Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Importance-Aware Bandwidth-Allocation Scheme for Point-Cloud Transmission in Multiple LIDAR Sensors

This paper addresses bandwidth allocation to multiple light detection and ranging (LIDAR) sensors for smart monitoring, which a limited communication capacity is available to transmit a large volume of point-cloud data from the sensors to an edge server in real time. To deal with the limited capacity of the communication channel, we propose a bandwidth-allocation scheme that assigns multiple point-cloud compression formats to each LIDAR sensor in accordance with the spatial importance of the point-cloud data transmitted by the sensor. Spatial importance is determined by estimating how objects, such as cars, trucks, bikes, and pedestrians, are likely to exist since regions where objects are more likely to exist are more useful for smart monitoring. A numerical study using a real point-cloud dataset obtained at an intersection indicates that the proposed scheme is superior to the benchmarks in terms of the distributions of data volumes among LIDAR sensors and quality of point-cloud data received by the edge server.
Linear Phased Array Antenna Fed by the Modified Dielectric Image Line

A linear phased array antenna fed by the modified dielectric image line (DIL) is proposed in this letter. The array contains 16 subarrays, and one subarray consists of eight patch antenna elements fed by one modified DIL through eight coupling apertures on the ground plane. The modified DIL can enhance the electromagnetic field confinement in the DIL, which is beneficial to reduce the mutual coupling between parallel DILs. According to the measured results, the coupling between two modified DILs has been reduced by about 5 dB compared with that in the original DILs when the length is 160.5 mm. Finally, the proposed array achieves a wide fractional bandwidth of over 20% with voltage standing-wave ratio (VSWR) less than 2.5 when scanning up to ±60° in the H-plane in 8∼10 GHz. The isolation between the two adjacent ports is higher than 20 dB. A 16 × 8 array prototype is measured to verify the validity of the design.
Hierarchical Head Design for Object Detectors

The notion of anchor plays a major role in modern detection algorithms such as the Faster-RCNN [1] or the SSD detector [2]. Anchors relate the features of the last layers of the detector with bounding boxes containing objects in images. Despite their importance, the literature on object detection has not paid real attention to them. The motivation of this paper comes from the observations that (i) each anchor learns to classify and regress candidate objects independently (ii) insufficient examples are available for each anchor in case of small-scale datasets. This paper addresses these questions by proposing a novel hierarchical head for the SSD detector. The new design has the added advantage of no extra weights, as compared to the original design at inference time, while improving detectors performance for small size training sets. Improved performance on PASCAL-VOC and state-of-the-art performance on FlickrLogos-47 validate the method. We also show when the proposed design does not give additional performance gain over the original design.
Joint DL/UL Decoupled Cell-Association and Resource Allocation in D2D-Underlay HetNets

With the rise of data-hungry applications and the rapid development of the Internet-of-Things (IoT), 5G and device-to-device (D2D) communications come into being. While such technologies are designed to support massive data access to meet the ever-increasing demands, the complexity and heterogeneity of the network structure bring bigger challenges for interference management. As such, downlink/uplink decoupling (DUDe) has become an attractive approach, as it offloads user equipments (UEs) to small-cells (SCells) and creates a more benign environment for D2D communication. In this paper, we investigate the application of DUDe in D2D-underlay heterogeneous networks, and propose an efficient joint cell-association, subchannel allocation, and power control scheme for network sum-rate maximization. The proposed scheme includes a minimum path-loss based DUDe to decide the uplink (UL) serving base-station, and a greedy coloring scheme with modified Munkres algorithm to cluster the UEs, and allocate subchannels. Furthermore, the difference of convex functions based programming algorithm for power control is utilized to maximize the network sum-rate while satisfying the UE transmit power and data rate constraints. The results presented show that the proposed scheme can achieve superior performance to the coupled benchmarks.
Not Only Look But Infer: Multiple Hypothesis Clustering of Data Association Inference for Semantic SLAM

In semantic visual simultaneous localization and mapping (VSLAM), accurate data association of semantic measurement from visual sensor is very crucial for robot state estimation and scene reconstruction. However, most of the related works assume a simple world for semantic association. It is still a challenge to deal with the ambiguity of data association in a cluttered environment. In this article, we propose a novel approach to reduce the uncertainty of data association via multiple hypothesis Dirichlet process (MHDP). The posterior distribution of data association is inferred by Dirichlet process (DP) first. Ambiguous associations from the distribution are tackled by a hypothesis tree, and hypothesis testing-based ambiguity judgment is then proposed for each object measurement to provide a strategy for branch growing of the hypothesis tree. Moreover, the proposed data association approach is integrated with a geometric featured-based simultaneous localization and mapping (SLAM) system in a tightly coupled way. The qualitative and quantitative evaluation on simulated and real-world public data sets demonstrates the robustness and effectiveness of our approach compared to other data association methods and the state-of-the-art SLAM system.
BAT Optimized CNN Model Identifies Water Stress in Chickpea Plant Shoot Images

Stress due to water deficiency in plants can significantly lower the agricultural yield. It can affect many visible plant traits such as size and surface area, the number of leaves and their color, etc. In recent years, computer vision-based plant phenomics has emerged as a promising tool for plant research and management. Such techniques have the advantage of being non-destructive, non-evasive, fast, and offer high levels of automation. Pulses like chickpeas play an important role in ensuring food security in poor countries owing to their high protein and nutrition content. In the present work, we have built a dataset comprising of two varieties of chickpea plant shoot images under different moisture stress conditions. Specifically, we propose a BAT optimized ResNet-18 model for classifying stress induced by water deficiency using chickpea shoot images. BAT algorithm identifies the optimal value of the mini-batch size to be used for training rather than employing the traditional manual approach of trial and error. Experimentation on two crop varieties (JG and Pusa) reveals that BAT optimized approach achieves an accuracy of 96% and 91% for JG and Pusa varieties that is better than the traditional method by 4%. The experimental results are also compared with state of the art CNN models like Alexnet, GoogleNet, and ResNet-50. The comparison results demonstrate that the proposed BAT optimized ResNet-18 model achieves higher performance than the comparison counterparts.
Classification and feature selection using a primal-dual method and projection on structured constraints

This paper concerns feature selection using supervised classification on high dimensional datasets. The classical approach is to project data onto a low dimensional space and classify by minimizing an appropriate quadratic cost. We first introduced a matrix of centers in the definition of this cost. Moreover, as quadratic costs are not robust to outliers, we propose instead to use an $\ell_{1}$ cost (or Huber loss to mitigate overfitting issues). While control on sparsity is commonly obtained by adding an $\ell_{1}$ constraint on the vectorized matrix of weights used for projecting the data, we propose to enforce structured sparsity. To this end we used constraints that take into account the matrix structure of the data, based either on the nuclear norm, on the $\ell_{2,1}$ norm, or on the $\ell_{1,2}$ norm for which we provide a new projection algorithm. We optimize simultaneously the projection matrix and the matrix of centers with a new tailored constrained primal-dual method. The primal-dual framework is general enough to encompass the various robust losses and structured constraints we use, and allows a convergence analysis. We demonstrate the effectiveness of this approach on three biological datasets. Our primal-dual method with robust losses, adaptive centers and structured constraints does significantly better than classical methods, both in terms of accuracy and computational time.
3D Audio-Visual Speaker Tracking with A Novel Particle Filter

3D speaker tracking using co-located audio-visual sensors has received much attention recently. Though various methods have been attempted to this field, it is still challenging to obtain a reliable 3D tracking result since the position of colocated sensors are restricted to a small area. In this paper, a novel particle filter (PF) based method is proposed for 3D audio-visual speaker tracking. Compared with traditional PF based audio-visual speaker tracking method, our 3D audio-visual tracker has two main characteristics. In the prediction stage, we use audio-visual information at current frame to further adjust the direction of the particles after the particle state transition process, which can make the particles more concentrated around the speaker direction. In the update stage, the particle likelihood is calculated by fusing both the visual distance and audiovisual direction information. Specially, the distance likelihood is obtained according to the camera projection model and the adaptively estimated size of speaker face or head, and the direction likelihood is determined by audio-visual particle fitness. In this way, the particle likelihood can better represent the speaker presence probability in 3D space. Experimental results show that the proposed tracker outperforms other methods and provides a favorable speaker tracking performance both in 3D space and on the image plane.
Easy-to-Design-and-Manufacture Frequency Selective Surfaces for Conformal Applications

A bendable frequency selective surface (FSS) is proposed in this letter; it is based on short carbon fibers embedded into an epoxy resin whose characteristics are set in order to provide the required mechanical flexibility. The frequency response of the FSS is of band-stop type due to the half-wavelength resonance of the used short dipoles. The FSS design and the manufacturing process are clearly highlighted to demonstrate the advantages of the proposed FSS in terms of costs, ease to design and build the FSS, and accuracy of the obtained stopband response. Both aluminum and carbon materials are considered for the fiber as a mean for achieving a mechanically reinforced structure; the effects of both materials from an electromagnetic propagation point of view are discussed. Finally, the proposed FSSs with different fiber materials and bending radii are manufactured and experimentally tested in a free-space setup to verify the design. The proposed FSS is mainly suitable for applications on parabolic reflectors and curved radomes.
The Influence of Team Human Capital on Safety Performance of Engineering Project Team: The Mediating Role of Safety Citizenship Behavior

With the further deepening and expansion of the “Belt and Road” initiative, the number and scale of various engineering project teams in China are continuously expanding, together with the associated security risks. To this respect, this paper studies the relationship among the team human capital, the overall level of safety citizenship behavior, and the team safety performance based on the division of the human capital components of the engineering project team, so as to provide theoretical reference for safety management and effect improvement. Empirical results show that: (1) The human capital of the project team and its three sub-dimensions (team capability capital, team psychological capital, and team social capital) have a significant positive impact on the team safety performance; (2) The human capital of the project team and its three sub-dimensions have a significant positive effect on the overall level of safety citizenship behavior of the team; (3) Safety citizenship behavior plays an intermediary role in the influence of team human capital and its three sub-dimensions on safety performance.
Validation of Positive-Sequence Modeling of Large-Disturbance Stability in a Distribution Network With Distributed Generation Using the Hybrid Comprehensive Simulator

One of the general trends in power industry is the penetration of distributed generation (DG) units. The ongoing transformation of electric power systems (EPS) due to this penetration leads to a significant change in the properties of power systems. The problem arises in the ensuring stability of distribution system with DG units and EPS as a whole, especially in the case of large disturbances. The main way of solving this problem is positive-sequence phasor time-domain simulation. However, the known simplifications and limitations are inevitably applied at such simulation. In this regard, the obtained simulation results need to be validated. This paper proposes an alternative approach for validation of processes calculations, based on the use of a benchmark tool instead of field data. The hybrid simulator is proposed to use as a benchmark tool, because such simulator allow to obtain comprehensive information about a single spectrum of wave, electromagnetic, electromechanical processes in power system in cases of different large disturbances. That makes possible to identify the impact of the applied simplifications and limitations in the positive-sequence simulation on the reliability of power system stability assessment in the case of large disturbances. The studies presented in this paper were carried out using the scheme of distribution system with DG units, which is part of a real EPS. The obtained results demonstrate the arising errors in stability calculations, the nature of their changing and causes of occurrence, as well as factors affecting them.
Improving Gravitational Wave Detection with 2D Convolutional Neural Networks

Sensitive gravitational wave (GW) detectors such as that of Laser Interferometer Gravitational-wave Observatory (LIGO) realize the direct observation of GW signals that confirm Einstein's general theory of relativity. However, it remains challenges to quickly detect faint GW signals from a large number of time series with background noise under unknown probability distributions. Traditional methods such as matched-filtering in general assume Additive White Gaussian Noise (AWGN) and are far from being real-time due to its high computational complexity. To avoid these weaknesses, one-dimensional (1D) Convolutional Neural Networks (CNNs) are introduced to achieve fast online detection in milliseconds but do not have enough consideration on the trade-off between the frequency and time features, which will be revisited in this paper through data pre-processing and subsequent two-dimensional (2D) CNNs during offline training to improve the online detection sensitivity. In this work, the input data is pre-processed to form a 2D spectrum by Short-time Fourier transform (STFT), where frequency features are extracted without learning. Then, carrying out two 1D convolutions across time and frequency axes respectively, and concatenating the time-amplitude and frequency-amplitude feature maps with equal proportion subsequently, the frequency and time features are treated equally as the input of our following two-dimensional CNNs. The simulation of our above ideas works on a generated data set with uniformly varying SNR (2–17), which combines the GW signal generated by PYCBC and the background noise sampled directly from LIGO. Satisfying the real-time online detection requirement without noise distribution assumption, the experiments of this paper demonstrate better performance on average compared to that of 1D CNNs, especially in the cases of lower SNR (4–9).
Enhanced Vote Network for 3D Object Detection in Point Clouds

In this work, we aim to estimate 3D bounding boxes by voting to object centers and then groups and aggregates the votes to generate 3D box proposals and semantic classes of objects. However, due to the sparse and unstructured nature of the point clouds, we face some challenges when directly predicting bounding box from the vote feature: the sparse vote feature may lack some necessary semantic and context information. To address the challenges, we propose a vote feature enhancement network that aims to encode semantic-aware information and aggravate global context for the vote feature. Specifically, we learn the point-wise semantic information and supplement it to the vote feature, and we also encode the pairwise relations to collect the global context. Experiments on two large datasets of real 3D scans, ScanNet and SUN RGB-D, demonstrate that our method can achieve excellent 3D detection results.
Optimizing Cooperative Pallet Loading Robots: A Mixed Integer Approach

We consider a system with a metering conveyor that feeds a palletizing machine. Multiple robots cooperatively move packages to obtain a desired final pallet layout. We address the problem of finding the optimal sequence of manipulations and packages positions on the conveyor that allow to maximize production. Our main contribution is the reformulation of this operation as a mixed integer linear programming (MILP) problem, that we solve with a metaheuristic that exploits a standard commercial solver. We also present some numerical experiments on randomly generated problems.
Broadband Target Simulator for FMCW Radar Sensors With Mirror Target Suppression

This article describes a target simulator for FMCW radar sensors which, working on the frequency shift principle, deals with the presence of an unwanted (mirror) target. A simple method is proposed to suppress the mirror target by utilizing an IQ mixer in simulator circuitry which enables single-sideband modulation. Achievable combinations of speed and distance of simulated targets are also presented. In addition, the generation of targets closer than the physical distance of the simulator, a radar parameter estimation and a simulator calibration are included. All presented principles were verified by a laboratory experiment with a 24 GHz FMCW radar module and other commercially available components.
A Modularized Interchangeable Multibeam Slot Array Antenna Using Hybrid Substrates for Mass Production

A substrate integrated waveguide multibeam antenna based on a parabolic lens beam-forming network is presented. The proposed antenna is comprised of a port deck (Rogers RT/duroid 5880, ϵr = 2.2, tanδ = 0.0009) and an antenna deck (Rogers RT/duroid 6002, ϵr = 2.94, tanδ = 0.0012). By fabricating the antenna deck with substrates of different permittivity, while the port deck does not have to be changed, the proposed antenna features flexible beam tunability. Experimental results show that the proposed antenna can operate from 27∼28 GHz with the gain up to 14.5 dBi. The maximum radiation efficiency is 85%. Five beams pointing from −59° to 62° are achieved. Compared with counterparts, the proposed antenna features desirable performance but simpler structure. The short turn-around design circle and low cost from the modularized interchangeable design make it preferable for 5G mobile terminals in mass production.
UWB Localization in a Smart Factory: Augmentation Methods and Experimental Assessment

The advent of the fourth industrial revolution (Industry 4.0) aims at increasing automation and efficiency in manufacturing processes by the adoption of information and communication technologies. Several of the proposed solutions rely on precise localization of material, equipment, or operators. This article investigates the employment of ultrawideband (UWB) real-time location systems (RTLS) in a factory environment and proposes an augmentation technique to mitigate the impairments that arise in such a complex scenario. A Bayesian filtering method is developed to jointly track the motion dynamics and the time-varying visibility conditions of the UWB antennas, with particle-based implementation to deal with the nonlinearity of the UWB measurements. Laboratory tests and industrial experiments are carried out to evaluate the performance of three commercial off-the-shelf UWB technologies: Decawave, Sewio, and Ubisense. The experimental data are then used to calibrate and test the developed filtering technique, showing that it is possible to significantly reduce the positioning error originating from dense multipath and NLOS effects by jointly tracking the target dynamics and visibility conditions.
Dynamic Unequally Sub-Connected Hybrid Beamforming Architecture for Massive MIMO Systems

In this work, we propose a novel dynamic unequal sub-array (DUESA) architecture for massive multiple-input multiple-output (MIMO) systems with partially connected hybrid beamforming (PC-HBF) that relaxes the antenna-allocation constraint in the conventional unequal sub-array (UESA) architecture. The proposed DUESA architecture dynamically allocates antennas to sub-arrays based on channel state information, which leads to a combinatorial problem to optimize the sum rate. The exact solution to this problem requires an exhaustive search; however, the total number of candidates to search through can be extremely large in massive MIMO systems. To address this challenge, we propose three sub-optimal algorithms. The first is a tabu search-based algorithm, which examines the neighbors of the current solution to antenna-to-sub-array allocation and moves to the best one in each iteration. The second is a sequential antenna-reallocation algorithm with reduced complexity, which updates an antenna-to-sub-array assignment in each iteration. The third algorithm, that is, greedy antenna allocation, is composed of two sub-optimal algorithms. In its first part, only a single antenna is allocated to each sub-array based on a conventional algorithm. Then, in the second part, the antennas are added sequentially to each sub-array to optimize the system sum rate. The numerical results demonstrate that the DUESA architecture achieves substantially higher total achievable rates than conventional PC-HBF schemes. Furthermore, it has approximately 10%–20% higher energy efficiency than the conventional UESA architecture.
Speeding-up pruning for Artificial Neural Networks: Introducing Accelerated Iterative Magnitude Pruning

In recent years, Artificial Neural Networks (ANNs) pruning has become the focal point of many researches, due to the extreme overparametrization of such models. This has urged the scientific world to investigate methods for the simplification of the structure of weights in ANNs, mainly in an effort to reduce time for both training and inference. Frankle and Carbin [1], and later Renda, Frankle, and Carbin [2] introduced and refined an iterative pruning method which is able to effectively prune the network of a great portion of its parameters with little to no loss in performance. On the downside, this method requires a large amount of time for its application, since, for each iteration, the network has to be trained for (almost) the same amount of epochs of the unpruned network. In this work, we show that, for a limited setting, if targeting high overall sparsity rates, this time can be effectively reduced for each iteration, save for the last one, by more than 50%, while yielding a final product (i.e., final pruned network) whose performance is comparable to the ANN obtained using the existing method.