Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence with Functional Maps

In this paper, we provide a theoretical foundation for pointwise map recovery from functional maps and highlight its relation to a range of shape correspondence methods based on spectral alignment. With this analysis in hand, we develop a novel spectral registration technique: Fast Sinkhorn Filters, which allows for the recovery of accurate and bijective pointwise correspondences with a superior time and memory complexity in comparison to existing approaches. Our method combines the simple and concise representation of correspondence using functional maps with the matrix scaling schemes from computational optimal transport. By exploiting the sparse structure of the kernel matrices involved in the transport map computation, we provide an efficient trade-off between acceptable accuracy and complexity for the problem of dense shape correspondence, while promoting bijectivity.1
Synthesis of 1-(4-Ethoxy-3-methoxybenzyl)-1,10-phenanthrolin-1-ium Bromide and Its Evaluation as Antiplasmodium through Heme Polymerization Inhibitory Activity (HPIA) Assay

This study describes the development of N-benzyl-1,10-phenantrolinium salt as an antiplasmodium agent. The salt, that is, 1-(4-ethoxy-3-methoxybenzyl)-1,10-phenanthrolin-1-ium bromide, was prepared using vanillin as the starting material in four simple synthetic steps. First, the alkylation of vanillin using diethyl sulfate produced 4-ethoxy-3methoxybenzaldehyde in 79% yield. Second, the reduction of the protected vanillin by NaBH4 through the grinding method allowed us to obtain 4-ethoxy-3-methoxybenzyl alcohol in 96% yield. Next, the bromination of the benzyl alcohol under solvent-free condition led to the formation of the corresponding benzyl bromide, which in turn underwent bimolecular nucleophilic substitution with 1,10-phenanthroline to produce the desired product, that is, 1-(4-ethoxy-3methoxybenzyl)-1,10-phenanthrolin-1-ium bromide, in 58% yield. The evaluation of N-benzyl-1,10-phenantrolinium salt as an antiplasmodium agent was conducted through heme polymerization inhibitory activity (HPIA) assay. The results showed that the phenantroline salt and chloroquine displayed the HPIA half maximal inhibitory concentrations of 3.63 and 4.37 mM, respectively. Therefore, 1-(4-ethoxy-3-methoxybenzyl)-1,10-phenanthrolin-1-ium bromide displays desirable HPIA and has a great potential to be further developed as an antiplasmodium.
Secrecy-enhancing design for two-way energy harvesting cooperative networks with full-duplex relay jamming

To guarantee secure information transmission within two sources, a full-duplex (FD) relay jamming scheme for two-way energy harvesting (EH) cooperative networks is proposed, in which the relay can achieve simultaneous information reception and jamming transmission. The EH relay node scavenges energy from two source signals, and then, uses the harvested energy for information relaying and jamming transmission by the power splitting (PS) protocol. Considering multiple eavesdroppers, the non-convex optimization for maximizing the smaller bidirectional secrecy rates is formulated by jointly optimizing the power allocation and PS factor. Furthermore, an iterative algorithm based on successive convex approximation and alternate search method is proposed to solve this non-convexity. Simulation results verify the proposed iterative algorithm and show that the proposed jamming scheme can achieve substantial performance gain than other conventional schemes.
DC Breakdown Modulated by Molecular Chains Movements and Trap Characteristics of HDPE/LDPE Blend Insulation

Polyethylene (PE) is an environmentally friendly material, and is generally used as cable insulation materials. This paper mainly researches the coupling effect of dielectric polarization behaviors and traps level characteristics on direct current (DC) electrical breakdown performance of five high density/low density polyethylene (HDPE/LDPE) blends specimens. HDPE/LDPE specimens are prepared by high temperature blending and hot-press forming methods. Broadband dielectric spectroscopy (BDS) and isothermal decay current (IDC) measurements are employed to reveal the dielectric polarization behaviors and traps level characteristics of five HDPE/LDPE specimens. A novel molecular chains movement model is employed to investigate the mechanism of the molecular chains movements in the HDPE/LDPE dielectrics. The breakdown field strength of five HDPE/LDPE specimens reaches maximum when the HDPE content increases to 15 wt%, and then decreases with a further addition of HDPE to 20 wt%. The dielectric relaxation polarization a is attributed to molecular chains movements in HDPE/LDPE blends, and a behavior intensity (Δ εα) increases with the HDPE content from 0 to 5 wt%, and then reduces with a further addition of HDPE content to 20 wt%. The molecular chains movements will be strengthened on account of the increase of (Δ εα), which provides charges with larger mean free volume to form high energy electrons. The increased traps density raises the possibilities of high energy electrons being captured, and enhance the breakdown performance of the specimen. It is deduced that the DC breakdown properties of HDPE/LDPE blends are influenced by molecular chains movements as well as traps characteristics.
TSEP-Sensitivity Study to Analyze the Soldering Process of a Power Diode

The use of SAC305 solder for connecting electronic components on a printed circuit board is standard for the microelectronics industry. It is well-known that the quality of solder joints decreases when voids are formed, as it impairs the heat dissipation and hence the reliability of the device. In this work, a sample series of 12 power diodes was analysed, regarding their voids in the solder and their thermal behavior. The integrity of the solder joints between the power diode and the FR4-based printed circuit board was investigated by X-ray tomography. Their void formation was studied in terms of their overall percentage, size and location. The thermal resistance, hence the thermal behavior, of the power diodes was obtained by a temperature-sensitive electrical parameter (TSEP) measurements. The correlation between the thermal data and the void formation promotes the awareness of the void's influence on thermal behavior. The findings were supported by finite element simulations.
Hardware-Oriented Algorithm for High-Speed Laser Centerline Extraction Based on Hessian Matrix

Centerline extraction is the basic and key procedure in line-structured laser 3-D scanners. In this article, we propose a hardware-oriented algorithm for fast and accurate extraction of a laser centerline based on the Hessian matrix. The algorithm is divided into three low-coupling modules that can be processed in parallel—coarse positioning, linewidth estimation, and precise positioning module. In the coarse positioning module, a window slider is used to traverse all image pixels in the raster-scan mode for collecting local image features, based on which the potential region of interest (ROI) containing laser lines is detected. In the linewidth estimation module, the second-order moment features in the detected ROI are calculated to estimate the linewidth according to the local rectangular similarity characteristics of the laser line. In the precise positioning module, the optimal Gaussian template estimated by the linewidth is convolved with the ROI to obtain the Hessian matrix. Based on the Hessian matrix, the normal direction of the laser line is obtained, and the second-order Taylor expansion is performed in that direction to determine the subpixel position of the center point. Finally, non-maximum suppression is used to remove noise points and obtain the most reliable single-pixel centerline. The proposed algorithm is evaluated using thousands of sample images with different materials, exposure times, and laser line shapes, and it presents high robustness for subpixel precision extraction. By implementing the proposed algorithm on a field-programmable gate array (FPGA), a high-speed, high-precision laser centerline extraction system is achieved, which operates at 1350 frames/s for a 1-million-pixel video stream.
Test Sequence Generation with Cayley Graphs

The paper presents a theoretical foundation for test sequence generation based on an input specification. The set of possible test sequences is first partitioned according to a generic “triaging” function, which can be created from a state-machine specification in various ways. The notion of coverage metric is then expressed in terms of the categories produced by this function. Many existing test generation problems, such as t-way state or transition coverage, become particular cases of this generic framework. We then present algorithms for generating sets of test sequences providing guaranteed full coverage with respect to a metric, by building and processing a special type of graph called a Cayley graph. An implementation of these concepts is then experimentally evaluated against existing techniques, and shows it provides better performance in terms of running time and test suite size.
Ant Colony Optimization for UAV-based Intelligent Pesticide Irrigation System

The application of unmanned aerial vehicle (UAV) to achieve precision irrigation in agriculture is a hot research topic in the industry. However, much spay and much leakage of pesticide are tricky for the current UAV-based irrigation methods to deal with. In this paper, we propose a new UAV-based irrigation system for precision agriculture. First, considering that different areas in the same farmland may have different pesticide shortage, a map preprocess strategy is introduced to divide the entire farmland into pieces. Second, we establish a UAV precision irrigation model and put forward an adaptive and fast dynamic ant colony optimization (AFD-ACO) algorithm to minimize the longest flight path with the lowest energy consumption and pesticide residues. In order to promote the efficiency and the optimization effect, we utilize the scent pervasion rule to make the global map preprocessed and the neighborhood adaptive search policy to accomplish planning work. Finally, comparing with other two ACO-based algorithms, the proposed algorithm is proved to be effective for the research problem, especially when the more pieces the farmland is divided, the better our solution performs.
Study on Typical Objects Three-Dimensional Modeling and Classification Technology Based on UAV Image Sequence

The cost of data acquisition is relatively high in airborne LiDAR platform, not suitable for the entire industry promotion. Point-Cloud extraction technique based on image, a method based on low-altitude drone image and flight control data for 3D reconstruction is adopted. Do not depend on prior knowledge of camera calibration or other premise, multi-angle, high-overlap image sequence acquired by visible light camera, to restore the three-dimensional reconstruction information. Aiming at the problem of insufficient memory when dealing with high-resolution unmanned aerial vehicle (UAV) images by SIFT (Scale-Invariant Feature Transform) feature extraction operator, an improved SFIT algorithm for image segmentation is proposed. The point cloud filtering is carried out by the method of orthogonal polynomial banding filtering, the ground point and the non-ground point are separated. The non-ground point is used to extract the roof set of the building. This experiment has obtained good test results based on the UAV sequence image in the test area, which has certain application value for low-cost building 3D modeling, 3D modeling of digital city, and damage building damage identification.
A Survey on Superpixel Segmentation as a Preprocessing Step in Hyperspectral Image Analysis

Recent developments in hyperspectral sensors have made it possible to acquire hyperspectral images (HSI) with higher spectral and spatial resolution. Hence, it is now possible to extract detailed information about relatively smaller structures. Despite these advantages, HSI suffers from many challenges also, like higher spatial variability of spectral signatures, the Hughes effect due to higher dimensionality, and a limited number of labeled training samples compared to the dimensions of the spectral space. Superpixels can be a potentially effective tool in tackling these challenges. Superpixel segmentation is a process of segmenting the spatial image into several semantic subregions with similar characteristic features. Such grouping by similarity can significantly ease the subsequent processing steps. Because of this, superpixels have been successfully applied to various fields of HSI processing such as classification, spectral unmixing, dimensionality reduction, band selection, active learning (AL), denoising, and anomaly detection. This article focuses on classification, presenting a detailed survey of superpixel segmentation approaches for the classification of HSI. The superpixel creation algorithm framework and postprocessing frameworks for superpixels in HSI are also analyzed. Also, a brief description of various application areas of superpixels is provided. An experimental analysis of existing superpixel segmentation approaches is also provided in this article, supported by quantitative results on standard benchmark datasets. The challenges and future research directions for the implementation of superpixel algorithms are also discussed.
Application of Sequence Embedding in Host-based Intrusion Detection System

In the field of host-based intrusion detection systems(HIDS), existing anomaly detection algorithms paid much attention to extracting system call features, such as N-gram, frequency-based and neural networks, whereas few literatures introduce effective methods of modeling system call sequences with semantic features. This paper proposes a new model to represent system call sequences with novel applications of embedding techniques. The method converts system calls into embedding vectors as inputs for anomaly detector, mainly includes two parts: 1) construct embedding vectors for all system calls; 2) model the sequences with system call embedding and weighting. This sequence representation model is intuitive and effective on ADFA-LD dataset. As is illustrated with our experiment, the FPR can be significantly reduced to 0.53%, while the TPR still reaches 91.7%, even by a simple 1-NN classifier.
An Embedding-based Deterministic Policy Gradient Model for Spatial Crowdsourcing Applications

Most spatial crowdsourcing systems are designed in a static mode with tasks allocated based on the historical interactions data between crowd participants and crowdsourcing tasks. However, these task assignment algorithms usually ignore the long-term feedback on interactive spatial crowdsourcing systems, resulting in performance degradation. Though reinforcement learning naturally fits the problem of maximizing long term crowdsourcing rewards, deep reinforcement learning-based task assignment is still facing the challenge of interactive spatial crowdsourcing. To address these issues, this paper investigates a challenge problem which we study how to intelligently task assignments for interactive spatial crowdsourcing applications. Therefore, we develop an advanced Embedding-based Deterministic Policy Gradient learning framework to maximize long term crowdsourcing rewards for task assignments, called EDPG-Assignment. EDPG-Assignment is based on deep actor critic learning and combines the improvements of two advanced methods, action embedding and neighbor-based deep Deterministic Policy Gradient, and employed this to optimize the task assignment in interactive crowdsourcing. A matrix factorization method to learn spatial crowdsourcing action embedding for neighbor-based discrete actions similarities evaluation in deep actor critic learning-based task assignment from generated crowdsourcing trajectories without any prior knowledge. The EDPG-Assignment algorithm provided a more stable learning process and showed improved results in real-world dataset.
Joint Discriminative Distribution Adaptation and Manifold Regularization for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) has become a basic technology for cross-domain recognition and has received extensive attention in recent years. UDA aims to obtain a classifier for the target domain by learning source instances with different data distributions. However, traditional domain adaptation algorithms cannot effectively explore the manifold structure of data while reducing the distribution differences between domains. To address this problem, this paper proposes a new UDA framework called Joint Discriminative Distribution Adaptation and Manifold Regularization (DDAMR). DDAMR makes full use of the category information and geometric structure of samples in the Grassmann manifold to learn the domain-invariant classifier. Specifically, DDAMR performs discriminative distribution adaptation during dynamic distribution calibration to enhance the discrimination ability of the feature space. In addition, DDAMR introduces manifold regularization that can maintain the proximity relationship of the samples. It can maximize effectively the consistency between the prediction structure of the domain-invariant classifier $f$ and the inherent manifold structure of the sample. A large number of results from cross-domain experiments have demonstrated the effectiveness of our DDAMR algorithm.
TELECOVID: Remote Vital Signs Monitoring of COVID-19 Risk Patients in Home Isolation With an In-Ear Wearable

The TELECOVID study (https://www.telecovid.de) is designed to remotely monitor the physiological health status of COVID-19 positive-risk patients in home isolation. Key vital parameters are measured day and night using an in-ear biosensor technology. These data are streamed to the clinic in real time to enable timely interaction in case of deterioration.
Analysis of Non-Idealities in the Generation of Reconfigurable Sinc-Shaped Optical Nyquist Pulses

Optical sinc-shaped Nyquist pulses are widely used in microwave photonics, optical signal processing, and optical telecommunications due to their numerous advantages, like rectangular shape in the frequency domain, the orthogonality and the consequential possibility to use these pulses to transmit data with the maximum possible symbol rate. Ideal sinc pulses with the rectangular spectrum are just a mathematical construct. However, high-quality sinc pulse sequences offer the same advantages and can be generated by a phase-locked rectangular frequency comb with mode-locked lasers, intensity modulators, and integrated devices. Nevertheless, any non-idealities in the pulse and comb generation might lead to a degradation of the system performance, especially for metrology. Here, we investigate and analyze the effect of three major non-idealities, namely, the roll-off factor, the side band suppression ratio (SSR), and the ripple of sinc-shaped reconfigurable optical Nyquist pulse sequences based on 3, 5, and 9-line optical phase-locked frequency combs. We compare these results with the existing literature for the three-line comb followed by the experimental verification of the simulation results. We illustrate that by increasing the number of comb lines, the pulse sequences have superior performance and contribute to lesser root-mean-square (r.m.s.) error. We also discuss the trade-off between the r.m.s. error and the optical power loss for increasing the SSR.
Task Offloading Algorithms for Novel Load Balancing in Homogeneous Fog Network

Fog computing has become an emerging distributed computing paradigm to provide services with low latency and high throughput. However, load unbalance is serious due to the difference in geography, which results in performance deterioration and low utilization of resources in the fog network. In this paper, the load is the tradeoff between the delay and energy consumption for fog nodes. Meanwhile, the problem of minimizing the maximum load in the homogeneous fog network is formulated and its NP-hardness is proved. Then, a greedy algorithm is proposed for solving the problem by giving the preference to offloading the task in the fog node with the maximum load to the fog node with the minimum load in the network. Moreover, for solving the problem with consideration of selfishness of fog nodes, a coalition based algorithm is proposed to encourage the fog nodes with a light load to share their resources to reduce the maximum load. We evaluate the performance of the proposed algorithms on NS-3 and simulation results show that the proposed algorithms outperform the existing algorithm about 40% in terms of the maximum load.
Device-Free Human Activity Recognition Based on GMM-HMM Using Channel State Information

This paper presents a machine learning method, Gaussian Mixture Hidden Markov Model (GMM-HMM), for device-free activity recognition using WiFi channel state information (CSI). The basic concept of CSI is introduced and signal changes caused by human activity are described, which demonstrates that human activity can be identified using a unique mapping between action and signal variations. The phase difference expanded matrix is built by the mean and standard deviation of phase difference as feature matrix after linear correction and Savitzky-Golay filter is performed on the CSI raw phase information. The GMM-HMM is used for classification as the human activity can be modeled as the Markov process and the complex activity patterns can be fitted by multiple Gaussian density functions, respectively. The proposed system is verified on the self-collected datasets and several factors affecting the recognition accuracy are analyzed. Furthermore, the system has compared with the previous work. High accuracy and robustness in universal scenarios are realized. Experimental results show that the average recognition accuracy of the proposed system is over 97%.
Adaptive Video Streaming Using Dynamic Server Push over HTTP/2

With the increasing popularity of video services, HTTP adaptive streaming (HAS) has become the mainstream technology for media streaming distribution. In traditional HAS over HTTP/1.1, the HAS server responds to each request from the client individually. This process adds additional round-trip time, resulting in underestimation of available bandwidth. As a result, the HAS client chooses a lower bitrate, which reduces network utilization and the user's quality of experience. In recent years, the HTTP/2 protocol has emerged, which allows server to actively push multiple data segments to the client. Pushing multiple segments can reduce the negative impact of network latency on estimating available bandwidth, thereby increasing the user's request bitrate and video quality. However, when the network is unstable, the more video segments that are pushed by the server, the more challenges the client encounters in responding to network fluctuations i n time, causing playback stalling and poor user experience. Therefore, this paper proposes a dynamic server push algorithm over HTTP/2, which chooses a different number of segments for server push according to network fluctuations. For the evaluation results, relative to its benchmarks, the proposed approach improves the average video request bitrate while minimizing the probability of playback stalling.