Miracle-dz/UniFAR-FTUs huggingface dataset:
An Examination of the State-of-the-Art for Multivariate Time Series Classification

The UEA Multivariate Time Series Classification (MTSC) archive released in 2018 provides an opportunity to evaluate many existing time series classifiers on the MTSC task. Nevertheless, although many new TSC approaches were proposed recently, a comprehensive overview and empirical evaluation of techniques for the MTSC task is currently missing from the time series literature. In this work, we investigate the state-of-the-art for multivariate time series classification using the UEA MTSC benchmark. We compare recent methods originally developed for univariate TSC, to bespoke methods developed for MTSC, ranging from the classic DTW baseline to very recent linear classifiers (e.g., MrSEQL, ROCKET) and deep learning methods (e.g., MLSTM-FCN, TapNet). We aim to understand whether there is any benefit in learning complex dependencies across different time series dimensions versus treating dimensions as independent time series, and we analyse the predictive accuracy, as well as the efficiency of these methods. In addition, we propose a simple statistics-based time series classifier as an alternative to the DTW baseline. We show that our simple classifier is as accurate as DTW, but is an order of magnitude faster. We also find that recent methods that achieve state-of-the-art accuracy for univariate TSC, such as ROCKET, also achieve high accuracy on the MTSC task, but recent deep learning MTSC methods do not perform as well as expected.
Energy-Saving Resource Allocation with Lightweight Blockchain in Maritime Wireless Communication Networks

With the exploration of the ocean resource, establishing an energy-efficient and reliable maritime wireless communication network becomes more and more urgent. In maritime wireless communication networks, data types, such as search and rescue information, business information and surveillance video, will be continuously generated and need to be scheduled and shared. At the same time, there will be some trading needs. How to realize information exchange and ensure safe transactions is an urgent problem to be solved. To this end, this paper aims to establish a distributed network architecture composed of buoys and base stations (BS) at sea, and uses blockchain technology to solve the problem of the completeness of its decentralized network system. To satisfy the computing requirement companied with the blockchain, buoys and BSs in networks also have the computing ability. Due to the fact that buoys deployed in the ocean are hard to be frequently charged, the energy consumption problem is studied in this paper via the joint optimization of the communication resource and computing resource. We combine the weighted shortest processing time first (WSPT) model and the dynamic frequency and voltage scaling (DVFS) energy consumption model to propose an improved energy-saving algorithm. Finally, numerical results verify the superiority of the proposed algorithm.
Facial Expression Recognition Using Hybrid Features of Pixel and Geometry

Facial Expression Recognition (FER) has long been a challenging task in the field of computer vision. Most of the existing FER methods extract facial features on the basis of face pixels, ignoring the relative geometric position dependencies of facial landmark points. This article presents a hybrid feature extraction network to enhance the discriminative power of emotional features. The proposed network consists of a Spatial Attention Convolutional Neural Network (SACNN) and a series of Long Short-term Memory networks with Attention mechanism (ALSTMs). The SACNN is employed to extract the expressional features from static face images and the ALSTMs is designed to explore the potentials of facial landmarks for expression recognition. A deep geometric feature descriptor is proposed to characterize the relative geometric position correlation of facial landmarks. The landmarks are divided into seven groups to extract deep geometric features, and the attention module in ALSTMs can adaptively estimate the importance of different landmark regions. By jointly combining SACNN and ALSTMs, the hybrid features are obtained for expression recognition. Experiments conducted on three public databases, FER2013, CK+, and JAFFE, demonstrate that the proposed method outperforms the previous methods, with the accuracies of 74.31%, 95.15%, and 98.57%, respectively. The preliminary results of Emotion Understanding Robot System (EURS) indicate that the proposed method has the potential to improve the performance of human-robot interaction.
A New Hybrid Resource Allocation Scheme in C-V2V Systems

In the centralized mode of the cellular-based vehicle-to-vehicle (C-V2V) system, the base station performs resource allocation based on location information from the vehicle user equipments (VUEs). To implement this location-based resource allocation, the base station and VUEs frequently exchange driving information with each other. This causes serious control overhead which includes the signaling that occurs between the base station and VUEs and the computing power needed to perform location-based resource allocation. In this paper, we propose a new hybrid resource allocation scheme designed to reduce control overhead while satisfying high communication reliability. The proposed scheme is composed of two phases, a centralized phase and a distributed phase. In the centralized phase, the base station performs location-based resource allocation. In the distributed phase, VUEs autonomously select the resource occupied by the VUE in front of them. This procedure fixes the geographical locations of the resources to specific locations and consequently reduces the frequency with which the base station performs resource allocation. Simulation results show that the proposed resource allocation scheme reduces the control overhead of the conventional resource allocation with centralized mode.
Energy Optimization on Joint Task Computation Using Genetic Algorithm

Joint computation is a form of collaborative job execution running at separate physical units, which are previously grouped by their unique functionalities. While existing studies have mainly utilized joint computation with direct coordination between nodes in different segments, it is worth considering another scenario where an additional node within a layer relays data to another layer. As a consequence, the node can serve as an aggregation point for data capture units prior to transmission to the sink node. However, this new arrangement produces additional transmission paths and can thus cause additional energy spending. This pilot study investigates the joint computation problem aiming at optimizing energy consumption. Relevant components, such as computation and communication, are taken into account and modeled into formal representation. A genetic algorithm-based solution is then used as a tool to optimize parameter setup. According to the experiment results, the metaheuristic algorithm has potential to achieve the optimal system configuration, emphasizing the data length that affects the final energy spending on communications. However, the algorithm cannot always guarantee the optimality as it relies on the random variable used in the process.
Service Migration for Multi-Cell Mobile Edge Computing

Mobile-edge computing (MEC) enhances the capacities and features of mobile devices via offloading computation-intensive tasks over wireless networks to the edge servers. One challenge faced by the deployment of MEC in cellular networks is to support user mobility, so that the offloaded tasks can be seamlessly migrated between base stations (BSs) without compromising the resource-utilization efficiency and link reliability. In this paper, we tackle the challenge by optimizing the policy for migration/handover between BSs by jointly managing computation-and-radio resources. The policy design is formulated as a multi-objective optimization problem that maximizes the sum offloading rate, quantifying MEC throughput, and minimizes the migration cost, where the issues of virtualization, I/O interference between virtual machines (VMs), and wireless multi-access are taken into account. To solve the complex combinatorial problem, we develop an efficient relaxation-and-rounding based approach, including an optimal iterative algorithm for solving the integer-relaxed problem and a novel integer-recovery design that exploits the derived problem properties. The simulation results show the close-to-optimal performance of the proposed migration policies under various settings, validating their efficiency in computation-and-radio resource management for joint service migration and BS handover in multi-cell MEC networks.
Binaural Reproduction Based on Bilateral Ambisonics and Ear-Aligned HRTFs

Reproduction of high quality spatial sound has gained considerable importance with the recent technology developments in the fields of virtual and augmented reality. Recently, the reproduction of binaural signals in the Spherical-Harmonics (SH) domain has been proposed. This is performed by using SH representations of the sound-field and the Head-Related Transfer Function (HRTF). These processes offer the flexibility to control the reproduced binaural signals, by manipulating the sound-field or the HRTFs using algorithms that operate directly in the SH domain. However, in most practical cases, the binaural reproduction is order-limited, which introduces truncation error that has a detrimental effect on the perception of the reproduced signals, mainly due to the truncation of the HRTF. A recent study showed that pre-processing of the HRTF by ear-alignment reduces its effective SH order, which may be beneficial for alleviating the above effect. In this paper, a method to incorporate the pre-processed ear-aligned HRTF into the binaural reproduction process is presented. The method uses Ambisonics representation of the sound-field formulated at the two ears, and is denoted here as Bilateral Ambisonics. The proposed method leads to a significant reduction in errors due to the limited-order reproduction, which yields a substantial improvement in perceived binaural reproduction quality even with SH as low as first order.
The Research of Leaf Area Index Analyzer based on Embedded Platform

With the continuous development of optical lens and imaging chip technology, the fisheye camera method has been widely studied in the world because of its characteristics of TRAC instrument and LAI-2200C coronal analyzer. To obtain the critical ecological parameter of the leaf area index at low cost, a synchronous LAI-2200C leaf area index hemispheric image acquisition system was proposed in this paper. The system consists of an embedded platform, a low-cost image sensor, and a fisheye lens, fixed to the LAI-2200C optical sensor detector, triggered by the LAI-2200C synchronous of hemispheric vegetation image. This study used the system to measure the coronary photos of the tall shrubs in the Chengdu area at different times. It used an image processing algorithm to analyze and obtain LAI. The results show that there is a significant linear correlation between LAI and LAI-2200C measurements obtained by the Fisheye Camera Method (DH-P) (R2= 0.814), the average square root error is 0.278. The test results show that the system can effectively collect the image of the vegetation canopy can be low-cost, and obtain a leaf area index results with minor error.
Low-Complexity Wideband Channel Estimation for Millimeter-Wave Massive MIMO Systems via Joint Parameter Learning

Massive multiple-input and multiple-output (MIMO), especially in the millimeter-wave (mmWave) frequency band, has been recognized as a promising technology for future wireless communications. However, most previous research in mmWave massive MIMO only uses conventional MIMO channel model without taking into account the spatial-wideband effect in mmWave massive MIMO systems, where the number of antennas is very large and the transmission bandwidth is very wide. In this paper, a novel mmWave massive MIMO channel model is introduced which embraces the spatial-wideband effect. Then, a joint parameter learning algorithm is proposed to extract different channel parameters from the received data. Furthermore, a low-complexity spatial wideband channel estimation scheme is proposed for the new channel model. Simulation results and complexity analysis show that the proposed scheme are capable of achieving a considerable reduction in complexity compared with the recently spatial-wideband channel estimation algorithm with a small performance loss. Moreover, our scheme can effectively estimate the novel channel even part of the antenna data is received.
Combining Leak feeder cable and Antenna to support Mobile network in underground mine environment

This paper reports the measurements of a hybrid radiating LTE base-station (BS) based on Software Defined Radio (SDR) platform. The hybrid radiating elements consisted of a hybrid architecture of 2 antennas and a Leaky Feeder (LF) cable to support LTE technology operating in Frequency Division Duplex (FDD) on 850Mhz radio band. All the measurements were made in a former underground gold mine. The used LF cable aimed to enhance the radio signal propagation, particularly in Non-Line Of Sight (NLOS) areas where signal propagation has difficulty to reach the destination. Also, LF cable avoided increasing the number of deployed BSs to deal with NLOS areas and reduced the transmission power of the mobile user equipment (UE) without losing the quality of service(QoS).
Sensor Data Fusion for Monitoring Water Quality Toward Sustainable Freshwater Fisheries

The use of wireless sensors is increasing day by day. Different types of wireless sensors are being used in fisheries sectors to monitor the water quality, growth of the fish, and health of the fish. Due to a brupt changes in water quality parameters, the rapid outbreak of fish disease has become a significant constraint for this sector's sustainability. Development of an early monitoring system of fish culture parameters through high resilience and efficiency wireless sensor networks (WSN) effectively assess water quality regulators instantly and thus take proper actions for sustainable management of freshwater resources. However, current observation systems only consider the data from a single sensor. We designed a data fusion model using Dempster-Shafer theory (DST) to fuse the monitoring sensor data from different sensors to calculate the fish's sustainable environment. Moreover, we evaluated our monitoring system results for different scenarios using the standard performance metrics, i.e., specificity, sensitivity, a ccuracy, and F-Score were calculated using the True Positives (TP), False Positive (LIP), True Negative (TN), and False Negative (LIN) values. Our model's finding shows that fusing data from different sensors provide a more accurate result for monitoring the water's sustainability.
A TSV Test Method for Resistive Open Fault and Leakage Fault Coexisting

Large-size TSVs have larger parasitic capacitance, which leads to more resolution challenges. At the same time, due to the opposite fault effect of resistive open fault and leakage fault in the ring oscillator, the coexistence of two types of faults will cause serious test confusion. These problems significantly increase the difficulty in the TSV test. In this paper, a test method using Schmitt trigger as a TSV receiver is presented to enhance test resolution and reduce test confusion when two kinds of faults exist simultaneously. Schmitt trigger that is characterized by two threshold voltages is used to improve the test resolution. By reducing the supply voltage and checking whether the ring oscillator oscillates, we can identify whether there are two kinds of faults coexisting. HSPICE simulation results based on $45~nm$ CMOS technology demonstrate that the proposed method offers better test resolution in terms of large-size TSVs compared with the existing similar methods, and can distinguish the misdiagnosis when there are two kinds of faults existing simultaneously in a TSV.
Towards a Service-Oriented Architecture for the Energy Efficiency of Buildings: A Systematic Review

Currently, smart buildings generate large amounts of data due to the many devices and equipment available. Hence, buildings implement building management systems (BMSs), which monitor, control, manage and analyze each of these components. However, current BMSs are incapable of managing a massive amount of data (big data) and therefore cannot extract knowledge or make intelligent decisions in quasi real time. In addition, there are serious limitations to integrating BMSs with other services since they generally use proprietary software. In this sense, service-oriented architecture (SOA) is an architectural style that allows one to build distributed systems and provide functionalities such as services to end users or other types of services. Therefore, an SOA has the great advantage of allowing the expansion of the functionalities of BMSs. In fact, there are several studies that address SOAs for building management. However, we have not found any description or systematic analysis in the literature that allows the development of a versatile and interoperable SOA focused on the energy efficiency of buildings and that can integrate massive data analysis features. For these reasons, this study seeks to fill this knowledge gap and, more specifically, to identify and analyze the various software requirements proposed in the literature and the characteristics of big data that allow for improving the energy efficiency of buildings. To this end, we performed an in-depth review of the literature according to the methodology proposed by Kitchenham. As a result of this review, we provide researchers with a specific vision of the requirements and characteristics to consider for software development aimed at the energy efficiency of unique or historic buildings.
Electromagnetic Metamaterials: A New Paradigm of Antenna Design

The progress of technology in consumer electronics demand an antenna having a compact size, high gain and bandwidth, and multiple antennas at transmitter and receiver to enhance the channel capacity. Over the last decade, numerous techniques are proposed to improve the performance of the antenna. One such technique is the use of metamaterials (MTMs) in antenna design. MTMs are artificial structures to provide unique electromagnetic properties that are not available in natural materials. The unique properties of these materials allow the design of high-performance antennas, filters, and microwave devices which cannot be obtained using traditional antennas. Loading antenna with the one, two, and three-dimensional MTM structures comprised of a periodic subwavelength unit cell exhibits RLC resonant structures and allows to manipulate electromagnetic waves in the antenna system. These structures offer low resonant frequency compared to the antenna resonant frequency resulting in antenna miniaturization and manipulation of electromagnetic waves helps in enhancing the gain and bandwidth, and achieving circular polarization (CP) of an antenna system. Also, metamaterial loading enhances isolation between the antenna elements in the multiple-input-multiple output (MIMO) system by suppressing the surface waves. In this paper, the electromagnetics of MTM with analytical expressions and its application in antenna design are discussed in detail. The MTM-based antennas are classified into MTM loading, MTM inspired antenna, metasurface loading, and composite right/left hand (CRLH) based antennas. The recent development in MTM inspired antenna and its application in antenna miniaturization, enhancing gain and bandwidth, achieving CP and mutual coupling suppression in MIMO antenna systems are discussed to make it useful for further research.
EBG Superstrate Loaded Circularly Polarized Fabry-Perot Cavity Antenna at Sub-6 GHz for Satellite and 5G Cellular Communications

This work presents a circularly polarized Fabry- Perot cavity antenna (FPCA) using a new uniplanar electro- magnetic bandgap (EBG) superstrate. Proposed EBG poses a frequency bandgap in the sub-6 GHz frequency range from 3.07-4 GHz. Unit cell has overall dimension of 0.071 λ0 × 0.071 λ0 × 0.018 λ0, where λ0 is the free space wavelength at center frequency of 3.53 GHz. A 7 × 7 EBG lattice is used as partially reflective surface (PRS) over a metamaterial based circularly polarized (CP) antenna to improve the directivity. Peak gain of the primary CP antenna is 7.51 dBic at 3.66 GHz and that of the EBG superstrate loaded FPCA is 9.67 dBic.
Loss Reduction Strategy for Low Voltage Three-phase Unbalance Distribution Network Considering Load Phase Adjustment

In recent years, with the rapid improvement of load level, the problem of three-phase imbalance in distribution network has become prominent, and the additional loss caused has been gradually increasing, the potential of loss reduction is huge. In this paper, the load intensive substation-area represented by urban residential quarter is taken as the research subject. Based on the root mean square current method, the quantitative method of three-phase unbalanced additional loss is constructed, and a real-time automatic load commutation strategy is proposed to realize the optimization of three-phase unbalance and loss reduction in substation-area with intensive load. The simulation results show that the loss quantization method and optimization model established are scientific and feasible.
Analysis of Correlated Rayleigh Fading Massive MIMO Uplink Channel: The IDM-CGF Approach

In this paper, we showcase the effectiveness of an infinitesimal dipole modelling-cross correlation Green's function (IDM-CGF) methodology, integrated with Karhunen-Loeve approach, for correlated massive MIMO uplink channel modeling. Considering a uniform rectangular array (URA) of horizon-tally/vertically polarized infinitesimal dipoles (IDs) in the massive MIMO base-station (BS) and four user-equipment (UE) units located along the azimuth angular direction, we use the proposed approach to visualize the channel and critically assess the effect of element pattern/polarization. We further demonstrate the impact of the number of BS-elements in tailoring the channel eigenspace, especially the singular value spread.