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In the field of paper conservation and archival research, identifying the raw materials of paper is important to elucidate its history and culture. As the most basic element of the raw materials for paper, fibers have not been sufficiently investigated. In this study, we propose a nondestructive method for estimating paper fibers from macro photographs ( $4000\times3000$ pixels) captured using a digital camera. The proposed method consists of background patch (500 pixels per side) detection (BPD), wherein background regions with no text are identified; patch fiber classification (PFC), wherein background patches obtained after BPD are analyzed for fiber classification; and paper fiber estimation (PFE), wherein macro images obtained after PFC are analyzed for fiber estimation. BPD and PFC are employed to perform patch-based classification on segmented macro images, which are reconstructed during PFE to obtain the final fiber estimation results. We performed experiments using 1337 macro images (64176 patches) to evaluate the fiber estimation accuracy for kozo, mitsumata, and gampi via three-fold cross-validation. The average fiber classification accuracy for patch images was observed to be 79.1%; accordingly, the average fiber estimation accuracy for macro images was 85.8%. Experimental results indicated that PFE can be realized in a nondestructive manner on macro images of paper captured using a digital camera.

Evaluating the long-term influence of academic papers in the early stage after publication is crucial for research management and decision-making. Although the total number of citations has been considered as a valid indicator to measure the academic influence of papers, there are still some bottleneck problems in predicting future citation counts. Firstly, it remains a challenging task to select the reasonable features due to the diversity of model features. Secondly, some prediction models are overly complex and their defects may cause prediction bias or even errors. Finally, the most important thing is that very few models possess long-term predictive ability. This paper proposes a prediction model that can provide an early evaluation of the long-term citations, or the long-term influence, of academic papers. The model uses features available immediately after publication or after a certain period of time. Taking academic papers in Information Science and Library Science as our experimental subject, we train the model using three machine learning algorithms and find that the artificial neural network performs satisfactorily. Our findings may offer guidance for research management and decision-making.

This research proposes an inkjet printed dual-band dual-sense circularly polarized antenna using CPW-feeding on PET substrate. The antenna is designed and optimized using ANSYS HFSS, which operates at 4.01 GHz - 5.05 GHz (22.96%) and 6.23 GHz - 7.58 GHz (19.55%) with a return loss of $0.44\lambda $ x $0.52\lambda $ x $0.002\lambda $ , where $\lambda $ is the free-space wavelength at the lowest circular polarization frequency. Measurement of the fabricated version shows good agreement with the simulated version. To the best of author’s knowledge, this proposed design is the first circularly polarized antenna integrating flexibility with dual-band and dual-sensing characteristics.

The novel approach to the Low Energy Bluetooth RSSI (Received Signal Strength Indicator) examination for a personal location during the evacuation process is presented in this paper. The presented system is based on stationary locating localization nodes installed inside the facility and portable wristbands worn by people. A method based on the propagation model and preliminary determination of its characteristics is used to calculate the wristband-locator distance. The accuracy of the distance estimations is increased by assuming Gaussian model of RSSI measurements and using the estimator of RSSI mean value. A modified multilateration approach is used to estimate the person’s position in the 2D Cartesian coordinate system. The paper also includes the outcomes of experiments conducted on the proposed approach as it was applied to the prototype evacuation supervision system. The paper presents a comparison of the position estimation error for the proposed method based on the mean RSSI value estimator with the results obtained when raw RSSI values were used. Analysis and discussion of wristband position estimation error are also included.

Due to the relatively low operational reliability of the MMC valve, it is necessary to establish corresponding test methods to examine the tolerance of the valve to electrical and thermal stresses under engineering requirements through operational tests, to verify its operational reliability. To meet the operational testing requirements for higher voltage and larger current converter valves, an appropriate test topology is required. The paper first analyzes the voltage and current stress characteristics of capacitor components and MMC converter valves in ±800 kV/8 GW Flexible DC transmission system, providing a theoretical basis for the stress of operational testing valve, requiring that to be equivalent to actual operating conditions. Addressing the operational testing demands for high voltage and large current converter valves in the ±800 kV/8 GW flexible DC transmission MMC, a test topology is proposed. This topology can meet the operating conditions of higher voltage and larger current and simultaneously conduct tests on valve segments and modules, significantly improving operational testing efficiency and reducing testing costs. Additionally, a control method applicable to this test topology is proposed, enabling flexible adjustment of DC and AC in the test circuit, thereby enhancing the equivalency of operational tests. In this paper, the proposed topology and control method have enhanced testing efficiency, reduced testing costs, improved testing equivalence, and enhanced the precision of current and capacitor voltage control. Finally, simulations and experiments are constructed to validate the feasibility and effectiveness of the proposed operational test topology and its control method through simulations and experiments.

The demand for personalized learning experiences and effective analytics in education has significantly increased. The integration of technology in education has brought about significant changes in teaching and learning practices. In the era of digital technology, the integration of education technology in the classroom has led to a change in teaching methods and learning strategies. In this paper, we introduce the KNIGHT (AI in Education at Hochschule für Technik (HFT) Stuttgart) framework, which is a holistic solution designed to tackle the complex issue of personalized education in a digital era. The paper explores the application of multimodal data integration, the novel application of deep reinforcement learning to education analytics, and the ethical consideration of privacy-preserving personalized feedback. The proposed framework’s efficacy is substantiated through a case study, demonstrating its potential to revolutionize personalized education. This paper provides a comprehensive overview of the current discourse, providing valuable insights for educators, policymakers, and researchers into the multifaceted landscape of modern education, contributing to ongoing discussions and advancements in educational technology.

Currently, weakly supervised semantic segmentation methods based on image-level annotation often rely on pseudo pixel masks generated by seed regions. However, the growth of seed regions is stochastic, and in cases where targets are occluded or overlapped in the image without additional reference information, the segmentation network may encounter issues of missed or incorrect segmentation. To address this problem, this paper proposes a polarized attention mechanism for weakly supervised semantic segmentation networks. The attention mechanism consists of a semantic perception branch and a boundary detection branch. The semantic perception branch allows the network to better distinguish the category of each pixel in the image. Subsequently, the boundary detection branch enables the seed region to naturally expand towards the target boundary. The pseudo pixel mask generated by this method provides better coverage of the target area and improves the performance of the segmentation network. The test set and validation set mean Intersection over Union (mIoU) of the PASCAL VOC 2012 dataset achieved 72.1% and 73.2%. The results of the experiments demonstrated the effectiveness of the proposed method. The experimental results indicated that the attention mechanism, as proposed in this paper, can effectively enhance the segmentation performance in situations where objects in the image are occluded or overlapped.

A broadband multimode circularly polarized (CP) stacked antenna with pattern diversity is proposed in this paper. The antenna consists of three layers, two substrates with annular-ring patches and a parasitic metal patch. The antenna can radiate different patterns by exciting different modes of annular-ring micro-strip antenna (ARMSA). When port 1 is excited, the antenna is excited at the second-order mode(TM21) to generate the conical radiation pattern, whereas the antenna is excited at the fundamental mode(TM11) to generate the broad beam radiation pattern when port 2 is excited. In order to generate circular polarization, ARMSAs are fed by two feeding points and both of the annular-ring patches are fed with the network in the same layer. The two-port antenna achieves the broadband property by parasitic patch. The parasitic top metal layer can broaden the impedance bandwidth of TM11 mode while the ground plane of TM11 mode can act as the parasitic patch to broaden the impedance bandwidth of TM21 mode. To verify the design, the antenna is fabricated and measured. The measured results show that the antenna proposed in this paper has an impedance bandwidth of 0.22GHz (1.92-2.14GHz) and 3dB axial ratio (AR) bandwidth of about 0.25GHz (1.90-2.15GHz).

This paper proposes scalable and foldable origami-inspired supernumerary robotic limbs that incorporate a continuous robotic mechanism to support activities of daily living and presents an analysis of the fabrication process, evaluation experiments, and potential applications of the proposed devices. These origami-inspired supernumerary robotic limbs can augment the wearer’s capabilities by adjusting the scale of the devices to various applications and attaching them to parts of the body, such as the hands, wrists, arms, and torso. The device’s foldable structure can be unfolded for use and folded when not in use to prevent physical contact with the wearer and the surrounding environment so that the user’s movements are not restricted. In addition, the robotic limb is made of lightweight and soft material, so it remains safe to use and fatigue-free even after prolonged use. To verify the durability of the origami module, we conducted an experiment in which the module underwent 1,000 compressions using different materials and layer counts based on the folding pattern outlined in the manufacturing diagram. The experimental results showed that polypropylene was a suitable material for reducing fatigue due to folding, compared to polypropylene synthetic paper or plain paper. We also observed decreased stiffness with an increase in the number of layers. We believe that the proposed origami-inspired supernumerary robotic limbs can enlighten novel approaches to combining soft robotic and wearable devices.

This research paper intends to provide real-life applications of Generative AI (GAI) in the cybersecurity domain. The frequency, sophistication and impact of cyber threats have continued to rise in today’s world. This ever-evolving threat landscape poses challenges for organizations and security professionals who continue looking for better solutions to tackle these threats. GAI technology provides an effective way for them to address these issues in an automated manner with increasing efficiency. It enables them to work on more critical security aspects which require human intervention, while GAI systems deal with general threat situations. Further, GAI systems can better detect novel malware and threatening situations than humans. This feature of GAI, when leveraged, can lead to higher robustness of the security system. Many tech giants like Google, Microsoft etc., are motivated by this idea and are incorporating elements of GAI in their cybersecurity systems to make them more efficient in dealing with ever-evolving threats. Many cybersecurity tools like Google Cloud Security AI Workbench, Microsoft Security Copilot, SentinelOne Purple AI etc., have come into the picture, which leverage GAI to develop more straightforward and robust ways to deal with emerging cybersecurity perils. With the advent of GAI in the cybersecurity domain, one also needs to take into account the limitations and drawbacks that such systems have. This paper also provides some of the limitations of GAI, like periodically giving wrong results, costly training, the potential of GAI being used by malicious actors for illicit activities etc.

Identity systems are fundamental to any network, just as a language is shaped by a consistent set of symbols and their interpretation. A network is defined by a consistent set of identities and their usages. However, as pressures mount to customize and adapt to these identity spaces, one ponders how a network sustains its utility through coherence. This study investigates evolving Internet identifiers and their supporting systems. Owing to the multi-disciplinary nature of the topic, this paper draws perspectives from a wide array of sources that contribute to Internet systems and digital library systems. Initially, this paper highlights the dynamism and role of namespaces, focusing on the critical need to maintain coherence in the public domain. It also discusses the impact of mobility and digital cloning on identifiers and explores their influence on identity and location. In addition, key Internet identifiers are analyzed, contrasting them with methodologies adopted by digital library systems to provide deeper insights into various identity models. Furthermore, this study explores the evolution of the Domain Name System (DNS) as an identity system, examining the tensions and adaptations driven by customization demands. Finally, this paper explores alternative namespaces beyond the DNS, considering potential responses to these evolutionary pressures and future implications.

In modern cities, consumers with distributed energy resources (DERs) can trade energy by managing their consumption and supply. Blockchain is considered to provide technical support for establishing a distributed energy market, while the current mainstream blockchain technology cannot meet the requirement of efficiency and scalability under such a transaction scenario. In addition, the potential defaults of market players also hinder market implementation. Therefore, a reputation-based distributed energy trading mechanism and the corresponding blockchain consensus method considering reputation management are proposed in this paper. Firstly, a distributed reputation management mechanism is designed to systematically evaluate the user’s behavior in the market. Secondly, to address the limitations of traditional consensus methods in the distributed energy market distributed reputation control is introduced into the node model of blockchain, and a new energy blockchain consensus method named DPoR is proposed to solve the technical bottleneck under the distributed energy trading scenario. We also present a corresponding transaction mechanism to encourage market participants to maintain their reputations. Finally, the simulation results verify the effectiveness of the proposed transaction mechanism in this paper, and the presented blockchain consensus mechanism can meet the performance requirements under distributed energy trading scenarios in terms of high efficiency, low consumption, and openness.

This systematic literature review delves into the dynamic realm of graphical passwords, focusing on the myriad security attacks they face and the diverse countermeasures devised to mitigate these threats. The core objective of this paper is to identify existing security threats to graphical password schemes and the corresponding countermeasures developed to mitigate these attacks. The study process begins by identifying the usable databases and search engines to identify all the relevant resources. The inclusion and exclusion criteria were carefully selected to prioritize the study, focusing mostly on attacks and countermeasures related to graphical password schemes between 2009 and 2023. After thorough identification and selection progress, 59 studies met all the criteria. Among these studies, 47 mentioned shoulder surfing as a threat to graphical password schemes, while 20 discussed brute force attacks. Additionally, there were 21 papers on dictionary attacks, 13 on smudge attacks, spyware attacks, and social engineering, and 19 that discussed guessing attacks as threats to graphical password schemes. Furthermore, the papers identified several other attacks, including frequency of occurrence analysis attacks, video recording, eavesdropping, computer vision, sonar, and image gallery attacks, with the corresponding numbers of papers being 9, 17, 5, 2, 2, and 1, respectively. The results also highlight the countermeasures proposed in the study papers to mitigate the aforementioned attacks. Among the various countermeasures identified, most revolve around randomization, obfuscation, and password space complexity as the most commonly used techniques for enhancing the security of graphical password schemes.

Real-time systems are becoming pervasive with the growing global connectivity and rising consumer demands. The need for real-time processing has become a crucial part of many business applications worldwide. A key factor that determines the time taken for an application to give out the result hinges on its ability to prioritize, manage, and execute real-time workloads. However, there are several difficulties and constraints connected with implementing tasks in a real-time context. This research study primarily focuses on load balancing for mixed real-time tasks on a multi-core system, one of the major challenges for executing real-time workloads. The purpose of load balancing is to distribute the load evenly among the processor(s) and maximize their utility while minimizing overall execution time. The goal of this paper is to present a critical analysis of existing load balancing techniques for both periodic and aperiodic tasks. The paper explores several factors including throughput, performance, migration time, response time, overhead, resource utilization, scalability, fault tolerance, power efficiency, and other variables that play a crucial role in assessing the efficacy of load balancing in real-time systems. The proposed has contributed in four folds. Firstly, the state-of-the-art of various load balancing algorithms are discussed followed by the architecture involved in real-time multi-core systems. Later, different load balancing based scheduling algorithms were compared on the basis of different schemas and metrics for algorithm evaluation is also provided. Finally, the paper also identifies areas that warrant further exploration or investigation, suggesting potential avenues for future research, and highlighting emerging trends or developments that may shape the field.

The paper develops datasets and methods to assess student participation in real-life collaborative learning environments. In collaborative learning environments, students are organized into small groups where they are free to interact within their group. Thus, students can move around freely causing issues with strong pose variation, move out and re-enter the camera scene, or face away from the camera. We formulate the problem of assessing student participation into two subproblems: (i) student group detection against strong background interference from other groups, and (ii) dynamic participant tracking within the group. A massive independent testing dataset of 12,518,250 student label instances, of total duration of 21 hours and 22 minutes of real-life videos, is used for evaluating the performance of our proposed method for student group detection. The proposed method of using multiple image representations is shown to perform equally or better than YOLO on all video instances. Over the entire dataset, the proposed method achieved an F1 score of 0.85 compared to 0.80 for YOLO. Following student group detection, the paper presents the development of a dynamic participant tracking system for assessing student group participation through long video sessions. The proposed dynamic participant tracking system is shown to perform exceptionally well, missing a student in just one out of 35 testing videos. In comparison, a state-of-the-art method fails to track students in 14 out of the 35 testing videos. The proposed method achieves 82.3% accuracy on an independent set of long, real-life collaborative videos.

This paper describes an Intrusion Detection System (IDS) which uses several existing known IDS algorithms and Triple Modular Redundancy (TMR) algorithm to make decision about eventual existing attack by majority voting in one constructed ensemble model which solves practically the problem of binary classification. Proposed novel model belongs to so called stacking ensemble methods of machine learning algorithms which uses exactly four algorithms from the group of best binary classification algorithms: Decision trees, Naive Bayes, Support Vector Machine, k-nearest neighbors, logistic regression and AdaBoost and is applicable for any similar problem. Using proposed method, we get a more precisely determined threshold than it is case using whatever of in method individual applied algorithm as well as those algorithms that are the state of the art in the field of binary classification. Besides that, one of the main disadvantages of classic TMR used for classification network traffic, which is the problem of bad over-voting was successfully avoided by improving classic TMR with a new algorithm proposed by the authors. Today a denial of service attacks (DoS) and distributed denial of service (DDoS) are one of most present type on Internet and that is why the authors in this paper paid special attention to them and because of that the authors of proposed method chose to use known KAGGLE dataset which contains data of these type of attacks for the examination of quality of proposed IDS method implemented in suitable software. The dataset itself consists of a wide range of simulated intrusions in a military network environment in United States. Obtained results showed that IDS software with implemented proposed method worked precise and timely, which means alarms was trigger properly and efficiently with better results of quality of classification in most important measures than the individual included algorithms who are the state of the art in binary classification.

Recently, the thermal management of power electronic converters has gained significant attention due to the continuous trend of developing very compact power electronic converters with high power density. With the evolution of power semiconductor devices, high operating temperatures and large thermal cycles have become possible, necessitating a significant improvement in thermal system designs. Researchers have made significant efforts to develop effective thermal management systems to improve the reliability and lifetime of power electronic converters. This article intends to present a thorough review of thermal management systems employed in power electronics cooling. The applied thermal management techniques have been reviewed from the perspective of electrical parameter regulation and heat dissipation control. Regulation of electrical parameters involves active thermal control, which is a method for controlling junction temperature and thermal cycling of power semiconductor devices. The active thermal control implementation processes reviewed in this article consist of increasing overload capacity, manipulating switching and conduction losses, employing modified modulation processes, balancing thermal stress at the converter level, and controlling thermal stress at the system level. Control of heat dissipation can be achieved through direct and indirect cooling of power electronic converters with air or liquid as the coolant. The effectiveness and implementation methods of these cooling techniques, such as channel cooling, phase change material-based cooling, immersion cooling, jet impingement and spray cooling, are reviewed in this paper. Moreover, performance-enhancing ideas and challenges for these techniques are discussed. The primary objective of this review paper is to bridge the existing gap in the literature by offering a comprehensive comparison of commonly employed thermal management techniques.

Airborne 3D imaging using ultrasound is a promising sensing modality for robotic applications in harsh environments. Over the last decade, several high-performance systems have been proposed in the literature. Most of these sensors use a reduced aperture microphone array, leading to artifacts in the resulting acoustic images. This paper presents a novel in-air ultrasound sensor that incorporates 1024 microphones, in a 32-by-32 uniform rectangular array, in combination with a distributed embedded hardware design to perform the data acquisition. Using a broadband Minimum Variance Distortionless Response (MVDR) beamformer with Forward-Backward Spatial Smoothing (FB-SS), the sensor is able to create both 2D and 3D ultrasound images of the full-frontal hemisphere with high angular accuracy with up to 70 $\mathrm { \text {dB}}$ main lobe to side lobe ratioin the array response in a single-source scenario. This paper describes both the hardware infrastructure needed to obtain such highly detailed acoustical images, as well as the signal processing chain needed to convert the raw acoustic data into said images. Utilizing this novel high-resolution ultrasound imaging sensor, we wish to investigate the limits of both passive and active airborne ultrasound sensing by utilizing this virtually artifact-free imaging modality.

Recently, there is rapid integration of power electronic converter (PECs) into the power grid. Most of these PECs are grid-following inverters, where weak grid operation becomes an issue. Research is now shifting focus to grid-forming (GFM) inverters, resembling synchronous generators. The shift towards converter-based generation necessitates accurate PEC models for assessing system dynamics that were previously ignored in conventional power systems. Data-driven modeling (DDM) techniques are becoming valuable tools for capturing the dynamic behavior of advanced control strategies for PECs. This paper proposes using power hardware-in-the-loop experiments to capture dynamic GFM data in the application of DDM techniques. Furthermore, the paper derives an analytical approach to obtaining a mathematical model of GFM inverter dynamics and compares it with the DDM. A square-chirp probing signal was employed to perturb the active and reactive power of the load inside an Opal-RT model. The dynamic response of the GFM inverter, including changes in frequency and voltage, was recorded. This data was then used in a system identification algorithm to derive the GFM DDMs. The effectiveness of DDM is cross-validated with an analytical approach through experimental simulation studies, and the goodness-of-fit for both approaches is compared. Both approaches show more than 85% accuracy in capturing the dynamic response of GFM inverters under different loading conditions.

The objective of this paper is to develop a novel emotion recognition system from electroencephalogram (EEG) signals using effective connectivity and deep learning methods. Emotion recognition is an important task for various applications such as human-computer interaction and, mental health diagnosis. The paper aims to improve the accuracy and robustness of emotion recognition by combining different effective connectivity (EC) methods and pre-trained convolutional neural networks (CNNs), as well as long short-term memory (LSTM). EC methods measure information flow in the brain during emotional states using EEG signals. We used three EC methods: transfer entropy (TE), partial directed coherence (PDC), and direct directed transfer function (dDTF). We estimated a fused image from these methods for each five-second window of 32-channel EEG signals. Then, we applied six pre-trained CNNs to classify the images into four emotion classes based on the two-dimensional valence-arousal model. We used the leave-one-subject-out cross-validation strategy to evaluate the classification results. We also used an ensemble model to select the best results from the best pre-trained CNNs using the majority voting approach. Moreover, we combined the CNNs with LSTM to improve recognition performance. We achieved the average accuracy and F-score of 98.76%, 98.86%, 98.66 and 98.88% for classifying emotions using DEAP and MAHNOB-HCI datasets, respectively. Our results show that fused images can increase the accuracy and that an ensemble and combination of pre-trained CNNs and LSTM can achieve high accuracy for automated emotion recognition. Our model outperformed other state-of-the-art systems using the same datasets for four-class emotion classification.

Fog computing is a promising and challenging paradigm that enhances cloud computing by enabling efficient data processing and storage closer to data sources and users. This paper introduces a game-theoretic approach called GTRADPMFC (Game-Theoretic Resource Allocation and Dynamic Pricing Mechanism in Fog Computing) to address resource allocation and dynamic pricing challenges in fog computing environments with limited resources. The proposed model features non-cooperative competition among fog nodes for resources and dynamic pricing mechanisms to encourage efficient resource utilization. Theoretical analysis and simulations demonstrate that GTRADPMFC improves resource efficiency and overall fog computing system performance. Additionally, the paper discusses how to handle situations with insufficient samples and provide flexibility for users unable to meet completion time requirements. GTRADPMFC effectively manages resource allocation by establishing pricing in fog computing, considering potential delays in completion time. This is achieved through research, simulations, convergence analysis, complexity evaluation, and optimization guarantees.

Traffic collisions pose a significant global concern, necessitating innovative solutions to enhance safety. Post-collision, it becomes crucial for the police to determine the responsibilities of the involved parties, distinguishing between criminal acts and non-criminal incidents. Insurance companies also rely on such investigations to compensate victims. Assessing responsibility after a crash is a complex task requiring advanced knowledge of road rules. For straightforward scenarios like crashes with traffic lights, decisions are fast and easy. However, in situations such as crashes without any traffic signs, expert knowledge is essential. Automating such tasks demands innovative approaches, representing a necessity for the future of the automobile and insurance industries. Despite these critical needs, there has been limited research in the domain. This paper introduces a system that is capable of detecting vehicle collision and implements an original responsibility assessment process to assess drivers responsibilities. This paper is an extended version of our previous work. In the previous work, the system supported only three types of head-on/angle crash scenarios without traffic lights within three weather conditions, in addition to those with traffic lights. This study presents the improved system which now accommodates six different types of head-on/angle crash scenarios without traffic lights within six harsh weather conditions, such as foggy and snowy days. Additionally, extensive experiments are conducted with results showing that the system performs better than its previous version, mainly during nighttime without traffic lights (up to 93% accuracy against up to 82.5% obtained previously). Moreover, through case studies and comparisons with existing research, the effectiveness and superiority of the system are demonstrated.

The most recent ultra-lightweight mutual authentication protocol (UMAP) adopts a mechanism for key updating to keep the security forward and backward. The back-end server updates the secret keys immediately after sending the final message. Certainly, an adversary can intercept the final messages so that the back-end server updates the secret keys while the RFID tag does not. The replay attack can send the last messages by continuing to render the back-end server and RFID tags unauthorized. The major problem faced by existing protocols, when using high cryptographic operations on low-cost RFID tags, requires more memory, and computational power makes them unusable for these types of tags. In addition, there are weaknesses in the design of the existing protocols that lead to the emergence of desynchronization, secret disclosure, and replay attacks. Therefore, this paper proposes the design and development of an ultra-lightweight mutual authentication protocol that overcomes the weaknesses in earlier protocols and builds by implementing the rotation operator, mechanism of the secret keys, T-function, and timestamp technique. The informal and formal analysis results using security protocol verification official tools (Scythe and AVISPA) show that the proposed protocol has the ability to prevent attacks, especially replay attacks. Experimental analyses of our protocol to measure the performance shows that the proposed protocol involves a lower overhead than previous protocols. The total execution time in milliseconds of the processes of the proposed protocol with the existing protocols is 0.031ms, and it achieves an average of 78.51% over other protocols. In addition, this paper provides a comparison with the most recent protocols on performance, privacy and security requirements. Our proposed protocol storage space requirement is lowest than the existing protocols, the cryptographic requirement is ultra-lightweight and efficient and shows that the communication messages for authentication in our protocol are lowest than existing protocols and our protocol fulfills the privacy and security requirements.

Path planning is one of the research hotspots for outdoor mobile robots. This paper addresses the issues of slow convergence and low accuracy in the Double Deep Q Network (DDQN) method in environments with many obstacles in the context of deep reinforcement learning. A new algorithm, Improve Double Deep Q Network (IDDQN), is proposed, which utilizes second-order temporal difference methods and a binary tree data structure to improve the DDQN method. The improved method evaluates the actions of the current robot using second-order temporal difference methods and employs a binary tree structure to store the results obtained from these methods, replacing the traditional experience pool structure. The environment is constructed using a grid method, programmed in the Python language, with two two-dimensional grid maps created for simple and complex environments. DDQN and four related deep reinforcement learning methods, such as Multi-step updates and Experience Classification Double Deep Q Network (ECMS-DDQN), are compared through simulation experiments with the IDDQN method. Simulation results indicate that the IDDQN method improves various path planning metrics compared to the DDQN method and other relevant reinforcement learning methods. In the simple environment, IDDQN method exhibits a 26.89% improvement in step convergence time, a 22.58% improvement in reward convergence time, and a 10.30% improvement in average reward value after convergence compared to the original DDQN algorithm. It also outperforms other simulated methods in the simple environment, although the difference is not significant. In the complex environment, the IDDQN method avoids falling into local optima compared to other methods, demonstrating the accuracy of its strategy in complex environments. Other methods show artificially high average reward values after converging in local optima, lacking reference value. In the complex environment, IDDQN method exhibits a 33.22% improvement in step convergence time and a 25.47% improvement in reward convergence time compared to the original DDQN algorithm, clearly surpassing other participating simulated methods. The data above indicate that the IDDQN method improves both convergence speed and accuracy compared to the DDQN method and the relevant improvement methods simulated in this paper. Particularly in environments with many obstacles, the performance improvement is evident, allowing for effective path planning in such environments.

We investigated low Earth orbit satellite (LEO) communications applying Multiple-Input/Multiple-Output (MIMO) technology to the feeder link transmission between a satellite and gateway antennas to improve the channel capacity. This paper calls LEO satellites using MIMO technology “LEO-MIMO” and considers satellite and gateway antenna configurations to improve the feeder link capacity. LEO-MIMO operates with a single LEO satellite equipped with multiple antenna elements and a gateway with multiple remotely located antennas. The conditions of the antenna configuration, such as the distances of satellite/gateway antenna elements and the number of antenna elements in the satellite and gateway, affect the channel capacity of LEO-MIMO. This paper reveals antenna configurations that improve the channel capacity for LEO-MIMO systems. Computer simulations show that the channel capacity of LEO-MIMO can achieve 4 bps/Hz at maximum higher than that of the conventional multi-beam system.

To address the challenges of road extraction in high-resolution remote sensing images, this paper presents an enhanced UNet++ road extraction method that incorporates CBAM. The original UNet++ network is referenced, and the loss function is improved by introducing a new joint loss function. The enhanced UNet++ network utilizes an attention mechanism to enhance the network’s ability to identify road features, thereby improving the accuracy of road extraction. Additionally, a new joint loss function is employed to enhance the network’s stability and further improve its road extraction capability. Experimental validation is performed on the Massachusetts roads dataset and DeepGlobal road dataset. The experimental results demonstrate that this method outperforms U-Net, SegNet, and UNet++ networks in terms of IoU, Recall, OA, and Kappa. Specifically, on the Massachusetts road dataset, the OA and Kappa values are 94.92% and 0.9202, respectively. On the DeepGlobal road dataset, the OA and Kappa values for this algorithm are 98.12% and 0.9515, respectively. The ablation experiment confirms the effectiveness of the proposed enhancements. In conclusion, this paper presents a method that effectively extracts roads from high-resolution remote sensing images, exhibits a certain level of generalization ability, and can provide valuable support for road protection and planning.

The breath rate can now be monitored remotely due to the advancements in digital stethoscope sensor technology, signal processing, and machine learning. Automatic breathing rate classification, on the other hand, provides additional benefits in medical diagnostics. In this paper, a lightweight convolutional neural network is proposed for automatic breathing rate classification utilizing the piezoresistive sensor data. In the proposed work, the raw signals from the piezoresistive sensor are pre-processed using a continuous wavelet transform to generate the corresponding images. These images are then fed into a lightweight convolutional neural network, which efficiently classifies the breathing rate into six classes based on the number of breaths per minute. Through extensive results, we show that the proposed model results in a classification accuracy of 96.40% which is higher than all the benchmark models considered in this paper. We also evaluate the performance of the proposed model using edge computing devices such as Raspberry Pi, Nvidia AGX Xavier, and Nvidia Jetson Nano.

Speech and voice disorders are highly prevalent and a significant concern, particularly among children. These disorders have a notable impact on individual’s personalities, academic performance and overall development. Early detection and intervention of these disorders surely help out and generate an effective outcome. Automatic detection of speech and voice disorders at an early stage plays a crucial role in identifying and addressing these communication challenges because of their efficiency over traditional diagnostic methods. Over time, numerous techniques were developed in the past, but some of them have become less efficient in the present scenario. Deep Learning has surfaced as a recent and significant advancement in detecting speech and voice disorders. This paper provides a systematic review of the utilization of deep learning techniques for the detection of speech and voice disorders. The review encompasses studies published from 2018 to 2023 exploring various architectures of deep learning models for capturing complex patterns in speech data. Each deep learning based speech sound disorder detection technique is discussed with critical appraisal and relevant benchmarks available for evaluation of results. This review holds significance for new researchers who are interested in exploring the field of automatic speech disorder detection as the paper concludes by discussing future directions and potential areas of improvement in automatic speech sound disorder detection using deep learning.

Data-driven methods have been shown to be suitable for the diagnosis and prognosis of the health of engineering systems. However, the training of data-driven methods usually requires a large amount of data, which is rarely available in industry. In addition, the prediction accuracy often degrades when operating or environmental conditions change or when there are similar systems with different technical characteristics. Transfer learning offers the possibility to transfer knowledge about the degradation behavior between such systems. However, there is a risk that the degradation behavior differs too much, which leads to a so-called negative transfer. Therefore, the authors argue that a similarity assessment of degradation behavior is essential. An assessment based on the operational data of systems seems particularly appropriate. In this paper, the suitability of time series similarity measures for such data-based similarity assessments is investigated. The current state of research is presented. Thereby, no studies on the similarity comparison of degradation curves of engineering systems using time series similarity measures are found. Furthermore, measures for assessing the similarity of degradation curves are identified and categorized. In a case study on filter clogging curves, similarity tests are performed using these measures to find the most similar time series. Various approaches are proposed for evaluation, two of which are used in this paper. The results show that mostly a good selection is made, with some measures performing particularly well. The work presented in this paper represents valuable groundwork for the use of time series similarity measures in transfer learning.

This paper investigates a class of sampled-data control systems in which at least one of the discrete-time zeros remains on the unit circle regardless of the sampling period. This type of zero is referred to as an “uniformly marginally stable” (UMS) zero. Utilizing this novel concept, we present necessary conditions as well as sufficient conditions for the continuous-time plant whose sampled-data model has an UMS zero. The theoretical finding of this paper newly highlights the significance of the symmetry regarding the zeros and poles of the continuous-time plant, as it plays a vital role in enforcing a discrete-time zero of the sampled-data system to be UMS, for both discretization zeros and intrinsic zeros. We develop the theory for sampled-data systems employing a conventional sampler and a zero-order hold together with a single-input single-output continuous-time plant, and then observe that certain results can be extended to the generalized holds. As an application, we construct a discrete-time stable inversion-based control in the sampled-data setting and establish a condition for continuous-time plants that allow for this type of control, in which absence of such an UMS zero is crucial.

The semiconductor industry has adopted heterogeneous integration (HI), incorporating modular intellectual property (IP) blocks (chiplets) into a unified system-in-package (SiP) to overcome the slowdown in Moore’s Law and Dennard scaling and to respond to the increasing demand for advanced integrated circuits (ICs). Despite the manifold benefits of HI, such as enhanced performance, reduced area overhead, and improved yield, this transformation has also led to security vulnerabilities in the SiP supply chain and in-field operations, ranging from chiplet piracy and SiP reverse engineering (RE) to information leakage. Although conventional countermeasures provide the desired robustness for monolithic ICs, they are insufficient for addressing these challenges in the context of HI. To address these concerns, this paper presents a novel root-of-trust architecture, augmenting the process of integration using a centralized chiplet hardware security module (CHSM), aiming to provide comprehensive and robust protection throughout the SiP supply chain and in-field operations. Also, the proposed architecture equipped with the CHSM effectively addresses potential security breaches while providing robust protection against zero-day attacks through its reconfigurable capabilities. Throughout five detailed case studies, this paper performs a comprehensive security analysis to illustrate the resilience of CHSM against contemporary attack scenarios in the HI domain.

In Software-Defined Networks (SDN), the ternary content addressable memory (TCAM) capacity in switches is limited, making them vulnerable to low-rate flow table overflow attacks. Most existing research in this field has not focused on the influence of flow entry eviction mechanisms on the effectiveness of such attacks. This paper proposes an adaptive low-rate flow table overflow attack (ALFO), which can adopt corresponding attack modes under different flow entry eviction mechanisms, significantly degrading network service quality. Due to the different features of ALFO under different attack modes, the existing attack detection methods are ineffective in this attack. Therefore, this paper proposes a detection and mitigation framework, which is called adaptive low-rate flow table overflow attack guard framework (ALFO-Guard). It extracts flow features from flow entry information in the switch and aggregates them into a current-time graph model. Then, combining graph neural networks, it performs graph anomaly detection and flow entry classification to identify attack flow entries. Finally, the attack can be eliminated by deleting the identified attack flow entries and blocking the attack flows. The effectiveness of ALFO and ALFO-Guard is validated through extensive experiments, and the experimental results demonstrate that ALFO-Guard can effectively defend against ALFO.

It is undeniable that the development of robot manipulators has gained significant attention due to its numerous applications in various sectors. In order to address and resolve several issues for robotic manipulators, this paper provides a review of recent control approaches that have been conducted. In the beginning, a succinct comprehensive introduction of the pertinent terminology, configurations, components, advantages/disadvantages, and applications is given. A state-of-the-art review of the various control strategies for robotic manipulator systems is then presented, along with potential solutions when the systems are faced with various obstacles and impediments. An analytical study is discussed showing percentages of the papers discussed in this study in terms of the different control strategies, link types, models, applications, and degrees of freedom of robotic manipulators. For academic, research, medical, and industrial uses, some off-the-shelf developments in robotic manipulators are also presented.

Joint Radar and Communications (JRC) can satisfy the apparent demand for applications based on object detection, tracking, ranging, and positioning. JRC is, therefore, often seen as candidate technology for 6G mobile systems. Implementing JRC will require novel approaches in many research and engineering fields, including protocol design, digital and analog signal processing, and hardware development. The ongoing debates on JRC already include many white papers and research articles ranging in content from very specific technical problems to comprehensive bird’s eye-level reviews. This paper represents the work within the Open6GHub research project in Germany, which aims to investigate and implement potential end-to-end solutions for 6G. In this framework, we propose a consolidated vision for potential JRC architectural approaches. The subsequent discussion on integrating radar sensing with communications highlights this technology’s state-of-the-art and presents relevant opportunities and challenges.

Deep learning has significantly transformed face recognition, enabling the deployment of large-scale, state-of-the-art solutions worldwide. However, the widespread adoption of deep neural networks (DNNs) and the rise of Machine Learning as a Service emphasize the need for secure DNNs. This paper revisits the face recognition threat model in the context of DNN ubiquity and the common practice of outsourcing their training and hosting to third-parties. Here, we identify backdoor attacks as a significant threat to modern DNN-based face recognition systems (FRS). Backdoor attacks involve an attacker manipulating a DNN’s training or deployment, injecting it with a stealthy and malicious behavior. Once the DNN has entered its inference stage, the attacker may activate the backdoor and compromise the DNN’s intended functionality. Given the critical nature of this threat to DNN-based FRS, our paper comprehensively surveys the literature of backdoor attacks and defenses previously demonstrated on FRS DNNs. As a last point, we highlight potential vulnerabilities and unexplored areas in FRS security.

This paper proposes a design principle for reactance compensation circuit for multiple-input multiple-output (MIMO) inductive power transfer (IPT) where the primary side employs a coil array to send power to a receiving coil array located at the secondary side. Delivering wireless power over multiple streams, MIMO topology is an effective approach to relax strict burdens on system components in high-power IPT applications. An important requirement in MIMO topology is a method to mitigate cross-coupling inside the coil arrays so that load-independent constant-current and constant-voltage can be realized in the same fashion as conventional single-input single-output IPT. Based on transfer matrix analysis, this paper proposes a design principle indicating that passive compensation circuit should implement the negative of the cross-coupling’s reactance matrix to cancel it in the reactance domain. The proposed design is independent of the transmission distance, applicable to MIMO IPT systems at arbitrary scale and does not require the coil arrays to employ any decoupling structure. Full-wave electromagnetic simulations with $3\times 2$ MIMO and experiments with $2\times 2$ MIMO are provided to verify effectiveness and feasibility of the proposed scheme.

Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning (DL) techniques. This review comprehensively outlines techniques and methods best suited for rare event detection across various modalities, while also highlighting future research prospects. To the extent of our knowledge, this paper is a pioneering SR dedicated to exploring this specific research domain. This SR identifies the employed methods and techniques, the datasets utilized, and the effectiveness of these methods in detecting rare events. Four modalities concerning RED are reviewed in this SR: video, sound, image, and time series. The corresponding performances for the different ML and DL techniques for RED are discussed comprehensively, together with the associated RED challenges and limitations as well as the directions for future research are highlighted. This SR aims to offer a comprehensive overview of the existing methods in RED, serving as a valuable resource for researchers and practitioners working in the respective field.

Neural cellular automata have been proven effective in simulating morphogenetic processes. Developing such automata has been applied in 2D and 3D processes related to creating and regenerating complex structures and enabling their behaviors. However, neural cellular automata are inherently uncontrollable after the training process. Starting from a neural cellular automaton trained to generate a given shape from one living cell, this paper aims to gain insight into the behavior of the automaton, and to analyze the influence of the different image characteristics on the training and stabilization process and its shortcomings in different scenarios. For each considered shape, the automaton is trained on one RGB image of size $72 \times 72$ pixels containing the shape on an uniform white background, in which each pixel represents a cell. The evolution of the automaton starts from one living cell, employing a shallow neural network for the update rule, followed by backpropagation after a variable number of evolutionary steps. We studied the behavior of the automaton and the way in which different components like symmetry, orientation and colours of the shape influence its growth and alteration after a number of epochs and discussed this thoroughly in the experimental section of the paper. We further discuss a pooling strategy, used to stabilize the model and illustrate the influence of this pooling on the training process. The benefits of this strategy are compared to the original model and the behavior of the automaton during its evolution is studied in detail. Finally, we compare the results of models using different filters in the first stage of feature selection. The main results of our study are the insights gained into how the neural cellular automaton works, what it is actually learning, and what influence this learning, as there are observable result differences depending on the characteristics of the input images and the filters used in the model.

With the increasing frequency and sophistication of network attacks, network administrators are facing tremendous challenges in making fast and optimum decisions during critical situations. The ability to effectively respond to intrusions requires solving a multi-objective decision-making problem. While several research studies have been conducted to address this issue, the development of a reliable and automated Intrusion Response System (IRS) remains unattainable. This paper provides a Systematic Mapping Study (SMS) for IRS, aiming to investigate the existing studies, their limitations, and future directions in this field. A novel semi-automated research methodology is developed to identify and summarize related works. The innovative approach not only streamlines the process of literature review in the IRS field but also has the potential to be adapted and implemented across a variety of research fields. As a result of this methodology, 287 papers related to the IRS were identified from a pool of 6143 studies extracted by the developed web robot based on initial keywords. This highlights its effectiveness in navigating and extracting valuable insights from the extensive body of literature. Furthermore, this research methodology allows the identification of prominent researchers, journals, conferences, and high-quality papers in the field of study.

This paper introduces a first-order method for solving optimal powered descent guidance (PDG) problems, that directly handles the nonconvex constraints associated with the maximum and minimum thrust bounds with varying mass and the pointing angle constraints on thrust vectors. This issue has been conventionally circumvented via lossless convexification (LCvx), which lifts a nonconvex feasible set to a higher-dimensional convex set, and via linear approximation of another nonconvex feasible set defined by exponential functions. However, this approach sometimes results in an infeasible solution when the solution obtained from the higher-dimensional space is projected back to the original space, especially when the problem involves a nonoptimal time of flight. Additionally, the Taylor series approximation introduces an approximation error that grows with both flight time and deviation from the reference trajectory. In this paper, we introduce a first-order approach that makes use of orthogonal projections onto nonconvex sets, allowing expansive projection (ExProj). We show that 1) this approach produces a feasible solution with better performance even for the nonoptimal time of flight cases for which conventional techniques fail to generate achievable trajectories and 2) the proposed method compensates for the linearization error that arises from Taylor series approximation, thus generating a superior guidance solution with less fuel consumption. We provide numerical examples featuring quantitative assessments to elucidate the effectiveness of the proposed methodology, particularly in terms of fuel consumption and flight time. Our analysis substantiates the assertion that the proposed approach affords enhanced flexibility in devising viable trajectories for a diverse array of planetary soft landing scenarios.

The evolution of wireless communication has led to the introduction of IEEE 802.11be, a leading-edge wireless standard also referred to as Extremely High Throughput (EHT) or Wi-Fi 7. Multi-Link Operation (MLO) is the most innovative and disruptive technology proposed in this amendment, which holds the potential to revolutionize wireless networks. MLO aims to increase throughput through link aggregation, enhance reliability through redundant frame transmission over multiple links, and decrease latency by utilizing the earliest available link. However, to the best of our knowledge, there is no specific survey or review paper is dedicated solely to MLO, despite the extensive research conducted on Wi-Fi 7. Thus, this survey paper will bridge the gap by presenting an exhaustive overview of cutting-edge research spanning from 2010 to 2023, exclusively focused on MLO characteristics, current status and future directions. This study has discovered several open issues, including backoff count overflow, channel selection, load balancing and channel access fairness. These findings lay the groundwork for future research that has the potential to impact the current status of wireless communication significantly.

Unmanned Aerial Vehicles (UAVs) are aerial vehicles that can go to a particular position without human control or with remote human control. It is because unmanned control mainly relies on position estimation that a lot of research has been studied on GPS spoofing attacks. Detection methods against GPS spoofing attacks mainly include monitoring RF signals or IMU sensor values inside UAVs. In this work, we analyze GPS attack and detection in an advanced autopilot system, PX4 without excluding RF detection. Recent studies have shown that GPS spoofing is unable to evade the detection using EKF sensor fusion. Therefore, this paper experiment whether the detection could be evaded by strengthening the attacker model. This paper classifies attacker models according to whether an attacker knows the true position of UAVs or the estimated position by UAV and proposed attack methods depending on each model in PX4. We inject GPS value which our attack method intends to UAV during mission flight in simulation environment. By manipulating the GPS driver code of PX4 controller, we inject GPS value the attack method wants into UAV in physical environment. Finally, we observed the innovation test ratio during GPS spoofing and demonstrate that GPS spoofing attack is possible keeping the innovation test ratio under the anomaly detection criterion. After that, we proposed our approach which scales the EKF’s Kalman gain randomly to increase the detection method’s efficiency and experiment with the attack methods. This detection method has little effect on the test ratio without the attack. But during the attack, the innovation test ratio was increased higher than the anomaly detection criterion so that the attack could be detected.

Chromosome karyotyping is an important yet labor-intensive procedure for diagnosing genetic diseases. Automating such a procedure drastically reduces the manual work of cytologists and increases congenital disease diagnosing precision. Researchers have contributed to chromosome segmentation and classification for decades. However, very few studies integrate the two tasks as a unified, fully automatic procedure or achieved a promising performance. This paper addresses the gap by presenting: 1) A novel chromosome segmentation module named ChrRender, with the idea of rendering the chromosome instances by combining rich global features from the backbone and coarse mask prediction from Mask R-CNN; 2) A devised chromosome classification module named ChrNet4 that pays more attention to channel-wise dependencies from aggregated informative features and calibrating the channel interdependence; 3) An integrated Render-Attention-Architecture to accomplish fully automatic karyotyping with segmentation and classification modules; 4) A strategy for eliminating differences between training data and segmentation output data to be classified. These proposed methods are implemented in three ways on the public Q-band BioImLab dataset and a G-band private dataset. The results indicate promising performance: 1) on the joint karyotyping task, which predicts a karyotype image by first segmenting an original microscopical image, then classifying each segmentation output with a precision of 89.75% and 94.22% on the BioImLab and private dataset, respectively; 2) On the separate task with two datasets, ChrRender obtained AP50 of 96.652% and 96.809% for segmentation, ChrNet4 achieved 95.24% and 94.07% for classification, respectively. The COCO format annotation files of BioImLab used in this paper are available at https://github.com/Alex17swim/BioImLab The study introduces an integrated workflow to predict a karyotyping image from a Microscopical Chromosome Image. With state-of-the-art performance on a public dataset, the proposed Render-Attention-Architecture has accomplished fully automatic chromosome karyotyping.

Metasurfaces can achieve multifunctional wavefront control, but most co-polarized metasurfaces can only achieve a 360° phase coverage at specific frequency points. In this paper, we propose a metasurface with efficient co-polarized Terahertz (THz) wave transmission and full 360° phase coverage over a wide band. The proposed metasurface consists of five metal layers and four dielectric layers, which can be equivalent to two cascade polarization converters to form a Fabry-Perot resonator. The forward incident y-polarized THz wave can transform into the same polarized transmission wave after transmitting the proposed metasurface. Electromagnetic waves interfere repeatedly inside the multilayer metasurface, resulting a high transmission efficiency; furthermore, by changing the structure parameters of the middle two metal layers, a full 360° phase coverage in the broadband is achieved. Wideband THz focus metalens, orbital angular momentum generator and holographic imaging are realized by properly arranging the metasurface unit cells. Simulation results show that the designed multifunctional metasurface can achieve good wavefront control over the band 0.7-1.0 THz. This paper provides a new idea for the design of broadband co-polarized metasurfaces, and has great potential value in communication, imaging, remote sensing and other fields.

Frequency-modulated continuous-wave (FMCW) radars are becoming increasingly important in industrial applications due to their low cost, simple signal processing, and high resolution. The research is driven by the growing trend of radar applications in industrial settings, including tasks like autonomous robot navigation, radar-based monitoring of humans, and enhancing safety across various scenarios, such as smart factories, traffic control, or environmental monitoring. The network of distributed sensor nodes is a feature to be implemented by the sixth generation of wireless mobile networks. As a result, 6G will become an enabler technology that will develop even further use cases and advance the expansion of these technologies. Therefore, the mutual interference of these sensor systems is becoming highly relevant since they can be avoided by scheduling the individual resources of the sensor nodes by a network structure. This paper investigates the interference behavior of FMCW radar sensors in a dense indoor scenario. In these environments, in particular, the reliability of the sensor system plays an important role, and disruption can have critical consequences like not recognizing a target, triggering a false alarm, or the complete failure of the sensing. To correctly classify the expected interference in the overall context, the paper investigates the physical layer effects on the performance, such as multipath propagation, channel spread, delay, Doppler shift, and attenuation. By evaluating these effects using state-of-the-art signal processing, it was revealed that a high channel spread in particular and the dynamics of the radar channel can significantly reduce the detection quality, as the interference power can exceed the received power by up to 20 dB, depending on the scenario. The simulation results given in this paper show that a potential communication between the radars and compliance with a synchronization delay, the interference problems can be reliably prevented with an appropriately designed anti-aliasing filter. Our simulations have quantified the detection performance under interference through the entire signal chain by evaluating the physical layer, the radar signal processing with non-idealities, and the hardware. The paper has investigated interference probability in indoor environments by examining radars with uncoordinated transmissions. Integrating findings from the physical propagation characteristics, the deviations in chirp durations, and the frequency of interference cases offers insights into the potential risk of interference.

Improving operational efficiency and reducing energy consumption are common objectives for all ports. In previous research by authors, a new high operation platform mode was proposed. It had been proven to reduce energy consumption and be economically feasible. On this basis, this paper studied the influence of high operation platform mode on automated container terminal (ACT) efficiency. This paper took U-shaped ACT as the research object, put forward the operation characteristics of U-shaped ACT under high operation platform mode, and constructed the operation time model of high and low operation platform mode respectively. This paper took 30 containers at the same bay as an example to calculate and verify. The computational results shows that when $V_{v}^{1}/V_{h}^{1}$ is above the $V_{v}^{1}/V_{h}^{1}-\alpha $ curve, the operation time of high platform mode is shorter, and the operation efficiency of ACT is higher ( $V_{v}^{1}$ represents the speed of spreader with container vertically moving; $V_{h}^{1}$ represents the speed of spreader with container horizontally moving.); when $V_{v}^{1}/V_{h}^{1}$ is under the $V_{v}^{1}/V_{h}^{1}-\alpha $ curve, the shortening time of quay cranes operation is greater than the increasing time of yard cranes operation, so the overall operational efficiency of ACT is still improved. Therefore, high operation platform mode can improve efficiency of ACT.

The Smart Grid is a modern power grid that relies on advanced technologies to provide reliable and sustainable electricity. However, its integration with various communication technologies and IoT devices makes it vulnerable to cyber-attacks. Such attacks can lead to significant damage, economic losses, and public safety hazards. To ensure the security of the smart grid, increasingly strong security solutions are needed. This paper provides a comprehensive analysis of the vulnerabilities of the smart grid and the different approaches for detecting cyber-attacks. It examines the different vulnerabilities of the smart grid, including system vulnerabilities and cyber-attacks, and discusses the vulnerabilities of all its elements. The paper also investigates various approaches for detecting cyber-attacks, including rule-based, signature-based, anomaly detection, and ma-chine learning-based methods, with a focus on their effectiveness and related research. Finally, prospective cybersecurity approaches for the smart grid, such as AI approaches and blockchain, are discussed along with the challenges and future prospects of cyberattacks on the smart grid. The paper’s findings can help policymakers and stakeholders make informed decisions about the security of the smart grid and develop effective strategies to protect it from cyber-attacks.

To address the limitation of traditional surprise boxes that only support single or fixed quantity customization, this paper proposed a new method: utilizing comparators to detect cascaded quantities for specific customization. This innovative approach aims to simplify the customization process of surprise boxes while avoiding the complexity of using micro-controllers for quantity determination. A dual comparator is employed to compare the results that are greater or smaller than the set quantity. Then the output of the dual comparator is connected to an AND gate circuit, triggering the hidden function of the central surprise box when the AND gate generates high levels. The system design involves setting the number of surprise boxes and connecting upper and lower limit comparators to the current surprise box connection signal. By selecting the size of the cascade resistor, the number of surprise boxes can be determined. This paper provides step-by-step instructions for calculating the cascade resistor. Furthermore, the proposed method’s reliability in determining the number of surprise boxes is demonstrated by setting the number of secondary surprise boxes to seven and four, with the allowed resistance error 6.4% and 11%, respectively. The results indicate that a higher number of connected surprise boxes leads to a smaller range of cascaded resistors and higher accuracy requirements. Through these case studies, it has been verified that the design using dual comparators can achieve the desired quantity detection, and the detection circuit exhibits a high degree of redundancy in resistance value error, resulting in increased reliability of the detection results.

This paper introduces the perpendicular parking algorithm of car-like robots, such that the generated path consists of a cusp. This path planning is based on the car’s turning radii, which can be determined by the car’s geometry and its maximum steering angle. As far as we know, this paper is novel in developing autonomous perpendicular parking based on circular arc and straight line segments, such that a cusp on the generated path is allowed. Since a cusp is allowed, the proposed parking approach is suitable for parking in a small space. The simulation results show the validity of the proposed approach.