Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Analysis for Anomaly Detection to Secure Rail Network

The security, safety and reliability of rail systems are of the utmost importance. In order to better detect and prevent anomalies, it is necessary to accurately study and analyze the network traffic and abnormal behaviors, as well as to detect and alert any anomalies if happened. This paper focuses on data analysis for anomaly detection with Wireshark and packet analysis system. An alert function is also developed to provide an alert when abnormality happens. Rail network traffic data have been captured and analyzed so that their network features are obtained and used to detect the abnormality. To improve efficiency, a packet analysis system is introduced to receive the network flow and analyze data automatically. The provision of two detection methods, i.e., the Wireshark detection and the packet analysis system together with the alert function will facilitate the timely detection of abnormality and triggering of alert in the rail network.
Quentin: an Ultra-Low-Power PULPissimo SoC in 22nm FDX

The End-Nodes of the Internet of Things (IoT) require extreme energy efficiency coupled with wide power performance operating range. Fully-depleted SOI (FD-SOI) is an attractive technology for ultra-low power and wide-range operation as it offers compelling options to tune power, performance, area (PPA) at design time as well as at run time. This paper describes Quentin: an MCU-class (32bit) open source RISC-V SoC featuring an autonomous I/O subsystem optimized to deal with the wide variety of sensors available in IoT end-nodes, coupled with a processor optimized for near threshold computation and a heterogeneous (standard-cell and SRAM) memory architecture to better exploit the low-voltage capabilities of 22nm FDX technology. The system runs up to 2400 million equivalent RV32IMC instructions per second (MOPS) and achieves best power density of $6 \mu \mathrm {W}/$MHz, resulting into an energy efficiency of 433 MOPS/mW.
A Minimum Task-Based End-to-end Delivery Delay Routing Strategy With Updated Discrete Graph for Satellite Disruption-Tolerant Networks

Recently, a variety of time-varying graphs, such as space-time graph and event-driven graph, are widely employed for modelling the dynamic topologies of satellite Disruption-Tolerant Networking (DTN) network with periodic orbital motions of satellite platforms. As the major criterion of delivering a file of targeted data, however, Task-based End-to-end Delivery Delay (TEDD) is hardly evaluated by using the current methods of graphs due to their intrinsic incapability in precision. In this paper, a topology-driven Updated Discrete Graph (UDG) is proposed for confining the low bound of TEDD with a given delivery task by using a tailored edges capacity. In particular, a Minimum TEDD Routing Strategy (MTRS) is designed through solving a correspondingly integral Min-Max optimization problem. The simulation results verifies the advantage of MTRS for TEDD compared with two typical graph algorithms under a group of specific satellite network scenarios.
A New Bearing Fault Diagnosis Method Based on Fine-to-Coarse Multiscale Permutation Entropy, Laplacian Score and SVM

Fault diagnosis of rotating machinery is vital to identify incipient failures and avoid unexpected downtime in industrial systems. This paper proposes a new rolling bearing fault diagnosis method by integrating the fine-to-coarse multiscale permutation entropy (F2CMPE), Laplacian score (LS) and support vector machine (SVM). A novel entropy measure, named F2CMPE, was proposed by calculating permutation entropy via multiple-scale fine-grained and coarse-grained signals based on the wavelet packet decomposition. The entropy measure estimates the complexity of time series from both low- and high-frequency components. Moreover, the F2CMPE mitigates the drawback of producing time series with sharply reduced data length via the coarse-grained procedure in the conventional composite multiscale permutation entropy (CMPE). The comparative performance of the F2CMPE and CMPE is investigated by analyzing the synthetic and experimental signals for entropy-based feature extraction. In the proposed bearing fault diagnosis method, the F2CMPE is first used to extract the entropy-based features from bearing vibration signals. Then, LS and SVM are used for selection of features and fault classification, respectively. Finally, the effectiveness of the proposed method is verified for rolling bearing fault diagnosis using experimental vibration data sets, and the results have demonstrated the capability of the proposed method to recognize and identify the bearing fault patterns under different fault states and severity levels.
Distributed Resource Allocation and Computation Offloading Scheme for Cognitive Mobile Edge Computing Networks with NOMA

Mobile edge computing (MEC) has become a key component of 5G, via bringing computation services to the edge of the mobile network to enhance the computational capacity of users. To improve spectrum efficiency, we adopt cognitive radio (CR) technology in the MEC system, which makes the resource allocation and computation offloading problem more complex. In this paper, we consider a multi-MEC and multi-user system combined with CR, where each user has a computation task to complete. To further improve the system efficiency, non-orthogonal multiple access (NOMA) is adopted by users. We formulate the resource allocation and computation offloading problem in our scenario as an optimization problem with multiple constraints which aims to maximize the system utility. Alternating direction method of multipliers (ADMM) is adopted to solve this problem in a distributed and efficient way. Simulation results show that our algorithm could achieve great performance comparing to state-of-art distributed and centralized approaches.
Prediction of Dialogue Success with Spectral and Rhythm Acoustic Features Using DNNS and SVMS

In this paper we investigate the novel use of exclusively audio to predict whether a spoken dialogue will be successful or not, both in a subjective and in an objective manner. To achieve that, multiple spectral and rhythmic features are inputted to support vector machines and deep neural networks. We report results on data from 3267 spoken dialogues, using both the full user response as well as parts of it. Experiments show an average accuracy of 74% can be achieved using just 5 acoustic features, when analysing merely 1 user turn, which allows both a real-time but also a fairly accurate prediction of a dialogue successfulness only after one short interaction unit. From the features tested, those related to speech rate, signal energy and cepstrum are amongst the most informative. Results presented here outperform the state of the art in spoken dialogue success prediction through solely acoustic features.
Performance Analysis of Three Dimensional Fading Channel Model with Angle Offset

As one of the most promising techniques for the 5th Generation (5G) mobile communication systems, Three dimensional (3D) Multiple Input and Multiple Output (MIMO) has the potential of improving spectrum efficiency, but it needs further research. For 3D channel model, the elevation angle should be accurately included. Conventionally 3D channel is modeled with an assumption that the mean elevation angle of multipath is coincided with the line of sight (LOS) direction. To investigate its authenticity, we conduct extensive 3D multiple antenna field channel measurements with 100 MHz bandwidth in a typical urban microcell (UMi) scenario. The elevation angles of each multipath is extracted from the measurement data and its statistical propagation characteristics are analyzed. It is found that for LOS case, this assumption holds. However, for none line of sight (NLOS) case, there exists an elevation angle difference between the mean and the line directly connecting transmitting and receiving antenna, which is defined as elevation angle offset. The performance of the 3D channel model with the elevation angle offset is validated in terms of channel capacity comparing to the 3D channel model without angle offset. Results show that the 3D channel model with angle offset fits the real measured data better.
An Exploration of Directly Using Word as ACOUSTIC Modeling Unit for Speech Recognition

Conventional acoustic models for automatic speech recognition (ASR) are usually constructed from sub-word unit (e.g., context-dependent phoneme, grapheme, wordpiece etc.). Recent studies demonstrate that connectionist temporal classification (CTC) based acoustic-to-word (A2W) models are also promising for ASR. Such structures have drawn increasing attention as they can directly target words as output units, which simplify ASR pipeline by avoiding additional pronunciation lexicon, or even language model. In this study, we systematically explore to use word as acoustic modeling unit for conversational speech recognition. By replacing senone alignment with word alignment in a convolutional bidirectional LSTM architecture and employing a lexicon-free weighted finite-state transducer (WFST) based decoding, we greatly simplify conventional hybrid speech recognition system. On Hub5-2000 Switchboard/CallHome test sets with 300-hour training data, we achieve a WER that is close to the senone based hybrid systems with a WFST based decoding.
A Multi-task Learning Approach for Weather Classification on Railway Transportation

Most of vision based urban transport dataset are designed to be executed in clear weather conditions. However, limited visibility in rain or cloudy strongly affects the accuracy of vision systems. To improve safety of railway transportation in actual weather situations, our newly constructed railway transportation dataset contains 4 situations from the real videos which has more adverse weather conditions. Taking into account railway transportation images are mainly single object with single background, which is limited to weather classification. We also collected a multi-class weather dataset to improve the generalization ability of the classification model. In order to capture a discriminate feature for each weather condition and avoid involving complicated pre-processing techniques. We provide a multi-task learning framework which formulate the classification problem as a multi-task regression problem by considering the classification on each weather class as a task. We capture the intrinsic relatedness among different tasks by a group Lasso regularization. With experiments on standard weather datasets and our own dataset, we demonstrate that the proposed framework achieves superior performance compared to the state-of-the-art methods.
HPC Software Verification in Action: A Case Study with Tensor Transposition

As HPC platforms get increasingly complex, the complexity of software optimized for these platforms has also increased. There is a pressing need to ensure correctness of scientific applications to enhance our confidence in the results they produce. In this paper, we focus on checking the functional equivalence of libraries providing a small but important functionality---tensor transposition---used in computational chemistry applications. While several correctness tools have been developed and deployed, there are several practical challenges in using them to check correctness of production HPC software. We present our experiences using two tools---CIVL and CodeThorn---in checking the functional equivalence of two index permutation libraries. We observe that, with some effort, the tools we evaluated can handle kernels from production codes. We present observations that will aid library writers to write code that can be checked with these tools.
Pathfinding Optimization when Solving the Paparazzi Problem Comparing A* and Dijkstra's Algorithm

This paper aims to compare the application of the A* algorithm and Dijkstra's algorithm to solve a particular variant of pathfinding problem based on the so-called paparazzi problem. The problem consists of a grid with different fixed obstacles, leading to different traversing time factors respectively costs. A specific model of this problem is derived and the performance of these two pathfinding algorithms that solve the paparazzi problem is compared with regard to the computation time and the number of iterations. The shortest versus the fastest path with lowest cost are discussed.
A Hybrid Approach for Weak Fault Feature Extraction of Gearbox

A novel hybrid fault diagnosis method based on ensemble empirical mode decomposition and weighted adaptive multi-scale morphological analysis (WAMMA) is proposed to detect the early damage of gearboxes. In this method, we propose a characteristic frequency ratio (CFR) method to determine the weighted coefficient for each scale of AMMA. First, multiple scales are obtained using the AMMA method. Second, the weighted coefficient of each scale in the AMMA method is calculated using the CFR. Third, the final results can be obtained by multiplying the weighted coefficients and filtering results with all scales. Since the performance of each scale of AMMA is evaluated using the CFR, the demodulation ability can be effectively improved. However, the WAMMA is easily disturbed by heavy noise when extracting early fault feature directly. A method combined EEMD with the WAMMA is proposed. The effectiveness of the proposed method has been verified using two experimental vibration signals of gearboxes. The results demonstrate that the proposed method has a superior performance in the extraction of weak fault characteristics of gearboxes in comparison with the WAMMA and EEMD-AMMA methods.
An Effective Infrastructure for Pocket Switch Network

In everyday life we are using Internet such as Broadband, mobile data etc. for communication. Pocket Switch Networks (PSN) uses phones as the medium to connect with others without a fixed cellular network or Internet. Since no fixed route to destination is available, problems arise when a node wants to send any message to a destination node. Packets are forwarded by flooding. Flooding causes increase in network congestion, resource consumption, delay and drop of packets. This approach can be applied to smaller networks but is inefficient in large scale. To deliver messages in a larger network with high throughput and low latency we propose an infrastructure for PSN called ZoneCluster. Firstly, we use a probabilistic approach to create clusters in the network. Secondly, we propose a routing algorithm for the clusters in the network. Lastly, we emphasize its application for larger networks such as an AD-Hoc WAN by comparing its performance with other protocols.
Appearance inspection of wire harness using RGB-D Sensor

To realize in hand inspection system which does not fix camera and target object, we employ RGB-D sensor to adjust relative position of camera and target object. This system uses a RGB-D sensor to get both color image and depth image, simultaneously, without fixing camera and target object. In this paper, we focus on wire harness inspection, especially for checking fuse box arrangement. From RGB-D images, first we extract fuses faces. Then, we obtain feature values for each fuse. By using machine learning, our system can discriminate fuse array almost accurately if the distance was within 25 cm and the angle was within 18 degrees.
Wideband Monopolar Transmitting Antenna for Wireless Power Transfer and Communication Application

A proximity-fed circular patch antenna with linear polarization and monopole-like radiation pattern is presented. The antenna achieves a bandwidth $(|Sl1|\lt \ -10 dB)$ of 50.4% (4.15-6.95 GHz) at the central frequency of 5.55 GHz. The microwave power transmission (MPT) band (5.8 GHz) and space shuttle communication band (4.60GHz) can be completely covered. The broadband performance is achieved by exciting multimodes (TM01 and TM02) in the patch. Three resonant points can be observed in the S-parameter curve. The measured minimum and maximum gains in the whole band are 5.1 dBi and 9.4 dBi, respectively. The simulated results and the measured ones are in good agreement. The antenna is pretty suitable for microwave power and information transmission in space shuttle.
Streaming Video Optimization in Mobile Communications

With the rapid development of streaming video service, video traffic has occupied the most of internet traffic. The rapid increase of mobile video traffic becomes a big challenge for the limited network resource. Latency and rebuffering (video freezes) therefore turn out to be the main problems of mobile video service. In this work, we present new network schemes to solve the problems of latency and rebuffering in mobile communications. We propose a new network architecture deploying Content Delivery Network (CDN) inside mobile networks, with the help of multi-access edge computing (MEC) technology. Users can get access to video service via local CDN without through traditional mobile core network. The latency and transmission distance can be significantly decreased. The bandwidth can be saved as well. Aiming to decrease rebuffering during video playing, we present a new bit rate adaptation scheme, with user equipment (UE) reporting parameters of video quality and wireless network quality to MEC. The bit rate adaptation algorithm in MEC decides whether to switch bit rate or not. On one hand, when network quality gets better, users can enjoy video service with a better resolution and quality. On the other hand, resolution is decreased once bandwidth cannot satisfy the download speed. In this way, rebuffering is prevented successfully. This work has been successfully verified in China Unicom Edge-Cloud pilot project under real 4G network environment. The user experience can be significantly improved using the proposed scheme.
Dynamic Switch Migration in Distributed Software-Defined Networks to Achieve Controller Load Balance

Multiple distributed controllers have been used in software-defined networks (SDNs) to improve scalability and reliability, where each controller manages one static partition of the network. In this paper, we show that dynamic mapping between switches and controllers can improve efficiency in managing traffic load variations. In particular, we propose balanced controller (BalCon) and BalConPlus, two SDN switch migration schemes to achieve load balance among SDN controllers with small migration cost. BalCon is suitable for the scenarios where the network does not require a serial processing of switch requests. For other scenarios, BalConPlus is more suitable, as it is immune to the switch migration blackout and does not cause any service disruption. Simulations demonstrate that BalCon and BalConPlus significantly reduce the load imbalance among SDN controllers by migrating only a small number of switches with low computation overhead. We also build a prototype testbed based on the open-source SDN framework RYU to verify the practicality and effectiveness of BalCon and BalConPlus. Experiment confirms the results of the simulations. It also shows that BalConPlus is immune to switch migration blackout, an adverse effect in the baseline BalCon.
Matching Heterogeneous Textual Data Using Spatial Features

An increasing amount of textual data is made available through different medium (e.g., social networks, company, data catalog, etc.). These new resources are highly heterogeneous, thus new methods are needed to extract information. Here, we propose a text matching process based on spatial features and compatible with heterogeneous textual data. Besides being compatible with heterogeneous data, we introduce two contributions. First, to be compared, spatial information is extracted then stored in a dedicated representation: STR, or Spatial Textual Representation. Second, to improve the approximation of the spatial similarity, we propose two transformations to apply on STR. To support our contributions, we evaluate the different aspects of the process using two corpora, including one corpus that is highly heterogeneous. Results obtained on both corpora demonstrate that relevant spatial matches can be obtained between the most similar STRs with an improvement due to STR transformation.
Thermoresponsive Nanohybrids for Tumor Imaging

We demonstrate the prospect of using thermoresponsive polymer capped gold nanosphere-cadmium telluride quantum dot (QD) nanohybrids for minimally invasive detection of cancerous tissue and for feedback control of tumor temperature in localized hyperthermia therapy conducted using gold nanorods, to minimize damage to the surrounding healthy tissue. Here, we use a generalized nonlocal optical response method based cavity quantum electrodynamical formalism and show that the enhancement of the Rayleigh scattering intensity of the nanohybrid over that of the isolated gold nanosphere follows an exponential scaling with varying nanohybrid centre separation distance, in the close vicinity of the QD resonance frequency, which makes them ideal candidates for a multitude of distance based sensing applications.
A 3D Printed TE10 Rectangular to TE01 Circular Waveguide Transition for Polymer Waveguide Characterization

In this paper we present a copper plated 3D printed implementation of the Marié rectangular to circular waveguide transition. The copper plated 3D printed structure allows to overcome machining limitation when dealing with curved surfaces, confining the signal propagation in air, thus avoiding losses from the printing polymers. Waveguide flanges are employed at both the rectangular (WR-12) and circular port to perform a full TRL calibration when the transitions are used in a VNA environment. A step diameter taper is used on the circular port of the transition to facilitate testing of circular polymer waveguides of different diameters. Experimental characterization is carried out for multiple samples of polymer (i.e., Teflon) waveguides in the 65–90 GHz range.
Verification of Relative Permittivity Models for Composite Nanodielectrics at Elevated Temperatures

Composite materials and determination of their properties is a much-discussed topic not only in the field of the high voltage insulation systems. In this field, one of the most important parameters which may be determined is relative permittivity, also known as the dielectric constant. In some of the times, few mathematical formulas, e.g. linear, Birchak’s, Landau-Lifshitz-Looyeng’s or Lichtenecker-Rother’s, were valid. The basic problem of usage of these formulas occurred with the application of filler with nano-dimensions, where the dielectric properties are affected by interaction zone between the surface of the filler and the matrix. The main aim of this paper is to use these formulas for calculations at elevated temperatures and compare them with the results from prepared samples of nanocomposite material. The main contribution of this paper is placed in the knowledge of the temperature dependence of the magnesium oxide filler which was measured separately as a compressed pellet and may be used for precise calculation. The values of relative permittivity were measured from 30 to $120^{\circ}\mathrm{C}$. The different amount of filler was used for this investigation. Individual sample set contains 5 samples 10x10x0.1 cm for better statistical evaluation. The average values of the relative permittivity of each set were used for comparison with theoretical calculations and suggestion of modified formula.