Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Change Detection Framework in Urban Area Using Multilevel Matching Feature and Automatic Sample Extraction Strategy

Urban change detection (CD) using remote sensing images is of great significance for monitoring and analyzing the spatial and temporal distribution of changes within cities, which can be used to guide urban management. In order to reduce false detections and improve the CD automation, a novel CD framework for high resolution images is proposed in this article. First, a novel multilevel matching feature is presented, combining structural invariant features with multiscale dense matching features to comprehensively describe the invariant properties of ground objects at different levels. Second, a newly automatic training sample extraction strategy is proposed, in which sufficient and accurate no-change samples can be obtained by Gaussian-weighted Dempster–Shafer evidence theory and L1-norm, meanwhile, typical change samples can be extracted by sequential spectral change vector analysis. Utilizing the automatic extracted samples, the final CD results are obtained using four supervised classifiers, respectively (k-nearest neighbor, support vector machine, rotation forest, and extra-trees). To validate the proposed CD framework, experiments are conducted in four datasets with spectral variability, spectral confusion between the changed objects and unchanged backgrounds, and misregistration. The results demonstrate that the proposed multilevel matching feature and automatic sample extraction strategy can obtain better results with different types of supervised classifiers, and can effectively improve automation of CD, which is applicable to the large spatial extent scene.
A proactive selection method for dynamic access points grouping in user-centric UDN

Network densification is a promising solution to fulfill network capacity requirement and transmission rate for beyond 5G and 6G wireless communications. Ultra-dense network (UDN) integrates heterogeneous network resources and coordinates technologies on quality of service controlling, to provide users with flexible service. However, dense deployment reduces coverage radius of the cell, resulting in an increase on handover frequency, which makes a serious impact on service continuity. In this paper, we propose a proactive selection method for dynamic access points grouping (DAPGing) in accordance with "user-centric" philosophy, which selects target Access Points (AP) and reduces handover times to ensure communication continuity. This method includes two criteria: 1) the user's sojourn time, which is determined by analyzing the AP coverage area; 2) neighbor relationship between APs, which is determined by coverage area and signal strength characteristics between neighboring APs. Therefore, candidate APs become the proactive selected ones to update the AP group. Stochastic geometry is used to build system model and performance metrics are analyzed, including AP group coverage probability and average update frequency. Experimental analysis shows that the proposed proactive selection method brings similar coverage probability to traditional handover method, while average update frequency is reduced more than 20% selection criteria.
Aging models for n- and p-type LDMOS covering low, medium and high VGS operation

In this paper, we present aging models to describe degradation in LDMOS transistors covering the full V<inf>GS</inf>/V<inf>DS</inf> space that the devices see during operation. Three distinct regions are identified that require dedicated modelling: low V<inf>GS</inf> (off-state), medium V<inf>GS</inf> (on-state) and high V<inf>GS</inf> regime. Models are presented for both n-type and p-type LDMOS and verified with experimental data.
Development and Optimization of Software Defined Networking Anomaly Detection Architecture by GRU-CNN under Deep Learning

Ensuring the network security, resists the malicious traffic attacks as much as possible, and ensuring the network security, the Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) are combined. Then, a Software Defined Networking (SDN) anomaly detection architecture is built and continuously optimized to ensure network security as much as possible and enhance the reliability of the detection architecture. The results show that the proposed network architecture can greatly improve the accuracy of detection, and its performance will be different due to the different number of CNN layers. When the two-layer CNN structure is selected, its performance is the best among all algorithms. Especially, the accuracy of GRU- CNN-2 is 98.7%, which verifies that the proposed method is effective. Therefore, under deep learning, the utilization of GRU- CNN to explore and optimize the SDN anomaly detection is of great significance to ensure information transmission security in the future.
The Scalable Group Testing of Invalid Signatures based on Latin Square in Wireless Sensors Networks

Digital signature is more appropriate for message security in Wireless Sensors Networks (WSNs), which is energy-limited, than costly encryption. However, it meets with difficulty of verification when a large amount of message-signature pairs swarm into the central node in WSNs. In this paper, a scalable group testing algorithm based on Latin square (SGTLS) is proposed, which focus on both batch verification of signatures and invalid signature identification. To address the problem of long time-delay during individual verification, we adapt aggregate signature for batch verification so as to judge whether there are any invalid signatures among the collection of signatures once. In particular, when batch verification fails, an invalid signature identification algorithm is presented based on scalable OR-checking matrix of Latin square, which can adjust the number of group testing by itself with the variation of invalid signatures. Comprehensive analyses show that SGTLS has more advantages, such as scalability, suitability for parallel computing and flexible design (Latin square is popular), than other algorithm.
Novel Three-Stage Feature Fusion Method of Multimodal Data for Bearing Fault Diagnosis

Bearing faults are among the most common causes of machine failures. Therefore, bearing fault diagnosis should be performed reliably and rapidly. Currently, many types of modal data for monitoring the running state of bearings are available. They include data from different kinds of sensors and various domains (such as time and frequency domains). However, obtaining fault features with high quality from single-modal data is difficult because of the complex working conditions. Therefore, extracting and integrating complementary fault features from multimodal monitoring data are problems that remain to be solved. To address these issues, this study considers vibration and torque signals and proposes a novel three-stage feature fusion method of multimodal data for the fault diagnosis of bearings. The method is called attention-based multidimensional concatenated convolutional neural network. The attention mechanism can learn global information and assign different weights to feature maps to highlight important features. The fused features are then used in fault classification via softmax regression. The effectiveness of the proposed method is verified through the Paderborn data set. Results show that diagnostic accuracy is significantly improved from 96.4% to 99.8%.
Efficient Classification of Diabetic Retinopathy using Binary CNN

Diabetic Retinopathy (DR) is a fastly spreading disease that may lead to loss of vision if not quickly detected and treated. Early-stage detection is beneficial to restrict the progress of disease and reduces the recovery expenditure. The current detection process of DR heavily depends on domain experts. Machine-dependent approaches are gain attention with large-scale fundus image repositories to overcome this difficulty. Recent techniques with deep learning are successful in getting noticeable results with pre-trained networks. However, the increase of memory occupancy and runtime with existing models is the bottleneck. We propose Binary Convolutional Neural Networks (BCNN), which drastically reduces memory consumption and faster the execution process to combat this problem. Our model is hardware friendly and efficient in DR classification with large scale fundus images. Experiments conducted using the Kaggle dataset reduce memory consumption by 37% and increase runtime by 49% compared to the base model.
Efficient Planar Near-Field Measurements for Radiation Pattern Evaluation by a Warping Strategy

The sampling of the near-field radiated by a planar source observed over a finite planar aperture is addressed. To this end, we employ the warping method that amounts to properly change the observation variables and finding the sampling points as those that allow to approximate the singular values of the radiation operator up to the so-called number of degrees of freedom. In particular, the warping transformations allow to approximate the kernel function of the relevant operator as a band-limited function and hence the sampling theorem is adopted to devise the discretization scheme. Here, we generalize the warping method to the full vector case and introduce a spatially varying oversampling strategy that allows to deal with measurement apertures which are larger than the source. It is shown that the sampling points need to be non-uniformly arranged across the measurement aperture but their number is generally much lower than classical half-wavelength sampling. A numerical analysis is included to support the theoretical arguments. Finally, numerical experiment-based results concerning the radiation pattern estimation of a planar array antenna are presented. To this end, experimental data collected under a uniform half-wavelength sampling scheme are first interpolated over the required non-uniform grid and then processed to obtain the radiation pattern.
Fast Algorithms for Spatial K-Core Discovery and Maintenance

With the proliferation of location-based services, there is a rapidly growing amount of spatial data, which tends to be large and complex. To make sense of these data, in this paper, we first propose a novel model, named spatial $k -$core, by leveraging the $k -$core concept. Given a set of 2-dimensional data nodes, the spatial $k -$core is the maximal subset of nodes, where each node has at least k close neighbors. Two nodes are close if their spatial distance is not larger than a given threshold. However, existing spatial graph analysis only focuses on static spatial graphs and ignores the evolution of spatial graphs caused by node movement. To address the concern above, we first develop efficient algorithms to discover the spatial $k -$core and then propose several pruning rules to accelerate the process of maintaining the spatial $k -$core in location evolving data. Extensive experiments validate the superiority of our proposed algorithms.
A Comparison Between two Different Techniques for Beam-scanning Reflectarray Antennas Design

The possibility of introducing beam-scanning capabilities in reflectarray (RA) antennas is becoming more and more important and several solutions have been proposed. Among them, a possible approach consists in using a passive RA and acting on the feed to achieve the beam-scanning. However, the fulfillment of the desired high gain constraints over the entire scanning range is still a challenging issue. Here, two different design techniques are considered and compared: a more conventional one, i.e. the bifocal design technique, and a more innovative approach, based on the use of an efficient evolutionary algorithm, the Social Network Optimization technique. The obtained results show the effectiveness of this last method, enabling the design of a RA with scanning capability over a wide angular range, outperforming the bifocal configuration.
High Resolution SAR Imaging Using a 240 GHz FMCW Radar System with Integrated On-Chip Antennas

In this paper high resolution SAR imaging results are presented, recorded with a compact 240 GHz radar sensor. The sensor is based on a fully integrated SiGe MMIC including on-chip antennas, allowing an ultra-compact, lightweight, and low energy sensor solution. The use of modern high-speed SiGe technology allows a high operating frequency reaching from 198 GHz to 250 GHz. While the power consumption of the whole sensor is 3.5 W, the overall size is given by 58x46x18 mm³, resulting in an remarkable small and energy efficient sensor. Additionally, a calibration technique is used to compensate the frequency response of the system, allowing a range resolution close to the theoretical limit given by the modulation bandwidth of up to 52 GHz. Combined with the large antenna beam, which gives a high resolution in cross-range direction, an ultra fine image quality can be achieved.
An Efficient Algorithm for Clarification of Rumors with Limited Costs in Social Networks

Nowadays, the information about COVID-19 is spreading widely on social networks while rumors about it are also propagating quickly. When users receive two opposing opinions, they are more likely to accept the information with greater impact. Therefore, there is an urgent need to clarify rumors. Given the cost Ctotal, the rumor clarifying problem aims to find a seed set with at most k users such that the total number of the users influenced by positive information can be maximized. In this paper, we propose a Cost-Randomized Greedy algorithm called CRG to solve the rumor clarifying problem. It runs in $O((n-r)mC_{total})$, where $n, m$ and r are the number of nodes, edges and the initial rumor nodes, respectively. Extensive contrast experiments on online networks show that our algorithm is much more efficient than the state-of-the art methods.
A New "X-Words Like" Approach to Two Dimensional Phase Retrieval Problems

A new approach to Phase Retrieval (PR) problem, which is of interest in antenna testing and synthesis, is introduced and discussed. The proposed strategy can be interpreted as a proper combination of solutions of simpler problems, which are herein 1-D PR problems. In particular, the 2-D PR problem is split into a number of 1-D PR problems (along diameters and circumferences) solved through the Spectral Factorization technique, and a subsequent crosswords-like selection scheme. It is worth to note that in the possible applications to antenna testing, opposite to the most widespread existing techniques, the approach just requires a single measurement surface plus the knowledge of the source’s support and of the field’s phase in a couple of sampling points.
DCache: A Distributed Cache Mechanism for HDFS based on RDMA

Hadoop Distributed File System (HDFS) is an important component of Hadoop, which provides data storage service. The performance of the IO subsystem has a great influence on data processing efficiency. In the Hadoop system, computing jobs are scheduled to the nodes where the data is located to reduce IO time. The job scheduler and data distribution have a great impact on the efficiency of data processing. HDFS provides a mechanism for users to specify files or directories to be cached. But the cached data is only accessible for the jobs running in the same data nodes. In this paper, we present a distributed cache mechanism for HDFS with RDMA-capable network. We separate the caching function from the original data nodes. We design and implement a new kind of component for the caching function. The cached data in these nodes can be accessed directly by any nodes in the cluster through Remote Direct Memory Access (RDMA). Compared with the existing caching mechanism, our approach improves the performance and stability of the IO process. We also accelerate the data writing process using our caching mechanism. The experiments show that compared with the existing caching mechanism of HDFS, our scheme improves the reading latency up to 3 times, improves the throughput by 30% $\sim$ 95% for large files, and the writing performance by 64%. When the task runs on different nodes, the IO fluctuation caused by different data distribution is dropped from 20.71 to 0.50.
Mutual Coupling Reduction of Dielectric Resonator Antennas by Metamaterial Inspired Diverter Elements

Mutual coupling (MC) of dielectric resonator antennas is investigated and decoupling is accomplished by metamaterial inspired diverter elements. The diverter elements consist of unit cells of metallic crosses surrounded by four L-shaped copper strips each, printed on both sides of a substrate. A one-unit cell diverter element brings about 3.8 dB reduction in MC and a 2-by-2 unit cell diverter element reduces MC by 3 dB at 25.4 GHz.
A Very-Low-Voltage Frequency Divider in Folded MOS Current Mode Logic With Complementary n- and p-type Flip-Flops

In this article, a static frequency divider based on folded MOS current mode logic (FMCML) is presented. The design is based on alternating FMCML flip-flops with complementary pMOS or nMOS input differential pairs since common-mode problems arise by using only one type of FMCML flip-flops. The design is carried out after detailed theoretical modeling and analysis versus the flip-flop bias current, thus allowing defining optimized design strategies for the maximum speed or the minimum power-delay product (PDP). The frequency divider architecture and design strategies are validated considering a commercial 28-nm FDSOI CMOS technology. Postlayout simulations of a divider-by-16 show a maximum frequency of about 12 GHz with 74-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> power consumption for the high-speed design and a maximum frequency of 10 GHz with 53-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> power consumption for the minimum PDP design.
CCIED: Cache-aided Collaborative Intelligence Between Edge Devices

Recently, deep learning technology has shined in the fields of computer vision, natural language processing and speech recognition, and related products have sprung up like mushrooms. Due to the storage and calculation of deep neural network (DNN) models are relatively large and mobile edge devices are often resource-constrained, how to efficiently deploy DNN models on resource-constrained edge devices has attracted great attention from academia and industry. There’s strength in numbers, so we propose CCIED, a framework which lets edge devices cooperate with each other to complete DNN inference tasks. Due to task inputs in the mobile edge computing scenarios usually have great similarities, the outputs of the middle layer of the neural network and the corresponding labels are cached. When a similar input already exists in the cache, the device does not need to perform the remaining calculations, but directly returns the cached results. One of the challenges of collaborative inference is that the communication overhead associated with transferring intermediate data can be significant. We therefore perform weight pruning only on the layer that obtains the intermediate results, which can greatly reduce the redundant parameters of the intermediate results, thereby reducing the time for transferring data between devices, and basically does not reduce the complexity of the model. Experimental results show that CCIED can efficiently deploy the DNN model on edge devices with almost no loss of precision, and can significantly reduce the total latency during cache hits.
Reflectarray Antenna Design for LEO Satellite Communications in Ka-band

This paper presents the design of a Ka-band reflectarray antenna, intended for LEO-satellite communications, which operates at 27 GHz. The phase tuning mechanism relies on variable size patches capable of achieving a 360° phase range, which enables the incoming wave to be scattered in any specific direction. In particular, the reflectarray antenna, which has a squared-shape of 30 cm each side, is constituted by a 50 × 50 radiating-patch elements, printed on a planar substrate of "Rogers TMM4" material. With a 27.41 dBi directivity, this configuration is able to generate a pencil-beam in perpendicular direction to the reflecting plane.
An Efficient Low Delay Task Scheduling Algorithm Based on Ant Colony System in Heterogeneous Environments

Low delay is an important issue in a variety of applications on heterogeneous computing platforms. A great number of algorithms including heuristics as well as metaheuristics have been designed to minimize the completion time (i.e., makespan) of applications and achieved good results. However, the demand of high performance never stops. In this paper, we propose a new and efficient Ant-Colony based Low Delay Scheduling (ACLDS) algorithm to minimize the makespan of applications which can form directed acyclic graphs and are executed on a heterogeneous system. Simulated results show that our proposed algorithm is highly efficient in reducing makespan in comparison with state-of-the-art scheduling algorithms. For instance, the reduction ratio can be up to 15.2174% for a Fast Fourier Transform (FFT) benchmark.
Compact and Broadband Uniplanar Yagi MSA for Sub-6 GHz 5G Frequency Band

A compact and broadband Yagi MSA is proposed with a simple feed to achieve endfire radiation for a frequency band of sub-6 GHz 5G. To make the antenna compact along the boom length, the concept of a microstrip antenna is used so that the distance between the reflector and the driven element is reduced significantly. To increase bandwidth, a semi-elliptical driven patch is used instead of a rectangular driven patch. A parametric study of various parameters of the antenna is carried out to see their effects on the gain, bandwidth, and radiation pattern of the antenna. The proposed antenna having size 0. 50λ0×0. 13λ0×0. 009λ0 provides a better figure of merit with a peak gain of 5.2 dBi, an average gain of 4.9 dBi, and 11.3% bandwidth.
Low-Power Retentive True Single-Phase-Clocked Flip-Flop With Redundant-Precharge-Free Operation

As basic components, optimizing power consumption of flip-flops (FFs) can significantly reduce the power of digital systems. In this article, an energy-efficient retentive true-single-phase-clocked (TSPC) FF is proposed. With the employment of input-aware precharge scheme, the proposed TSPC FF precharges only when necessary. In addition, floating node analysis and transistor level optimization are employed to further ensure the high energy efficiency of the FF without significantly increasing the area. Postlayout simulations based on SMIC 55-nm CMOS technology show that at a supply voltage of 1.2 V, the power consumption of the proposed FF is 84.37% lower than that of conventional transmission-gate flip-flop (TGFF) at 10% data activity. The reduction rate is increased to 98.53% as the data activity goes down to 0%. When the supply voltage decreases to 0.6 V, the proposed FF consumes only 0.411 fJ/cycle at 10% data activity, which is 84.23% lower than TGFF. Measurement results of ten test chips demonstrate the great energy efficiency of the proposed FF. Furthermore, the CK-to-Q delay of the proposed FF is 26.18% lower than that of TGFF at a supply voltage of 1.2 V.
Design of ME-dipole antennas for 5G phased array applications at 28 GHz

Phased arrays are one of the most critical blocks in 5G networks. In the case of the point-to-multipoint link, they have to provide beam reconfigurability over a large scanning range while maintaining a low profile and a limited cost. Moreover, operation at millimeter wave frequencies poses several critical challenges especially in terms of integration and, in turn, analysis. This work provides an introduction to the different simulation challenges involved in the design of a phased array differentiating them into two categories: i) cross-domain effects due to the different integration and packaging technologies present in the array along its vertical dimension and ii) mutual coupling effects taking place across the array horizontal dimension. As a design example, an ultra-low profile Magneto Electric (ME) dipole antenna design for 5 G applications is reported.