Miracle-dz/UniFAR-FTUs huggingface dataset:
YOLO-GFD: A Fast and Accurate Fabric Defect Detection Model

Fabric defect detection plays a pivotal role in ensuring quality management within the textile industry, enabling timely identification and remediation of defects. This research addresses key challenges in fabric defect detection, including slow detection speed, significant variations in defect scale, and the difficulty in detecting small defects. To tackle these challenges, this study presents the “Yolo-GFD” fabric defect detection model, a refined version of YOLO5, designed to strike a harmonious balance between detection speed and accuracy. Firstly, “G-bottleneck” is utilized instead of the original bottleneck module in the backbone and neck network, which greatly reduces the parameters of the model and improves the detection speed. In addition, we design a new feature fusion network that better fuses features across scales and effectively enhances the detection of small targets. Finally, we use the k-means algorithm to design a priori frames suitable for fabric blemish detection and introduce the feature enhancement module “DSE-Block” behind the backbone network to make our detection model more adaptable to the scale variation of fabric blemishes. Experimental results on the dataset used in this paper show that compared with the original algorithm, the mean average precision (map) is dramatically improved by 6.72%, and the detection speed reaches 22. 39 fps, which effectively meets the requirements of real-time defect detection in industrial environments.
A Ka-band Two-way Combined Power Amplifier in 65-nm CMOS Technology

A fully integrated two-way differential power amplifier with two stages is designed and simulated in 65-nm CMOS technology. The circuit uses parallel power combination and stacked transistor techniques with neutral capacitors to improve high frequency gain and circuit stability. The driver stage applies a common-source architecture while the amplifier stage applies a common-source common-gate architecture (cascode), both of which are biased in Class AB. The power amplifier achieves a saturated output power of 23.2 dBm and a maximum peak efficiency of 26.6% at 30 GHz. The saturated output power is more than 20 dBm over the frequency band of 25 GHz to 35 GHz.
A Fast Startup Voltage Controlled Oscillator Design for IR-UWB Applications

This work presents a fast startup wide turning range LC oscillator for Impulse Ratio Ultrawideband (IR-UWB) applications. This work uses an asymmetric structure in the resonator and the negative-gm transistors of the crossed-couple LC Voltage Controlled Oscillator (VCO) core, which can enlarge the noise swing in the setting process of the startup phase in oscillation to shorten the startup time. But this method could deteriorate the phase noise performance. This work uses the gm-boost technology with a filter capacitor to overcome this side effect. A VCO with the proposed architecture has been designed and fabricated in GlobalFoundries (GF) 0.13um Complementary Metal Oxide Semiconductor (CMOS) technology. Post-simulation results show the worst phase noise is -117.1dBc/Hz @1MHz. The proposed VCO demonstrates a turning range of 7-9GHz with a measured 13.5mW in the continuous oscillating condition and 1.2V DC supply.
A Single-layered, Low-profile, Wideband and Dual-polarized Antenna in the Millimeter-wave Band

This paper reports the design of a novel millimeter-wave antenna with single-layered geometry and low profile. The antenna is working under two resonate modes, i.e. patch and inverted-F antenna (IFA) modes. The patch and IFA are located in the same substrate layer, leading to a single-layered geometry. The dual resonate modes contribute to the wideband operation of the antenna. To improve the port isolation, an L-shaped metal strip has been introduced. The design concept has been experimentally verified by a Ka-band 2×2 array antenna.
Design of 1-Bit Folded Transmitarray Based on Magneto-Electric Dipole Elements

This paper presents the design of a 1-bit folded transmitarray based on the wideband magneto-electric (ME) dipole elements. The proposed folded transmitarray is composed of a polarization selective transmitting surface and a polarization rotation (PR) surface. The electromagnetic wave radiated from the feed is reflected twice between the transmitting surface and the PR surface so that the profile of the proposed transmitarray is reduced to one-third of the traditional transmitarray. The simulated results of the proposed folded transmitarray show a 3-dB bandwidth of 22.8% (9.3-11.7 GHz) and a peak aperture efficiency of 15.3% at 10.1 GHz.
Digital Protection and Management of Cultural Heritage Based on Deep Learning Technology

In order to solve the problem that there is no publicly available large-scale multi-category image dataset of cultural relics collections, the research on single-label and multi-label classification of heritage images based on deep learning is proposed. In the research, two representative datasets, DPM dataset and MET dataset, are constructed for domestic and foreign collection types respectively through a network approach for single-label classification research, which are useful for the construction of large-scale deep learning datasets in related fields. The experimental results show that for the problem of small samples in DPM dataset, DPM dataset is first classified by means of deep transfer learning for mainstream deep learning models, among which ReSNet50 model Dovo achieves the accuracy of nearly 87%. To address the problem of large intra-class differences and small inter-class differences in heritage images, a multi-feature fusion classification method combining point convolution and integration learning is proposed, in which the locally connected point convolution-based method finally improves the classification accuracy by nearly 5 percentage points on the DPM dataset. It is concluded that the scoring layer fusion method based on the locally connected point convolution+SL algorithm proposed in the research achieves the best results among all fusion methods, which proves the effectiveness of the point convolution+SL method.
Deep Learning for Android Malware Detection and Classification Using Hybrid-Based Analysis: A Comparative Study

The Android operating system is the fastestgrowing mobile operating system due to its open-source nature. However, the popularity of Android does not always have a positive impact on its users. One of the threats to Android users is the presence of malicious software (malware). Therefore, preventive measures need to be taken to detect and classify malware. One way to do this is through deep learning. This study compares single-view deep learning and multi-view deep learning architectures to detect and classify Android malware by utilizing system calls, network flows, and static features of an application. The deep learning models are built using three different algorithms, namely Bi-LSTM and Bi-GRU, to process system calls, and MLP, to process network flows and static features. In the single-view deep learning architecture, each feature is processed separately in the model. In contrast, in multi-view deep learning, all three features are processed in a single model combined with the concatenate function. After going through the model training and evaluation stages, the research results show that the best models for binary classification architecture are the Bi-GRU and Bi-LSTM models with results of up to 100%. Meanwhile, in the multi-class classification architecture, the MLP model based on static features obtained results of 94%.
TMFER: Multimodal Fusion Emotion Recognition Algorithm Based on Transformer

According to the problems of the existing emotion recognition algorithms, which are not rich in emotion information, weak in feature representation and not high in recognition accuracy, this paper proposes a multimodal fusion emotion recognition algorithm based on Transformer (TMFER), which fuses three modalities of text, speech and image information for emotion recognition. For the different characteristics of each modal information, Bert model pre-training processing, MFCC feature extraction and CNN feature extractor extraction methods are used to extract features for each modality respectively, to explore deeper features. To address the problem of unreasonable combination of multi-modal features, the Transformer Encode multi-headed attention mechanism is used to build a feature fusion module to extract and combine potential feature information in different modalities in parallel. The fused data are fed into the algorithm classification module for sentiment recognition classification, and a joint supervised loss function based on large margin learning is customized to solve the problem of unbalanced classification and feature confounding in the baseline model. Finally, based on the IEMOCAP and MELD multimodal datasets, the TMFER algorithm is experimentally compared with current algorithms in the field that are more effective in emotion recognition classification. The experimental results show that the TMFER algorithm outperforms other algorithms in all evaluation metrics.
Text-based File Security with Signcryption Using AAβ and DSA Algorithms

The swift development of the current digital era is directly proportional to the emergence of several security issues in digital data transmission, especially in data confidentiality and authentication. The modernisation of this way of communication method must be given more attention to the security system because of its increasing popularity. In this case, users can overcome it by using various encryption method approaches, one of many ways that can be done is signcryption. Signcryption is a scheme that combines encryption and digital signatures. Where the encryption process provides confidentiality while the signature offers authenticity. Signcryption itself is utilized in providing solutions to the security problems mentioned earlier by combining a relatively new public key algorithm called $AA_{\beta}$ with DSA, which is the standard in digital signatures, in the hope that this combination of algorithms could provide integrity, confidentiality, and authentication. The final result of this research is that signcryption with $AA_{\beta}$ and DSA algorithms has been successfully carried out by producing an execution time of 1.189 seconds on the signcryption process of the smallest file size of 1 KB and resistant to brute force attacks.
Performance Analysis of an Embedded System for E-Waste Recognition

This paper presents a performance analysis of an embedded e-waste recognition system, with the goal of evaluating its effectiveness and efficiency. The system under consideration uses advanced computer vision and machine learning techniques, making it an inferential alternative for solving the pressing e-waste problem. Key performance metrics including accuracy, latency, memory usage, and power consumption are evaluated. These metrics are essential for evaluating the practical feasibility of the embedded system in real applications.We present a custom software tool developed to visualize and record the images and classification results generated by the embedded system in real-time. This tool allows a detailed and in-depth investigation of the system's performance, enabling us to obtain valuable information about its efficiency. The analysis of the obtained results gives an objective insight into the strengths and weaknesses of the embedded system. Recommendations for potential improvements and optimizations are derived.The findings presented in this paper contribute to ongoing efforts to develop effective and reliable e-waste recognition solutions. This supports sustainable waste management and environmental protection.
A Novel 90° Filtering Coupler With Low Amplitude Imbalance

In this article, a novel 90° filtering coupler with low amplitude imbalance is proposed. This novel design is based on the traditional filtering coupler, placing two waveguide-height discontinuity in the middle of the coupler symmetrically, which can introduce controllable ripple in the whole working frequency band, then achieve better amplitude imbalance. The center frequency of the filtering coupler is 140 GHz, which works in 138-142GHz. in the range of 138-142Ghz band, |S11| and |S43| are lower than −20 dB and |S33| and |S21| are lower than −20dB. The maximum magnitude imbalance is 0.148 dB, and the maximum phase error is 0.2°, Compared with the traditional structure, the amplitude imbalance is reduced to a great extent.
Scattering Reduction of Antenna Array Based on Phase Optimization

A novel strategy for optimizing the scattering cross section (SCS) of array antenna is proposed. According to the derived array scattering factor expression, the improved genetic algorithm is used to generate the required size of scattering field to offset the scattering field of active element by optimizing the distribution position and additional phase of passive element. Then, the array scattering factor is optimized by changing the installation height of the array element. The scattering cross section peak value of array antenna can be suppressed obviously with good radiation performance. A microstrip array antenna is adopted to verify the feasibility of the optimization method. The simulation results agree well with the optimization results, which shows that the optimized array has obvious scattering control.
TCM Tongue Segmentation and Analysis with a Mobile Device

Mobile devices and applications provide increased accessibility to things like Traditional Chinese Medicine (TCM). Ideally, one would be able to seek out a diagnosis to the tongue using advanced machine learning models in a mobile device. However, such kind of models are still not yet available for the public. In this research, we investigated a way of combining machine learning with a mobile application to support tongue diagnosis for the public. For tongue segmentation, we experimented with an unsupervised approach (K-means), a supervised method and a deep learning model. There are two outcomes presented in this research. Firstly, the user can seamlessly take a picture from a mobile phone and send it over to the server for a segmentation process. Secondly, the image processed by the server would have at least some levels of autonomous analysis preformed on it with acceptable results using machine learning methods. Our current results indicate that we were able to finish some key steps in this process.
Data Driven Approach for Heart Failure Analysis

Identifying individuals who may be at risk of developing heart failure is crucial in the healthcare industry. By applying preventative therapies and managing the condition, patient outcomes can be improved and the burden on healthcare systems can be reduced. Machine learning-based predictive models have shown promise in this regard, offering the potential for precise risk stratification and tailored care. The process of creating a heart failure prediction model involves several steps, including data collection, preprocessing, feature selection, model construction, evaluation, and clinical application. With the help of this technology, healthcare professionals and researchers can develop and validate heart failure prediction models, leading to better outcomes for patients.
Modified Compact High-Q Coaxial Cavity Resonator With Its Applications

This article presents a method to modify the coaxial resonator by bending the inner cylinder coaxial into the gradient dumbbell shape coaxial. The new coaxial resonator, gradient dumbbell coaxial resonator (GDCR), could provide very compact sizes while remaining high-quality factors (Qu). Then the GDCR resonator was deployed to design a four-pole bandpass filter (BPF) centred at 3.5 GHz with a fractional bandwidth (FBW) of 5.71%, and a pair of symmetrical transmission zeros (TZs) located at 3.27 and 3.64 GHz for demonstration. The TZs were generated by introducing the capacitive-dominant cross-coupling between resonator 1 and resonator 4, which avoids using the support material as the conventional methods. The theoretical responses were also given for comparison, which showed a good agreement with simulated ones.
Transfer-Based Deep Learning Technique for PCOS Detection Using Ultrasound Images

Polycystic ovary syndrome, or PCOS, is a complex problem of hormones that affects up to 10% of women of childbearing age. Correct treatment and control of PCOS depend on a correct diagnosis and classification of the disease. The majority of people who have PCOS are overweight, have more severe symptoms, have irregular periods, and have an increased amount of body hair. It is critical to acquire a diagnosis of polycystic ovary syndrome (PCOS) as quickly as possible so that you may begin treatment for the symptoms and reduce the associated health concerns. At the moment, physicians and radiologists employ ovarian ultrasonography to detect PCOD by determining the size of the eggs and calculating the number of eggs present in the ovary. This is one of the PCOS diagnosis variables that might be challenging. In this work, we suggest a way to classify people with PCOS by using ultrasound images of the ovaries and transfer learning-based deep learning models. Our model is trained on a large set of ultrasound images and was already trained using predefined InceptionV3 models on a set of medical images in general. We were able to classify PCOS with 99.48% accuracy, which shows that transfer learning is a good way to improve the accuracy of PCOS detection. Our experimental results demonstrate that transfer learning-based classification achieves superior performance in PCOS subtype classification compared to traditional machine learning approaches. Clinicians may be able to better diagnose and treat PCOS with the help of our suggested model.
SOH Estimation of Lithium Batteries Based on New Health Factors and Optimized BP Neural Networks

The study of the state of health (SOH) of lithium-ion batteries can avert major mishaps while also grading retired batteries for secondary use. To overcome the flaws of the traditional back propagation (BP) neural network technology, which has low precision and a poor fitting effect, this study proposes a model for forecasting the SOH of lithium-ion batteries based on a novel health factor and an upgraded BP algorithm. To begin, the NASA lithium battery database is evaluated offline, and the metrics with the highest correlation in the charging process are chosen as new health variables. On the other hand, simulation experiments are carried out on various groupings of battery data, and the BP algorithm is optimized using a genetic algorithm. The experimental results show that the errors in the classic BP models are all less than 2.4%, whereas the errors in the optimized BP neural network model are all less than 0.38%, and that the fitting effect has better generalization than the traditional BP model, which improves the estimation accuracy of SOH of lithium batteries, and that the proposed health factor can be directly collected by the battery management system (BMS), which has some practical implications.
Brain Stroke Detection from CT Images using Transfer Learning Method

Recently, with the production of high-capacity computers, artificial intelligence methods have been used in many areas. In particular, in the field of health, artificial intelligence methods are used to detect diseases and determine the treatments to be applied. Deep learning methods, a sub-branch of artificial intelligence, show a high success in diagnosing many diseases thanks to its deep CNN networks. In this study, firstly, it was tried to determine which deep learning methods are more successful for the detection of brain stroke from computerized tomography images. This study is of great importance in terms of determining which deep learning architectures we will focus on in our future studies on brain stroke. In this study, brain stroke disease was detected from CT images by using the five most common used models in the field of image processing, one of the deep learning methods. The pre-trained ResNetl01, VGG19, EfficientNet-B0, MobileNet-V2 and GoogleNet models were run with the same dataset and same parameters. As a result, the EfficientNet-B0 architecture showed the highest performance with 97.93% accuracy. ResNetl01, VGG19, MobileNet-V2 and GoogleNet models showed 94.32%, 97.28%, 92.30% and 91.61% accuracy rates, respectively. With this study, it was concluded that the EfficientNet architecture is more suitable for the detection of brain stroke disease from CT images.
Floating Point Multipliers Based On Vedic Mathematics

Multipliers form the core of all Digital systems. There is a growing demand for low-delay multipliers in a wide range of digital signal processing applications. These applications need high-speed processing and low latency. As demand for these applications continues to grow, the need for floating-point arithmetic that is both fast and accurate has also increased. In this paper, the Vedic mathematics technique Urdhva-Tiryakbhyam (UT) sutra is used to build a Floating-Point Multiplier. The paper aims to put forth the better performance of Vedic multipliers. The Vedic Multiplier is analyzed and compared with a Conventional Multiplier- Array Multiplier as well as with a widely used Wallace tree Multiplier. The designs were created using Cadence EDA Tool's 90nm standard cell library. The comparisons and benchmarking are done by the tools provided in Virtuoso. The Vedic multiplier is implemented with different adders and compared, and a conventional multiplier is also implemented and compared with the Vedic counterpart. The power and delay are compared among the multipliers.
High Selectivity UWB Bandpass Filter Based on Dual Radial Open Resonators

In this article, a unique microstrip ultra-wideband (UWB) bandpass filter (BPF) is introduced which employs dual radial open resonators (DRORs). The filter exhibits remarkable skirt selectivity and favorable passband reflection characteristics. To begin with, we conduct a theoretical analysis of the innovative DROR based on the odd-even mode theory to understand its resonant modes distribution. We then utilize parallel coupling lines with a defective ground structure (DGS) to achieve a flat passband. Additionally, we introduce two parallel tapered resonators to enhance the filter's out-of-band performance. The inclusion of radial stepped impedance open stub (RSIOS) at the center improves the filter's impedance matching, leading to better in-band performance. The results of the simulation demonstrate a close agreement with the theoretical analysis.
Does Artificial Intelligence Help Reduce Audit Risks?

This article aims to discover how AI-powered systems facilitate auditing, what risks emerge for AI-assisted audits and how to deal with these new risks. The paper studies the impact of cognitive computing on audit risk. AI-powered software is capable of self-learn so that it can identify patterns in data and codify them in predictions, rules and decisions. This self-learning ability can become both a benefit and, at the same time, insecurity. Although AI-self-learning helps make the process more efficient and calculations more accurate by improving the algorithm, eliminating errors and reducing risks, it creates new previously unknown threats. We discovered inherent limitations of cognitive-based technologies and risks for the audit process associated with using AI systems. We also proposed a complex security model that can reduce the uncertainty of AI-enabled audit and provides insight into future research opportunities.