Miracle-dz/UniFAR-FTUs huggingface dataset:
CrowdBLPS: A Blockchain-Based Location-Privacy-Preserving Mobile Crowdsensing System

With the popularization of intelligent terminals, especially current trends, such as “Industrie 4.0” and the Internet of Things, mobile crowdsensing is becoming one of the promising applications built on smart devices in mobile networks. However, the existing mobile crowdsensing models are mostly based on a centralized platform, which is not fully trusted in reality and results in the existence of fraud and other security problems. Furthermore, the data quality collected through crowdsensing is varied, and the location privacy is difficult to guarantee, especially at the worker selection stage. To solve these two problems, an effective blockchain-based location-privacy-preserving crowdsensing model, CrowdBLPS, is proposed in this article. First, the idea of a blockchain is introduced into this model. The decentralized structure and the consensus approach are applied to realize the nonrepudiation and nontampering of information. Second, to improve the data sensing quality and protect worker privacy, a two-stage approach, including the preregistration stage and the final selection stage, is proposed. Finally, we further implement a prototype on the Ethereum public testing network, and the experimental results show the feasibility, availability, and reliability of CrowdBLPS.
3D Hole Center and Surface Normal Estimation in Robot Vision Systems

In this paper, we present a novel stereo camera-based 3D hole center and surface normal estimation method which can compute the relative pose (i.e., position and orientation) of a hole to a camera precisely. Unlike conventional methods which are completely dependent on either the geometrical model of a target or the visual feedback, the proposed method takes a novel open-loop approach on the basis of specific stereo image processing. Therefore, the computational speed of the proposed method is high enough to be applicable to real-time tasks in industrial applications while maintaining requirements for the localization accuracy. These breakthroughs are achieved by suppressing 3D geometrical errors from the stereo image processing based on the well-known DLT (Direct Linear Transformation) technique. In extensive experiments, the proposed method shows very promising performance in terms of both processing speed and pose estimation accuracy in a typical robot vision system which is equipped with a binocular eye-in-hand apparatus.
Noise-Robust, Reconfigurable Canny Edge Detection and its Hardware Realization

Edge detection is a common operation in image/video processing applications. Canny edge detection, which performs well in different conditions, is one of the most popular and widely used of these algorithms. Canny’s superior performance is due mainly to its provision of the ability to adjust the output quality by manipulating the edge detection parameters, Sigma and Threshold. Calculating values for these two parameters on-the-fly and based on the application’s circumstances requires additional preprocessing, which increases the algorithm’s computational complexity. To reduce the complexity, several proposed methods simply employ precalculated, fixed values for the Canny parameters (based on either the worst or typical conditions), which sacrifices the edge detection’s performance in favor of the computational complexity. In this paper, an adaptive parameter selection method is proposed that selects values for the Canny parameters from a configuration table (rather than calculating in run-time), based on the estimated noise intensity of the input image and the minimum output performance that can satisfy the application requirements. This adaptive implementation of the Canny algorithm ensures that, while the edge detection performance (noise robustness) is higher than state-of-the-art counterparts in different circumstances, the execution time of the proposed Canny remains lower than those of recent cutting-edge Canny realizations.
Pilot-Assisted Methods for Channel Estimation in MIMO-V-OFDM Systems

Multiple-input multiple-output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) technology has both the advantages of MIMO and OFDM. Vector Orthogonal Frequency Division Multiplexing (V-OFDM) is an extension of OFDM, which makes data transmission flexible. In MIMO systems using V-OFDM technology, different novel schemes are proposed to improve channel estimation performance for different channel sparsity. The 2-D Kriging interpolation scheme is proposed for the non-sparse channels, which can significantly improve the performance of conventional Least Square (LS) and Minimum Mean Square Error (MMSE) algorithms. When the channel is sparse, the estimation process can be modeled as a sparse recovery problem using compressed sensing (CS) theory. In this paper, the measurement matrix is determined by pilot locations, and a pilot search algorithm based on random genetic algorithm (RGA) is proposed to minimize the cross-correlation value of the measurement matrix. Besides, a variable threshold sparsity adaptive matching pursuit (VTSAMP) algorithm is designed to obtain more accurate estimates, which achieves better Normalized Mean Square Error (NMSE) performance, higher calculation speed, and lower implementation complexity.
A Multi-Objective Optimization Perspective for Joint Consideration of Video Coding Quality

Traditional efforts for video coding usually focus on either rate-distortion (R-D) performance or quality smoothness (QS), which may not effectively achieve the desirable visual quality of experience for reconstructed videos. Single-objective optimization cannot guarantee the performance of the other one. In this paper, we would like to introduce a new perspective for the better joint consideration of video coding quality, where both R-D and QS are simultaneously evaluated, and then the traditional video coding optimization problem is converted from single objective to multiple objectives. We provide the details to demonstrate the establishment method of the joint video coding quality (JVCQ), which is given as the comprehensive analytical model to jointly considers spatial and temporal artifacts. The FixedQP results and consumed bits are referred to construct the comparable JVCQ results. Finally, by collecting JVCQ results for four different target bit rates and five different weighting strategies, we can obtain the rate-averaged JVCQ (RJVCQ), weighting-averaged JVCQ (WJVCQ) and rate-weighting-averaged JVCQ (RWJVCQ), respectively. By comparing the different rate control (RC) algorithms, experiments validate the consistency and applicability of the proposed JVCQ with the original two separate evaluation metrics, and the proposed RC algorithms can have better performances. Besides the video coding quality evaluation, the group of JVCQs can be used to guide the optimization process of video coding to achieve gains on both evaluation metrics simultaneously.
An overview of Process Mining and its applicability to complex, real-life scenarios

Process models are considered to be the cornerstone of Business Process Management since they enable the documentation, analysis and improvement of business processes. Traditionally, these process models are subjective and depend strongly on perceptions.Process mining remedies the subjectivity issue by utilizing trails left by operational processes (event logs). This novel research field tightly couples event data and process models. Thus, it offers a comprehensive set of tools capable of providing fact-based insights into actual end-to-end processes.The different approaches to process mining and the plethora of tools it offers, make it applicable to a large spectrum of domains. However, blindly applying process mining to real-life, complex and highly flexible processes, proves to be inefficient. In fact, it is necessary to adapt the adopted approach to the particularities of each case study, and to frame its application by the understanding the problem at hand.This paper illustrates, through studies documented in scientific literature, that the applicability and efficiency of process mining in real-life scenarios relies on adapting the approach to their inherent challenges: Some processes might be too complex (e.g. healthcare processes), or too flexible in the absence of a system that enforces a normative model (e.g. loan applications, interaction with radiology equipment…). Event logs might present some challenges as well: Some might be extremely large, heterogenous, related to different processes or suffer from missing data. Plus, in some scenarios, we might need to account for some other aspects that traditional process mining doesn’t consider (e.g. the financial aspect of processes). The papers reviewed, try to adapt to the challenges faced by defining a clear set of questions and combining process mining techniques with other analysis approaches. The papers are related to two application domains. The first is healthcare since medical procedures are too complex and sometimes involve different intertwined processes. The second is banking, finance and audit, since it is highly flexible, and in some cases, it requires the integration of the financial dimension.
Frequency Stability of Synchronous Machines and Grid-Forming Power Converters

An inevitable consequence of the global power system transition toward nearly 100% renewable-based generation is the loss of conventional bulk generation by synchronous machines (SMs), their inertia, and accompanying frequency- and voltage-control mechanisms. This gradual transformation of the power system to a low-inertia system leads to critical challenges in maintaining system stability. Novel control techniques for converters, so-called grid-forming strategies, are expected to address these challenges and replicate functionalities that, so far, have been provided by SMs. This article presents a low-inertia case study that includes SMs and converters controlled under various grid-forming techniques. In this article, the positive impact of the grid-forming converters (GFCs) on the frequency stability of SMs is highlighted, a qualitative analysis that provides insights into the frequency stability of the system is presented, we explore the behavior of the grid-forming controls when imposing the converter dc and ac current limitations, the importance of the dc dynamics in grid-forming control design as well as the critical need for an effective ac current limitation scheme are reported, and finally, we analyze how and when the interaction between the fast GFC and the slow SM dynamics can contribute to the system instability.
A Multi-Access Session Management for ATSSS Support in 5G Network

The 3rd Generation Partnership Project (3GPP), which has developed the most successful standard technologies in the mobile communication market, is currently carrying out the 5G standardization of both access network (AN) and core network (CN) in 5G system. The 5G core (5GC) was designed to be commonly connected to multiple access networks in the first phase (Release 15) of normative standardization. In the next phase (Release 16), Access Traffic Steering, Switching and Splitting (ATSSS), one of work items to converge multiple access networks in 5GC, is under standardization. In this paper, we propose an efficient session management in multiple access environment to support ATSSS function for 5G broadband multimedia service. Proposed multi-access (MA) session management for ATSSS achieves radio resource saving and signaling reduction by using existing policy rules and signaling procedures.
Segmentation and Averaging of sEMG Muscle Activations Prior to Synergy Extraction

Averaging electromyographic activity prior to muscle synergy computation is a common method employed to compensate for the inter-repetition variability usually associated with this kind of physiological recording. Capturing muscle synergies requires the preservation of accurate temporal and spatial information for muscle activity. The natural variation in electromyography data across consecutive repetitions of the same task raises several related challenges that make averaging a non-trivial process. Duration and triggering times of muscle activity generally vary across different repetitions of the same task. Therefore, it is necessary to define a robust methodology to segment and average muscle activity that deals with these issues. Emerging from this need, the present work proposes a standard protocol for segmenting and averaging muscle activations from periodic motions in a way that accurately preserves the temporal and spatial information contained in the original data and enables the isolation of a single averaged motion period. This protocol has been validated with muscle activity data recorded from 15 participants performing elbow flexion/extension motions, a series of actions driven by well-established muscle synergies. Using the averaged data, muscle synergies were computed, permitting their behavior to be compared with previous results related to the evaluated task. The comparison between the method proposed and a widely used methodology based on motion flags, shown the benefits of our system maintaining the consistency of muscle activation timings and synergies.
Targets Recognition Based on Deep Learning

A deep learning based recognition algorithm to identify various targets was proposed in this paper. Compared with traditional machine learning, deep learning can extract the features of recognized targets better and obtain higher accuracy. We first simulate electromagnetic scattering of targets and acquired the scattering electric field of targets at different frequencies and scattering angles. Then we use the scattering electric field to get the ISAR image. Then we input ISAR images to the deep convolutional neural networks for training, and extract the deeper features of the targets. In order to improve the accuracy of recognition, we combine different polarization ISAR images as one sample. Numerical results show that the average recognition accuracy of our proposed method is 99.72%, which verifies the effectiveness of the method.
Forest Canopy Volume Density Index Inversion Method Using Polarization Decomposition

A method for retrieval of the volume density index of forest canopy using Freeman decomposition is derived for coniferous forest. Volume scattering component, double-bounce scattering component and surface scattering component corresponding to three main scattering mechanisms of forest vegetation are obtained by Freeman decomposition. According to the volume scattering component correlated quite well with ground truth forest canopy’s volume density, relationship function between the volume scattering component and the volume density index is established, and an empirical model of inversion forest canopy volume density index is obtained. Simulation results show the volume scattering component plays a dominant role in the scattering mechanism of forest canopy, and the forest canopy volume density index inversion error is less than 5% with these parameters, which indicating that the method can effectively monitor the density change of coniferous forest canopy.
Data Augmentation for GrossMotor-ActivityRecognition Using DCGAN

Gross Motor Activity Recognition(GM-AR) AI has been studied to develop assessment AI that automatically evaluates child’s GM skill. However, collecting and creating datasets takes a lot of effort and time. Therefore, the goal is to automatically generate a GM-AR data set. Since the data set used in this paper is a special time series image, DCGAN was used for data generation. Therefore, this paper aims to generate time-series images by GM-GAN using CNN for GMAR as Discriminator. Newly designed GM-AR could achieve 68.8 % accuracy. The generated image of GM-GAN could be converted into a skeleton with both hands and feet, and the skeleton was confirmed to work with 40 frames. Therefore, GM-GAN was able to generate PK time-series images.
Privacy-Aware Switch-Controller Mapping in SDN-Based IoT Networks

Software Defined Networking (SDN) based solutions have been proposed to address challenges related to IoT traffic management and security. However, the SDN architecture introduces new threats that can impact the privacy of IoT devices. A compromised controller in the network can infer sensitive information by analyzing the network traffic of IoT devices and predict future user behavior. To minimize the information availability to controllers, we propose DPAM, a Dynamic Privacy-Aware Mapping approach, which changes switch-controller mapping in a scheduled manner to prevent controllers from monitoring the same switches continuously for long durations. As a part of DPAM, we formulate an optimization problem whose solution generates a switch-controller mapping schedule that minimizes the maximum information that is available to a controller from a switch while reducing the overhead incurred due to frequent mapping changes. We obtain optimal solution to our problem and evaluate DPAM's performance by comparing it against other dynamic mapping approaches. We demonstrate in our performance study that DPAM reduces the maximum amount of information available to a controller by up to 40% as compared to the other approaches, while achieving better controller load balancing.
Combining Deep Gaussian Process and Rule-Based Method for Decision-Making in Self-Driving Simulation with Small Data

Self-driving vehicle is a popular and promising field in artificial intelligence. Conventional architecture consists of multiple sensors, which work collaboratively to sense the units on road to yield a precise and safe driving strategy. Besides the precision and safety, the quickness of decision is also a major concern. In order to react quickly, the vehicle need to predict its next possible action, such as acceleration, brake and steering angle, according to its latest few actions and status. In this paper, we treat this decision-making problem as a regression problem and use deep gaussian process to predict its next action. The regression model is trained using simulation data sets and accurately captures the most significant features. Combined with rule-based method, it can be used in Torcs simulation engine to realize successful loop trip on virtual roads. The proposed method outperforms the existing reinforcement learning methods on the performance indicators of time consumption and the size of data volume. It may be useful for real road tests in the future.
A Real-Time Sliding-Window-Based Visual-Inertial Odometry for MAVs

This article presents a sliding widow-based visual-inertial odometry to deal with the micro air vehicle (MAV) pose estimation problem. Errors caused by inertial measurement unit (IMU) preintegration, visual landmarks reprojection, and marginalization, are unified into a nonlinear residual minimization framework. Furthermore, a dual-step marginalization method has been proposed to increase the computational efficiency. Experiments have been conducted on publicly available datasets, as well as customized handheld and MAV platform, where state-of-the-art approaches have served as the baselines for comparison. According to the results, the proposed method has a comparative accuracy, which can run in real-time on an onboard minicomputer.
Multipath Protections and Dynamic Link Recoveryin Softwarized 5G Networks Using Segment Routing

Future softwarized 5G networks have to be robust enough so as to ensure high network reliability and services availability. 5G network architecture should make sure that any failed parts in the network are detected, restored and recovered within a permissible period of time and at the lowest achievable cost. In this paper, we propose a Multipath Protection and Link-Failure free MPTCP/SR-based SDN/NFV architecture that increases survivability, resilience, availability of services in 5G networks. We present our system model and a multiPath protection and dynamic Link- Failure free algorithm called "PathReLief" that greatly reduces the failure recovery time and avoids link congestion in MPTCP/SR SDN/NFV 5G networks. To demonstrate the effectiveness of our proposal, we compare the performance of the proposed algorithm and the conventional topology discovery mechanisms for link/node failures in POX and OpenDaylight controllers. Preliminary results show that, our approach outperforms others used in the commonly used controllers (i.e., POX and OpenDaylight), in terms of reduced failure recovery time and localization time.
Multi-frequency Array Beam-forming

In this paper, we address the problem of optimal beam-forming for an antenna array generating beams at multiple frequencies simultaneously. The key challenge here is to perform automatic current allocation among antenna elements, which is a hard combinatorial problem. We formulate it mathematically and propose a fast algorithm that solves it using available optimisation tools. We also derive conditions under which the unconstrained beam-forming problem can be solved exactly. The conditions lead to several uncertainty principles for the antenna design. The developed algorithm is tested in numerical simulations and its performance is demonstrated.
Angularly Stable Bandpass Frequency Selective Surface Based on Metasurface

A novel bandpass frequency selective surface (FSS) working at 2.4 GHz is proposed in this paper based on metasurface with independent-polarization, wide rejection band and good angular stability. The 10-dB upper stopband could cover 3.35-9.4 GHz with a fractional bandwidth of 94.9%. In addition, the filtering response could remain stable under different polarizations and incidence angles. Parameter analysis and equivalent circuit model are illustrated to explain the resonance behavior of the proposed FSS. It is demonstrated that the lower resonant point in the stopband could be tuned independently by changing the length of small rectangular patches in the top layer. While the higher resonant point could be changeable independently by adjusting the diameter of circular gap in the bottom layer. A prototype of the proposed FSS is fabricated and its simulated results are coincided with measurements.
Multichannel environmental sound segmentation

This paper proposes a multichannel environmental sound segmentation method. Environmental sound segmentation is an integrated method to achieve sound source localization, sound source separation and classification, simultaneously. When multiple microphones are available, spatial features can be used to improve the localization and separation accuracy of sounds from different directions; however, conventional methods have three drawbacks: (a) Sound source localization and sound source separation methods using spatial features and classification using spectral features trained in the same neural network, may overfit to the relationship between the direction of arrival and the class of a sound, thereby reducing their reliability to deal with novel events. (b) Although permutation invariant training used in autonomous speech recognition could be extended, it is impractical for environmental sounds that include an unlimited number of sound sources. (c) Various features, such as complex values of short time Fourier transform and interchannel phase differences have been used as spatial features, but no study has compared them. This paper proposes a multichannel environmental sound segmentation method comprising two discrete blocks, a sound source localization and separation block and a sound source separation and classification block. By separating the blocks, overfitting to the relationship between the direction of arrival and the class is avoided. Simulation experiments using created datasets including 75-class environmental sounds showed the root mean squared error of the proposed method was lower than that of conventional methods.