Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Divider Performance Using Microstrip Coupled Lines using Inductor Compensation

This article focuses on the study and improvement of a Wilkinson power divider (WPD) circuit design to a complex size. The enhancement process primarily concentrates on the circuit’s structure, inserted using microstrip coupled lines with inductor compensation at centre coupled lines. The involves detailed design and simulation using Sonnet LiteTM software at 0.9GHz frequency on FR4 substrate. Experimental results indicate that the insertion loss values for $S_{12}$ and $S_{13}$ are approximately $-3.12 \mathrm{~dB}$, return loss values for $S_{11}, S_{22}$, and $S_{33}$ A comparison between the modified WPD and the conventional design shows that the insertion loss and isolation loss can be reduced by more than $-20 \mathrm{~dB}$ within the electrical length $(\theta)$ of the parallel coupled microstrip lines.
Experimental Direction Finding Results Using a Time-Multiplexed Co-Array Approach

This paper addresses the problem of multi-source direction finding with a system that employs fewer receiver channels than antenna elements. To verify previous theoretical work [1], an experimental sensor system was built up and used for measurements in an anechoic chamber. The system consists of a specifically arranged (co-)array antenna divided into a 6-element uniform circular prototype array coupled through a switch to one receiver channel, and two directly connected reference antenna elements. Experimental results show that time-multiplexed (switched) direction of arrival estimation with number of sources equal to the number of receiver channels is feasible. Therefore, co-array techniques are utilized at each switch position and the received signal covariance matrix of the prototype array is reconstructed by means of spatial smoothing. The combined switched co-array approach is compared to the standard time-multiplex method in the sense of direction finding and source identification capability. Furthermore, direction finding results of a full 8-channel receiver system with the same array antenna used to span the co-array geometry are presented and discussed.
Synchronization Control of Dual Motor Driving Steer by Wire System

The Steer by Wire (SbW) system is a system that replaces the mechanical connection between the steering wheel and the vehicle's wheels with electrical signals to control steering. Dual motor driving SbW is designed with physical redundancy in case the SbW system does not operate normally. The system's mechanism requires synchronization control between the two motors to maintain driver safety in response to external disturbances and changes in driving conditions. This paper presents a position control scheme that utilizes the mean and difference of the two motor angles to directly control tracking performance and synchronization error. To apply the proposed method, the system's dynamics were theoretically analyzed, and a controller was designed based on that analysis. Finally, the proposed control scheme's performance is validated through experiments.
Cryptanalysis of a Reversible Data Hiding Scheme in Encrypted Images by Improved Redundant Space Transfer

In this paper, we propose a novel attack model called the Got Plaintext Attack (GPA), where the attacker only requires one plaintext and the ciphertext image set stored in the cloud to attack the content of the ciphertext image. Using this model, we examine the security of the Improved Redundant Space Transfer (IRST) encryption method. To this end, we define an ordered characteristic matrix based on the properties of the three keys used in IRST. By comparing the histogram distance of the ordered characteristic matrix, we are able to obtain a plain-ciphertext pair. Furthermore, by leveraging the invariant properties of the ordered characteristic matrix of image blocks in the plain-ciphertext pair, we estimate the block permutation Π2 and the bit-plane permutation sequence Π1. Our experiments show that the accuracy of estimating Π2 is higher than 70% for block sizes of 3x3 pixels or larger. Despite a 40% accuracy in estimating Π1, the content information of the ciphertext image can still be exposed.
An Improved DDQN Method for Throughput in RIS-Assisted UAV System

This research examines a system supported by reconfigurable intelligent surface (RIS) and unmanned aerial vehicle (UAV), used to ensure the users’ minimum throughput under limited transmission power. To reduce the discomfort of power allocation and unreasonable UAV scheduling, causing the additional waste of power and dissatisfaction with user quality of service(QoS), we propose a novel algorithm named scheduling double deep Q-Network to solve the dynamic resource allocation problem based on users’ position information and UAV-mounted base station(UAV-BS) position information. By jointly optimizing the trajectory, transmission power, and scheduling of the UAV-BS, aiming to ensure the QoS as much as possible while minimizing transmission power. The outcomes of the simulation demonstrate that this algorithm surpasses other methods in its performance within the RIS-assisted UAV network.
Hybrid Model with Discrete-Event Simulation and Repeated Machine Learning Prediction-Based Quality Inspection of Inbound Distribution Center Deliveries

Business-to-business distributors deem it necessary to inspect the quality of inbound deliveries to their distribution centers. This paper observes a company that experiences an inefficient quality inspection and wishes to improve the process. The broader product-receiving process is under-researched in warehousing literature but possesses similarities with manufacturing quality control. Moreover, the paper aims to extend prediction-based quality inspection to the warehousing field. It applies a hybrid model, combining discrete-event simulation and machine learning multi-label classification to decrease the required inspection volume and evaluate its effects on the ability of the inspection and the workload and costs of distribution center operations. The results show that the inspection volume can drastically be decreased, reducing the workload and costs at the expense of the inspection capability of infrequently occurring delivery quality flaws in training data. The configuration of the classification model determines the degree of inspection volume reduction and wrongly predicted delivery quality flaws.
Semi-Supervised Tooth Lesion Segmentation Algorithm based on Generative Adversarial Network

The tooth lesion segmentation algorithm can be used to detect and locate pathological areas, and it is one of the key technologies for achieving automated diagnosis of oral diseases. In response to the problem of expert-guided annotation and the difficulty of pixel-level annotation for lesion image labeling, a semi-supervised tooth lesion segmentation algorithm based on generative adversarial networks is proposed. Firstly, the generator adopts an improved DeepLabV3+ segmentation algorithm to extract foreground information of the lesion area. Secondly, the discriminator uses a fully convolutional network to generate a confidence map consistent with the input image size as pseudo-labels for the semi-supervised training process. Finally, supervised and semi-supervised training are alternated to improve the segmentation accuracy of the network using unlabeled data. The experimental results show that training with unlabeled data can effectively improve the performance of the segmentation network. The proposed method has improved the segmentation accuracy and other indicators such as mIoU.
Spike-Timing-Dependent Plasticity for a Hafnium-Oxide Memristive Synapse

Spike-timing-dependent plasticity (STDP) is a widely used technique for online learning that updates synapse weights based on the timing of pre- and post-synaptic spikes. Metal-oxide memristors, including HfO2, are promising in emerging synaptic circuits due to their inherent plasticity, but their non-idealities must be carefully considered in the design process. In this paper, a current-controlled memristive synapse with an STDP learning circuit is presented for improved reliability. The design limits the operating range to resistance states near the low resistance state (LRS) to account for device non-idealities at the high resistance state (HRS). This work implements a mixed-signal STDP design consisting of two parts: one programming voltage generation part, and another pulse generation part. Simulation using a 65nm CMOS process shows reliable operations both in potentiation and depression regions. Results also show good performance in terms of power and energy.
Transformer-based spatial-temporal feature lifting for 3D hand mesh reconstruction

This paper presents a novel model for reconstructing hand meshes in video sequences. The model extends the MobRecon [1] pipeline and incorporates a variant of the Transformer architecture which effectively models both spatial and temporal relationships using distinct positional encodings. The Transformer encoder enhances the feature representation by modeling joint relationships and learning hidden depth information. Leveraging temporal information from consecutive frames, the Transformer decoder further enhances the feature representation for the mesh decoder’s final prediction. Additionally, we incorporate techniques such as Twice-LN, confidence-based attention, scaling in place of Softmax, and learnable encodings to improve the feature representation. Experimental results demonstrate the superiority of the proposed method over existing approaches.
A 1.5 mW, 28 GHz Noise-Cancelling LNA in 65-nm CMOS

A low power 28 GHz noise-cancelling low noise amplifier (NC-LNA) employing common gate (CG) and common source (CS) cascode topologies is presented. Designed in a 1P8M standard 65-nm complementary metal oxide semiconductor (CMOS) process, the proposed NC-LNA uses a transformer-based input matching network to decrease required transconductance $(\mathrm{g}_{\mathrm{m}})$ of CG stage, allowing for very low power consumption (~1.5 mW). When consuming 1.5 mA from 1V supply, the proposed LNA exhibits $\mathrm{S}_{21} > 17\text{dB}, \mathrm{S}_{11} < -14\text{dB}$, and noise figure (NF) < 5.5 dB at 28 GHz, with $\text{Pi}_{\text{dB}}\sim-13\text{dBm}$ and $\text{IIP}_{3}\sim-3.7\text{dBm}$.
Intelligent optimization method for time period of power grid operation based on knowledge model

Modern power systems impose higher requirements on the time period selection of power grid operation. On one hand, factors influencing operating costs have become more intricate, and on the other hand, the disparities in costs across different time periods have become more pronounced. Therefore, based on a knowledge model, this paper proposes a method to determine the optimal time period for operations. Firstly, this paper analyzes various factors that affect the operation cost and provides a quantitative calculation model. Then, to accelerate the optimization process, a knowledge model for optimizing the operation time period is established based on historical operating data, especially operation ticket data. Finally, the proposed optimization method is applied to the power grid in northwest China. The results demonstrate that the proposed method can identify the optimal operational time period and has a higher computational efficiency compared to the traditional enumeration method.
Probabilistic Spiking Neural Networks Training with Expectation - Propagation

In this paper, we propose to use variational inference based on expectation-propagation (EP) for training probabilistic spiking networks. We adopt a Bayesian formalism and formulate the training process as finding parametric approximations for the marginal distribution of the network weights given training data. We investigate two variants of EP and through an image classification problem, we illustrate how EP methods can handle large training sets and multimodal, sparsity promoting priors. This preliminary study thus provides with methodological tools that can be leveraged to increase the interpretability and potentially accelerate the training of deeper and more complex spiking networks for computer vision tasks.
Improved Trans-Z-Source Multi-level Inverter with Continuous Input Current for Induction Motor Drives

This paper presents an improved trans-Z-source network for multilevel voltage source inverters (VSI). A coupled inductor is used in the impedance network for voltage boosting. It has continuous input current and high boost factor. Proposed topology has got three degrees of freedom. Wide range of output voltage can be obtained by adjusting the turn ratio of the coupled inductor or by varying the shoot-through time period or the modulation index. This circuit operates with good performance for entire power factor loads. Modulation technique used is simple boost control with space vector PWM. Performance of the proposed topology is validated using MATLAB simulation on a 3HP, 220V scalar controlled three-phase IM drive.
Hierarchical Structure Based Explainable Pre-Trained Model for Legal Provisions Recommendation

Explainable Artificial Intelligence (XAI) is a crucial research topic in the intersection of social science and artificial intelligence. In legal provisions recommendation, the existing models insufficiently utilize legal provisions which have the hierarchical structure. The hierarchical structure is based on granular computing. That overlooks the relationship between legal provisions and related case details. Concurrently, category imbalance problem exists in current judicial documents. This can easily lead to model training imbalance, subsequently making it difficult to accurately predict legal provisions which appear less frequently. The inherent hierarchical structure of legal provisions is exploited to learn the semantic expressions of major crime categories and legal provisions. Simultaneously, the collaborative attention mechanism is introduced to enhance the explainability. In addition, the legal cases can be augmented with text enhancement strategies. We propose a pre-trained language model incorporated with prior knowledge. The experiments were conducted on CAIL2018 and compared with six benchmark models and two ablation models. The results show our model's effectiveness and explainability.
Discovery of Disease Evolution by Graph Curvature

Geometric representation learning has played a positive role in disease diagnosis by effectively extracting feature in-formation from medical datasets to improve diagnostic accuracy. Current geometric learning methods can only utilize Euclidean metrics to explore the local or global geometric structures of the dataset. However, they have limitations in exploring the nonlinear complex structures of the dataset distribution, leading to poor interpretability of the algorithms. Therefore, this paper proposes a novel interpretable geometric representation learning methods. We utilize adjacency graphs to represent the topological structure of the dataset while introducing edge curvature on the graph to explore the nonlinear geometric structure of the dataset distribution. In terms of interpretability, we discover that edge curvature can serve as a quantitative index for multi-clusters recognition and has explanatory power for the evolution patterns of diseases. In the experiment, we select several medical imaging datasets. The relevant experiments demonstrate that our method outperforms traditional manifold learning methods.
Multi-round Consensus Scheme that Utilizes Statemerge to Reduce the Rollback Probability under State Sharding

Sharding is one of the core technologies to solve the scaling problem of today's blockchain systems, among which state sharding is most likely to essentially solve the scalability problem of public blockchian. However, after sharding, the number of nodes is much lower than that before sharding, which reduces the cost of attack and makes it easy to reach a high proportion of Byzantine nodes in a shard, thus failing to reach a consensus. Secondly, each node under state sharding only keeps the state information of its own shard, so other shards cannot work directly instead of the attacked shard. Therefore, a multi-round consensus scheme that utilizes state-merge to reduce the rollback probability under state sharding is proposed. First, this paper proposes a state-merge model for the case of transaction rollback in the attacked shards under state sharding, which allows highperformance nodes in the base shards to synchronize the state information of their brothers, and then revert it to the upper virtual shard, thus enabling the completion of consensus verification of the transactions in the base shard. After that, a state synchronization method based on alternate nodes is proposed, and the nodes that have completed the synchronization enter the sequence of alternate nodes, which in turn can complete the state merging and the subsequent multiple rounds of consensus. Experiments show that for the case of transaction rollback in the attacked shards under state sharding, this scheme can restore the consensus efficiency of nodes, which ensures the performance of the blockchain system, and at the same time safeguards the security of the blockchain network.
Utilizing the NEEP Model to Predict the Compressive Strength of Cement

In the construction industry, cement is widely used, and its quality standards are particularly stringent. Among these standards, the compressive strength of cement is one of the most crucial quality indicators. However, existing measurement methods are time-consuming and resource-intensive. This study adopts an innovative symbolic regression methodology to swiftly evaluate cement's compressive strength for quality assessment, even prior to the initiation of the hydration process. Additionally, it facilitates parameter analysis to explore the impact of cement raw material ratios on compressive strength. Additionally, it allows for parameter analysis, enabling research into the impact of cement raw material ratios on compressive strength. Experimental results demonstrate that this algorithm accurately predicts strength and exhibits excellent generalization capabilities.
Automatic Detection of Puncture Needle from CT Image with Deep Learning and Difference of CT Value Along Craniocaudal Direction

We have developed a CT guided needle puncture robot (Zerobot) to assist in interventional radiology surgery. Currently, Zerobot is operated remotely, and the next goal is to perform automatic needle puncture surgery. There is a challenge that automatic detection of puncture needle from CT images for first step of automatic puncture surgery. Because there is the case that the form of puncture needle is curved, it is necessary to detect the needle from CT image instead of estimating the needle position from arm of Zerobot. First, the method detects ROI with ResNet. Next, difference of the CT value is calculated for each pixel in the ROI, and a linear approximation is performed for to detect the needle shape. Images from animal experiments were used to evaluate the learner and image processing. We confirmed that the proposed method can detect needles in a single image and in multiple images.
Hardware-Efficient Accurate and Approximate FPGA Multipliers for Error-Tolerant Applications

FPGAs are widely used in various applications such as communications, digital signal processing, and artificial intelligence. As a basic arithmetic unit in these applications, the multiplier's optimization can significantly improve hardware efficiency and performance. This paper proposes an optimized unsigned Booth multiplier by leveraging the hardware feature of FPGA, decreasing 36.4% and 45.5% in the area over the Xilinx LogiCORE multiplier and the Xilinx Synthesis Default, respectively. Additionally, an approximate unsigned multiplier with probabilistic analysis compensation is proposed. The error compensation scheme is implemented by the FPGA's carry chain without additional resource consumption. This design saves 34.8 % area compared to the state-of-the-art paper with higher precision. The bit error rate deterioration of the proposed multiplier is less than 0.0008 when applied to a 5G MIMO wireless communication receiver.
Effects of Initialization Methods on the Performance of Multi-Objective Evolutionary Algorithms

Population initialization is always needed in evolutionary multi-objective optimization (EMO) algorithms. Intuitively, a well-designed initialization method can help facilitate the evolutionary process and improve the performance of EMO algorithms. However, very few studies have investigated the effects of initialization methods on the performance of EMO algorithms. Many existing EMO algorithms randomly generate an initial population to start the evolutionary process. To fill this research gap and attract more attention from EMO researchers to this important yet under-explored issue, in this paper, we examine the effects of various initialization methods that may become promising alternatives to the commonly-used random initialization method. Each initialization method is evaluated through computational experiments on test problems of various sizes with 5–1000 decision variables. Experimental results clearly demonstrate the advantage of well-designed initialization methods over the random initialization method. This study provides useful insights into EMO algorithm design and motivates further research on population initialization.
Clustering on the Stiefel Manifold with Symmetric Block Term Decomposition

In this paper, we consider the problem of clustering a collection of tall matrices with respect to their partially “common” column spaces. We show that the considered problem is closely related to Subspace Clustering and Generalized Canonical Correlation Analysis, while it can be posed in terms of the Block Term Decomposition of a tensor. In order to address instances of this problem, we propose an optimization algorithm for learning the memberships and the partially “common” subspaces. We compare the proposed approach to several state of the art Subspace Clustering methods using synthetic data and demonstrate the superiority of the proposed method in terms of performance and robustness to strong noise and interference.
Top-k query optimization on the hierarchical memory structure

With the development of the Internet, the information stored on the network in text form is exploding. The large accumulation of dynamic information hinders its effective human use. As an information retrieval tool on the large-scale text collection, the search engine has become an important tool to solve the network information access at the beginning of its birth, and occupies an increasingly important position in the human information life in the subsequent development. For a certain query, the search engine may hit hundreds of millions of memory query results, while users are often only concerned about the optimal dozens of results that meet their query requirements. How to quickly and accurately find the set of results that best meet the query requirements from the large number of results hit by the search engine, which constitutes a key problem in the search engine design is the top-k query problem. The Top-k queries target the objects scattered in different information sources, and find out the k objects with the best score according to the aggregation function. It has wide applications in the field of information retrieval and is a key component affecting the performance of search engines. In order to improve the data processing power of top-k queries and accelerate the computation process of top-k queries, this paper targets the distributed memory system to study the key technologies of top-k query parallel algorithm design and performance optimization. The study of parallel query algorithm and performance optimization technology can make full use of the processing power of existing parallel computers to solve the problems of massive data processing and real-time performance in top-k query, which has important academic value and application value.