Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of an Active Beam-Steering Array With a Perforated Wide-Angle Impedance Matching Layer

In this communication, a tapered slot antenna array with a perforated wide-angle impedance matching (WAIM) layer is studied for active beam steering. To achieve the wide-angle beam scanning characteristic, a tapered slot antenna is used in the array antenna design. For active impedance matching in a wide beam-steering angle, a thin dielectric type of WAIM is introduced, and an optimal WAIM characteristic is realized by changing the effective dielectric constant by adding an air hole to the WAIM. To verify the characteristics of the designed antenna, a <inline-formula> <tex-math notation="LaTeX">$16\times 16$ </tex-math></inline-formula> array is fabricated and measured. The fabricated array shows active <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>-parameter characteristics of less than −9.13 dB during E-plane scanning and −9.31 dB during H-plane scanning when the beam is steered up to 70° on both planes. It was also confirmed that when the beam is steered to 70°, no scan blindness or grating lobes occur within the operating frequency.
EfficientPillarNet: A Fast Deep Network for Semantic Segmentation of Large-scale Point Clouds from Lidar

Semantic segmentation in large-scale point clouds has brought increasing research in the 3D vision inspection field of many robotic applications, such as auto-driving and drone. To segment 3D point clouds end-to-end, there are two typical ways recently: using 3D convolution on the structure-specific 3D point clouds tends to be more accurate, while encoding point clouds into 2D pseudo graph tend to be faster. In this paper, we propose EfficientPillarNet, a real-time processing network, which projects disorder point clouds into bird’s-eye-view by using factorized convolutions and dilated convolutions in order to gain state-of-art operating efficiency while remaining outstanding performance. Our approach can realize pixel-wise semantic segmentation in real-time in a single GPU. Our experiment shows that our model’s segmentation performance can run at 20Hz on the 1080Ti and main classes of mIoU can reach 58.8% on the lidar dataset. This makes our model an ideal approach for scene understanding of large-scale point clouds.
Scaling and Adopting a Multimodal Learning Analytics Application in an Institution-Wide Setting

Multimodal learning analytics, which is collection, analysis, and report of diverse learning traces to better understand and improve the learning process, has been producing a series of interesting prototypes to analyze learning activities that were previously hard to objectively evaluate. However, none of these prototypes have been taken out of the laboratory and integrated into real learning settings. This article is the first to propose, execute, and evaluate a process to scale and deploy one of these applications, an automated oral presentation feedback system, into an institution-wide setting. Technological, logistical, and pedagogical challenges and adaptations are discussed. An evaluation of the use and effectiveness of the deployment shows both successful adoption and moderate learning gains, especially for low-performing students. In addition, the recording and summarizing of the perception of both instructors and students point to a generally positive experience in spite of the common problems of a first-generation deployment of a complex learning technology.
Towards Automated Software Testing with Generative Adversarial Networks

This paper discusses software testing methods based on Generative Adversarial Network (GAN). GAN is a generative model that can create new data instances that resemble training data. A GAN consists of a generator network and a discriminator network. In our testing scheme, the trained generator network is used as a test case generator. In addition, we propose a framework with GAN, which is a testing strategy used to increase the test coverage.
HI-FFT: Heterogeneous Parallel In-Place Algorithm for Large-Scale 2D-FFT

Fast Fourier Transform (FFT) is a fundamental operation for 2D data in various applications. To accelerate large-scale 2D-FFT computation, we propose a Heterogeneous parallel In-place 2D-FFT algorithm, HI-FFT. Our novel work decomposition method makes it possible to run our parallel algorithm on the original data (i.e., in-place), unlike prior parallel algorithms that require additional memory space (i.e., out-of-place) to guarantee independence among sub-tasks. Our work decomposition method also removes the duplicated operations on the out-of-place approaches. Using our decomposition method, we introduced an in-place heterogeneous parallel algorithm that utilizes both multi-core CPU and GPU simultaneously. To maximize the utilization efficiency of the computing resources, we also propose a priority-based dynamic scheduling method. We compared the performance of seven different 2D-FFT algorithms, including ours, for large-scale 2D-FFT problems whose sizes varied from 20K2 to 120K2. As a result, we found that our method achieved up to 2.92 and 4.42 times higher performance than the conventional homogeneous parallel algorithms based on the state-of-the-art CPU and GPU libraries, respectively. Also, our method showed up to 2.27 times higher performance than the prior heterogeneous algorithms while requiring two times less memory space. To check the benefit of our HI-FFT on an actual application, we applied it to a CGH (Computer Generated Holography) process. We found that it successfully reduces the hologram generation time. These results demonstrate the advantage of our approach for large-scale 2D-FFT computation.
SBR: A Novel Architecture of Software Defined Network Using the RPL Protocol for Internet of Things

This paper proposes a novel architecture named SBR, a software defined network (SDN) architecture using as a routing protocol RPL, a Protocol for Low-Power, and Lossy Networks for Internet of Things (IoT). The architecture includes the function SIGMA, an objective function used in IoT applications to take routing decisions. This research represents an effort to relate IoT and SDN. This association was developed using RPL-SIGMA to solve various IoT and Wireless Sensor Networks (WSN) problems. At the end, a comparison between SBR (SDN-RPL with SIGMA) and SDN-RPL (without SIGMA) was included. The simulations show that SBR has a better performance concerning packet loss, latency, and energy consumption over an architecture based on IoT without SIGMA. The simulation also demonstrates that SDN can be used to manage the IoT network efficiently.
Unified Approach for Synthesis and Analysis of Non-Isolated DC-DC Converters

Transformational techniques unifying synthesis of two-state DC-DC converters and analytical synthesis techniques allowing generation of all possible converters meeting a certain criteria already exist. The analysis of a family of converters derived from a single converter cell has also been unified. Current waveforms generated by the family of converters were shown to be related. However, a concept or basic building blocks that facilitate unified synthesis, analysis, prediction of current waveforms and assignment of switch states over a very wide range of DC-DC converters is still lacking. This study will propose three 3-terminal basic building blocks and one 3-terminal filter block. It will be shown that between them, they are sufficient for realizing all non-isolated DC-DC converters excluding those with coupled inductors. The various DC-DC converters fall into those realized through cascade, stacked, stacked plus cascade, interleaved/paralleled or differential connection of the basic building blocks. A systematic approach for evaluating input-output current gains will be presented. Moreover, a basic building block will be shown to have fixed switching states for proper operation. This gives rise to the generation of a unique set of current waveforms at the three terminals irrespective of where a basic building block is embedded. It has been shown that the effort and time needed to design DC-DC converters can be reduced as switching device stresses can be estimated without the need for tedious first principle derivations.
A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

With the popularization of machine learning (ML) techniques and the increased chipset’s performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.
A Model for User Interface Adaptation of Multi-Device Media Services

In the current audiovisual arena the user has the chance to consume different contents at once across multiple Internet-connected devices, such as smart TV, smartphone, tablet or laptop. However, few existing solutions are able to dynamically adapt a multitude of contents as a single experience across multi-device contexts and the existing ones are very scenario specific. To address this gap, we propose a general multi-device adaptation model, that provides a framework for building adaptive User Interfaces (UIs) for multi-screen applications under the Create Once and Publish Everywhere (COPE) concept. Hybrid broadcast-broadband TV is a reference application field for the adaptation model. However, our model is universal in the sense that it can be applied to any kind of content, device or user; in different multi-device scenarios; and in other fields such as industry or crisis management. The proposed model is an outcome of an extensive research that arose from a previous multi-device media service deployment with broadcasters, which helped to identify a set of guidelines and improvements. In order to meet this goal, the UI is formalised as a set of elements including components, devices and layouts and the adaptation process is modelled as a function of these components. Additionally, an evaluation model is provided which allows the validation of the approach in terms of quality, efficiency and universality. The tests carried out show that the model provides efficient solutions which, although they are suboptimal, have enough quality to meet the expectations of broadcasters and researchers in different scenarios.
Prediction Method of Optical Cable Line Fault Probability Based on Improved KNN Algorithm

In order to overcome the problem that it is difficult to predict the damage and interruption of optical cable line in advance to take targeted maintenance, this paper proposes a fault probability prediction method based on the type of optical cable laying and the state of the surrounding environment of the cable line, so as to predict the fault probability of optical cable line. By learning the historical data of optical cable line fault and using KNN (K Nearest Neighbor) algorithm for reference, the probability of fault in different segments of optical cable line can be effectively estimated to enhance the pertinency of operation and maintenance. The test of the method shows that the maintenance based on the probability prediction method proposed in this paper can effectively reduce the occurrence of cable fault.
Gob-side entry retaining formed by roof cutting without roadside support

When using gob-side entry retaining (GSER) under hard-roof conditions, the roadside support deforms and destroys easily due to the great overburden stress induced by the large length and time of the hanging roof. Selecting the 20196 working face in the Linhua coal mine located in western China as an example, the technology of GSER formed by roof cutting without roadside support (RCWRS) is studied. The overlying strata movement characteristics, support system of the surrounding rocks, and pre-splitting construction technique are developed. And stress transfer and bearing of the hanging roof structure above retaining roadway are discussed. The deformation of surrounding rocks of the retaining roadway is also monitored. The engineering application shows that this technology can ensure the stability of the retaining roadway. The maximum roof subsidence is 172 mm, the maximum floor heave is 37 mm, and the coalside deformation is 56 mm. The above observations are significant for coal mining under hard-roof conditions. [Received: May 24, 2017; Accepted: December 4, 2017]
Building Damage Detection Method Based on Normal Vector Computation of Airborne Point Cloud Data

Earthquake is one of the major natural disaster which endangers the existence and development of mankind. The collapse of buildings caused by earthquake is one of the important reasons causing the casualties. Therefore, it is very important to recognize and extract the damage of buildings quickly. Airborne LiDAR can acquire high resolution point cloud data, which can provide height or other information for building damage detection. This paper finds an effective factor which can be used to detect building damage when we only have post-earthquake point cloud data. The factor is an angle, which is calculated from the normal vector and zenith of point cloud data. Through the test of large scale of point cloud data, the Kappa coefficient is 0.788, which proves the validity of this method. The result provides new ideas for the detection of building damage type and damage degree.
A fast point cloud precision registration method based on spatial grid division

Point cloud precision registration is the most time-consuming link in registration, and the main factor that hinders point cloud registration from meeting the real-time requirement. In this paper we propose a fast point cloud precision registration method based on spatial grid division. By virtue of the spatial grid, the scattered point cloud is transformed into the spatial grid sequence. During the applicaton of the Iterative Closest Point (ICP) algorithm, we can get the neighborhood information of any point with the aid of the neighborhood information of the grid sequence to efficiently determine the nearest neighbor points. When calculating the nearest neighbor points, the grids don’t interfere with each other. Hence, we can use GPU parallel processing to accelerate the search of the nearest neighbor points. Spatial grid division and parallel processing greatly improve registration efficiency.
Deep Learning Framework for Preoperative Recognition of Inverted Papilloma and Nasal Polyp

Surgery is the most commonly used method of curing inverted papilloma (IP) or nasal polyp (NP). Although accurate preoperative recognition by computed tomography (CT) is a critical aspect of surgical planning, the minor CT imaging differences in such lesions may be a challenge. Therefore, we have devised a deep learning framework for automatic recognition of IP and NP in CT. The proposed framework involves two major steps: (a) use of a convolutional neural network (CNN) to preclassify lesions and (b) automatic IP/NP recognition. The preclassify CNN enables classification of CT slices according to anatomic structure. Separate networks are then implemented to differentiate IP and NP accordingly. Once the framework was trained using a CT dataset (5681 slices) from 136 patients, it outperformed other methods during evaluation, achieving 89.30% accuracy (area under the curve [AUC]=0.95) in classification. The proposed framework has clear potential as a clinical tool, enabling effective and highly accurate preoperative recognition of IP and NP.
Sequence-to-Point Learning Based on Temporal Convolutional Networks for Nonintrusive Load Monitoring

Nonintrusive load monitoring (NILM) is performed by monitoring the total electricity consumption data from the customer’s meter and decomposing it with a load decomposition algorithm to get real-time electricity consumption data for individual appliances. Nonintrusive load decomposition is a single-channel blind source separation problem, which is difficult to implement because of the inherently unidentifiable problem. Recent studies have shown that deep learning is gradually becoming the mainstream approach to solve the NILM problem owing to the availability of data, computing power, and deep network training algorithm models. Among them, the sequence-to-point (seq2point) (Zhang) load decomposition model achieves state-of-the-art prediction because it concentrates on the representation ability of the network at one point. In this article, we optimize the structure of the network model based on the seq2point model, use a temporal convolutional network (TCN) with a more flexible perceptual field of view to train the load decomposition model, and replace the traditional activation function Relu with the activation function GELU. Finally, we test the algorithm in the public dataset UK-DALE and select the mean absolute error and F-score as the main evaluation indexes of the algorithm performance. Comparing the decomposition results of the two algorithms for the same period dataset, the results show that the temporal convolutional network model has substantially improved the performance index of load decomposition while reducing the model parameters.
Enhanced Pencil-Beam Scanning CTS Leaky-Wave Antenna Based on Meander Delay Line

This letter presents a millimeter-wave (mmW) continuous transverse stub (CTS) leaky-wave antenna (LWA) with pencil-beam broadside radiation, high scanning-rate, and low sidelobe levels (SLLs) characteristics. A parallel-plate waveguide (PPW) meander delay line is employed to control the phase constant of the proposed LWA. By adjusting the length of the transverse electromagnetic TEM-mode PPW delay line, the phase difference between adjacent radiating elements can be altered, achieving flexible control of scanning-rate. Match stubs close to the radiation slots are utilized to suppress the open stopband, which help enlarge the beam scanning range. A linear source generator excited by multiple ports with amplitude tapering is employed to generate the quasi-TEM wave. Combined with additional amplitude tapering among the CTS radiation slots, low SLLs at both principal planes are realized. Experimental results show that the LWA with 12 radiation elements has a scanning range of −68° to 42° over the frequency range of 26–34 GHz (a scanning-rate of 13.75°/GHz) with SLLs lower than 13.5 dB/16.6 dB and a peak gain of over 22.5 dBi.
Automatic Bone Age Assessment of Adolescents Based on Weakly-Supervised Deep Convolutional Neural Networks

Hand bone age, as the biological age of humans, can accurately reflect the development level and maturity of individuals. Bone age assessment results of adolescents can provide a theoretical basis for their growth and development and height prediction. In this study, a deep convolutional neural network (CNN) model based on fine-grained image classification is proposed, using a hand bone image dataset provided by the Radiological Society of North America (RSNA) as the research object. This model can automatically locate informative regions and extract local features in the process of hand bone image recognition, and then, the extracted local features are combined with global features of a complete image for bone age classification. This method can achieve end-to-end bone age assessment without any image annotation information (except bone age tags), improving the speed and accuracy of bone age assessment. Experimental results show that the proposed method achieves 66.38% and 68.63% recognition accuracy of males and females on the RSNA dataset, and the mean absolute errors are 3.71 ± 7.55 and 3.81 ± 7.74 months for males and females, respectively. The test time for each image is approximately 35 ms. This method achieves good performance and outperforms existing methods in bone age assessment based on weakly supervised fine-grained image classification.
Boosting Lightweight CNNs Through Network Pruning and Knowledge Distillation for SAR Target Recognition

Deep convolutional neural networks (CNNs) have yielded unusually brilliant results in synthetic aperture radar (SAR) target recognition. However, overparameterization is a widely-recognized property of deep CNNs, and most previous works excessively pursued high accuracy but neglected the requirement of model deployment in radar systems, i.e., small computations and low memory cost. Therefore, further research on lightweight CNNs for SAR target recognition is necessary. In this article, we devise an effective CNN with channel-wise attention mechanism for SAR target recognition and then compress the network structure and recover lightweight network performance through network pruning and knowledge distillation, respectively. The attention values produced by the network are utilized to evaluate the importance of convolution kernels, and unimportant kernels are pruned. In addition, a novel bridge connection based knowledge distillation method is proposed. Instead of directly mimicking the hidden layer output or artificially designing a function to extract the knowledge in hidden layers, bridge connections are introduced to distill internal knowledge via teacher network. Experiments are conducted on the moving and stationary target acquisition and recognition benchmark dataset. The proposed network has excellent generalization performance and reaches an accuracy of 99.46% on the classification of ten-class targets without any data augmentation. Furthermore, through the network pruning and knowledge distillation algorithm, we cut down 90% parameters of the proposed CNN while maintaining model performance.