Implementation of several wireless applications such as radar systems and source localization is possible with direction of arrival (DOA) estimation, an array signal processing technique. In the past, we proposed a DOA estimation method using deep neural networks (DNNs), which presented very good performance compared to the traditional root multiple signal classification (root-MUSIC) algorithm when the number of radio wave sources is two. However, once three radio wave sources are considered, the performance of that proposed DNN decays especially at low and high signal-to-noise ratios (SNRs). In this paper, mainly focusing on the case of three sources, we present two additional strategies based on our previous method and capable of dealing with each SNR region. The first, which supports DOA estimation at low SNRs, is a scheme that makes use of principal component analysis (PCA). By representing the DNN input data in a lower dimension with PCA, it is believed that the noise corrupting the data is greatly reduced, which leads to improved performance at such SNRs. The second, which supports DOA estimation at high SNRs, is a scheme where several DNNs specialized in radio waves with close DOA are accordingly selected to produce a more reliable angular spectrum grid in such circumstances. Finally, in order to merge both ideas together, we use our previously proposed SNR estimation technique, with which appropriate selection between the two schemes mentioned above is performed. We have verified the superiority of our methods over root-MUSIC and our previous technique through computer simulation when the number of sources is three. In addition, brief discussion on the performance of these proposed methods for the case of higher number of sources is also given.

Radio Frequency Identification (RFID) is one of the key technologies of the Internet of Things. However, during its application, it faces a huge challenge of co-frequency interference cancellation, that is, the tag collision problem. The multi-tag anti-collision problem is modeled as a Blind Source Separation (BSS) problem from the perspective of system communication transmission layer signal processing. In order to reduce the cost of the reader antenna, this paper uses the boundedness of the tag communication signal to propose an underdetermined RFID tag anti-collision method based on Bounded Component Analysis (BCA). This algorithm converts the underdetermined tag into the signal collision model is combined with the BCA mechanism. Verification analysis was conducted using simulation data. The experimental results show that compared with the nonnegative matrix factorization (NMF) algorithm based on minimum correlation and minimum volume constraints, the bounded component analysis method proposed in this article can perform better. Solving the underdetermined collision problem greatly improves the effect of eliminating co-channel interference of tag signals, improves the system bit error rate performance, and reduces the complexity of the underdetermined model system.

Sign constraints are a handy representation of domain-specific prior knowledge that can be incorporated to machine learning. This paper presents new stochastic dual coordinate ascent (SDCA) algorithms that find the minimizer of the empirical risk under the sign constraints. Generic surrogate loss functions can be plugged into the proposed algorithm with the strong convergence guarantee inherited from the vanilla SDCA. The prediction performance is demonstrated on the classification task for microbiological water quality analysis.

Transcatheter renal denervation (RDN) is a treatment for resistant hypertension, which is performed by ablating the renal nerves located outside the artery using a catheter from inside the artery. Our previous studies simulated the temperature during RDN by using constant physical properties of biological tissue to validate the various catheter RDN devices. Some other studies report temperature dependence of physical properties of biological tissues. However, there are no studies that have measured the electrical properties of low water content tissues. Adipose tissue, a type of low water content tissue, is related to RDN closely. Therefore, it is important to know the temperature dependence of the electrical constants of adipose tissue. In this study, we measured the relationship between the electrical constants and the temperature of bovine adipose tissue. Next, the obtained equation of the relationship between relative permittivity of adipose tissue and temperature was introduced. In addition, the temperature dependence of the electrical constants of high water content tissues and the temperature dependence of the thermal constants of biological tissues were also introduced into the temperature analysis. After 180 seconds of heating, the temperature of the model with the temperature dependence of the physical properties was 7.25°C lower than the model without the temperature dependence of the physical properties at a certain position. From the results, it can be said that the temperature dependence of physical properties will be significant when an accurate temperature analysis is required.

In order to support a person’s various activities (e.g., primary industry, elderly care), an IoT (Internet of Things) system supporting sensing technologies that observe the status of various people, things, and places in the real world is attracting attention. The existing studies have utilized the camera device for the sensing technology to observe the condition of the real world, but the use of the camera device has problems related to the limitation of the observation period and the invasion of privacy. Therefore, new IoT systems utilizing three-dimensional LiDAR (Light Detection And Ranging) have been proposed because they can obtain three-dimensional spatial information about the real world by solving problems. However, several problems exist with the use of 3D LiDAR in the deployment on the real fields. The 3D LiDAR requires much electric power for observing the three-dimensional spatial information. In addition, the annotation process of a large volume of point-cloud data for constructing a machine-learning model requires significant time and effort. Therefore, in this study, we propose IoT systems utilizing 3D LiDAR for observing the status of targets and equipping them with new technologies to improve the practicality of the use of 3D LiDAR. First, a linkage function is designed to achieve power saving for an entire system. The linkage function operates only a sensing technology with low power consumption during normal operation and activates the 3D LiDAR with high power consumption only when it is estimated that an observation target is approaching. Second, a self-learning function is built to analyze the data collected by not only the 3D LiDAR but also the camera for automatically generating a large amount of training data with the correct label which is estimated by analyzing the camera images. Through the experimental evaluations using a prototype system, it is confirmed that the sensing technologies can correctly be interconnected to reduce the total power consumption. In addition, the machine-learning model constructed by the self-learning function can accurately estimate the status of the targets.

Accurately simulating physical systems is essential in various fields. In recent years, deep learning has been used to automatically build models of such systems by learning from data. One such method is the neural ordinary differential equation (neural ODE), which treats the output of a neural network as the time derivative of the system states. However, while this and related methods have shown promise, their training strategies still require further development. Inspired by error analysis techniques in numerical analysis while replacing numerical errors with modeling errors, we propose the error-analytic strategy to address this issue. Therefore, our strategy can capture long-term errors and thus improve the accuracy of long-term predictions.

Renal Denervation (RDN) has been developed as a potential treatment for hypertension that is resistant to traditional antihypertensive medication. This technique involves the ablation of nerve fibers around the renal artery from inside the blood vessel, which is intended to suppress sympathetic nerve activity and result in an antihypertensive effect. Currently, clinical investigation is underway to evaluate the effectiveness of RDN in treating treatment-resistant hypertension. Although radio frequency (RF) ablation catheters are commonly used, their heating capacity is limited. Microwave catheters are being considered as another option for RDN. We aim to solve the technical challenges of applying microwave catheters to RDN. In this paper, we designed a catheter with a helix structure and a microwave (2.45 GHz) antenna. The antenna is a coaxial slot antenna, the dimensions of which were determined by optimizing the reflection coefficient through simulation. The measured catheter reflection coefficient is -23.6 dB using egg white and -32 dB in the renal artery. The prototype catheter was evaluated by in vitro experiments to validate the simulation. The procedure performed successfully with in vivo experiments involving the ablation of porcine renal arteries. The pathological evaluation confirmed that a large area of the perivascular tissue was ablated (> 5 mm) in a single quadrant without significant damage to the renal artery. Our proposed device allows for control of the ablation position and produces deep nerve ablation without overheating the intima or surrounding blood, suggesting a highly capable new denervation catheter.

This paper introduces a computational approach for transient analysis of extensive scattering problems. This novel method is based on the combination of physical optics (PO) and the fast inverse Laplace transform (FILT). PO is a technique for analyzing electromagnetic scattering from large-scale objects. We modify PO for application in the complex frequency domain, where the scattered fields are evaluated. The complex frequency function is efficiently transformed into the time domain using FILT. The effectiveness of this combination is demonstrated through large-scale analysis and transient response for a short pulse incidence. The accuracy is investigated and validated by comparison with reference solutions.

This paper proposes an anomaly-detection method using the Fast xFlow Proxy, which enables fine-grained measurement of communication traffic. When a fault occurs in services or networks, communication traffic changes from its normal behavior. Therefore, anomalies can be detected by analyzing their autocorrelations. However, in large-scale carrier networks, packets are generally encapsulated and observed as aggregate values, making it difficult to detect minute changes in individual communication flows. Therefore, we developed the Fast xFlow Proxy, which analyzes encapsulated packets in real time and enables flows to be measured at an arbitrary granularity. In this paper, we propose an algorithm that utilizes the Fast xFlow Proxy to detect not only the anomaly occurrence but also its cause, that is, the location of the fault at the end-to-end. The idea is not only to analyze the autocorrelation of a specific flow but also to apply spatial analysis to estimate the fault location by comparing the behavior of multiple flows. Through extensive simulations, we demonstrate that base station, network, and service faults can be detected without any false negative detections.

This paper presents a comprehensive design approach to load-independent radio frequency (RF) power amplifiers. We project the zero-voltage-switching (ZVS) and zero-voltage-derivative-switching (ZVDS) load impedances onto a Smith chart, and find that their loci exhibit geodesic arcs. We exploit a two-port reactive network to convert the geodesic locus into another geodesic. This is named geodesic-to-geodesic (G2G) impedance conversion, and the power amplifier that employs G2G conversion is called class-G2G amplifier. We comprehensively explore the possible circuit topologies, and find that there are twenty G2G networks to create class-G2G amplifiers. We also find out that the class-G2G amplifier behaves like a transformer or a gyrator converting from dc to RF. The G2G design theory is verified via a circuit simulation. We also verified the theory through an experiment employing a prototype 100 W amplifier at 6.78 MHz. We conclude that the presented design approach is quite comprehensive and useful for the future development of high-efficiency RF power amplifiers.

This article describes the idea of utilizing Attested Execution Secure Processors (AESPs) that fit into building a secure Self-Sovereign Identity (SSI) system satisfying Sybil-resistance under permissionless blockchains. Today’s circumstances requiring people to be more online have encouraged us to address digital identity preserving privacy. There is a momentum of research addressing SSI, and many researchers approach blockchain technology as a foundation. SSI brings natural persons various benefits such as owning controls; on the other side, digital identity systems in the real world require Sybil-resistance to comply with Anti-Money-Laundering (AML) and other needs. The main idea in our proposal is to utilize AESPs for three reasons: first is the use of attested execution capability along with tamper-resistance, which is a strong assumption; second is powerfulness and flexibility, allowing various open-source programs to be executed within a secure enclave, and the third is that equipping hardware-assisted security in mobile devices has become a norm. Rafael Pass et al.’s formal abstraction of AESPs and the ideal functionality $\color{brown}{\mathcal{G}_\mathtt{att}}$ enable us to formulate how hardware-assisted security works for secure digital identity systems preserving privacy under permissionless blockchains mathematically. Our proposal of the AESP-based SSI architecture and system protocols, $\color{blue}{\Pi^{\mathcal{G}_\mathtt{att}}}$, demonstrates the advantages of building a proper SSI system that satisfies the Sybil-resistant requirement. The protocols may eliminate the online distributed committee assumed in other research, such as CanDID, because of assuming AESPs; thus, $\color{blue}{\Pi^{\mathcal{G}_\mathtt{att}}}$ allows not to rely on multi-party computation (MPC), bringing drastic flexibility and efficiency compared with the existing SSI systems.

2D and 3D semantic segmentation play important roles in robotic scene understanding. However, current 3D semantic segmentation heavily relies on 3D point clouds, which are susceptible to factors such as point cloud noise, sparsity, estimation and reconstruction errors, and data imbalance. In this paper, a novel approach is proposed to enhance 3D semantic segmentation by incorporating 2D semantic segmentation from RGB-D sequences. Firstly, the RGB-D pairs are consistently segmented into 2D semantic maps using the tracking pipeline of Simultaneous Localization and Mapping (SLAM). This process effectively propagates object labels from full scans to corresponding labels in partial views with high probability. Subsequently, a novel Semantic Projection (SP) block is introduced, which integrates features extracted from localized 2D fragments across different camera viewpoints into their corresponding 3D semantic features. Lastly, the 3D semantic segmentation network utilizes a combination of 2D-3D fusion features to facilitate a merged semantic segmentation process for both 2D and 3D. Extensive experiments conducted on public datasets demonstrate the effective performance of the proposed 2D-assisted 3D semantic segmentation method.

Existing weakly-supervised segmentation approaches based on image-level annotations may focus on the most activated region in the image and tend to identify only part of the target object. Intuitively, high-level semantics among objects of the same category in different images could help to recognize corresponding activated regions of the query. In this study, a scheme called Cycle-Consistency of Semantics Network (CyCSNet) is proposed, which can enhance the activation of the potential inactive regions of the target object by utilizing the cycle-consistent semantics from images of the same category in the training set. Moreover, a Dynamic Correlation Feature Selection (DCFS) algorithm is derived to reduce the noise from pixel-wise samples of low relevance for better training. Experiments on the PASCAL VOC 2012 dataset show that the proposed CyCSNet achieves competitive results compared with state-of-the-art weakly-supervised segmentation approaches.

At Crypto 2019, Gohr first adopted the neural distinguisher for differential cryptanalysis, and since then, this work received increasing attention. However, most of the existing work focuses on improving and applying the neural distinguisher, the studies delving into the intrinsic principles of neural distinguishers are finite. At Eurocrypt 2021, Benamira et al. conducted a study on Gohr’s neural distinguisher. But for the neural distinguishers proposed later, such as the r-round neural distinguishers trained with k ciphertext pairs or ciphertext differences, denoted as NDcpk_r (Gohr’s neural distinguisher is the special NDcpk_r with K = 1) and NDcdk_r , such research is lacking. In this work, we devote ourselves to study the intrinsic principles and relationship between NDcdk_r and NDcpk_r. Firstly, we explore the working principle of NDcd1_r through a series of experiments and find that it strongly relies on the probability distribution of ciphertext differences. Its operational mechanism bears a strong resemblance to that of NDcp1_r given by Benamira et al.. Therefore, we further compare them from the perspective of differential cryptanalysis and sample features, demonstrating the superior performance of NDcp1_r can be attributed to the relationships between certain ciphertext bits, especially the significant bits. We then extend our investigation to NDcpk_r, and show that its ability to recognize samples heavily relies on the average differential probability of k ciphertext pairs and some relationships in the ciphertext itself, but the reliance between k ciphertext pairs is very weak. Finally, in light of the findings of our research, we introduce a strategy to enhance the accuracy of the neural distinguisher by using a fixed difference to generate the negative samples instead of the random one. Through the implementation of this approach, we manage to improve the accuracy of the neural distinguishers by approximately 2% to 8% for 7-round Speck32/64 and 9-round Simon32/64.

The design of automobile lamps requires accurate estimation of heat distribution to prevent overheating and deformation of the product. Traditional heat resistant analysis using Computational Fluid Dynamics (CFD) is time-consuming and requires expertise in thermofluid mechanics, making real-time temperature analysis less accessible to lamp designers. We propose a machine learning-based temperature prediction system for automobile lamp design. We trained our machine learning models using CFD results of various lamp designs, providing lamp designers real-time Heat-Resistant Analysis. Comprehensive tests on real lamp products demonstrate that our prediction model accurately estimates heat distribution comparable to CFD analysis within a minute. Our system visualizes the estimated heat distribution of car lamp design supporting quick decision-making by lamp designer. It is expected to shorten the product design process, improving the market competitiveness.

To achieve precise measurement without landing, the high-mobility vehicle-mounted theodolite needs to be leveled quickly with high precision and ensure sufficient support stability before work. After the measurement, it is also necessary to ensure that the high-mobility vehicle-mounted theodolite can be quickly withdrawn. Therefore, this paper proposes a hierarchical automatic leveling strategy and establishes a two-stage electromechanical automatic leveling mechanism model. Using coarse leveling of the first-stage automatic leveling mechanism and fine leveling of the second-stage automatic leveling mechanism, the model realizes high-precision and fast leveling of the vehicle-mounted theodolites. Then, the leveling control method based on repeated positioning is proposed for the first-stage automatic leveling mechanism. To realize the rapid withdrawal for high-mobility vehicle-mounted theodolites, the method ensures the coincidence of spatial movement paths when the structural parts are unfolded and withdrawn. Next, the leg static balance equation is constructed in the leveling state, and the support force detection method is discussed in realizing the stable support for vehicle-mounted theodolites. Furthermore, a mathematical model for “false leg” detection is established furtherly, and a “false leg” detection scheme based on the support force detection method is analyzed to significantly improve the support stability of vehicle-mounted theodolites. Finally, an experimental platform is constructed to perform the performance test for automatic leveling mechanisms. The experimental results show that the leveling accuracy of established two-stage electromechanical automatic leveling mechanism can reach 3.6″, and the leveling time is no more than 2 mins. The maximum support force error of the support force detection method is less than 15%, and the average support force error is less than 10%. In contrast, the maximum support force error of the drive motor torque detection method reaches 80.12%, and its leg support stability is much less than the support force detection method. The model and analysis method proposed in this paper can also be used for vehicle-mounted radar, vehicle-mounted laser measurement devices, vehicle-mounted artillery launchers and other types of vehicle-mounted equipment with high-precision and high-mobility working requirements.

This paper proposes a determination method of the cascaded number for lumped parameter models (LPMs) of the transmission lines. The LPM is used to simulate long-distance transmission lines, and the cascaded number significantly impacts the simulation results. Currently, there is a lack of a system-level determination method of the cascaded number for LPMs. Based on the theoretical analysis and eigenvalue decomposition of network matrix, this paper discusses the error in resonance characteristics between distributed parameter model and LPMs. Moreover, it is deduced that optimal cascaded numbers of the cascaded π-type and T-type LPMs are the same, and the Γ-type LPM has a lowest analog accuracy. The principle that the maximum simulation frequency is less than the first resonance frequency of each segment is presented. According to the principle, optimal cascaded numbers of cascaded π-type, T-type, and Γ-type LPMs are obtained. The effectiveness of the proposed determination method is verified by simulation.

The prediction of peak power load is a critical factor directly impacting the stability of power supply, characterized significantly by its time series nature and intricate ties to the seasonal patterns in electricity usage. Despite its crucial importance, the current landscape of power peak load forecasting remains a multifaceted challenge in the field. This study aims to contribute to this domain by proposing a method that leverages a combination of three primary models - the GRU model, self-attention mechanism, and Transformer mechanism - to forecast peak power load. To contextualize this research within the ongoing discourse, it’s essential to consider the evolving methodologies and advancements in power peak load forecasting. By delving into additional references addressing the complexities and current state of the power peak load forecasting problem, this study aims to build upon the existing knowledge base and offer insights into contemporary challenges and strategies adopted within the field. Data preprocessing in this study involves comprehensive cleaning, standardization, and the design of relevant functions to ensure robustness in the predictive modeling process. Additionally, recognizing the necessity to capture temporal changes effectively, this research incorporates features such as “Weekly Moving Average” and “Monthly Moving Average” into the dataset. To evaluate the proposed methodologies comprehensively, this study conducts comparative analyses with established models such as LSTM, Self-attention network, Transformer, ARIMA, and SVR. The outcomes reveal that the models proposed in this study exhibit superior predictive performance compared to these established models, showcasing their effectiveness in accurately forecasting electricity consumption. The significance of this research lies in two primary contributions. Firstly, it introduces an innovative prediction method combining the GRU model, self-attention mechanism, and Transformer mechanism, aligning with the contemporary evolution of predictive modeling techniques in the field. Secondly, it introduces and emphasizes the utility of “Weekly Moving Average” and “Monthly Moving Average” methodologies, crucial in effectively capturing and interpreting seasonal variations within the dataset. By incorporating these features, this study enhances the model’s ability to account for seasonal influencing factors, thereby significantly improving the accuracy of peak power load forecasting. This contribution aligns with the ongoing efforts to refine forecasting methodologies and addresses the pertinent challenges within power peak load forecasting.

Highly conflicting evidence that may lead to the counter-intuitive results is one of the challenges for information fusion in Dempster-Shafer evidence theory. To deal with this issue, evidence conflict is investigated based on belief divergence measuring the discrepancy between evidence. In this paper, the pignistic probability transform belief χ2 divergence, named as BBχ2 divergence, is proposed. By introducing the pignistic probability transform, the proposed BBχ2 divergence can accurately quantify the difference between evidence with the consideration of multi-element sets. Compared with a few belief divergences, the novel divergence has more precision. Based on this advantageous divergence, a new multi-source information fusion method is devised. The proposed method considers both credibility weights and information volume weights to determine the overall weight of each evidence. Eventually, the proposed method is applied in target recognition and fault diagnosis, in which comparative analysis indicates that the proposed method can realize the highest accuracy for managing evidence conflict.

This paper discusses the resilience of networks based on graph theory and stochastic process. The electric power network where edges may fail simultaneously and the performance of the network is measured by the ratio of connected nodes is supposed for the target network. For the restoration, under the constraint that the resources are limited, the failed edges are repaired one by one, and the order of the repair for several failed edges is determined with the priority to the edge that the amount of increasing system performance is the largest after the completion of repair. Two types of resilience are discussed, one is resilience in the recovery stage according to the conventional definition of resilience and the other is steady state operational resilience considering the long-term operation in which the network state changes stochastically. The second represents a comprehensive capacity of resilience for a system and is analytically derived by Markov analysis. We assume that the large-scale disruption occurs due to the simultaneous failure of edges caused by the common cause failures in the analysis. Marshall-Olkin type shock model and α factor method are incorporated to model the common cause failures. Then two resilience measures, “operational resilience” and “operational resilience in recovery stage” are proposed. We also propose approximation methods to obtain these two operational resilience measures for complex networks.