Electronics, Volume 13, Issue 11 (June-1 2024) – 230 articles

Achieving accurate and high-sensitivity liquid level detection in medical instruments has always been a knotty task. In this paper, a high-precision, non-contact, flexible capacitive liquid level sensor is proposed, aiming to apply capacitive sensors in test tube liquid level measurement and improving the sensitivity of real-time liquid level sensors. The simulation study is conducted using ANSYS Maxwell and demonstrates the correlation between test tube thickness and sensitivity. A geometric model of the test container and sensing electrodes is established to optimize the design strategy for the physical dimensions of the sensor’s interdigitated (IDT) electrodes based on a flexible printed circuit (FPC). The hardware and software designs are completed based on the FDC2214 capacitive-to-digital converter to collect the capacitance variation data of the sensing electrodes accurately. To assess the system’s performance, an experimental platform for a liquid level sensor system has been constructed, facilitating the measurement, communication, processing, and visualization of liquid levels. The performance results demonstrate that the system is capable of accurately measuring the effective liquid level range within a standard 5 mL test tube with a resolution of up to 1 mm, as well as a sensitivity of 78.68 fF/mm, verifying the simulation results and exhibiting excellent linearity. Full article

The high-frequency (HF) voltage injection method is widely applied in achieving position-sensorless control for interior permanent-magnet synchronous motors (IPMSMs). This method necessitates precise and rapid extraction of the current signal for accurate position estimation and field-oriented control (FOC). In the traditional methods, the position error signal and fundamental current are extracted from the current signal using band-pass filters (BPFs) and low-pass filters (LPFs), or a method based on time-delay filters. However, the traditional extraction method falls short in ensuring simultaneous dynamic performance and accuracy, particularly when the switching frequency is limited or when encountering harmonic and noise interference. In this article, a novel HF pulsating square-wave voltage injection method based on an improved current signal-extraction strategy is proposed to improve the extraction accuracy while maintaining good dynamic performance. The newly devised current signal-extraction method is crafted upon a notch filter (NF). Through harnessing NF’s effective separation characteristics of specific frequency signals, the current signal is meticulously processed. This process yields the extraction of the position error signal and fundamental-current component, crucial for accurate position estimation and motor FOC. Simulation and hardware-in-the-loop (HIL) testing are conducted to validate the effectiveness of the proposed approach. Full article

The RPL protocol is essential for efficient communication within the Internet of Things (IoT) ecosystem, yet it remains vulnerable to various attacks, particularly in dense and mobile environments where it shows certain limitations and susceptibilities. This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the Hello Flood attack in mobile environments. Using four different group mobility models—the Column Mobility Model (CMM), Reference Point Group Mobility Model (RPGM), Nomadic Community Mobility Model (NCM), and Pursue Mobility Model (PMM)—within the Cooja simulator, this study uniquely investigates the Hello Flood attack in mobile settings, an area previously overlooked. Our systematic evaluation focuses on critical performance metrics, including the Packet Delivery Ratio (PDR), End-to-End Delay (E2ED), throughput, Expected Transmission Count (ETX), and Average Power Consumption (APC). The findings reveal several key insights: PDR decreases significantly, indicating increased packet loss or delivery failures; ETX values rise, necessitating more packet retransmissions and routing hops; E2ED increases, introducing delays in routing decisions and data transmission times; throughput declines as the attack disrupts data flow; and APC escalates due to higher energy usage on packet transmissions, especially over extended paths. These results underscore the urgent need for robust security measures to protect RPL-based IoT networks in mobile environments. Furthermore, our work emphasizes the exacerbated impact of the attack in mobile scenarios, highlighting the evolving security requirements of IoT networks. Full article

This review paper introduces the modeling, measurement, identification and control of direct current motors based on the state space modeling and the transfer function representation. These models are identified by real laboratory measurements, and the simulated results are compared with the measurements. Continuous-time and discrete-time PID (Proportional-Integral-Derivative) controllers, discrete-time state feedback and linear quadratic controllers are designed mathematically. The designed controllers are realized by the microcontroller Arduino UNO, and the behavior of the controllers is compared and analyzed. The noisy current signal has been measured by a discrete-time observer, steady-state Kalman filtering is also studied. The practical results of the implemented controllers support the theoretical results very well. Full article

This paper extends the three-dimensional inverted pendulum (spherical inverted pendulum or SIP) in a polar coordinate system to simulate human walking in free fall and the energy recovery when the foot collides with the ground. The purpose is to propose a general model to account for all characteristics of the biped and of the gait, while adding minimal dynamical complexity with respect to the SIP. This model allows for both walking omnidirectionally on a flat surface and going up and down staircases. The technique does not use torque control. However, for the gait, the only action is the change in angular velocity at the start of a new step with respect to those given after the collision (emulating the torque action in the brief double stance period) to recover from the losses, as well as the preparation of the position in the frontal and sagittal planes of the swing foot for the next collision for balance and maneuvering. Moreover, in climbing or descending staircases, during the step, the length of the supporting leg is modified for the height of the step of the staircase. Simulation examples are offered for a rectilinear walk, ascending and descending rectilinear or spiral staircases, showing stability of the walk, and the expenditure of energy. Full article

Light-field video provides a detailed representation of scenes captured from different perspectives. This results in a visualisation modality that enhances the immersion and engagement of the viewers with the depicted environment. In order to perform research on compression, transmission and signal processing of light field data, datasets with light-field contents of different categories and acquired with different modalities are required. In particular, the development of machine learning models for quality assessment and for light-field processing, including the generation of new views, require large amounts of data. Most existing datasets consist of static scenes and, in many cases, synthetic contents. This paper presents a novel light-field plenoptic video dataset, KULFR8, involving six real-world scenes with moving objects and 336 distorted light-field videos derived from the original contents; in total, the original scenes in the dataset contain 1800 distinctive frames, with angular resolution of $5\times 5$ with and total spatial resolution of $9600\times 5400$ pixels (considering all the views); overall, the dataset consists of 45,000 different views with spatial resolution of $1920\times 1080$ pixels. We analyse the content characteristics based on the dimensions of the captured objects and via the acquired videos using the central views extracted from each quilted frame. Additionally, we encode and decode the contents using various video encoders across different bitrate ranges. For quality assessments, we consider all the views, utilising frames measuring $9600\times 5400$ pixels, and employ two objective quality metrics: PSNR and SSIM. Full article

With the rise of Large Language Models (LLMs), distinguishing between genuine and AI-generated content, particularly in finance, has become challenging. Previous studies have focused on binary identification of ChatGPT-generated content, overlooking other AI tools used for text regeneration. This study addresses this gap by examining various AI-regenerated content types in the finance domain. Objective: The study aims to differentiate between human-generated financial content and AI-regenerated content, specifically focusing on ChatGPT, QuillBot, and SpinBot. It constructs a dataset comprising real text and AI-regenerated text for this purpose. Contribution: This research contributes to the field by providing a dataset that includes various types of AI-regenerated financial content. It also evaluates the performance of different models, particularly highlighting the effectiveness of the Bidirectional Encoder Representations from the Transformers Base Cased model in distinguishing between these content types. Methods: The dataset is meticulously preprocessed to ensure quality and reliability. Various models, including Bidirectional Encoder Representations Base Cased, are fine-tuned and compared with traditional machine learning models using TFIDF and Word2Vec approaches. Results: The Bidirectional Encoder Representations Base Cased model outperforms other models, achieving an accuracy, precision, recall, and F1 score of 0.73, 0.73, 0.73, and 0.72 respectively, in distinguishing between real and AI-regenerated financial content. Conclusions: This study demonstrates the effectiveness of the Bidirectional Encoder Representations base model in differentiating between human-generated financial content and AI-regenerated content. It highlights the importance of considering various AI tools in identifying synthetic content, particularly in the finance domain in Pakistan. Full article

In shaft mining, monitoring the deformation of the roadway due to mining pressure is of great significance to the safe production of coal mines. For this reason, a distributed high-density anchor (cable) support force monitoring system was designed by developing a low-cost anchor (cable) stress monitoring device, which consists of an anchor (cable) stress sensor and a data acquisition device. The whole system consists of an anchor bar (cable) stress monitoring device and a mine roadway deformation monitoring substation. The signals collected by the anchor force sensors are processed by the data acquisition device and sent to the self-developed mine roadway deformation monitoring substation through Long Range Radio (LoRa) wireless communication. All data from the monitoring substation are transmitted to the ground control center in real time via the Message Queuing Telemetry Transport (MQTT) network transmission protocol. The distributed high-density arrangement of monitoring nodes reflects the deformation trend of the whole section of the roadway by monitoring the anchor bar (cable) support force data of multiple sections, which effectively ensures the safety of the roadway. Full article

This work describes a system to help in the remote assessment of fall risk in elderly people. A portable hardware system equipped with an RGB-D sensor is used for motion capture. A set of anonymous frames, representing the process of skeleton tracking, and a collection of sequences of interesting features, obtained from body landmark evaluations through time, are stored in the Cloud for each patient. A WEB dashboard allows for tailored tests to be designed, which include the typical items within well-known fall risk evaluation tests in the literature. Such a dashboard helps therapists to evaluate each item from the analysis and observation of the sequences and the 3D representation of the body through time, and to compare the results of tests carried out in different moments, checking on the evolution of the fall risk. The software architecture that implements the system allows the information to be stored in a safe manner and preserves patients’ privacy. The paper shows the obtained results after testing an early prototype of the system, a discussion about its advantages, and the current limitations from the Human–Computer Interaction point of view, and a plan to deploy and evaluate the system from the usability perspective in the near future. Full article

This paper discusses the construction and operating principle of an axial flux electric machine with hybrid excitation. Based on computer simulations using the Finite Element Method, an analysis was conducted with changes in the geometry of the magnetic circuit, which involves the rotation of the rotor disks relative to each other on the operating parameters of the machine. Both the generator state of operation, in the meaning of analyzing the induced voltage (adjustment at −11% ÷ +64%) and the cogging torque, and the motor state of operation, in the meaning of analyzing the ripple of the electromagnetic torque (possible reduction by almost 30%), were examined. The article concludes with observations on how the change in the angle of the rotor disks affects the efficiency of the disk machine with axial flux and hybrid excitation. Full article

In response to the issue of low positioning accuracy and insufficient robustness in small UAVs (unmanned aerial vehicle) caused by sensor noise and cumulative motion errors during flight in complex environments, this paper proposes a multisource, multimodal data fusion method. Initially, it employs a multimodal data fusion of various sensors, including GPS (global positioning system), an IMU (inertial measurement unit), and visual sensors, to complement the strengths and weaknesses of each hardware component, thereby mitigating motion errors to enhance accuracy. To mitigate the impact of sudden changes in sensor data, a high-fidelity UAV model is established in the digital twin based on the real UAV parameters, providing a robust reference for data fusion. By utilizing the extended Kalman filter algorithm, it fuses data from both the real UAV and its digital twin, and the filtered positional information is fed back into the control system of the real UAV. This enables the real-time correction of UAV positional deviations caused by sensor noise and environmental disturbances. The multisource, multimodal fusion Kalman filter method proposed in this paper significantly improves the positioning accuracy of UAVs in complex scenarios and the overall stability of the system. This method holds significant value in maintaining high-precision positioning in variable environments and has important practical implications for enhancing UAV navigation and application efficiency. Full article

Smart home devices are becoming more popular over the years. A diverse range of appliances is being created, and Ambient Intelligence is growing in homes. However, there are various producers of these gadgets, different kinds of protocols, and diverse environments. The lack of interoperability reduces comfort of the user and turns into a barrier to smart home adoption. Matter is growing by constructing an open-source application layer protocol that can be compatible with all smart home ecosystems. In this article, a Matter overview is provided (namely, of the Commissioning stage), and a Matter Accessory using ESP32-S3 is developed referring to the manufacturer’s SDKs and is inserted into an existent household ecosystem. Its behavior on the network is briefly analyzed, and interactions with the device are carried out. The simplicity of these tasks demonstrates accessibility for developers to create products, especially when it comes to firmware. Additionally, device commissioning and control are straightforward for the consumer. This capacity of gadget incorporation into diverse ecosystems using Matter is already on the market and might result in higher device production and enhanced smart home adoption. Full article

Against the backdrop of rapidly advancing cloud storage technology, as well as 5G and 6G communication technologies, group key management faces increasingly daunting challenges. Traditional key management encounters difficulties in key distribution, security threats, management complexity, and issues of trustworthiness. Particularly in scenarios with a large number of members or frequent member turnover within groups, this may lead to security vulnerabilities such as permission confusion, exacerbating the security risks and management complexity faced by the system. To address these issues, this paper utilizes blockchain technology to achieve distributed storage and management of group keys. This solution combines key management with the distributed characteristics of blockchain, enhancing scalability, and enabling tracking of malicious members. Simultaneously, by integrating intelligent authentication mechanisms and lightweight data update mechanisms, it effectively enhances the security, trustworthiness, and scalability of the key management system. This provides important technical support for constructing a more secure and reliable network environment. Full article

This paper investigates the temperature-dependent effects of gamma-ray irradiation on β-Ga₂O₃ vertical Schottky barrier diodes (SBDs) under a 100 V reverse bias condition at a total dose of 1 Mrad(Si). As the irradiation dose increased, the radiation damage became more severe. The total ionizing dose (TID) degradation behavior and mechanisms were evaluated through DC, capacitance–voltage (C-V), and low-frequency noise (LFN) measurements by varying irradiation, and the test results indicated that TID effects introduced interface defects and altered the carrier concentration within the material. The impact of TID effects was more pronounced at lower temperatures compared to higher temperatures. Additionally, the annealing effect in the high-temperature experimental conditions ameliorated the growth of interface trap defects caused by irradiation. These results suggest that compared to low-temperature testing, the device exhibits higher TID tolerance after high-temperature exposure, providing valuable insights for in-depth radiation reliability studies on subsequent related devices. Full article

As the next generation video coding standard, Versatile Video Coding (VVC) significantly improves coding efficiency over the current High-Efficiency Video Coding (HEVC) standard. In practice, this improvement comes at the cost of increased pre-processing complexity. This increased complexity faces the challenge of implementing VVC for time-consuming encoding. This work presents a technique to simplify VVC intra-prediction using Discrete Cosine Transform (DCT) feature analysis and a concatenate-designed CNN. The coefficients of the (DTC-)transformed CUs reflect the complexity of the original texture, and the proposed CNN employs multiple classifiers to predict whether they should be split. This approach can determine whether to split Coding Units (CUs) of different sizes according to the Versatile Video Coding (VVC) standard. This helps to simplify the intra-prediction process. The experimental results indicate that our approach can reduce the encoding time by 52.77% with a minimal increase of 1.48%. We use the Bjøntegaard Delta Bit Rate (BDBR) compared to the original algorithm, demonstrating a competitive result with other state-of-the-art methods in terms of coding efficiency with video quality. Full article

Existing point cloud object detection algorithms struggle to effectively capture spatial features across different scales, often resulting in inadequate responses to changes in object size and limited feature extraction capabilities, thereby affecting detection accuracy. To solve this problem, we present a point cloud object detection method based on multi-scale feature fusion of the original point cloud and projection, which aims to improve the multi-scale performance and completeness of feature extraction in point cloud object detection. First, we designed a 3D feature extraction module based on the 3D Swin Transformer. This module pre-processes the point cloud using a 3D Patch Partition approach and employs a self-attention mechanism within a 3D sliding window, along with a downsampling strategy, to effectively extract features at different scales. At the same time, we convert the 3D point cloud to a 2D image using projection technology and extract 2D features using the Swin Transformer. A 2D/3D feature fusion module is then built to integrate 2D and 3D features at the channel level through point-by-point addition and vector concatenation to improve feature completeness. Finally, the integrated feature maps are fed into the detection head to facilitate efficient object detection. Experimental results show that our method has improved the average precision of vehicle detection by 1.01% on the KITTI dataset over three levels of difficulty compared to Voxel-RCNN. In addition, visualization analyses show that our proposed algorithm also exhibits superior performance in object detection. Full article

Ransomware is one of the most extended cyberattacks. It consists of encrypting a user’s files or locking the smartphone in order to blackmail a victim. The attacking software is ordered on the infected device from the attacker’s remote server, known as command and control. In this work, we propose a method to recover from a Locker.CB!tr ransomware attack after it has infected and hit a smartphone. The novelty of our approach lies on exploiting the communication between the ransomware on the infected device and the attacker’s command and control server as a point to reverse disruptive actions like screen locking or file encryption. For this purpose, we carried out both a dynamic and a static analysis of decompiled Locker.CB!tr ransomware source code to understand its operation principles and exploited communication patterns from the IP layer to the application layer to fully impersonate the command and control server. This way, we gained full control over the Locker.CB!tr ransomware instance. From that moment, we were able to command the Locker.CB!tr ransomware instance on the infected device to unlock the smartphone or decrypt the files. The contributions of this work are a novel method to recover the mobile phone after ransomware attack based on the analysis of the ransomware communication with the C&C server; and a mechanism for impersonating the ransomware C&C server and thus gaining full control over the ransomware instance. Full article

Optical fibres are gaining popularity for relative dosimetry in proton therapy due to their spatial resolution and ability for near real-time acquisition. For FLASH proton therapy, these fibres need to handle higher dose rates and larger doses than for conventional proton dose rates. We developed a multi-point fibre sensor embedded in a 3D-printed phantom which can measure the profile of a FLASH proton beam. Seven PMMA fibres of 1 mm diameter were embedded in a custom 3D-printed plastic phantom of the same density as the fibres. The phantom was placed in a proton beam with FLASH dose rates at the TRIUMF Proton Therapy Research Centre (PTRC). The sensor was exposed to different proton energies, 13.5 MeV, 19 MeV and 40.4 MeV, achieved by adding PMMA bolus in front of the phantom and three different beam currents, varying the dose rates from 7.5 to 101 Gy/s. The array was able to record beam profiles in both transverse and axial directions in relative agreement with measurements from EBT-XD radiochromic films (transverse) and Monte Carlo simulations (axial). A decrease in light output over time was observed, which might be caused by radiation damage in the matrix of the fibre and characterised by an exponential decay function. Full article

This paper works on detecting a person in bed for sleep routine and sleep pattern monitoring based on the Micro-Electro-Mechanical Systems (MEMS) accelerometer and Internet of Things (IoT) embedded system board. This work provides sleep information, patient assessment, and elderly care for patients who live alone via tele-distance to doctors or family members. About 216,000 pieces of acceleration data were collected, including three classes: no person in bed, a static laying position, and a moving state for Artificial Intelligence (AI) application. Six well-known Machine-Learning (ML) algorithms were evaluated with precision, recall, F1-score, and accuracy in the workstation before implementing in the STM32-microcontroller for real-time state classification. The four best algorithms were selected to be programmed into the IoT board and applied for real-time testing. The results demonstrate the high accuracy of the ML performance, more than 99%, and the Classification and Regression Tree algorithm is among the best models with a light code size of 1583 bytes. The smart bed information is sent to the IoT dashboard of Node-RED via a Message Queuing Telemetry broker (MQTT). Full article

Digital twin technology is expected to transform agriculture. By creating the virtual representation of a physical entity, it assists food producers in monitoring, predicting, and optimizing the production process remotely and even autonomously. However, the progress in this area is relatively slower than in industries like manufacturing. A systematic investigation of agricultural digital twins’ current status and progress is imperative. With seventy published papers, this work elaborated on the studies targeting agricultural digital twins from overall trends, focused areas (including domains, processes, and topics), reference architectures, and open questions, which could help scholars examine their research agenda and support the further development of digital twins in agriculture. Full article

In this paper, we present a novel approach to state estimation in dynamic systems by combining Physics-Informed Neural Networks (PINNs) with an adaptive Unscented Kalman Filter (UKF). Recognizing the limitations of traditional state estimation methods, we refine the PINN architecture with hybrid loss functions and Monte Carlo Dropout for enhanced uncertainty estimation. The Unscented Kalman Filter is augmented with an adaptive noise covariance mechanism and incorporates model parameters into the state vector to improve adaptability. We further validate this hybrid framework by integrating the enhanced PINN with the UKF for a seamless state prediction pipeline, demonstrating significant improvements in accuracy and robustness. Our experimental results show a marked enhancement in state estimation fidelity for both position and velocity tracking, supported by uncertainty quantification via Bayesian inference and Monte Carlo Dropout. We further extend the simulation and present evaluations on a double pendulum system and state estimation on a quadcopter drone. This comprehensive solution is poised to advance the state-of-the-art in dynamic system estimation, providing unparalleled performance across control theory, machine learning, and numerical optimization domains. Full article

In this study, a tyre defect detection model is improved and optimized under the YOLOv5 framework, aiming at radial tyre defects with characteristics such as an elongated shape and various target sizes and defect types. The DySneakConv module is introduced to replace the first BotteneckCSP in the Backbone network. The deformation offset of the DySneakConv module is used to make the convolutional energy freely adapt to the structure to improve the recognition rate of tyre defects with elongated features; the AIFI module is introduced to replace the fourth BotteneckCSP, and the self-attention mechanism and the processing of large-scale features are used to improve the recognition rate of tyre defects with elongated features using the AIFI module. This latter module has a self-attention mechanism and the ability to handle large-scale features to solve the problems of diverse tyre defects and different sizes. Secondly, the CARAFE up-sampling operator is introduced to replace the up-sampling operator in the Neck network. The up-sampling kernel prediction module in the CARAFE operator is used to increase the receptive field and allow the feature reorganization module to capture more semantic information to overcome the information loss problem of the up-sampling operator. Finally, based on the improved YOLOv5 detection algorithm, the Channel-wise Knowledge Distillation algorithm lightens the model, reducing its computational requirements and size while ensuring detection accuracy. Experimental studies were conducted on a dataset containing four types of tyre defects. Experimental results for the training set show that the improved algorithm improves the mAP0.5 by 4.6 pp, reduces the model size by 25.6 MB, reduces the computational complexity of the model by 31.3 GFLOPs, and reduces the number of parameters by 12.7 × 10⁶ compared to the original YOLOv5m algorithm. Experimental results for the test set show that the improved algorithm improves the mAP0.5 by 2.6 pp compared to the original YOLOv5m algorithm. This suggests that the improved algorithm is more suitable for tyre defect detection than the original YOLOv5. Full article

This study presents three structures for clearly expressing functional requirements (FRs) and quantitative non-functional requirements (qt-NFRs). Expressing requirements with these structures will allow the understanding of requirements by stakeholders and software developers. The first structure is the SURE format, which is composed of three main sections: a title, a short definition, and a detailed description. The second proposed structure is a template to facilitate the definition of the title and description of unambiguous FRs. It is based on the application of CRUD operations on a certain entity, calling it the “CRUDE” structure. Finally, the third structure serves as a template to make it easier to clearly define the description and title of qt-NFRs. It is based on the application of system properties to computer events or actions, calling it the “PROSE” structure. In this, it is very important to specify those metric values that are desired or expected by the stakeholder. To know how much the definition of FRs and qt-NFRs improved when the proposed structures were used, 46 requirement specification documents elaborated as homework by students of the “Requirement Engineering” course offered at the University of Guayaquil between 2020 and 2022 were evaluated by five experts with more than 10 years of experience in software development for Ecuadorian companies. The findings showed that students reduced the percentage of unambiguous FRs and qt-NFRs from over 80% to about 10%. In conclusion, the findings demonstrate how crucial the three structures proposed in this paper are to helping students develop the ability to clearly express requirements. Full article

Semiconductors continue to shrink in die size because of benefits like cost savings, lower power consumption, and improved performance. However, this reduction leads to more defects due to increased inter-cell interference. Among the various defect types, customer-found defects are the most costly. Thus, finding the root cause of customer-found defects has become crucial to the quality of semiconductors. Traditional methods involve analyzing the pathways of many low-yield wafers. Yet, because of the extremely limited number of customer-found defects, obtaining significant results is difficult. After the products are provided to customers, they undergo rigorous testing and selection, leading to a very low defect rate. However, since the timing of defect occurrence varies depending on the environment in which the product is used, the quantity of defective samples is often quite small. Unfortunately, with such a low number of samples, typically 10 or fewer, it becomes impossible to investigate the root cause of wafer-level defects using conventional methods. This paper introduces a novel approach to finding the root cause of these rare defective chips for the first time in the semiconductor industry. Defective wafers are identified using rare customer-found chips and chip-level EDS (Electrical Die Sorting) data, and these newly identified defective wafers are termed vBADs (virtual bad wafers). The performance of root cause analysis is dramatically improved with vBADs. However, the chip-level analysis presented here demands substantial computing power. Therefore, MPP (Massive Parallel Processing) architecture is implemented and optimized to handle large volumes of chip-level data within a large architecture infrastructure that can manage big data. This allows for a chip-level defect analysis system that can recommend the relevant EDS test and identify the root cause in real time even with a single defective chip. The experimental results demonstrate that the proposed root cause search can reveal the hidden cause of a single defective chip by amplifying it with 90 vBADs, and system performance improves by a factor of 61. Full article

Computational intelligence technologies have been extensively applied for the interpretation of remote sensing imagery. Recently, the computational-intelligence-based Transformer change detection (CD) approach has attracted increasing attention. However, the current Transformer-based CD method can better capture global features, but there is no good solution for the loss of local detail information. For this reason, introducing semantic and frequency information from the perspective of a dual-domain can be beneficial for improving the representation of detailed features to improve CD performance. To overcome this limitation, a dual-domain Transformer (D²Former) is proposed for CD. Firstly, we adopt a semantic tokenizer to capture the semantic information, which promotes the enrichment and refinement of semantic change information in the Transformer. Secondly, a frequency tokenizer is introduced to acquire the frequency information of the features, which offers the proposed D²Former another aspect and dimension to enhance the ability to detect change information. Therefore, the proposed D²Former employs dual-domain tokenizers to acquire and fuse the feature representation with rich semantic and frequency information, which can refine the features to acquire more fine-grained CD ability. Extensive experiments on three CD benchmark datasets demonstrate that the proposed D²Former obviously outperforms some other existing approaches. The results present the competitive performance of our method on the WHU-CD, LEVIR-CD, and GZ-CD datasets, for which it achieved F1-Score metrics of 92.85%, 90.60%, and 87.02%, respectively. Full article

With the rapid advancement of deep learning techniques, deep learning-based flood prediction models have drawn significant attention. However, for short-term prediction in small- and medium-sized river basins, models typically rely on hydrological data spanning from the past several hours to one day, utilizing a fixed-length input window. Such input limits the models’ adaptability to diverse rainfall events and restricts their capability to comprehend historical temporal patterns. To address the underutilization of historical information by existing models, we introduce a Pre-training Enhanced Short-term Flood Prediction Method (PE-SFPM) to enrich the model’s temporal understanding without necessitating changes to the input window size. In the pre-training stage, the model uses a random masking and prediction strategy to learn segment features, capturing a more comprehensive evolutionary trend of historical floods. In the flow forecasting stage, temporal features and spatial features are captured and fused using the temporal attention, spatial attention, and gated fusion. Features are further enhanced by integrating segment features using a feed-forward network. Experimental results demonstrate that the proposed PE-SFPM model achieves excellent performance in short-term flood prediction tasks. Full article

To improve the accuracy of signal recognition in delay-based optical reservoir computing (RC) systems, this paper proposes the use of nonlinear algorithms at the output layer to replace traditional linear algorithms for training and testing datasets and apply them to the identification of frequency-modulated continuous wave (FMCW) LiDAR signals. This marks the inaugural use of the system for the identification of FMCW LiDAR signals. We elaborate on the fundamental principles of a delay-based optical RC system using an optical-injected distributed feedback laser (DFB) laser and discriminate four FMCW LiDAR signals through this setup. In the output layer, three distinct training algorithms—namely linear regression, support vector machine (SVM), and random forest—were employed to train the optical reservoir. Upon analyzing the experimental results, it was found that regardless of the size of the dataset, the recognition accuracy of the two nonlinear training algorithms was superior to that of the linear regression algorithm. Among the two nonlinear algorithms, the Random Forest algorithm had a higher recognition accuracy than SVM when the sample size was relatively small. Full article

Due to the long-term service through wheel-rail rolling contact, the train wheelset tread will inevitably suffer from different types of defects, such as wear, cracks, and scratches. The effective detection of wheelset tread defects can provide critical support for the operation and maintenance of trains. In this paper, a new method based on a local inference constraint network is proposed to detect wheelset tread defects, and the main purpose is to address the issue of insufficient feature spaces caused by small samples. First, a generative adversarial network is applied to generate diverse samples with semantic consistency. An attention mechanism module is introduced into the feature extraction network to increase the importance of defect features. Then, the residual spine network for local input decisions is constructed to establish an association between sample features and defect types. Furthermore, the network’s activation function is improved to obtain higher learning speed and accuracy with fewer parameters. Finally, the validity and feasibility of the proposed method are verified using experimental data. Full article

The DFIG-based wind farm faces sub-synchronous oscillation (SSO) when it is integrated with a series-compensated transmission system. The equivalent SSO damping is influenced by both wind speed and compensation level. However, it is hard for the wind farm to obtain a compensation level in time to predict the SSO risk. In this paper, an SSO risk prediction method for a DFIG wind farm is proposed based on the characteristics identified from noise-like signals. First, SSO-related parameters are analyzed. Then, the potential SSO frequency and damping are identified from signals at normal working points by integration using variational mode decomposition and Prony analysis. Finally, a fuzzy inference system is established to predict the SSO risk of a DFIG wind farm. The effectiveness of the proposed method is verified by simulation. The proposed prediction method can predict SSO risks caused by the variation in wind speed, while the transmission line parameters are undetectable for the wind farm. Full article

The emergence of illegal (gambling, pornography, and attraction) websites seriously threatens the security of society. Due to the concealment of illegal websites, it is difficult to obtain labeled data with high quantity. Meanwhile, most illegal websites usually disguise themselves to avoid detection; for example, some gambling websites may visually resemble gaming websites. However, existing methods ignore the means of camouflage in a single modality. To address the above problems, this paper proposes MEDAL, a multimodality-based effective data augmentation framework for illegal website identification. First, we established an illegal website identification framework based on tri-training that combines information from different modalities (including image, text, and HTML) while making full use of numerous unlabeled data. Then, we designed a multimodal mutual assistance module that is integrated with the tri-training framework to mitigate the introduction of error information resulting from an unbalanced single-modal classifier performance in the tri-training process. Finally, the experimental results on the self-developed dataset demonstrate the effectiveness of the proposed framework, performing well on accuracy, precision, recall, and F1 metrics. Full article