Miracle-dz/UniFAR-FTUs huggingface dataset:
ROV assisted magnetic induction communication field tests in underwater environments

Magnetic Induction (MI) is a promising technique for near-field wireless underwater communications. Although the literature has some theoretical analyses and lab experiments for underwater MI communication, there is a lack of field tests in underwater environments, especially in subsea environments. In this paper, we leverage the remotely operated vehicle (ROV) and the remotely controlled boat (RCB) to develop an MI wireless communication system, and conduct field tests for MI communication performance in both fresh water and sea water. The experiment results show that even in the most challenging subsea environment, the MI communication has very good near-field transmission performance with a small coil antenna and low power consumption.
Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm

The early detection of diseases is important in agriculture for an efficient crop yield. The bacterial spot, late blight, septoria leaf spot and yellow curved leaf diseases affect the crop quality of tomatoes. Automatic methods for classification of plant diseases also help taking action after detecting the symptoms of leaf diseases. This paper presents a Convolutional Neural Network (CNN) model and Learning Vector Quantization (LVQ) algorithm based method for tomato leaf disease detection and classification. The dataset contains 500 images of tomato leaves with four symptoms of diseases. We have modeled a CNN for automatic feature extraction and classification. Color information is actively used for plant leaf disease researches. In our model, the filters are applied to three channels based on RGB components. The LVQ has been fed with the output feature vector of convolution part for training the network. The experimental results validate that the proposed method effectively recognizes four different types of tomato leaf diseases.
Aalborg Universitet An Experimental Study of Embodied Interaction and Human Perception of Social Presence for Interactive Robots in Public Settings Heath,

The human perception of cognitive robots as social depends on many factors, including those that do not necessarily pertain to a robot’s cognitive functioning. Experience Design offers a useful framework for evaluating when participants interact with robots as products or tools and when they regard them as social actors. This study describes a between-participants experiment conducted at a science museum, where visitors were invited to play a game of noughts and crosses with a Baxter robot. The goal is to foster meaningful interactions that promote engagement between the human and robot in a museum context. Using an Experience Design framework, we tested the robot in three different conditions to better understand which factors contribute to the perception of robots as social. The experiment also outlines best practices for conducting human-robot interaction research in museum exhibitions. Results from the study indicate that perceived social presence can be evaluated using a combination of HRI and Experience Design methods that measure co-presence and co-experience.
Double-layer Cubature Kalman Filter

The cubature Kalman filter (CKF) algorithm is not suitable for non-Gaussian environments. The cubature particle filter (CPF) algorithm can solve the problem of the CKF algorithm, but it will introduce the problem of a large computational complexity. To solve the above problems, a Double-Layer Cubature Kalman Filter (DLCKF) algorithm is proposed. The DLCKF algorithm uses the state estimation of the inner CKF to replace the state transition density function of the outer CKF and updates the weights of each deterministic sampling point of the outer CKF with the latest measurements. Finally, the state estimation at each time is obtained. Simulation results show that, compared with the CKF and the CPF, the proposed algorithm not only has a low computational complexity, but also has very good estimation accuracy.
Region Expansion Algorithm: A Well-Quality Region Proposal Generation

This paper proposes an efficient algorithm to appropriately generate a limited number of regions, socalled a region proposal algorithm, for resolving computer vision problems. The most important challenge in region proposal technique is to accurately produce a limited number of regions. This literature introduces an algorithm to answer to the challenge through expanding region. The proposed algorithm comprises of two components and in a triple way. The first component divides an image into some non-overlapping regions. Afterwards, each region is developed in adjacent regions based on the 8-connectivity in the second component. In fact, the image is represented by overlapping expanded regions. This component can be executed to three different modes as fixed, all, and efficient-mode. Our proposed algorithm shows better results than existing state-of-the-art segmentation algorithms, including EGBS, Quick shift, and SLIC. The quality of the regions is measured according to the best overlap and recall metrics at MSRC dataset. The results show the good achievement of the overlap and recall as well. The best result is acquired by EGBS and efficient-mode around 24% and 13% improvement for recall and the best overlap, respectively.
Sampling and Reconstruction of Multiband Signals in Multiresolution Subspaces Associated With the Fractional Wavelet Transform

The fractional wavelet transform (FrWT), which generalizes the ordinary wavelet transform, is a very promising tool for signal processing and analysis. A sampling theory for signals in FrWT-based multiresolution subspaces has been proposed recently. However, the signals to be sampled are regarded as lowpass signals indiscriminately. When dealing with bandpass or multiband signals, the sampling rate could be unnecessarily high. This letter investigates the sampling theory of multiband signals in multiresolution subspaces associated with the FrWT. The nonideal sampling and reconstruction scheme we proposed can guarantee lower sampling rate, fewer sampling points but subequal reconstruction performances. Numerical experiments are presented to validate the theoretical derivations.
CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring

This paper proposes a Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) combination scheme for anomaly detection to train feature extraction and to test anomaly prediction by using Stacked Convolutional Neural Networks (S-CNNs), Stacked Gated Recurrent Units (S-GRUs) as the typical model of RNNs, and a linear regression layer. In this proposed model, the S-CNNs layers firstly capture spatial feature extraction of the input sequence data of vibration sensor, and the result is used to temporal feature learning secondly with the S-GRUs. After this procedure, finally a regression layer predicts an anomaly detection. The experimental results of bearing data in NASA prognostics data repository not only show the accuracy of the proposed model in anomaly prediction for rotating machinery diagnostics, but also suggest the better performance than other state-of-the-art algorithms such as a plain RNN and GRU of the individual model.
A Novel Ant-Based Multiple Cells Tracking Approach with Cardinality Estimation

Cell migration is an important process in normal tissue, organ or entire organism development and disease. This paper proposes an ant algorithm based on cardinality estimation for clustered cells state and number estimator simultaneously. Cardinality prediction and updating model based on the existence probability of pheromone field are derived for effectively estimating the number of cells. In order to separate clusters cells, an ant work model based on the pheromone gradient information is developed to guide ants movement towards center of interested cells. Experiment results show that our algorithm could automatically track clustered cells in various scenarios, and, it is more accurate than other popular tracking methods.
Fuzzy PI Controller for Mobile Robot Navigation and Tracking

The precise mobile robot control is an interesting field in robotics. In this work, three fuzzy logic PI controllers are adapted for the purpose of goal attain and trajectory tracking. Each controller consists on designing a classical PI controller and a fuzzy inference system with two inputs and two outputs. The inputs are the error and the error derivative; however the outputs of the fuzzy unit are the parameter corrections of the gains of the PI controller. These controllers are designed to regulate the motor velocities of our khepera II mobile robot. Navigation and tracking are graphically presented to demonstrate the efficacy of this proposed fuzzy PI controller. Also, a comparison with classical methods is exposed.
Book2Vec: Representing Books in Vector Space Without Using the Contents

This paper presents book2vec, a neural network based embedding approach for creating book representations. In this work, a well-known method from natural language processing domain, namely word2vec, is applied to a dataset of the books read by different users from the Goodreads website. Unlike previous works, we use non-textual features, considering only the book IDs. We represent the books read by each user as a sentence where the books' IDs are the words in the sentences. The results show that this approach can find meaningful representation of the books.
Anticipatory User Plane Management for 5G

The 5G user plane is bound to play a significant role in fulfilling the dynamic demand created from the heterogeneous device layer, with novel concepts introducing the flexible deployment of user plane functions and per-user traffic management. The focus in this paper lies on the dynamic control of 5G's SDNized transport network to optimize the user plane management for mobile users. For this purpose we propose the concept of Anticipatory User Plane Management for 5G, which aims at optimized, learning-based and foresighted user plane management, reducing the user plane reconfiguration latency caused by users' mobility. In particular we contribute with two different approaches that exploit the prediction of user behavior for improved post-handover procedures, i) by suitable selection of intermediate UPFs based on the anticipated user behavior, and ii) by applying a pre-configuration of the user data plane via the means of a novel UPF mode.
Design and Vehicle Implementation of Autonomous Lane Change Algorithm based on Probabilistic Prediction

This paper describes design, vehicle implementation and validation of a motion planning and control algorithm of autonomous driving vehicle for lane change. Autonomous lane change is necessary for high-level autonomous driving. A vehicle equipped with diverse devices like sensors and computer is introduced for implementation and validation of autonomous driving. The autonomous driving system consists of three parts: perception, motion planning and control. In a perception part, surrounding vehicles' states and lane information are estimated. In motion planning part, using these information and chassis information, probabilistic prediction is conducted for ego vehicle and surrounding vehicle separately. And then, driving mode are decided among three modes: lane keeping, lane change and traffic pressure. Driving mode is determined based on a safety distance by predicting states of surrounding vehicles and ego vehicle. If the ego vehicle cannot perform lane change when the lane change is required, the most proper space is selected considering the probabilistic prediction information and the safety distance. Target states are defined based on driving mode and information of surrounding vehicles behaviors. In control part, the distributed control architecture for real time implementation to the vehicle. A linear quadratic regulator (LQR) optimal control and a model predictive control (MPC) are used to obtain the longitudinal acceleration and the desired steering angle. The proposed automated driving algorithm has been evaluated via vehicle test, which has used one autonomous vehicle and two normal vehicles.
Community Detection in Complex Networks by Detecting and Expanding Core Nodes Through Extended Local Similarity of Nodes

As the community detection is able to facilitate the discovery of hidden information in complex networks, it has been drawn a lot of attention recently. However, due to the growth in computational power and data storage, the scale of these complex networks has grown dramatically. In order to detect communities by utilizing global approaches, it is required to have all the global information of the whole network; something which is impossible, because of the rapid growth in the size of the networks. In this paper, a local approach has been proposed based on the detection and expansion of core nodes. First, a community’s central node (core node) which has a high level of embeddedness is detected based on the similarity between graph’s nodes. By using this, the total weights of a weighted graph’s edges created. Following by that, the expansion of these nodes will be considered, by utilizing the concept of node’s membership based on the definition of strong community for weighted graphs. It can be seen that in detecting communities, the more accurate the weights of edges detected based on the node similarity, the more precise the local algorithm will be. In fact, the algorithm has the ability to detect all the graph’s communities in a network using local information as well as identifying various roles of nodes, either being (core or outlier). Test results on both real-world and artificial networks prove that the quality of the communities which are detected by the proposed algorithm is better than the results which are achieved by other state-of-the-art algorithms in the complex networks.
Evaluation of the Effect of Frame Size on Speech Emotion Recognition

Speech emotion recognition aims to determine the emotional state in people by processing the speech with digital signal processing methods. Speech emotion recognition is influenced by many factors such as the language, the demographic characteristics of the participants, and the signal processing methods used. By pre-processing the speech signals before emotion recognition, the signal is improved and the recognition performance is being increased. The framing involved in preprocessing methods is the division of the speech signal into small pieces. In this study, we investigated the effect of the frame size used in framing on the emotion recognition. In addition, the most appropriate frame size for the most commonly used datasets in the literature has been determined. According to the results obtained, the frame size to be used differs according to the dataset used. Furthermore, even the 1ms difference in frame size causes a change in the rate of emotion recognition.
Multi-person Sleeping Respiration Monitoring with COTS WiFi Devices

Recently, non-intrusive respiration monitoring has attracted much attention. Many respiration monitoring systems using the commercial off-the-shelf WiFi devices have been developed. However, these systems mainly have difficulties in the presence of multiple persons. The difficulty generally comes from the separation of the effects of multiple persons' respiration on the received WiFi signals. Another problem is that even though the separation can be feasible with some complicated algorithms, it is still impossible to map the multiple identified respiration states to the corresponding persons. In this paper, we study the problem of multi-person sleeping respiration monitoring and try to address the above challenges. Instead of focusing on developing complicated signal processing algorithms, we take another approach: via the deployment of WiFi transceivers. The key insight comes from the WiFi Fresnel zone model, which indicates that a carefully placed WiFi transceiver may only be affected by the person in a certain location. Furthermore, we consider the sleeping movements of people as well as the sleeping posture change to improve the robustness of the system. Extensive experiments show that we can successfully estimate the respiration rate of multiple persons, with the Mean Absolute Error (MAE) of 0.5 bpm - 1 bpm.
Analysis of Calculation Models for Device Resonance in Critical Mode Converters

Critical Mode (CRM) converters, superior for high density applications at high frequencies, rely on precision gating signals for best timing of device resonance. Previously, two calculation models were used, one based on device time related output capacitance, Co(tr), and another on device output charge, Qoss. This work applies them to GaN and Si power devices, and analyzes the differences. Results show that the Qoss model is better for both devices in predicting resonant period. However, for prediction current/voltage waveforms the Co(tr)model is better for GaN while the Qoss model better for Si devices. The Qoss model is then simplified and applied to a 1 MHz and 1.4 kW CRM boost converter built with GaN devices. The prototype achieves >99% efficiency from 600% to 100% load at 240V input and 400V output.
Graph Embedding for Citation Recommendation

As science advances, the academic community has published millions of research papers. Researchers devote time and effort to search relevant manuscripts when writing a paper or simply to keep up with current research. In this paper, we consider the problem of citation recommendation on graph and propose a task-specific neighborhood construction strategy to learn the distributed representations of papers. In addition, given the learned representations, we investigate various schemes to rank the candidate papers for citation recommendation. The experimental results show our proposed neighborhood construction strategy outperforms the widely-used random walks based sampling strategy on all ranking schemes, and the model based ranking scheme outperforms embedding based rankings for both neighborhood construction strategies. We also demonstrated that graph embedding is a robust approach for citation recommendation when hidden ratio changes, while the performance of classic methods drop significantly when the set of seed papers is becoming small.
Intelligent Driving Data Recorder in Smartphone Using Deep Neural Network-Based Speedometer and Scene Understanding

This paper proposes a smartphone-based Driving Data Recorder (DDR). The proposed DDR has the functions of accurate speed estimation and intelligent traffic scene understanding. DDRs are used to store the relevant driving data to provide feedback on driver behavior for accident analysis, insurance issue, and so on. The conventional DDRs are standalone devices with multiple sensors. The current DDR products record many useless data or lose important information. On the other hand, the widely used smartphones already have the hardware conditions to replace the conventional DDR products. This paper proposes to develop the intelligent DDR in the smartphones. Considering the requirements of the DDRs, two functions are developed in this paper: motion sensor-based speedometer and vision sensor-based scene understanding. The proposed speedometer function adopts double-layered Long Short-Term Memory (LSTM) network as the model, which can estimate the vehicle speed directly from gyroscope and accelerometer of a smartphone. The scene understanding function can detect road facilities such as traffic lights, crosswalks, and stop lines. The driving data recorded in those areas are very important for analyzing driver behaviors. In the development of the scene understanding function, maintaining high detection accuracy with reduced computation cost is significant due to the limitation of smartphones’ processing resources. This paper uses a lightweight architecture deep learning network to achieve the goal. The proposed system has been evaluated using the real traffic data. Speed estimation function only has 1.8 km/h of speed mean error. In addition, there is no accumulated error even for a long time driving. The evaluation of the scene understanding function indicates that the proposed method can provide a high-accuracy detection at 2 FPS, which is faster than the state-of-the-art method.
SD-WAN Source Route Based on Protocol-oblivious Forwarding

Larger companies need more sites in the wide area network (WAN). However, internet service providers cannot obtain sufficient capacity to handle peak traffic, causing a terrible delay. The software-defined network (SDN) allows to own more programmability, adaptability, and application-aware, but scalability is a critical problem for merging both. This paper proposes a solution based on Protocol-Oblivious Forwarding (POF). It is a higher degree of decoupling control and data planes. The control plane uses fields unrelated to the protocol to unify packet match and route, and the data plane uses a set of general flow instructions in fast forwarding. As a result, we only save three flow tables on the forwarding paths so that each packet keeps a pipeline in the source route header to mark the next output ports. This solution can support a constant delay while the network expands.
An Adaptive Online Learning Model for Flight Data Cluster Analysis

Safety is a top priority for civil aviation. To help airlines further improve safety, various clustering-based methods were developed to better understand their current flight operations and detect unknown risks from onboard flight data. However, existing methods can only be carried on historical data in batches, resulting in its inability to update and adjust as new data come in. New onboard flight data related to anomaly detection are generated at airlines every day. The addition of new data will inevitably cause changes in the clustering results. Yet it would be computational costly to run clustering on all data as they accumulate. Therefore, anomaly detection methods that allow real-time update of cluster models as new data come in are more practical for airlines. This paper presents a reinforcement learning method to identify common patterns in flight data via cluster analysis and update its clusters as new data come in. This method is based on Gaussian Mixture Model (GMM) and uses online (recursive) expectation-maximization (EM) algorithm to update clustering results over time. An initial result of clusters can be obtained by performing GMM-based clustering on historical flight data. Then, as new data come in, the parameters of GMM are updated via an online EM algorithm. By recording the GMM parameters, the method can also track changes in clusters over time. We demonstrated the proposed method using Flight Data Recorder (FDR) data from real operations of an airline. The evolution of clusters was observed as new batches of flight data are fed into the proposed method.