We present Ksformer, utilizing Multi-scale Key-select Routing Attention (MKRA) for intelligent selection of key areas through multi-channel, multi-scale windows with a top-k operator, and Lightweight Frequency Processing Module (LFPM) to enhance high-frequency features, outperforming other dehazing methods in tests.

Escalators are an indispensable facility in public places. While they can provide convenience to people, abnormal accidents can lead to serious consequences. Yolo is a function that detects human behavior in real time. However, the model exhibits low accuracy and a high miss rate for small targets. To this end, this paper proposes the Small Target High Performance YOLO (SH-YOLO) model to detect abnormal behavior in escalators. The SH-YOLO model first enhances the backbone network through attention mechanisms. Subsequently, a small target detection layer is incorporated in order to enhance detection of key points for small objects. Finally, the conv and the SPPF are replaced with a Region Dynamic Perception Depth Separable Conv (DR-DP-Conv) and Atrous Spatial Pyramid Pooling (ASPP), respectively. The experimental results demonstrate that the proposed model is capable of accurately and robustly detecting anomalies in the real-world escalator scene.

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.

We proposed a procedure to pre-process data used in a vector autoregressive (VAR) modeling of a temporal point process by using kernel density estimation. Vector autoregressive modeling of point-process data, for example, is being used for causality inference. The VAR model discretizes the timeline into small windows, and creates a time series by the presence of events in each window, and then models the presence of an event at the next time step by its history. The problem is that to get a longer history with high temporal resolution required a large number of windows, and thus, model parameters. We proposed the local density estimation procedure, which, instead of using the binary presence as the input to the model, performed kernel density estimation of the event history, and discretized the estimation to be used as the input. This allowed us to reduce the number of model parameters, especially in sparse data. Our experiment on a sparse Poisson process showed that this procedure vastly increases model prediction performance.

White-box cryptographic implementations often use masking and shuffling as countermeasures against key extraction attacks. To counter these defenses, higher-order Differential Computation Analysis (HO-DCA) and its variants have been developed. These methods aim to breach these countermeasures without needing reverse engineering. However, these non-invasive attacks are expensive and can be thwarted by updating the masking and shuffling techniques. This paper introduces a simple binary injection attack, aptly named clear & return, designed to bypass advanced masking and shuffling defenses employed in white-box cryptography. The attack involves injecting a small amount of assembly code, which effectively disables run-time random sources. This loss of randomness exposes the unprotected lookup value within white-box implementations, making them vulnerable to simple statistical analysis. In experiments targeting open-source white-box cryptographic implementations, the attack strategy of hijacking entries in the Global Offset Table (GOT) or function calls shows effectiveness in circumventing run-time countermeasures.

With the development of the Semantic Web, an increasing number of researchers are utilizing ontology technology to construct domain ontology. Since there is no unified construction standard, ontology heterogeneity occurs. The ontology matching method can fuse heterogeneous ontologies, which realizes the interoperability between knowledge and associates to more relevant semantic information. In the case of differences between ontologies, how to reduce false matching and unsuccessful matching is a critical problem to be solved. Moreover, as the number of ontologies increases, the semantic relationship between ontologies becomes increasingly complex. Nevertheless, the current methods that solely find the similarity of names between concepts are no longer sufficient. Consequently, this paper proposes an ontology matching method based on semantic association. Accurate matching pairs are discovered by existing semantic knowledge, and then the potential semantic associations between concepts are mined according to the characteristics of the contextual structure. The matching method can better carry out matching work based on reliable knowledge. In addition, this paper introduces a probabilistic logic repair method, which can detect and repair the conflict of matching results, to enhance the availability and reliability of matching results. The experimental results show that the proposed method effectively improves the quality of matching between ontologies and saves time on repairing incorrect matching pairs. Besides, compared with the existing ontology matching systems, the proposed method has better stability.

Multi-focus image fusion involves combining partially focused images of the same scene to create an all-in-focus image. Aiming at the problems of existing multi-focus image fusion algorithms that the benchmark image is difficult to obtain and the convolutional neural network focuses too much on the local region, a fusion algorithm that combines local and global feature encoding is proposed. Initially, we devise two self-supervised image reconstruction tasks and train an encoder-decoder network through multi-task learning. Subsequently, within the encoder, we merge the dense connection module with the PS-ViT module, enabling the network to utilize local and global information during feature extraction. Finally, to enhance the overall efficiency of the model, distinct loss functions are applied to each task. To preserve the more robust features from the original images, spatial frequency is employed during the fusion stage to obtain the feature map of the fused image. Experimental results demonstrate that, in comparison to twelve other prominent algorithms, our method exhibits good fusion performance in objective evaluation. Ten of the selected twelve evaluation metrics show an improvement of more than 0.28%. Additionally, it presents superior visual effects subjectively.

Modern memory devices such as DRAM are prone to errors that occur because of unintended bit flips during their operation. Since memory errors severely impact in-memory key-value stores (KVSes), software mechanisms for hardening them against memory errors are being explored. However, it is hard to efficiently test the memory error handling code due to its characteristics: the code is event-driven, the handlers depend on the memory object, and in-memory KVSes manage various objects in huge memory space. This paper presents MemFI that supports runtime tests for the memory error handlers of in-memory KVSes. Our approach performs the software fault injection of memory errors at the memory object level to trigger the target handler while smoothly carrying out tests on the same running state. To show the effectiveness of MemFI, we integrate error handling mechanisms into a real-world in-memory KVS, memcached 1.6.9 and Redis 6.2.7, and check their behavior using the MemFI prototypes. The results show that the MemFI-based runtime test allows us to check the behavior of the error handling mechanisms. We also show its efficiency by comparing it to other fault injection approaches based on a trial model.

1 millisecond (1-ms) vision systems are gaining increasing attention in diverse fields like factory automation and robotics, as the ultra-low delay ensures seamless and timely responses. Superpixel segmentation is a pivotal preprocessing to reduce the number of image primitives for subsequent processing. Recently, there has been a growing emphasis on leveraging deep network-based algorithms to pursue superior performance and better integration into other deep network tasks. Superpixel Sampling Network (SSN) employs a deep network for feature generation and employs differentiable SLIC for superpixel generation. SSN achieves high performance with a small number of parameters. However, implementing SSN on FPGAs for ultra-low delay faces challenges due to the final layer’s aggregation of intermediate results. To address this limitation, this paper proposes an aggregated to pipelined structure for FPGA implementation. The final layer is decomposed into individual final layers for each intermediate result. This architectural adjustment eliminates the need for memory to store intermediate results. Concurrently, the proposed structure leverages decomposed layers to facilitate a pipelined structure with pixel streaming input to achieve ultra-low latency. To cooperate with the pipelined structure, layer-partitioned memory architecture is proposed. Each final layer has dedicated memory for storing superpixel center information, allowing values to be read and calculated from memory without conflicts. Calculation results of each final layer are accumulated, and the result of each pixel is obtained as the stream reaches the last layer. Evaluation results demonstrate that boundary recall and under-segmentation error remain comparable to SSN, with an average label consistency improvement of 0.035 over SSN. From a hardware performance perspective, the proposed system processes 1000 FPS images with a delay of 0.947 ms/frame.

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.

In recent years, the declining birthrate and aging population have become serious problems in Japan. To solve these problems, we have developed a system based on edge AI. This system predicts the future heart rate during walking in real time and provides feedback to improve the quality of exercise and extend healthy life expectancy. In this paper, we predicted the heart rate in real time based on the proposed system and provided feedback. Experiments were conducted without and with the predicted heart rate, and a comparison was made to demonstrate the effectiveness of the predicted heart rate.

In this paper, we propose the transient analysis technique to analyze the multilayered dispersive media by using a combination of fast inversion Laplace transform (FILT) and the continued fraction expanded methods. Numerical results are given by the reflection response, inside-time response waveforms, and electric field distributions of the reflection component. Further, we verify the calculation accuracy of FILT method for the two types using a convergence test.

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.

In this paper, a single-layer circular polarizer for linear polarized horn antenna is proposed. The multiple reflected waves between the aperture and array provide desired phase differences between vertical and horizontal polarizations. The measured gain of the fabricated antenna is 14.4 dBic and the half power beamwidths of the vertical polarization are 28 and 24 deg. and those of the horizontal polarization are 31 and 23 degrees in the vertical and horizontal planes. The polarizer has a low impact on the gain and beamwidth of the primary horn antenna and their changes are within 1.7 dB and 10 degrees. The 3 dB fractional bandwidth of the axial ratio is measured to be 1.4%.

We report on a method for reconstructing the spectrum of incident light from a single image captured by a snapshot multispectral camera. The camera has a dielectric multilayer multispectral filter array (MSFA) integrated onto a CMOS image sensor. Sparse estimation algorithm was applied to reconstruct the spectrum. Using Gaussian functions with various bandwidths and central wavelengths as the bases matrix, the algorithm has been shown to be highly accurate for estimating the spectra of both narrowband monochromatic and broadband fluorescent light emitting diodes (LEDs), regardless of the wavelength band.

Adaptive bitrate (ABR) video streaming is an important application on the Internet. To ensure that users enjoy high-quality services, ABR control mechanisms need to be designed that select chunks wisely on the basis of the available network throughput. To address the chunk selection problem, this paper describes an adaptive bitrate control mechanism that leverages long-term throughput information in the chunk selection process. While previous work has considered how quality should be requested on a per-chunk basis, the proposed method increases the timeframe of the analysis and allows higher quality of experience (QoE) to be reached. This is done by appropriately selecting a sequence of consecutive chunks’ quality values instead of a single chunk’s value. Simulation results are reported on a large variety of real-world network conditions and various throughput prediction algorithms and show the benefit of the proposed method over conventional ABR control mechanisms.

This paper studies the secrecy outage probability of transmit antenna selection (TAS) with hybrid generalized selection combining (GSC)/selection combining (SC) in amplify and forward (AF)-multiple input multiple output (MIMO) relay system. This paper derives the exact cumulative distribution (CDF) expression of the received signal to noise ratio (SNR) for TAS with hybrid GSC/SC system. Using derived CDF, this work derives the lower bound and asymptotic forms for the hybrid combining system for the secrecy outage probability. Asymptotic results shows that the proposed hybrid system provides the secrecy diversity of product of the number of antennas in the relay node and the number with the smaller number of antennas among the source node and user node. An interesting result is that the secrecy diversity order is independent of the number of combining signals and the number of eavesdroppers.

In this paper, a novel Enhanced Spatial Modulation-based Orthogonal Time Frequency Space (ESM-OTFS) is proposed to maximize the benefits of enhanced spatial modulation (ESM) and orthogonal time frequency space (OTFS) transmission. The primary objective of this novel modulation is to enhance transmission reliability, meeting the demanding requirements of high transmission rates and rapid data transfer in future wireless communication systems. The paper initially outlines the system model and specific signal processing techniques employed in ESM-OTFS. Furthermore, a novel detector based on sparse signal estimation is presented specifically for ESM-OTFS. The sparse signal estimation is performed using a fully factorized posterior approximation using Variational Bayesian Inference that leads to a low complexity solution without any matrix inversions. Simulation results indicate that ESM-OTFS surpasses traditional spatial modulation-based OTFS, and the newly introduced detection algorithm outperforms other linear detection methods.

Intelligent reflecting surface (IRS) is an effective technology to improve the energy and spectral efficiency of wireless powered communication network (WPCN). Under user cooperation, we propose an IRS-assisted WPCN system where the wireless devices (WDs) collect wireless energy in the downlink (DL) and then share data. The adjacent single-antenna WDs cooperate to form a virtual antenna array so that their information can be simultaneously transmitted to the multi-antenna common hybrid access point (HAP) through the uplink (UL) using multiple-input multiple-output (MIMO) technology. By jointly optimizing the passive beamforming at the IRS, the active beamforming in the DL and the UL, the energy consumed by data sharing, and the time allocation of each phase, we formulate an UL throughput maximization problem. However, this optimization problem is non-convex since the optimization variables are highly coupled. In this study, we apply the alternating optimization (AO) technology to decouple the optimization variables and propose an efficient algorithm to avoid the difficulty of directly solving the problem. Numerical results indicate that the joint optimization method significantly improves the UL throughput performance in multi-user WPCN compared with various baseline methods.

Phased-array technology is primarily employed in atmospheric and wind profiling radars for meteorological remote sensing. As a novel avenue of advancement in phased-array technology, the Multiple-Input Multiple-Output (MIMO) technique, originally developed for communication systems, has been applied to radar systems. A MIMO radar system can be used to create a virtual receive antenna aperture plane with transmission freedom. The MIMO technique requires orthogonal waveforms on each transmitter to identify the transmit signals using multiple receivers; various methods have been developed to realize the orthogonality. In this study, we focus on the Doppler Division Multiple Access (DDMA) MIMO technique by using slightly different frequencies for the transmit waveforms, which can be separated by different receivers in the Doppler frequency domain. The Middle and Upper atmosphere (MU) radar is a VHF-band phased array atmospheric radar with multi-channel receivers. Additional configurations are necessary, requiring the inclusion of multi-channel transmitters to enable its operation as a MIMO radar. In this study, a comparison between the brightness distribution of the beamformer, utilizing echoes reflected from the moon, and the antenna pattern obtained through calculations revealed a high degree of consistency, which means that the MU radar functions effectively as a MIMO radar. Furthermore, it is demonstrated that the simultaneous application of MIMO and Capon techniques has a mutually enhancing effect.