different conditions. The experimental results pertaining to module plane bending agree with the theoretical values, which verify the accuracy of the modeling method. Moreover, fixed point docking and two modules docking experiments demonstrate the validity of the modeling method and the docking ability. The modeling method proposed in this paper can well guide the docking of soft modular robots.
Sampling H- & V-Polarized Antennas using a Single ADC for Digital Antenna Arrays by Exploiting Multi-Dimensional Signal Processing RF Circuits

Digital array receivers increasingly require both H and V polarization of the incident RF waves while supporting full-band operation. A wideband ADC is required for each polarization at every location of the array, leading to 2N ADCs for N locations. The paper proposes exploiting multidimensional sparsity in the spatio-temporal frequency domain to reduce the number of ADCs from 2N to N, while supporting two polarizations and wideband RF-digital operation. By using spatiotemporal sparsity with multi-dimensional linear transforms, it is proposed to combine the H and V array signals without interference, such that, the combined array signal can be sampled using just N ADCs. This allows a modern RFSoC or array receiver with N wideband ADC inputs to process N spatial locations in an array where each location contains a cross-polarized element measuring both H and V components, doubling the information carrying capacity of the N-ADC system provided that the elements limit the field-of-view to about 60°.
Uplink Performance Improvement by Frequency Allocation and Power Control in Heterogeneous Networks

The cell association based on the maximum downlink received power is optimal in a conventional homogeneous network, but this association is not particularly suitable for heterogeneous networks (HetNets). Therefore, the concept of downlink and uplink decoupling (DUDE) is proposed for improving uplink performances of HetNets. However, in DUDE association scheme, macro user equipment (MUE) will suffer more interference from the offloaded users in uplink. In this paper, we investigate uplink interference mitigation through frequency reuse and power control schemes. The reverse frequency allocation (RFA) scheme is adopted to alleviate the cross-tier interference by increasing the distance between users of the same frequency. Then, a dynamic distributed power control (DDPC) with user admission control is proposed to further improve the uplink users performance and system spectrum utilization. The DDPC mitigates co-channel interference by dynamically updating transmitting power of active users and new access users, meanwhile it can maintain the link quality of active users above given signal to interference plus noise ratio (SINR) thresholds at all times. The dynamics and performance of the network are investigated through simulation experiments in terms of average SINR, evolution of uplink transmitting power, uplink SINR, and dynamic adjusting factor. The simulation results show that, in comparison with the DUDE scheme and DUDE with RFA scheme, DUDE with RFA and DDPC scheme can achieve a better quality of service (QoS) and uplink data rate performance.
Distributed Channel Allocation for D2D-Enabled 5G Networks Using Potential Games

Frequency channel allocation is a key technique for improving the performance of cellular networks. In this paper, we address the channel allocation problem for a 5G multi-cell system. We consider a heterogeneous network in which cellular users, micro-cell users, and device-to-device (D2D) communications coexist within the radio footprint of the macro cell. We maximize the aggregate transmission rate, exploiting channel diversity and managing both the inter-cell interference, typical of cellular networks and the intra-cell interference generated by the nonorthogonal transmissions of the small-cell and D2D users. By modeling the allocation problem as a potential game, whose Nash equilibria correspond to the local optima of the objective function, we propose a new decentralized solution. The convergence of our scheme is enforced by using a better response dynamic based on a message passing approach. The simulation results assess the validity of the proposed scheme in terms of convergence time and achievable rate under different settings.
Compensation of Parameter Uncertainty Using an Adaptive Sliding Mode Control Strategy for an Interior Permanent Magnet Synchronous Motor Drive

This paper designs an adaptive sliding mode speed controller (ASMSC) that can compensate for parameter uncertainty of an interior permanent magnet synchronous motor (IPMSM) drive. Unlike the previous control systems, the proposed ASMSC guarantees a precise speed tracking capability in the presence of severe parameter variations without accurate knowledge on the motor parameter values and uncertainty bounds. In particular, the proposed adaptive switching gain tuning (ASGT) term can effectively solve the excessive input energy consumption problem due to an unnecessarily overestimated switching gain. Next, the convergence and stability analysis are proven through a Lyapunov function. The feasibility of the proposed approach is verified via experimental results using a prototype IPMSM test bed with a TI TMS320F28335 digital signal processor. In this paper, the proposed SMSC is investigated with two control terms: without an ASGT term and with an ASGT term. In addition, the conventional proportional–integral speed controller and conventional linear matrix inequalities based sliding mode speed controller are chosen for the performance comparison. Finally, the proposed ASMSC can assure more robust and faster speed performances against the parameter uncertainty under three dynamic load conditions.
Design of a Quad-Polarization-Agile Antenna Using a Switchable Impedance Converter

This letter presents a novel quad-polarization-agile aperture-coupled antenna using a reconfigurable impedance converter. The designed reconfigurable converter can achieve impedance and phase changes under the control of p-i-n diodes. Based on this converter, a quad-mode switchable feeding network can be produced. Utilizing the feeding network to excite a dual-polarized aperture-coupled patch antenna, polarization reconfigurability is realized. By switching the p-i-n diodes, two orthogonal linear polarizations (LPs) and two orthogonal circular polarizations can be generated. Measurement results show that its return-loss bandwidth is wider than 2.5% at LP modes, its axial-ratio bandwidth is better than 1.8%, and its cross polarizations are below –20 dB for all the states.
Advanced Ear Detection Algorithm Using Faster R-CNN, Refocus Filters, and the Gradient Map

Generally, the ear is a distinctive feature in face images. Many researchers carrying out the studies on face image processing attempt to extract the features of ears to improve the performances of face detection and recognition. In this paper, we propose a novel and efficient algorithm to detect ears from profile images automatically. The proposed algorithm is based on the techniques of the convolutional neural network (CNN), the object refocus filter, and the gradient map to avoid illumination variation and make the features of ears more prominent. We perform several simulations on well-known datasets and show that the proposed algorithm methods can achieve even higher detection rates than state-of-the-art methods.
High-Capacity Multi-Stage Operation of Polarization-Diversity Silicon Photonics 8 × 8 Optical Switch

For practical and wide use of optical switches, cascadability and high throughput are of great significance. We demonstrate up to 8-stage cascaded operation of a polarization-independent silicon photonics 8 × 8 optical switch where 11-channel 32/44-Gbaud DP-16QAM signals are loaded up on all the paths in a cyclic way. The results verify that the 8 × 8 switch is capable of offering a throughput of 48.2 Tb/s with a wall-plug energy efficiency of 0.37 pJ/bit. Using an additional optical module with the characteristics equivalent to the 8 × 8 switch, 9-stage cascaded operation is also emulated. The successful performance observed in these experiments promises the realization of optical switch fabrics with higher port counts by arranging the 8 × 8 switches in a multi-folded Clos network.