Miracle-dz/UniFAR-FTUs huggingface dataset:
Pruning Method Using Correlation of Weight Changes and Weight Magnitudes in CNN

Very complex deep learning models need to be compressed to be memory and cost effective, especially for applications on a mobile platform. We propose a new method of selecting weights to prune to compress convolutional neural networks. To select unimportant weights and get the best result, we combine typical weight magnitude pruning method with our method, which evaluates correlation coefﬁcients of weights to measure the strength of a relationship between weight magnitudes and weight changes through the iterations. In the experimental section, we show our result of pruning 94% of weights in LeNet-5 without signiﬁcant accuracy loss.
LazyDB: I/O Optimization of Blockchain Storage System Based on Non-Volatile Memory

For the blockchain storage system, this paper proposes an I/O optimization scheme based on non-volatile memory based on the storage performance requirements of the blockchain system application. This scheme adopts the key-value separation strategy. Due to the uncertain size of transaction data, by separating the “key” and “value” of transaction data, the inherent read-write amplification problem of the LSM-Tree structure is solved. The key-value separation strategy redesigns the storage system's space management and garbage collection mechanisms. Therefore, this chapter uses the byte-addressing characteristics of non-volatile memory to build a new data storage structure, a lazy compression mechanism, and a lazy garbage collection mechanism. On this basis, this paper builds a LazyDB database system for blockchain storage. To ensure the safe and reliable operation of the blockchain system, it can make full use of the storage medium and data characteristics, reducing the blockchain reading and writing process. The I/O overhead in the storage system improves the storage performance of the system. The experimental results show that compared with the most commonly used databases, such as LevelDB and RocksDB in the blockchain system, LazyDB effectively reduces the write amplification of the system and has higher read, write, and data update performance.
Deep Semantic Understanding and Accurate Classification of Public Interest Litigation Case Texts

With the burgeoning number of public interest litigation cases, their effective classification has become imperative. However, this task is fraught with challenges, given the inconsistency in text length, the intricate nature of legal terms, and the structural complexity within the text. To mitigate these hurdles, this paper presents a BERT-Att-MultiTextCNN-based text classification model designed for public interest litigation cases. The model capitalizes on BERT's deep semantic understanding capabilities, extracting key textual information via the Attention mechanism. Additionally, it employs the MultiTextCNN model to capture local features, thereby ensuring a more precise text classification. This model was validated using the datasets of public interest litigation cases, THUCNews, and SST-2, achieving accuracy levels of 93.5%, 94.8%, and 94.1%, respectively. The experimental findings underscore this model's significant advantages over conventional text classification models. The study's outcomes suggest that the proposed BERT-Att-MultiTextCNN-based model holds promising potential for text classification tasks, especially those involving the categorization of text in public interest litigation cases.
Exploiting the Collision-Free Region with Slotting Access for Data-Gathering in the Linear UANs

Long transmission range and non-negligible propagation delay are the unique features of medium accessing in underwater acoustic networks (UANs). In this paper, we study the medium access control (MAC) problem during data gathering from large-scale regular linear UANs where the underwater sensors are deployed in a one-dimensional chain with equal distance. We suppose each underwater sensor in the linear network are capable to transmit their packets to the receiver through one-hop transmission directly. During the data-gathering mission, the underwater sensors and the receiver construct a star-connected topology. In star-connected topology, it is possible to exploit the collision-free regions and reuse the medium while adopting the slotting technique. On this basis, we propose a slotted-based lightweight scheduling MAC (SLS-MAC) protocol. By jointly designing the slot length and scheduling the receiver position, the SLS-MAC can achieve collision-free data gathering and spatial reuse in the sink node. In the ideal case, the SLS-MAC can achieve almost 1 in normalized network throughput. Considering the limitation of the physical modem, in the worse case, the achievable normalized network throughput of SLS-MAC is also greater than 0.5, which is much greater than conventional slotted access MAC. The NS-3 simulator is used to validate our analysis and evaluate the network performance of the proposed SLS-MAC.
Probe Selection Method based on Switch Matrix for 5G Massive MIMO Base Station OTA Testing

The sectored multi-probe anechoic chamber method (MPAC) is considered to be the most promising method for 5G massive multiple-input multiple-output (MIMO) base station (BS) over-the-air (OTA) testing. In this case, the number of channel simulators and probes used in the testing system is increasing to reproduce the wireless propagation channel in a laboratory environment, which leads to a significant increase in the cost of the testing system and the complexity of probe selection. In this paper, a group-based switch matrix and corresponding probe selection algorithm are proposed. The simulation results indicate that the proposed method can accurately reconstruct the channel environment around the device-under-test (DUT) with fewer probes and lower computational complexity. Additionally, the impact of different probe configurations on the accuracy of channel emulation is also discussed.
The geomorphological characterisation of Digital Elevation Models

Techniques and issues are considered surrounding the characterisation of surface form represented by Digital Elevation Models (DEMs). A set of software tools suitable for use in a raster based Geographical Information System (GIS) is developed. Characterisation has three specific objectives, namely to identify spatial pattern, to identify scale dependency in form and to allow visualisation of results. An assessment is made of the characteristics of error in DEMs by identifying suitable quantitative measures and visualisation processes that may be enabled within a GIS. These are evaluated by contour threading a fractal surface and comparing four different spatial interpolations of the contours. The most effective error characterisations are found to be those that identify high frequency spatial pattern. Visualisation of spatial arrangement of DEM error is used to develop a deterministic error model based on local surface slope and aspect. DEMs are parameterised using first and second derivatives of quadratic surfaces fitted over a range of scales. This offers advantages over traditional methods based on a 3 by 3 local window, as geomorphometric form can be characterised at any scale. Morphometric parameters are combined to give a feature classification that may also be applied over a range of scales. Multi-scale measurements are combined to give a feature membership function that describes how properties change with scale. These functions are visualised using modal and entropy measures of variability. An additional method of visualising scale dependency is suggested that graphically represents statistical measures of spatial pattern over a variety of spatial lags. This is found most appropriate for detecting structural anisotropy in a surface. Characterisation tools are evaluated by applying them to uncorrelated surfaces, fractal surfaces and Ordnance Survey DEMs of Lake District, Peak District and Dartmoor.
Intelligent Reflecting Surface-aided Robust Directional Modulation with Hardware Impairments

In practice, hardware impairments of physical-layer secure communication systems are inevitable. Residual hardware impairments of the transceiver inevitably result in distortion noise, leading to performance loss. In this study, we focus on a multiple-input multiple-output communication system with distortion noise that utilizes both intelligent reflecting surface (IRS) and directional modulation (DM) for secure transmission. DM can prevent eavesdropping, ensuring the safety of legitimate users. IRS aids transmission from base stations to users, playing a crucial role in complex wireless environments. We explore a robust transmission design and propose an efficient algorithm for solving non-convex problems by the alternative iterative method to maximize the secrecy rate. Specifically, we use the successive convex approximation to solve the directional modulation optimization problem and the semidefinite program method to obtain the phase shift of the IRS. Simulation results show that the proposed algorithm can achieve significant performance improvement compared to conventional non-robust schemes.
Overload capability test of a permanent magnet synchronous motor for e-bike application

The electric bicycle (e-bike) industry is currently experiencing a rapid transformation, with e-bikes becoming increasingly popular as a form of urban transportation. In order to better understand the performance of e-bikes and inform their continued development, this study conducts a comprehensive analysis of a typical electric motor configuration used in such application. The focus of the study is on evaluating the overload capability of the motor. By utilizing both finite element simulations and experimental testing, this study provides a thorough examination of the motor performance under different operating conditions, including nominal and overload operation.
Effects of Rotor Asymmetries on d-q Axis Parameters in Synchronous Reluctance Machines

A recent research trend on synchronous reluctance machines (SynRMs) involve devising asymmetric rotor designs, since they promise to reduce torque ripple and improve the overall performance of the machine. This paper analyzes the suitability of estimating the torque output of an asymmetric SynRM by means of traditional torque estimation equations. It is proven that rotor asymmetry introduces a variation in the magnetic axes that is not considered by traditional equations and renders the torque estimation inaccurate. This shift could lead to an imprecise control and estimation of the required torque angle. In this paper, a 4-pole 48-slot single barrier synchronous reluctance machine was employed to investigate this phenomenon by means of finite element analysis. A correction factor is proposed and validated for one-barrier, two-barrier and three-barrier SynRMs.
Ray Tracing Based 6G RIS-Assisted MIMO Channel Modeling and Verification

Reconfigurable intelligent surface (RIS) has become one of the key technologies for the sixth-generation (6G) communication systems due to its advantages of being programmable, low energy consumption, and easy deployment. In this paper, a novel RIS-assisted deterministic channel model is proposed based on ray tracing (RT). The multipath components (MPCs) in the wireless propagation environment are divided into two types, i.e. MPCs with and without interaction to RIS. Multipath information of those two types of MPCs are obtained through the designed segmented RT simulations. Then the channel impulse response (CIR) of RIS-assisted channel can be extracted through data processing. To verify the accuracy and effectiveness of the proposed method, the simulation results including path loss (PL), delay power spectral density (PSD), angular PSD, delay spread (DS), and angular spread (AS) are compared with the channel measurement data. Good agreements are obtained regardless of whether RIS is in the mirror placement state.
Application of Geometric Features on Lung Lesion and Non-Lesion Segmentation

Lung cancer is the most prevalent cancer globally, with 1.6 million deaths annually. This is very crucial to develop a new CAD system that will aid medical experts in treating patients and increase the survival rate. Segmenting lung lesions is one of the most important tasks in a CAD system for lung cancer. A new procedure for lung lesion and non-lesion segmentation based on geometric features is presented in this paper with the aim to separate lung lesions and non-lesion from the lung region and to minimise the non-lesion without removing the potential lesion. The procedure’s focus is on geometric feature extraction. The procedure was applied to 300 lung CT scan images that were collected from Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia. Since the lung CT scans image have low contrast, contrast stretching is used to improve the quality of the image. Diameter and roundness were used as geometric features to provide additional information to reduce the number of non-lesions existing in the segmented image. As a contribution, the average lung lesion and non-lesion segmentation accuracy is 99.70%. When compared to manual delineation by radiologists, the study revealed that 83% of the overall lesion existing in the dataset was successfully segmented using the proposed method. The experiment’s findings provided a strong contribution to this research’s next phase.
SSD Multi-Level Parallel Garbage Collection

Solid-state drives (SSDs) based on NAND flash memory have the advantages of fast access speed, low power consumption and high reliability, have been widely used in storage systems. However, I/O requests from users may be significantly affected by SSD internal garbage collection (GC), and GC operations have always been the core issue limiting SSD performance. In order to solve the problem, this paper proposes a multi-level parallel garbage collection scheme (MP-GC) based on the new SSD flash memory chip architecture. In this scheme, the page-level parallelism and plane-level parallelism of GC operations are realized through the new SSD flash memory chip architecture, the valid pages in the victim block are migrated in parallel, and the victim blocks of multiple planes are reclaimed in parallel. This scheme reduces the time of the GC process and reclaims more invalid pages within a given erasure time. It can also delay future GC operations and effectively reduce the frequency of GC. We extend Flashsim to implement and evaluate our scheme. The experimental results show that compared with the GC scheme based on the traditional SSD flash memory chip architecture, MP-GC reduces the total GC time by 58.2% and the latency by 40% on average.
Adaptive Notch Filter Design of a Single-Link Flexible Manipulator based on System Identification

This paper proposes an adaptive notch filter (ANF) control method based on recursive least squares (RLS) parameter identification, to suppress the vibration at the end of a single-link flexible manipulator (SFM), for the end inherent vibration frequency generated during operation. Firstly, the damping of SFM is considered, and its transfer function model is established to analyze the causes of vibration generated at the end. Then, the self-tuning expressions for the notch filter (NF) parameters are derived based on the results of RLS parameter identification. Finally, the simulation and experimental results show that the method can automatically suppress the vibration problem caused by the end of SFM during the motion online.
Deep Reinforcement Learning for Network Security Applications With A Safety Guide

Most of the typical reinforcement learning algorithms help wireless devices choose the security policy such as the moving strategy and communication policy by exploring all the possible state-action pairs including the risky policies that cause a severe collision or network disaster. In this paper, we design a safe reinforcement learning algorithm for safety-critical applications (e.g., intelligent transportation systems) to guide the learning agent to avoid exploring risky policies. This algorithm uses Q-network (i.e., a convolutional neural network or a deep neural network) to choose the policy and designs a safety guide to modify the chosen policy that results in dangerous status. More specifically, the safety guide includes a risk alarm module that evaluates the immediate warning value corresponding to the risk of each state-action pair and a G-network that estimates the long-term risk value. By adding the long-term risk value and the long-term expected reward output by the Q-network, this algorithm uses a safety dock to modify the chosen policy. This algorithm uses the immediate warning value to formulate a safe buffer and a risky buffer for the G-network updating to ensure fully exploration in the initial learning process. As a case study, we apply the designed algorithm in a cargo transportation system, in which the experimental results verify the effectiveness of our algorithm compared with the benchmark safe deep Q-network.
Ranking Cellular Internet Card User VIP Values Based on Graph Clustering with Usage Data

Internet card (IC) is a new business model in telecom operators, which has attracted numerous users. However, the high user churn rate and the lack of IC user VIP value classification methods make the development of IC business encounter a bottleneck. In this paper, we conduct an in-depth study of IC user VIP value characterization and ranking by utilizing a large-scale dataset collected from a provincial cellular operator of China. We combine the knowledge in the field of traditional business user value management and new feature mining, and devise a data-driven system named REAL, i.e., Ranking cellular InternEt cArd user VIP vaLues, which consists of a dataset collection component, a feature extraction component, a graph clustering model (GCW) and a result output component. In REAL, consumption features, dependency features, and penalty features are extracted, and GCW model is devised to learn the common relationship among different IC users. Finally, a weight-based approach is designed to rank each user and output their VIP grades according to a threshold. Extensive experiments corroborate the efficacy of REAL in ranking IC users’ VIP values.
Channel Estimation for mmWave Massive MIMO Systems Using Graph Neural Networks

For massive multiple-input multiple-output(MIMO) systems, large-scale array antennas are used at both the base station and the receiver, which poses a challenging problem in channel estimation. Moreover, existing deep learning (DL) based channel estimation approaches generalize poorly when there is significant model mismatch between the train stage and the testing stage. In this paper, we propose a graph neural network (GNN) based channel estimation method by incorporating the graph topology of the system into the neural network architecture design. The mapping from the initial estimated channel matrix to the real channel matrix is learned by the proposed GNN, which inherits permutation equivariance and good generalization properties. Simulation results show that the proposed GNN based method generalizes well to different numbers of antennas at the user and maintains good performance when the number of antennas at the user increases dramatically.
Energy-Efficient and Link-Stability based Ant Colony Optimization Routing Protocol for Underwater Acoustic Networks

An energy-efficient and link-stable based ant colony optimization routing protocol (ELACO) based on Q-learning optimization and Quality of Service (QoS) constraints is proposed for solving the problem of high data transmission delay and weak dynamic adaptability in underwater acoustic communication. The local QoS constraints and global intelligent decision-making are included in the protocol. In the stages of the path discovery and maintenance, the intelligent ant is relied on for the construction of network topology environment and information exchange between nodes, as well as network maintenance. In the Q-learning stage, the node energy, depth, network transmission delay and link quality are considered in protocol design. Therefore, the network’s life cycle can be extended, and the system energy consumption and delay are reduced. QoS constraints are also imposed on local node pheromone increments to remove low-quality nodes and enhance link stability. Finally, the simulation results show that the ELACO is better than Q-learning aided ant colony routing protocol (QLACO) and Q-learning-based energy-efficient and lifetime-aware routing protocol (QELAR), as well as the depth based routing (DBR) algorithm in terms of energy consumption, latency, and network life cycle.
Cooperative Coverage Path Planning for AUVs in Integrated Underwater Acoustic Communication and Detection Networks

In this paper, we propose the cooperative coverage path planning (CPP) scheme for AUVs in integrated underwater acoustic communication and detection networks (UCDNs) to explore the unknown oceanic environment. Firstly, we design a virtual force-based task allocation strategy to allocate the coverage area for multiple AUVs. In addition, to provide the AUV with an adaptive and flexible path planning capability, we design a complete coverage path planning algorithm based on Q-learning (Q-CPP-C) in UCDNs with short path lengths and obstacle avoidance. Finally, the task is reallocated based on the residual energy of AUVs for the multi-cycle continuous operation. Simulation results demonstrate that the proposed CPP scheme outperforms the baseline in terms of energy consumption and time consumption.
Cooperative Control Strategy Based on MTPA for A Partitioned Stator DC-Biased Hybrid Excited Permanent Magnet Machine

This paper proposes a partitioned stator DC-biased hybrid excited permanent magnet machine and a cooperative control strategy based on maximum torque per ampere (MTPA) is established and verified. Firstly, the working principle of the proposed machine is introduced, and the dual three-phase configuration of the motor drive is presented. The model of the DC-biased hybrid excited machine based on dual three-phase configuration is established, and the cooperative control method based on maximum torque per ampere is derived. Various simulation and comparison studies of the proposed machine based on the cooperative control are conducted, and the results verify the control performance
Two-Stage Beamforming Design for Massive MIMO with Finite-Alphabet Inputs

In this paper, we investigate the two-stage beamforming design based only on channel statistic information (CSI) for the massive multiple-input multiple-output (MIMO) downlink transmission with finite-alphabet inputs. To achieve lower computational complexity, a lower bound on the achievable ergodic rate with closed-form expression is derived. Then, assuming a zero-forcing inner precoder, an outer precoder is designed by maximizing the minimum rate lower bound among clusters. An iterative algorithm based on concave-convex procedure (CCCP) is developed to obtain an optimal solution to the proposed problem. Numerical results show the tightness of the achievable ergodic rate lower bound and the performance gain over existing methods.
Dynamic Service Migration Method Based on User Mobility

With the rapid development of 5th Generation Mobile Communication Technology (5G), mobile edge computing (MEC) has played an important role in improving user experience and reducing energy consumption and latency. Mobile edge computing will decentralize some of the computing and storage problems of the central cloud to the network edge, so that the data generated by terminal devices can be processed quickly, but how to ensure that users can obtain good performance when moving between different locations is a key issue. In order to solve this problem and reduce end-to-end delay and energy consumption, a service migration algorithm based on node residual energy hybrid migration strategy for mobile edge computing (HOS-RE) is proposed. The experimental results show that compared with other methods, this method can reduce the time of service migration and ensure the continuity of services.