Miracle-dz/UniFAR-FTUs huggingface dataset:
Dielectric Material-Assisted Optical Tamm Mode Localization for Enhanced Photonic Spin Hall Effect

A dielectric material-aided excitation technique is presented in this research to confine optical Tamm mode (OTM) for enhanced photonic spin hall effect (PSHE) generation. The design comprises a bilayer photonic crystal (PhC) structure having a top defect layer. The structural dispersion analysis is carried out to validate the localization of the OTM and the generation of corresponding PSHE. Further, the impact of incidence angle and operating wavelength is also investigated towards enhancing PSHE. With an optimized defect layer thickness of 220 nm, the proposed structure exhibits substantial OTM confinement. Finally, a maximum transverse displacement of ≈ λ/2 at an incidence angle of 46.944° is reported.
Deep Convolutional Neural Network using Orthogonal Projection Loss for Palm-Vein Recognition

With the rise of the internet, people tend to value their safety on the web more and more. This work proposes an EfficientNetV2 model combined using orthogonal projection loss function (OPL) to build a palm-vein recognition system. The experimental results indicate that the proposed model using OPL can improve the accuracy in the multispectral palmprint of the public dataset CASIA. Compared with other palm-vein recognition works, this work has a higher model recognition ability, with a 97.35% rate of accuracy in the CASIA dataset.
Case Study of a Differential Single-Pole Double-Throw RF Switch Using Memristors

Nanotechnology-enabled novel RF switches have emerged as a promising alternative to MEMS, owing to current technological limitations of the latter. Additionally, recent research endeavors in the telecommunications sector have aimed on the gradual replacement of MEMS with memristive switches harnessing the unique properties of memristors. Such switches have been recently fabricated and characterized, providing optimal behavior under high-frequency stimuli. In this study, a novel double-pole double-throw (DPDT) memristor-based switch is proposed, that can be also utilized as a differential single-pole double-throw (SPDT) switch for use in RF applications. Memristor elements are incorporated into the circuit design, providing a more area-, power-, and cost-efficient approach. The designed circuits utilize a widely employed memristor model and are properly matched for 50Ω loads. The performance of the three circuit variants is evaluated using various key figures of merit, and the selected topology is further assessed under noisy input stimuli. Through this work, we aim to contribute to the ongoing discussion on the application of memristor-based switches in RF circuits, highlighting their potential advantages and addressing the need for more efficient and reliable alternatives.
Floating-Point Formats and Arithmetic for Highly Accurate Multi-Layer Perceptrons

The data precision can significantly affect the accuracy and overhead metrics of hardware accelerators for different applications such as artificial neural networks (ANNs). This paper evaluates the inference and training of multi-layer perceptrons (MLPs), in which initially IEEE standard floating-point (FP) precisions (half, single and double) are utilized separately and then compared with mixed-precision FP formats. The mixed-precision calculations are investigated for three critical propagation modules (activation functions, weight updates, and accumulation units). Compared with applying a simple low-precision format, the mixed-precision format prevents an accuracy loss and the occurrence of overflow/underflow in the MLPs while potentially incurring in less hardware overhead in terms of area/power. As the multiply-accumulation is the most dominant operation in trending ANNs, a fully pipelined hardware implementation for the fused multiply-add units is proposed for different IEEE FP formats to achieve a very high operating frequency.
Few-Shot Signal Recognition for UAV Based on Prototype Network

In recent years, the frequent occurrence of illegal use of unmanned aerial vehicles (UAVs) has posed a serious threat to societal security. Therefore, the identification and classification of UAVs have become critically important. However, UAV signal recognition based on deep learning has faced technical challenges due to the lack of large-scale UAV datasets. To address this issue, a prototype network based on a composite loss is proposed to tackle insufficient samples in UAV signal recognition. The proposed network combines cross-entropy loss and label smoothing loss, and then utilizes a Residual Network (ResNet) for feature extraction of UAV signals. To evaluate the performance of the proposed network, we compare it with the prototype network only with cross-entropy loss, and conduct tests with 1, 5, 10, 15 and 20 samples. Experimental results demonstrate that the proposed network exhibits excellent performance in dealing with few-shot classification problems. Particularly, when the number of samples exceeds 15, the accuracy of the proposed network can reach 98%.
Efficient Deployment of User Satisfaction Optimization in Multi-UAV Networks

In this paper, we study the deployment of unmanned aerial vehicle mounted base stations (UAV-BSs) in 3D space. We model the deployment problem as a user satisfaction maximization problem and propose an algorithm for serving non-uniformly distributed ground users outdoors. This algorithm can avoid serving too many ground users (GUs) per UAV-BS, thereby increasing the allocated data rate of ground users. Therefore, limiting the number of GUs served by each UAV-BS will improve user satisfaction. Preliminary simulation results show that our proposed algorithm improves user satisfaction by 19% compared to the existing counterclockwise spiral deployment method.
Constellation Optimization Assisted OTFS with Index Modulation

OTFS is recognized as a promising modulation technology to enhance and extend coverage for high-mobility communications. In this paper, we propose a Modified-Constellation assisted Orthogonal Time-Frequency-Space with Index Modulation (MCOTFS-IM) scheme to improve BER performance over doubly selective fading channels. The proposed scheme optimizes the positions of QAM constellation points that are close to the origin, such that the Euclidean distance for detecting active or inactive status of a grid point in Delay-Doppler domain can be enlarged. This adjustment offers a more accurate detection rate, thus improving the overall BER across index bits and information bits for index modulation. We further apply the Jacobi preconditioning Conjugate gradient (Jac-CG) method to achieve performance of MMSE detection with relatively low complexity. Simulation results demonstrate that the proposed MCOTFS-IM outperforms OTFS-IM in terms of BER under various system setups.
Exploiting Multi-Head Attention Maps Into A Deep Riemannian Representation to Quantify Pulmonary Nodules

Nodules are the principal biomarker of lung cancer, observed in CT scans as masses with abnormal variations in size and texture. Today, the radiological finding characterization and disease stratification remain highly subjective and expert-dependent. Deep representations have recently addressed diagnosis support, but their effectiveness strongly depends on a significant amount of balanced and annotated data. This work introduces a Riemannian mechanism that fully exploits attention map outputs, dealing with textural nodule variability in scarce data scenarios. The end-to-end strategy first recovers non-local textural relationships from multi-head attention modules. Then, using noise-robust statistics, a symmetric positive embedding (SPD) captures pairwise relationships between nodule patterns. The resultant SPD embeddings are mapped to deep geometrical modules to learn compact and discriminative descriptors with respect to the disease. The proposed approach was validated on the LIDC-IDRI dataset demonstrating competitive results in the SOA with a 91.83% on F1-score, and 95.19% on AUC.
Evaluation of machine learning based optimized feature selection approaches and classification methods for Intrusion Detection System

An Intrusion Detection System (IDS) is used in the field of cyber security to prevent and mitigate threats during transmitting data between the devices and server. An IDS using Machine Learning (ML) can be applied to identify and classify security threats. Various ML techniques have been applied to improve the model performance of IDS. In addition, the quality of the dataset is also an important determinant that can greatly improve the detection accuracy. The authors propose an IDS framework based on SVM ensemble with feature selection and balancing dataset. The experiment results show that the proposed model can achieve a high detection performance.
Cost-Effective Scheduling for Dependent Tasks With Tight Deadline Constraints in Mobile Edge Computing

In Mobile Edge Computing (MEC), latency-sensitive mobile applications comprising dependent tasks can be scheduled to edge or cloud servers to reduce latency and execution costs. However, existing algorithms based on deadline distribution can hardly satisfy tight application deadlines in heterogeneous MEC due to lacking a global view of the future impacts on descendant tasks. To fill in this gap, we formulate the deadline-constrained cost optimization problem for dependent task scheduling in MEC and propose a low-complexity scheduling algorithm that considers a single task's future impacts in two stages. Specifically: (1) In the edge scheduling stage, each task is scheduled according to its successors’ latest start times instead of its sub-deadline to alleviate the lateness of its successors. An edge-only schedule plan is generated by scheduling tasks only on edge servers to save execution costs. (2) In the cloud offloading stage, in order to utilize the powerful cloud resources to satisfy the deadline, the edge-only schedule plan missing the deadline is efficiently modified by properly offloading multiple successive tasks to the cloud. Simulation results show the substantial advantage of the proposed algorithm over baselines in both online and offline scenarios.
Bag-of-Functions Denoising: Extracting main components in time series

Denoising sequences of time series is one of the elementary preprocessing steps in data mining. Current statistical methods work on the univariate input data stream and do not obtain long dependencies over the whole series. Due to advanced architectures and learning techniques, machine learning approaches are getting more effective. However, none of the existing methods propose a learnable denoising process to select interpretable components out of a given subset of time dependencies. Therefore, we propose a novel denoising method in the field of machine learning. In particular, we decompose multivariate time series data in a dense representation of selected functional relationships. The decomposition process is assembled as a residual stacking of autoencoders, while the last remainder denotes the extracted noise. We elaborate our approach on a comparison between statistical and machine learning methods, dealing with synthetic as well as industrial data. Beside the performance boost, the approach is capable to learn the main components of a time series as interpretable features in the latent space representation, even on the difficult blind denoising task.
A Note on Discriminator Updating Method based on Weights of Other Models and its Verification

Mobile edge computing has been introduced in various fields. The discriminator of the edge device needs to work for local optimized problems. However, since the edge devices are worked in limited areas, these cannot obtain novel learning data by themselves. Therefore these discriminators need to update by using the knowledge of other devices. Thus, in this paper, a method of updating models using weights of the Neural Networks by transferring and fitting is proposed. It is expected models can be updated for solving local optimized problems for sharing the knowledge of another model that are effective for learning.
Evaluation of AIoT-based Smart Lighting System: An IDex Case Study

Smart lighting system is a crucial part of the smart city, which covers all corners of human life and industrial production. The salient feature of a smart lighting system is the involvement of AI and IoT techniques, which makes it more intelligent. However, the employment of AI and IoT algorithms will also raise new challenges, such as security. As such, a universal evaluation criterion is lacking to explore the performance of smart lighting systems. The maturity IoT index (IDex), developed by the IEEE 2668 working group, aims to comprehensively evaluate smart applications in terms of performance and requirements and provides the IDex level for reference. Furthermore, IDex could give suggestions for developers to improve the application. This paper discusses an IDex case study in the smart lighting system.
Design and Analysis of Secure Localization Against Vulnerability-Induced Attack for Internet of Things

This paper investigates the problem of secure localization where the device vulnerabilities are used to perform adversarial attacks in the Internet of Things. Compared with existing work, a practical vulnerability-induced adversarial attack model is considered, where a malicious user is able to make use of vulnerable nodes to launch attacks, which may destroy the localization services or cause private location information leakage. To address the above issue, the Quality of Security (QoSec) is introduced to quantitatively describe the capability of anchor users to provide secure data. Based on the maximum path attack probability, the QoSec is calculated by taking into account the attack value, social value and device vulnerability. Furthermore, from the perspective of the anchor security assessments, this paper designs a Secure Localization (SLoc) algorithm through selecting the anchor users with high QoSec values. Theoretical analyses are performed to prove that the SLoc algorithm is secure, correct, and accurate. Experiments and simulations are conducted to demonstrate the superior performance of the SLoc algorithm and validate the analytical results.
A High Angular Resolution Transceivers Cascaded Automotive Front Radar with Novel Antennas

With 2 transceivers cascaded and multiple-input and multiple-output radar technology used, the angle detection resolution for azimuth direction can reach better than 1.4 degrees, which will bring better angular resolution than the current automotive front radar. What’s more, a new array is proposed for automotive front radar application. According to simulation results,the radiation patterns of the proposed antenna can cover medium and long range field of view.
Using Synthetic Training Data in Neural Networks for the Estimation of Fiber Orientation Distribution Functions from Single Shell Data

Several studies have investigated the possibility of predicting the fiber orientation distribution function (fODF), which is obtained using the very accurate multi-shell multi-tissue constrained spherical deconvolution (MT-CSD) from single-shell or low angular resolution multi-shell diffusion magnetic resonance imaging (dMRI) data using deep learning.While all these approaches show promising results, the vast majority have in common that they require multi-shell high angular resolution diffusion imaging (HARDI) data to calculate the ground truth fODF using the MT-CSD for training their networks. This data, however, is difficult to acquire in a clinical context and it is yet unclear how well networks trained on data acquired on a certain scanner with a certain protocol would generalize to different data.In this work, we address these shortcomings and present a method that can estimate an accurate fODF from single-shell diffusion data without the need for multi-shell data for training. This is achieved by generating patient-, acquisition-and scanner-specific synthetic single voxel diffusion signals with a known ground truth fODF from single shell data that can be used to train the neural network. The trained network will then be applied to the real patient data to predict the fODF with a quality standard close to that of an MT-CSD and the ability to determine if white matter (WM) is present in the underlying voxel.The approach is evaluated on 20 subjects from the Human Connectome Project (HCP) for all three shells (b=1000, 2000, 3000 s/mm2). When comparing both this approach and a single shell constrained spherical deconvolution (CSD) to the results of the MT-CSD, this work outperforms the single shell CSD in terms of the angular correlation coefficient and root mean squared error on all three shells.
Oxide-based Memory Devices as Artificial Dendrites for Neuromorphic Hardware

In this paper, we propose the emulation of artificial dendrite-like behavior by utilizing Oxide-based Resistive Memory (OxRAM) devices for brain-inspired neuromorphic computing hardware. Through our experiments, we demonstrate non-linear integration and excitatory/ inhibitory characteristics of the proposed dendrite behavior. To demonstrate the temporal filtering behavior i.e low pass filtering phenomenon observed in biological counterpart, we characterized our proposed electronic dendrite from 19.23 Hz to 2.38 kHz frequency range. The proposed artificial dendrite exhibits the linearly decreasing trend of output current gain versus frequency. Electrical characterizations of the proposed OxRAM based dendrite over multiple cycles ensures reliable operation. Moreover, we compared the proposed work with other artificial dendrite emulations in literature. Our proposed electronic dendrite can save >5× die-area and achieves ~2.5× higher integration to filtering current ratio (ILRS/IHRS as compared to CMOS/NVM (Non-Volatile Memory) counterparts.
Sustainability of ICPS from a Safety Perspective: Challenges and Opportunities

The growth of Industrial Cyber-Physical Systems (ICPS) has boosted industrial processes’ effectiveness, security, and sustainability. Nevertheless, preserving the sustainability of ICPS is a complex challenge, especially in dynamic and unpredictable conditions. Fault diagnosis, prognosis, and remaining useful life prediction are crucial for ICPS sustainability in terms of safety, as they enable timely and effective maintenance to extend the system’s lifespan. This paper presents a perspective on the current state-of-the-art in the safety of ICPS, emphasizing fault diagnosis, prognosis, and remaining useful life prediction. Key findings from existing literature are summarized, academic gaps are identified, and research challenges are highlighted. The paper offers suggestions for addressing the gaps, advancing ICPS sustainability, and ensuring secure and reliable operation.
Application of an improved lightweight YOLOv5 model to wheat stripe rust detection

Aiming at the problems that the existing field wheat stripe rust detection model has many network parameters, slow calculation speed, and is difficult to integrate into agricultural UAV equipment, this study designed a lightweight ShuffleNetv2 based YOLOv5s network model, The backbone feature extraction network CSP is replaced by ShuffleNetv2 network, and the Focus slicing layer in the network is removed to avoid multiple slice operations, which greatly reduces the number of calculation parameters of the model. At the same time, the deep separable convolutional DWConv is introduced into the YOLOv5 detection head network to realize the separation of convolutional channels and regions. The Loss function was replaced by EIOU and Focal Loss was introduced to optimize the sample imbalance in the bounding box regression task. In the post-processing stage, Soft-NMS was used to replace NMS, which solved the problem of wheat missing detection caused by violent rejection detection box. These improvements significantly simplified the size of the model, further improved the detection accuracy of wheat stripe rust, and effectively overcame the problem of missing detection of target wheat. The experimental results show that the mAP of the improved YOLOv5s model on the test data set reaches 87.53%, which is better than 86.2% of the initial YOLOv5s network and Faster R-CNN, YOLOv4 and SSD networks. In addition, the size of the improved YOLOv5s model is only 6.7MB, which is significantly reduced from the 14.8MB of the original YOLOv5s model, greatly improving the detection speed of the model for wheat stripe rust, and making it easier to deploy to farmland UAV equipment. This study can provide a method reference for detecting other crop diseases and pests by using lightweight YOLOv5 model.
Dynamic Asset Allocation Using Trading Agent

We study the use of PPO (Proximal Policy Optimization) algorithm for trading ETFs belonging to different asset classes. The studied asset classes include common stocks, bonds, REIT (Real Estate Investment Trust), gold, and future contracts on agricultural commodities. When properly training the PPO agent with the proposed ratio allocation strategy, the agent outperforms the static, periodical rebalancing approach.
A Rate-Splitting-Based Approach for Sum-Rate Optimization Scheme in UAV-Assisted Emergency Communication

Taking advantage of the rapid deployment of unmanned aerial vehicles (UAVs) can provide temporary relay access for users in disaster areas. However, sharing spectrum resources between UAVs and ground networks will lead to severe air-to-ground (AtG) interference during the wireless backhaul transmission phase, which may reduce the channel capacity of the UAV relay backhaul link. In this paper, we propose an uplink RSMA-based joint 3D UAV trajectory design, and power allocation optimization (ADM-TPO) algorithm to maximize the achievable rate of the UAV relay wireless backhaul link. Specifically, considering the time-varying channel conditions caused by UAV mobility characteristics, we design an adaptive decoding mechanism to improve the communication capacity of the system by dynamically adjusting the successive interference cancellation (SIC) decoding order at the receiver. We formulate the achievable rate maximization problem by considering information causality and rate fairness constraints. As the proposed problem is non-convex, we decompose it into three subproblems, i.e., decoding order selection, trajectory optimization, and power allocation subproblems. Then we solve them by using successive convex approximation (SCA) methods. Simulation results show that the proposed ADM-TPO algorithm can effectively improve the communication capacity of the UAV relay backhaul link compared with other conventional approaches.