Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Game-based Caching Replacement in Edge Networks

In this paper, we design a caching replacement algorithm for edge networks to enhance the cache hit ratio of the edge network and reduce the traffic usage in the backbone network. Firstly, we design a dynamic-game model to represent the caching behavior between edge servers where each server takes the cache content of other servers into account to make their own caching decisions. Secondly, we analyze the utility function of edge servers from the perspective of caching contents. Finally, we derive the Nash equilibrium solution of the dynamic-game model and design a caching replacement algorithm to obtain the Nash equilibrium solution. Numerical results prove that the proposed edge caching replacement algorithm based on dynamic-game model improves the edge hit ratio up to 80% and meanwhile reduces 40% backbone traffic compared to the baseline solution.
A WMMSE Approach to Distortion-Aware Beamforming Design for Millimeter-Wave Massive MIMO Downlink Communication

Hardware impairments, such as power amplifier (PA) nonlinear distortion, present as the key source to system performance degradation in millimeter-wave (mmWave) communications. In this paper, we consider a mmWave massive MIMO downlink system with nonlinear PA at each transmit antenna and investigate the design of distortion-aware beamforming for efficient data transmission. In particular, we formulate a sumrate optimization problem that accounts for the characteristics of PA distortion. Rather than directly solving the original highly non-convex sum-rate optimization problem, we embrace the classic weighted-minimum-mean-square-error (WMMSE) framework and propose an algorithm to solve its equivalent WMMSE optimization problem for beamforming design. Numerical results confirm the effectiveness of our proposed WMMSE-based algorithm, which outperforms one existing distortion-aware beamforming algorithm and provides significant performance gain compared to conventional beamforming baselines that do not account for the knowledge of the PA distortion.
A Neural Network with Spatial Attention for Pixel-Level Crack Detection on Concrete Bridges

Concrete bridges play a very important role in transportation. As the main type of concrete bridges' damage, crack detection is of great significance to ensure the safety of bridges. In order to avoid the influence of subjective factors, methods based on deep learning develop rapidly. In this paper, a new network model in the form of encoder-decoder is proposed. It achieves the crack detection on pixel-level, which means that the detection results can be further quantified in the future. Meanwhile, the model proposed adds Spatial Attention to take advantage of crack's spatial characteristics. By doing this, more crack details can be found in the test results.
Design and development of a robust vision-based tactile sensor

For robots to perform advanced manipulation of objects, touch is a critical source of information, and a high-quality tactile sensor is essential. Image-based optical tactile sensors, and its inheritances, which have soft touch interfaces, can provide high-resolution tactile images of the contact geometry, contact pressure, and slip conditions. However, due to the lack of robustness provided by the current tactile sensors, the ability to grasp hard or sharp objects is minimal. In this work, we propose an image-based optical tactile sensor and overcome the above limitation of poor robustness by introducing a latex layer on the touch interface. We use a combination of silicone elastomer covered with a latex material and an acrylic sheet to support the silicone elastomer. A camera placed at the bottom of the sensor housing captures the deformation of the elastomer surface illuminated by an inner light. To evaluate the performance, we carried out a series of experiments. First, we evaluated the mechanical characteristics of the silicone elastomer with three types of coating, namely latex membrane, metallic coating, and no coating. The proposed latex membrane clearly outperformed the other two in terms of robustness. Second, we carried out the force-displacement experiments quantitatively to further study the sensitivity and robustness. Last, we validated the sensor performance in terms of its spatial resolution by applying the VGG-19 neural network for classifying touch patterns captured by the sensor. Overall, the proposed sensor achieved the desired robustness, sensitivity, and spatial resolution performance.
The Design of Robust Adaptive Controller Based on JITL Method for Nonlinear Process

In this paper, a robust adaptive control strategy is presented based on just-in-time learning(JITL) method for nonlinear processes. The dynamic system model of the nonlinear process can be represented by a set of local linear models based on the JITL method, and the database of JITL model is updated according to current input and output. Considering the tracking error, we introduce a feed-forward compensation which is consists of proportional and integration link to the control system, and then we can obtain a robust PID controller based on uncertain local state-space models. As a result, the simulation results show that the proposed control system has good robustness and excellent real-time control performance, and it provides a new way for the control of nonlinear processes.
A Localization and Trajectory Planning Method for UAVs with Visual-Inertial Odometry

Unmanned aerial vehicles (UAV) are capable of operating tasks in challenging environments of high risk for humans. However, these tasks usually require UAVs to plan motion in environments where GPS is unavailable. This paper implements a UAV system of motion planning and obstacle avoidance in GPS-denied indoor environments with the visual-inertial odometry (VIO) approach. Collecting measurements from stereo cameras and IMU, the visual-inertial odometry estimates the states of the UAV by jointly minimizing visual and inertial residuals and solving the nonlinear optimization problem. Then we utilize the minimum snap method to generate feasible trajectories based on the estimated states provided by VIO and an obstacle map of the indoor environment. In order to verify the effectiveness of the proposed system, we perform experiments on a UAV platform and the results show that the UAV accurately follows the planned trajectory and avoids obstacles without the aid of GPS.
Road Markings and Road Edges Mapping With Inverse Visual Detector Model

The paper proposes an algorithm for mapping linear features detected on the roadway – road markings, curbs, road boundaries. The algorithm is based on an occupancy grid mapping method with an inverse sensor model. We propose an inverse sensor model that takes into account the spatial error of the linear feature detector and study the influence of various parameters of the model on the resulting quality of mapping. The mapping algorithm was tested on data recorded on a real autonomous vehicle. The quality of the mapping algorithm was assessed by several common quality metrics. In addition, the mapping problem was considered as a binary classification problem, in which each map cell may or may not contain the linear feature, and the ROC curve was used to assess the quality. The experimental results have confirmed that the proposed algorithm can effectively filter detection noise and false detections, which confirms the applicability of the proposed algorithm and the inverse sensor model for solving practical road markings mapping problems.
Control-Aware Scheduling Optimization of Industrial IoT

In this paper, we elaborate on the frequency resource allocation problem of Wireless Networked Control Systems (WNCS). We consider a multi user wireless environment (e.g., factory) where the users are remote industrial Internet of Things (IIoT) devices competing for network resources and a centralized network base station needs to assign the resources to each device accordingly in order to keep the overall control system stability. We design a joint network and control scheduler solution, where we can estimate the degradation of the control system for a given network state and use this information to assign the frequency resource to each device. We show that the proposed solution outperforms traditional scheduling baselines, including genetic algorithms, assuming polynomial complexity in worst case scenario and generalizing for different control and network configurations.
A Hybrid Deep Learning Model for the Blood Glucose Prediction

Blood glucose (BG) control is important for diabetics to avoid hyperglycemia and hypoglycemia events in their daily life. And the forecasting of BG becomes one of the most important sections of the blood glucose control system. Accurate prediction makes patients can react to the abnormal values in advance so that precisely controlling their BG level can be realized. In this study, we propose a novel method to predict BG levels for 30 min of prediction horizon (PH) with a hybrid deep learning model, which integrates multi-layer perceptron, stacked bidirectional gated recurrent unit (Bi-GRU) based recurrent neural network (RNN), and the attention mechanism (AM). Firstly, after pre-processing, the real patient data (in this work, the history BG only) as the input is transmitted to the network used for training and testing. Subsequently, the stacked Bi-GRU layers are used for extracting temporal characteristics of time sequence data, the AM is used for paying more attention to the temporal information which has higher relevance with outputs by assigning weights and then multi-layer perceptron for rapid gradient descent and calculating the regression output of our neural network (in this work, the predict BG). Finally, some important criteria of predictive evaluation are used to demonstrate the effect of our method.
Pseudo-label Estimation Based on EDA for Semi-supervised Soft Sensor Development

Data-driven soft sensor is widely used in the process industry to realize online prediction of difficult-to-measure variables. However, the lack of labeled data and the abundance of unlabeled data in the process industry restrict the development of semi-supervised soft sensor. Assigning high-quality pseudo-labels to expand labeled data is an effective method for improving the semi-supervised model. In this respect, this paper proposes a method based on the estimation of distribution algorithm (EDA), known as EDA-based pseudo label estimation (EDPLE), which combines traditional pseudo-label generation and confidence evaluation into an optimal estimation problem of label probability distribution. The error accumulation in traditional iterative learning is alleviated considerably via this method. In the EDPLE method, the optimization objective consists of prediction accuracy, prediction uncertainty and manifold learning, and a multivariable probability model is designed. Subsequently, the EDA is used to solve the optimization problem to obtain high-confidence pseudo-labels. Therefore, a semi-supervised soft-sensor model based on labeled and pseudo-labeled data is established in this study. In real-world industrial cases, the proposed method outperforms the existing pseudo-label-based semi-supervised soft-sensor method.
An Experience Report on the Suitability of a Distributed Group Encryption Scheme for an IoT Use Case

The critical component in any IoT application is the communication between devices. This must not only function smoothly, but also be secured. An important step in securing IoT communication is its encryption. However, in order for IoT devices to encrypt their communication with each other, they must first agree on appropriate cryptographic keys. In practice, the generation and distribution of such keys is usually managed by a central authority. However, this centralized approach has the disadvantages that (i) a central authority must be trusted, (ii) the central authority represents a single point of failure, and (iii) the central authority may be far away and thus communication with it takes a long time. To overcome these drawbacks, distributed group key agreement approaches have also been proposed. Since these distributed approaches were not originally developed for IoT devices, their performance on such devices is unknown. Therefore, in this work, we investigate the performance of a distributed group encryption scheme on IoT devices. To this end, we have built a measurement environment for distributed group encryption schemes and compare centralized and distributed group encryption schemes for IoT. Our measurements show that under perfect network conditions, the distributed approach performs worse than the centralized approaches in terms of time and memory requirements. However, our measurements also show that the distributed approach allows a group of 5 members to agree on a key in less than a minute. Thus, the distributed method can be used for small IoT groups if agreeing on a key is not time-sensitive.
Convolutional Neural Networks-aided Canonical Correlation Analysis to Fault Classification

In industrial, the data are multivariable, high-dimensional, and nonlinear. Therefore, it is impossible to extract sufficient information from the process data in time. To solve this problem, this paper proposes a fault classification method based on the canonical correlation analysis (CCA) assisted by convolutional neural network(CNN). The proposed method bears data feature extraction at Case Western Reserve University and performs multiple fault classification. The experimental results verified the applicability and effectiveness of the proposed method.
ANÁLISE DE UM PROJETO DE INOVAÇÃO TECNOLÓGICA E O USO ABORDAGEM CANVAS

O dinamismo do mercado dos ultimos tempos e a exigencia cada vez maior do mercado na busca por novos processos, produtos e servicos cada vez mais alinhados com as necessidades organizacionais o processo de inovacao passou a ser extremamente relevante e investir em novos conhecimentos tornou-se essencial para organizacao manter-se no mercado. Partindo disso, a abordagem Canvas busca desenvolver modelos com ampla visao que proporcione o atendimento das necessidades das organizacoes, acelerando este processo de inovacao de forma participativa. O presente artigo teve como objetivo o mapeamento de uma das inovacoes na empresa, bem como a sua apresentacao por meio da abordagem de 2 modelos Canvas. Foi utilizada a metodologia de estudo de caso unico, com coleta dos dados realizada pela aplicacao de entrevista semi-estruturada. Os resultados indicam que a abordagem Canvas pode melhorar o planejamento de processos de inovacao, onde os envolvidos diretamente no projeto passam a ter uma visao ampla na busca pela solucao dos problemas enfrentados e na obtencao da criacao de vantagens competitivas.
Content Sharing in Pedestrian-based Micro Clouds

The continuous growth of the urban population and the high development and maintenance costs of infrastructure-based approaches make it necessary the utilization of distributed schemes. Among distributed systems, there is an increasing interest in edge cloud models, both for vehicular and pedestrian applications. In this paper, we developed a distributed application based on the micro cloud concept formulated in the field of vehicular edge computing for spreading content items in an indoor pedestrian environment. Results highlight how it is possible to reach both 100% content items spread and up to 66% reduction over channel collisions.
Kinect-based 3D Human Motion Acquisition and Evaluation System for Remote Rehabilitation and Exercise

With the increasingly serious aging situation, more and more elderly people are physically disabled. In addition, the current rehabilitation resources have the problems of shortage and uneven distribution, coupled with the impact of COVID-19 in early 2019, most patients have been greatly restricted from going to the rehabilitation center for training. To solve these problems, we propose a "Kinect-based 3D Human Motion Acquisition and Evaluation System for Remote Rehabilitation and Exercise" which uses the Kinect3 camera to obtain human motion with an error rate of only 3% when the body is in front of the camera. Then we use Unity to create a humanoid virtual model and interactive scene and synchronize the real body motion to the virtual model with an average error less than 1%. At the same time, our system provides reliable and highly accurate methods for evaluating actions based on angles and trajectories. What’s more, users don’t need to wear any wearable devices when using the system. It is a mark-less motion acquisition system, which reduces the cost and improves the usability and scalability of the system. And the interactive virtual scenes also increase the training motivation of users.
Orthogonal Time Frequency Space with Generalized Spatial Modulation

Orthogonal time frequency space (OTFS) is a two-dimensional modulation scheme designed in the delay-Doppler domain, for combating the high mobility environment toward future wireless communications. In this paper, we propose a novel generalized spatial modulation combined OTFS (GSMOTFS) system, aiming at achieving better system performance compared to the conventional multiple-input multiple-output OTFS (MIMO-OTFS) and GSM orthogonal frequency division multiplexing (GSM-OFDM) systems. Furthermore, a decision feedback detector based on the minimum mean square error (MMSE) criterion is also developed toward efficient signal detection for the proposed GSM-OTFS system.
Adaptive Trajectory Tracking for Linear Delay Systems with Application to Rotary Motors

An adaptive trajectory tracking controller is proposed for linear systems with input delay, unknown plant parameters and constant disturbance. The controller employs predictor feedback to compensate the effect of input delay and the projection-type adaptation laws are designed to tackle parametric uncertainties. By the stability analysis with a Lyapunov function in logarithmic form, the controller guarantees the convergence of the tracking error when the design parameters such as adaptation gains meet certain conditions. An application to the speed control of the rotary motor is conducted. The simulation and experiment results demonstrate the effectiveness of the proposed controller.
Yarn-dyed Shirt Piece Defect Detection Based on U-shaped Swin Transformer Auto-encoder

Automatic defect detection is an essential and challenging problem in the yarn-dyed weaving production process, this paper proposed a novel U-shaped Swin Transformer auto-encoder reconstructed model for yarn-dyed shirt piece defect detection. This method uses the model of Transformer to extract the global features of the image better and reconstruct it more accurately, which solves the problems of scarce and unbalanced type of defect samples and high cost in actual production. Firstly, for a certain pattern, using defect-free samples adding Gaussian noise to train the reconstruction model. Then, the image to be tested is input into the Transformer model to obtain the corresponding output image. Subsequently, the residual image is calculated by subtracting the input image and its corresponding output image. Finally, the defect localization can be achieved through thresholding and morphological operation. The experiment result verifies the effectiveness of the proposed method on various types of yarn-dyed shirt pieces.
Successive Interference Cancellation for Communication and Radar Coexistence

The ever increasing demand for high transmission rate towards futuristic scenarios stimulates communication systems to utilize a broad spectrum which is supposed to be partially overlapped by the spectrum used by radar. In light of this, communication and radar coexistence (CARC) has developed to be the candidate scheme for the 6th generation (6G) mobile communication. However, the ineluctable interference between the two systems posed by spectral overlap will cause a sharp deterioration on communication performance and most existing approaches to suppress such inter-system interference neglect the scatters of radar signal whose power is relatively large compared with communication transmission. To this end, based on orthogonal frequency division multiplexing (OFDM) communication technique, we propose a successive interference cancellation (SIC) scheme for CARC taking the interference posed by scattering radar signal from the target into account. At the communication receiver, channel impulse response (CIR) of the scattering path is obtained through subtracting the reconstructed communication waveform so that the inter-system interference is estimated and removed. Numerical results with respect to bit error rate (BER) and spectral efficiency (SE) performances verify the effectiveness of our design.
Deep Point Cloud Normal Estimation Via Triplet Learning

Current normal estimation methods for 3D point clouds often show limited accuracy in predicting normals at sharp features (e.g., edges and corners) and less robustness to noise. In this paper, we propose a novel normal estimation method for point clouds which consists of two phases: (a) feature encoding to learn representations of local patches, and (b) normal estimation that takes the learned representation as input and regresses the normal vector. We are motivated that local patches on isotropic and anisotropic surfaces respectively have similar and distinct normals, and these separable features or representations can be learned to facilitate normal estimation. To realise this, we design a triplet learning network for feature encoding and a normal estimation network to regress normals. Despite having a smaller network size compared with most other methods, experiments show that our method preserves sharp features and achieves better normal estimation results especially on computer-aided design (CAD) shapes.
Spatio-temporal Attention Transformer Model for Future Trajectory Forecast

Human trajectory prediction is very important for people's safety. And human motion is essentially a multimodal motion, which brings great challenges to the trajectory prediction. Meanwhile, most sequence-to-sequence models only consider one of the temporal and spatial characteristics of pedestrian motion and ignore other. Therefore, based on the temporal and spatial attention mechanism and the transformer model, we fuse the temporal and spatial characteristics of the imputation, and propose a deep learning prediction model based on the spatial-temporal attention transformer mechanism (STATM). Our model designs a temporal attention module based on multi-head attention, a spatial attention module based on GAT, and finally maps spatial-temporal features to trajectory destinations through a transformer-decoder-like module. Compared with other methods, our model achieves better results on ETH and UCY public datasets.
Application of Distributed Brillouin Optical Fiber Sensor and MVO-DBSCAN for OPGW Outlier Identification

The entire transmission line of the optical fiber composite overhead ground wire (OPGW) is formed by splicing multiple optical cable segments in the splice box. The Brillouin frequency shift change at the splice point and the density based spatial clustering of applications with noise (DBSCAN) algorithm can be used to realize the clustering of the Brillouin frequency shift, and then locate the splice point and outliers. However, the DBSCAN algorithm is very sensitive to the input parameters, which can easily cause poor clustering effect. In this paper, grid search is used to optimize the parameter selection of DBSCAN algorithm, and the effective parameter interval is obtained. The Multi-Verse Optimizer (MVO) algorithm is further used to obtain the optimal solution of DBSCAN parameters, and the actual line data is used to verify the effectiveness of DBSCAN after MVO optimization.