Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduced-Dimension Design of MIMO AirComp for Data Aggregation in Clustered IoT Networks

One basic operation of Internet-of-Things (IoT) networks is to acquire a function of distributed data collected from sensors over wireless channels, called wireless data aggregation (WDA). Targeting dense sensors, low-latency WDA poses a design challenge for high-mobility or mission critical IoT applications. A promising solution is a low- latency multi-access scheme, called over-the-air computing (AirComp), that supports simultaneous transmission such that an access point (AP) can estimate and receive a summation-form function of the distributed data by exploiting the waveform- superposition property of multi-access channels. In this work, we propose a multiple-input-multiple-output (MIMO) AirComp framework for an IoT network with clustered multi-antenna sensors and an AP with large receive arrays. The contributions of this work are two-fold. Define the AirComp error as the error in the functional value received at AP due to channel noise. First, under the criterion of minimum error, the optimal receive beamformer at the AP, called decomposed aggregation beamformer (DAB), is shown to have a decomposed architecture: the inner component focuses on channel-dimension reduction and the outer component focuses on joint equalization of the resultant low-dimensional small-scale fading channels. Second, to provision DAB with the required channel state information (CSI), a low-latency channel feedback scheme is proposed by intelligently leveraging the AirComp principle to support simultaneous channel- feedback by sensors.
Low Power Clock Tree Optimization by Clock Buffer/Inverter Reduction

Low power clock tree optimization method is proposed for clock buffer/inverter reduction. Redundant clock buffer can be found and deleted by “driving redundant” check and timing check. In inverter tree, four types of clock structure are discussed and method is proposed to find and delete redundant clock inverters for “1-to-1” structure and “1-to-n” structure. Test results show 7.7%-39% improve on buffer tree and 6.5%-21% improve on inverter tree.
Vibration and Noise of High Speed Amorphous Alloy Permanent Magnet Synchronous Motor with Different Slot Width

This paper presents an analytical model of the electromagnetic vibration and noise for a High Speed Amorphous Alloy Permanent Magnet Synchronous Motor (HSAAPMSM) whose stator cores are made from amorphous alloy magnetic material. Firstly, air gap flux density of HSAAPMSM with different slot width is calculated by two methods of Finite Element Analysis (FEA) and analytical. By comparison, accuracy of the modeling is validated. Then, through the finite element coupling analysis, the electromagnetic force acting on the surface of the stator teeth is obtained. In addition, the space harmonic analysis of air gap flux density and electromagnetic force is carried out. The electromagnetic vibration and noise of HSAAPMSM with different slot width are calculated respectively and the influence of laminating effect is taken into account.
A 1–10-MHz Frequency-Aware CMOS Active Rectifier With Dual-Loop Adaptive Delay Compensation and >230-mW Output Power for Capacitively Powered Biomedical Implants

This article presents a novel CMOS active rectifier for the emerging modality of capacitive wireless power transfer to biomedical implants with high power budgets. For operation versatility in terms of the input frequency, output power level, input voltage range, and load value, dual-loop adaptive delay compensation is utilized to provide both high resolution and high dynamic range in switched-offset currents of comparators that can be reconfigured for low- and high-speed operation modes for automatic adaptation to the input frequency. Fabricated in 0.18 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> 1P/6M CMOS, the rectifier features power conversion efficiency (PCE) of >84.4% (peak of 91.5%) and voltage conversion ratio (VCR) of >88.6% (peak of 95.1%) when operating in 1–10 MHz and driving a load of 300 <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula>. Moreover, for a load range of 20 <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula>–1 <inline-formula> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula> at 5 MHz, PCE of 81%–91.8% and VCR of 78.4%–95.4% are achieved, with a maximum output power of ~232 mW at 50 <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> with a PCE of 90.1%. The active rectifier is also interfaced with a series-resonant capacitive link formed with coated flexible copper plates around a 3-mm-thick layer of muscle tissue, demonstrating an end-to-end power transfer efficiency of >40% in 2–10 MHz and with a load of 300 <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula>.
Stator Tum-to-Tum Short-Circuit Fault Analysis in Line Start Permanent Magnet Synchronous Machine using Finite Element Method

Permanent Magnet Synchronous Motors are being adopted in industries due to the demand for machines with increased power density. Line Start Permanent Magnet Synchronous Motor (LSPMSM) is commonly used for constant speed applications in the place of Induction Motor. But LSPMSM tends to undergo a variety of faults during run-time. Electrical faults contribute heavily to the total percentage of faults. In this paper, stator turn-to-turn short-circuit fault has been simulated in an LSPMSM using Finite Element Method (FEM) and its effects on various analytical and electromagnetic parameters of the machine has been analyzed. The analysis has been carried out using signal processing. Finally, Motor Current Signature Analysis (MCSA) is chosen as a potential fault detector for the type of fault considered.
Spectral–Spatial–Temporal MAP-Based Sub-Pixel Mapping for Land-Cover Change Detection

The maximum a posteriori (MAP) estimation model-based sub-pixel mapping (SPM) method is an alternative way to solve the ill-posed SPM problem. The MAP estimation model has been proven to be an effective SPM approach and has been extensively developed over the past few years, as a result of its effective regularization capability that comes from the spatial regularization model. However, various spatial regularization models do not always truly reflect the detailed spatial distribution in a real situation, and the over-smoothing effect of the spatial regularization model always tends to efface the detailed structural information. In this article, under the scenario of time-series observation by remote sensing imagery, the joint spectral–spatial–temporal MAP-based (SST_MAP) model for SPM is proposed. In SST_MAP, a newly developed temporal regularization model is added to the MAP model, based on the prerequisite for a temporally close fine image covering the same study region. This available fine image can provide the specific spatial structures most closely conforming to the ground truth for a more precise constraint, thereby reducing the over-smoothing effect. Furthermore, the three dimensions are mutually balanced and mutually constrained, to reach an equilibrium point and achieve restoration of both smooth areas for the homogeneous land-cover classes and a detailed structure for the heterogeneous land-cover classes. Four experiments were designed to validate the proposed SST_MAP: three synthetic-image experiments and one real-image experiment. The restoration results confirm the superiority of the proposed SST_MAP model. Notably, under the background of time-series observation, SST_MAP provides an alternative way of land-cover change detection (LCCD), achieving both detailed spatial-scale and high-frequency temporal LCCD observation for the study case of urbanization analysis within the city of Wuhan in China.
A Trend based Forecasting Model Using Fuzzy Time Series and PSO Algorithm

Time series prediction methods are vital in many areas, such as science, meteorology, agriculture, stock indices, economics, etc. To improve the forecasting accuracy, fuzzy approaches and evolutionary methods are widely used in time series forecasting models in recent years. Therefore, a hybrid method based on fuzzy time series and particle swarm optimization algorithm is proposed to establish a forecasting model. This study considers the scenario using the trend-based weighted strategy in hybrid fuzzy PSO model (THFPSO) to estimate the unknown future values. The export orders index (EOI) data of Taiwan are selected as the experimental dataset. The experimental results show that the proposed model can reduce the mean square error (RMSE) and improve accuracy comparing with other models based on fuzzy time series.
A New Network Structure for Semantic Segmentation of Ship Targets in Remote Sensing

Accurate detection of ship targets is a research hotspot in computer vision. Most of the researches have achieved instance-level detection in the way of bounding box. But we intend to achieve more accurate detection of ship targets in pixel-level through semantic segmentation. However, there are still two main challenges: the first one is the difficulty to segment small targets caused by the difference among ship targets' scales, and the other one is the lack of localization information caused by insufficient recovery ability of the decoder part. In this paper, we propose an effective solution. First, a multi-scale pooling fusion module is proposed to fuse multi-scale feature maps and acquire more multi-scale context information, then we improve the capability of precise decoding by taking the place of convolution operation with deconvolution in the decoder part to gather more localization information. At last, we integrate above two schemes into an encoder-decoder symmetry training network with less training parameters and less training time. Furthermore, we construct a dataset for ship semantic segmentation called HRSC2016-SS by labeling HRSC2016 dataset to evaluate our solution. Experiments show that comparing with the existing methods, our proposed solution has a better performance.
Dual Band Slot Antenna with F-Shaped Slits for C-band and X-band Applications

This paper proposes a dual band CPW-fed slot antenna design for wireless network applications which operates at 4.9 GHz (X-band) and 7.7 GHz (C-band). The main idea is to embed two symmetrical F-shaped slits to generate a second resonant frequency. The measured results show that the antenna achieves a -10 dB bandwidths of 500 MHz at 4.9 GHz and 400 MHz at 7.7 GHz with reflection coefficients of -18 dB for both x-band and C-band. Moreover, the total gains of the proposed antenna are 5.9 dBi and 4.7 dBi at 5.1 and 7.9 GHz respectively. Measured results are in good agreement with the simulation results.
CNN for Butterfly Classification

In the world of widely available mobile phones and tablets, electronic field guides for the identification of different biodiversity components are emerging as a reality. This project aims to classify butterfly images based on their species using a convolutional neural network in TensorFlow. The goal is to get an accuracy of 80% or above on a butterfly data set. The result of this project includes a testing accuracy of 0.47 by a model that was trained for only 18 hours, i.e. partially trained. Another additional goal is to show that a neural network once trained can be used to identify the species of the butterfly in rather short time, which will make identification and tracking of different butterfly species much easier and efficient.
Electronic invoice system database architecture issues and the possible solutions

This paper elucidates electronic invoice system’s database architecture requirements and its building issues that one may encounter. Enumerated requirements and issues were taken into consideration and it is suggested to use three types of DB in the system. NoSQL model of database for its numbers of advantages is recommended for the information reused by users to store and querying them. The database is able to split database loadequally with the help of horizontal sharding. Making partition numbers equal to the number which has common divisors as much as possible will let splitting load amount equally among possible additional infrastructure. A special approach is required to each element of system to perfectly create architecture of the information system's database.
Object Detection in High Resolution Remote Sensing Imagery Based on Convolutional Neural Networks With Suitable Object Scale Features

Object detection in high spatial resolution remote sensing images (HSRIs) is an important part of image information automatic extraction, analysis, and understanding. The region of interest (ROI) scale of object detection and the object feature representation are two vital factors in HSRI object detection. With respect to these two issues, this article presents a novel HSRI object detection method based on convolutional neural networks (CNNs) with suitable object scale features. First, the suitable ROI scale of object detection is obtained by compiling statistics for the scale range of objects in HSRIs. Then, a CNN framework for object detection in HSRIs is designed using a suitable ROI scale of object detection. The object features obtained using a CNN have good universality and robustness. Finally, a CNN framework with a suitable ROI scale of object detection is trained and tested. Using the WHU-RSONE data set, the proposed method is compared with the faster region-based CNN (Faster-RCNN) framework. The experimental results show that the proposed method outperforms the Faster-RCNN framework and provides good object detection results in HSRIs.
Layered Antisymmetry-Constructed Clipped Optical OFDM for IM/DD Systems

In this paper, antisymmetry-constructed clipped optical OFDM (AC- OFDM) is proposed for intensity modulation with direct detection (IM/DD) systems, in which an antisymmetry property is imposed directly in time domain. AC-OFDM has the same spectral efficiency as traditional asymmetrically clipped optical OFDM (ACO-OFDM) but is less complex to implement. Layered AC-OFDM (LAC-OFDM) is then proposed as an extension to further improve spectral efficiency, where different layers of AC-OFDM signals are added in the time domain and transmitted simultaneously. Computational complexity analysis and numerical results show that LAC-OFDM has the same spectral efficiency as layered asymmetrically clipped optical OFDM (LACO-OFDM) and enhanced unipolar OFDM (eU-OFDM) but is less complex in terms of both the number of real multiplications and real additions required. Specifically, LAC-OFDM requires less than half the multiplication and addition operations compared to the comparable LACO-OFDM scheme. Monte Carlo simulation results show that LAC- OFDM has the same bit error rate (BER) performance as LACO- and eU-OFDM.
Subcarrier Scheduling for Joint Data Transfer and Key Generation Schemes in Multicarrier Systems

In computational complexity and latency con- strained emerging 5G applications, e.g., autonomous vehicles, haptic communications and enhanced reality, secret key generation (SKG) at the physical layer could be considered as an alternative to currently used key agreement schemes. In this framework, we study the optimal subcarrier scheduling in multicarrier systems when a subset of the subcarriers are used for SKG and the rest for data transmission, under both security and power constraints. The amount of data that can be transmitted with a single key is determined by the cryptographic suites used, so that realistic key rate constraints can be identified. This allows us to formulate the subcarrier allocation as a subset-sum 0-1 knapsack optimization problem that we solve using i) the standard dynamic programming approach and ii) a greedy heuristic approach of linear complexity. We show that the proposed heuristic induces virtually no loss in performance. Furthermore, a comparison with a baseline scheme in which SKG and data transfer are performed sequentially, shows that the proposed parallel approach offers gains in terms of efficiency.
Grant-Free Uplink Transmission in Self-Powered IoT Networks

Ambient radio frequency (RF) energy harvesting is widely promoted as an enabler for self-powered wireless networks. This paper jointly characterizes the harvested energy and the packet transmission success probability in grant-free uplink IoT networks energized via harvesting downlink energy. To do that, a joint queueing theory and stochastic geometry model is exploited and a spatiotemporal analytical model is developed accordingly. Particularly, the harvested energy and packet transmission success probability are characterized using tools from stochastic geometry. Moreover, each device is modeled using a two-dimensional discrete-time Markov chain (DTMC) to track the time evolution of the joint states of the scavenged energy and the data buffer. Consequently, the adopted queueing model represents the devices as spatially interacting queues. To that end, the network performance is assessed in light of the packet throughput, the average waiting time, and the average buffer size which offer valuable insights for network design.
Detection of Abdominal and Mediastinal LYMPH Nodes Using CT Images

Lymph nodes are small structures that contain immune cells which help to carry the unwanted substances to be removed and also fight against infections. The condition of lymph nodes in cancer staging is very important, which decides the treatment to be used, and also determines the prognosis. Computed Tomography (CT) imaging modality is most commonly used in clinical routine for lymph node assessment. Due to the low contrast of surrounding structures in CT, it is difficult to segment the lymph nodes manually. Thus Lymph node segmentation is an important yet challenging problem in medical image analysis due to its varying size, shapes, and similar surrounding structures. In this paper, our aim is to automatically segment Lymph nodes in abdominal and mediastinal CT images. To get clear visibility, firstly the CT image was pre-processed using TopHat Filter to enhance the contrast of the image and then the lymph nodes was segmented automatically using Histogram Thresholding, K means Clustering and Region growing The presented method helps physicians for proper detection of lymph nodes for diagnosis of cancer.
PETRA: A Crowdsourcing-Based Platform for Rocks Data Collection and Characterization

This paper presents details of a distributed platform intended for data acquisition, evaluation, storage and visualization, which is fully implemented under the crowdsourcing paradigm. The proposed platform is the result from collaboration between computer science and petrology researchers and it is intended for academic purposes. The platform is designed within a MTV (Model, Template and View) architecture and also designed for a collaborative data store and managing of rocks from multiple readers and writers, taking advantage of ubiquity of web applications, and neutrality of researchers from different communities to validate the data. The platform is being used and validated by students and academics from our university; in the near future it will be open to other users interested on this topic.
Study of Transmission/Distribution Network With Large Number of Power Electronic Devices Using Hybrid Simulation

Power System Networks are generally regarded as the most complex structures ever built by humanity. In an interconnected power system, there are tens of thousands of buses, transmission lines, transformers, generators etc., and they work together in one large network to supply the necessary power to the human society. While the behaviors of the power system are subject to basic physical laws, such as the Kirchhoff Law where sum of all currents injected to a node is zero, to study or predict such behaviors turned out to be quite complex. Digital time-domain simulation tools have been extensively used in power system studies since 1960's. In many areas including transient stability analysis, switching over-voltage analysis, and power electronic studies, such simulation platforms have become the industry standard tools.
Tunnel Section Extraction and Deformation Analysis Based on Mobile Laser Scanning

With the increase of tunnel operating pressure and the complex surrounding building structures, it is necessary to accurately, rapidly and automatically monitor the deformation of tunnels to ensure safe operation of subway trains. By taking into account the shortcomings of traditional detection methods, such as splicing of sub-stations, low detection efficiency and large amount of manual work, this paper proposes a tunnel section deformation detection method based on mobile laser scanning technology. Combined with tunnel shape characteristics, a denoising method based on point cloud data by ellipse model with the random sample consensus (RANSAC) algorithm is proposed. In the process of iteration, the adaptive threshold is set to filter out disturbing points. The section fitting is implemented in every point cloud segment by the Non-Uniform Rational B-Splines (NURBS) curve algorithm, and the fitting effect was evaluated by calculating the shortest distance from the point on the fitting curve to the original point. Finally, the accuracy of this method is verified by comparing it to the section data obtained by total station and applied to some circular shield tunnel. The tunnel detection railcar was used to obtain point cloud data, and the deformation of the tunnel was analyzed by comparing it with the theoretical section.
Trajectory Planning for Multi-UAV Assisted Wireless Networks in Post-Disaster Scenario

Recently, unmaned aerial vehicle (UAV) assisted wireless network has been recognized as a promising technology for post-disaster communications, in which multiple UAVs are dynamically deployed in the air as cellular base stations to provide public wireless connectivities when the ground infrastructure collapses. However, due to the non- uniformity of post-disaster traffic distribution and the large scale of post-disaster area, trajectory planning is considered as a major challenge for UAV- assisted post-disaster communications in many aspects including coverage, energy efficiency and computational complexity. In this paper, we consider a general trajectory planning problem for multi-UAV assisted wireless networks in a post-disaster scenario. We show that the problem can be formulated as a multi-depot vehicle routing problem. Then, we propose two heuristic algorithms that can efficiently utilize the battery of UAVs to improve the coverage performance. Simulation results show that, compared to the intuitive greedy algorithm, the coverage ratio can be improved by 8% and 28%, respectively, by using the proposed algorithms.