In software defined network (SDN), packet processing is commonly implemented using match-action model, where packets are processed based on matched actions in match action table. Due to the limited FPGA on-board resources, it is an important challenge to achieve large-scale high throughput based on exact matching (EM), while solving hash conflicts and out-of-order problems. To address these issues, this study proposed an FPGA-based EM table that leverages shared rule tables across multiple pipelines to eliminate memory replication and enhance overall throughput. An out-of-order reordering function is used to ensure packet sequencing within the pipelines. Moreover, to handle collisions and increase load factor of hash table, multiple hash table blocks are combined and an auxiliary CAM-based EM table is integrated in each pipeline. To the best of our knowledge, this is the first time that the proposed design considers the recovery of out-of-order operations in multi-channel EM table for high-speed network packets processing application. Furthermore, it is implemented on Xilinx Alveo U250 field programmable gate arrays, which has a million rules and achieves a processing speed of 200 million operations per second, theoretically enabling throughput exceeding 100 Gbps for 64-Byte size packets.

In this paper, we present a novel photometric compensation network named CASEformer, which is built upon the Swin module. For the first time, we combine coordinate attention and channel attention mechanisms to extract rich features from input images. Employing a multi-level encoder-decoder architecture with skip connections, we establish multiscale interactions between projection surfaces and projection images, achieving precise inference and compensation. Furthermore, through an attention fusion module, which simultaneously leverages both coordinate and channel information, we enhance the global context of feature maps while preserving enhanced texture coordinate details. The experimental results demonstrate the superior compensation effectiveness of our approach compared to the current state-of-the-art methods. Additionally, we propose a method for multi-surface projection compensation, further enriching our contributions.

High-performance deep learning-based object detection models can reduce traffic accidents using dashcam images during nighttime driving. Deep learning requires a large-scale dataset to obtain a high-performance model. However, existing object detection datasets are mostly daytime scenes and a few nighttime scenes. Increasing the nighttime dataset is laborious and time-consuming. In such a case, it is possible to convert daytime images to nighttime images by image-to-image translation model to augment the nighttime dataset with less effort so that the translated dataset can utilize the annotations of the daytime dataset. Therefore, in this study, a GAN-based image-to-image translation model is proposed by incorporating self-attention with cycle consistency and content/style separation for nighttime data augmentation that shows high fidelity to annotations of the daytime dataset. Experimental results highlight the effectiveness of the proposed model compared with other models in terms of translated images and FID scores. Moreover, the high fidelity of translated images to the annotations is verified by a small object detection model according to detection results and mAP. Ablation studies confirm the effectiveness of self-attention in the proposed model. As a contribution to GAN-based data augmentation, the source code of the proposed image translation model is publicly available at https://github.com/subecky/Image-Translation-With-Self-Attention

This paper presents a hippocampal cognitive prosthesis chip designed for restoring the ability to form new long-term memories due to hippocampal system damage. The system-on-chip (SOC) consists of a 16-channel micro-power low-noise amplifier (LNA), high-pass filters, analog-digital converters (ADCs), a 16-channel spike-sorter, a generalized Laguerre-Volterra model multi-input, multi-output (GLVM-MIMO) hippocampal processor, an 8-channel neural stimulator and peripheral circuits. The proposed LNA achieved a voltage gain of 50dB, input-referred noise of 3.95µVrms, and noise efficiency factor (NEF) of 3.45 with the power consumption of 3.3µW. High-pass filters with a 300-Hz bandwidth are used to filter out the unwanted local field potential (LFP). 4 12-bit successive approximation register (SAR) ADCs with a signal-to-noise-and-distortion ratio (SNDR) of 63.37dB are designed for the digitization of the neural signals. A 16-channel spike-sorter has been integrated in the chip enabling a detection accuracy of 98.3% and a classification accuracy of 93.4% with power consumption of 19µW/ch. The MIMO hippocampal model processor predict output spatio-temporal patterns in CA1 according to the recorded input spatio-temporal patterns in CA3. The neural stimulator performs bipolar, symmetrical charge-balanced stimulation with a maximum current of 310µA, triggered by the processor output. The chip has been fabricated in 40nm standard CMOS technology, occupying a silicon area of 3mm2.

To simultaneously enhance data rate and physical layer security (PLS) for low-luminance smartphone screen to camera uplink communication, space division multiplexing using high-luminance cell-size reduction arrangement is numerically analyzed and experimentally verified. The uplink consists of a low-luminance smartphone screen and an indoor telephoto camera at a long distance of 3.5 meters. The high-luminance cell-size reduction arrangement avoids the influence of spatial inter-symbol interference (ISI) and ambient light to obtain a stable low-luminance screen. To reduce the screen luminance without decreasing the screen pixel value, the arrangement reduces only the high-luminance cell area while keeping the cell spacing. In this study, two technical issues related to high-luminance cell-size reduction arrangement are solved. First, a numerical analysis and experimental results show that the high-luminance cell-size reduction arrangement is more effective in reducing the spatial ISI at low luminance than the conventional low-luminance cell arrangement. Second, in view point of PLS enhancement at wide angles, symbol error rate should be low in front of the screen and high at wide angles. A numerical analysis and experimental results show that the high-luminance cell-size reduction arrangement is more suitable for enhancing PLS at wide angles than the conventional low-luminance cell arrangement.

This paper focuses on the potential value and future prospects of using virtual reality (VR) technology in online education. In detailing online education and the latest VR technology, we focus on metaverse construction and artificial intelligence (AI) for educational VR use. In particular, we describe a virtual university campus in which on-demand VR lectures are conducted in virtual lecture halls, automated evaluations of student learning and training using machine learning, and the linking of multiple digital campuses.

Privacy violations via spy cameras are becoming increasingly serious. With the recent advent of various smart home IoT devices, such as smart TVs and robot vacuum cleaners, spycam attacks that steal users' information are being carried out in more unpredictable ways. In this paper, we introduce a new spycam attack on a mobile WebVR environment. It is performed by a web attacker who maliciously accesses the back-facing cameras of victims' mobile devices while they are browsing the attacker's WebVR site. This has the power to allow the attacker to capture victims' surroundings even at the desired field of view through sophisticated content placement in VR scenes, resulting in serious privacy breaches for mobile VR users. In this letter, we introduce a new threat facing mobile VR and show that it practically works with major browsers in a stealthy manner.

A new approach for multi-target tracking in an occlusion environment is presented. In pedestrian tracking using a video camera, pedestrains must be tracked accurately and continuously in the images. However, in a crowded environment, the conventional tracking algorithm has a problem in that tracks do not continue when pedestrians are hidden behind the foreground object. In this study, we propose a robust tracking method for occlusion that introduces a degeneration hypothesis that relaxes the track hypothesis which has one measurement to one track constraint. The proposed method relaxes the hypothesis that one measurement and multiple trajectories are associated based on the endpoints of the bounding box when the predicted trajectory is approaching, therefore the continuation of the tracking is improved using the measurement in the foreground. A numerical evaluation using MOT (Multiple Object Tracking) image data sets is performed to demonstrate the effectiveness of the proposed algorithm.

In this study, we investigate outdoor propagation measurements performed in an industrial park environment at 28.3GHz band. The propagation characteristics were evaluated with the measurement result regarding the path loss characteristics. Ray tracing simulation was also studied and compared with the measurement data to evaluate the quantitative accuracy of ray tracing in millimeter-wave band wireless propagations. Ray tracing, whose accuracy was evaluated based on a comparison with the measurement results, can aid in the theoretical design of the coverage area and deterministic channel modeling.

We propose an augmented-reality-based method for arranging furniture using natural markers extracted from the edges of the walls of rooms. The proposed method extracts natural markers and estimates the camera parameters from single images of rooms using deep neural networks. Experimental results show that in all the measurements, the superimposition error of the proposed method was lower than that of general marker-based methods that use practical-sized markers.

Bitcoin is currently a hot issue worldwide, and it is expected to become a new legal tender that replaces the current currency started with El Salvador. Due to the nature of cryptocurrency, however, difficulties in tracking led to the arising of misuses and abuses. Consequently, the pain of innocent victims by exploiting these bitcoins abuse is also increasing. We propose a way to detect new signatures by applying two-fold NLP-based clustering techniques to text data of Bitcoin abuse reports received from actual victims. By clustering the reports of text data, we were able to cluster the message templates as the same campaigns. The new approach using the abuse massage template representing clustering as a signature for identifying abusers is much efficacious.

Observed results of arc discharges generated between the brush and commutator are reported. The motion of the arc discharges was observed by a high-speed camera. The brush and commutator were installed to an experimental device that simulated the rotational motion of a real DC motor. The aim of this paper is to investigate the occurring position, dimensions, and moving characteristics of the arc discharges by means of high-speed imaging. Time evolutions of the arc voltage and current were measured, simultaneously. The arc discharges were generated when an inductive circuit was interrupted. Circuit current before interruption was 4A. The metal graphite or graphite brush and a copper commutator were used. Following results were obtained. The arc discharge was dragged on the brush surface and the arc discharge was sticking to the side surface of the commutator. The positions of the arc spots were on the end of the commutator and the center of the brush in rotational direction. The dimensions of the arc discharge were about 0.2 mm in length and about 0.3 mm in width. The averaged arc voltage during arc duration became higher and the light emission from the arc discharge became brighter, as the copper content of the cathode decreased.

Automobile companies have been trying to replace side mirrors of cars with small cameras for reducing air resistance. It enables us to apply some image processing to improve the quality of the image. Contrast Limited Adaptive Histogram Equalization (CLAHE) is one of such techniques to improve the quality of the image for the side mirror camera, which requires a large computation performance. Here, an implementation method of CLAHE on a low-end FPGA board by high-level synthesis is proposed. CLAHE has two main processing parts: cumulative distribution function (CDF) generation, and bilinear interpolation. During the CDF generation, the effect of increasing loop initiation interval can be greatly reduced by placing multiple Processing Elements (PEs). and during the interpolation, latency and BRAM usage were reduced by revising how to hold CDF and calculation method. Finally, by connecting each module with streaming interfaces, using data flow pragmas, overlapping processing, and hiding data transfer, our HLS implementation achieved a comparable result to that of HDL. We parameterized the components of the algorithm so that the number of tiles and the size of the image can be easily changed. The source code for this research can be downloaded from https://github.com/kokihonda/fpga_clahe.

Dependences of arc duration D and contact gap at arc extinction d on contact opening speed v are studied for break arcs generated in a 48VDC resistive circuit at constant contact opening speeds. The opening speed v is varied over a wide range from 0.05 to 0.5m/s. Circuit current while electrical contacts are closed I0 is varied to 10A, 20A, 50A, 100A, 200A, and 300A. The following results were obtained. For each current I0, the arc duration D decreased with increasing contact opening speed v. However, the D at I0=300A was shorter than that at I0=200A. On the other hand, the contact gap at arc extinction d tended to increase with increasing the I0. However, the d at I0=300A was shorter than that at I0=200A. The d was almost constant with increasing the v for each current I0 when the I0 was lower than 200A. However, the d became shorter when the v was slower at I0=200A and 300A. At the v=0.05m/s, for example, the d at I0=300A was shorter than that at I0=100A. To explain the cause of the results of the d, in addition, arc length just before extinction L were analyzed. The L tended to increase with increasing current I0. The L was almost constant with increasing the v when the I0 was lower than 200A. However, when I0=200A and 300A, the L tended to become longer when the v was slower. The characteristics of the d will be discussed using the analyzed results of the L and motion of break arcs. At higher currents at I0=200A and 300A, the shorter d at the slowest v was caused by wide motion of the arc spots on contact surfaces and larger deformation of break arcs.

In this paper, we present an efficient and robust method for estimating Homography matrix for soccer field registration between a captured camera image and a soccer field model. The presented method first detects reliable field lines from the camera image through clustering. Constructing a novel directional feature of the intersection points of the lines in both the camera image and the model, the presented method then finds matching pairs of these points between the image and the model. Finally, Homography matrix estimations and validations are performed using the obtained matching pairs, which can reduce the required number of Homography matrix calculations. Our presented method uses possible intersection points outside image for the point matching. This effectively improves robustness and accuracy of Homography estimation as demonstrated in experimental results.

This paper presents an efficient method for solving PnP, PnPf, and PnPfr problems, which are the problems of determining camera parameters from 2D-3D point correspondences. The proposed method is derived based on a simple usage of linear algebra, similarly to the classical DLT methods. Therefore, the new method is easier to understand, easier to implement, and several times faster than the state-of-the-art methods using Gröbner basis. Contrary to the existing Gröbner basis methods, the proposed method consists of three algorithms depending on the number of the points and the 3D point configuration. Experimental results show that the proposed method is as accurate as the state-of-the-art methods even in near-planar scenes while achieving up to three times faster.

Coded-aperture imaging has been utilized for compressive light field acquisition; several images are captured using different aperture patterns, and from those images, an entire light field is computationally reconstructed. This method has been extended to dynamic light fields (moving scenes). However, this method assumed that the patterns were gray-valued and of arbitrary shapes. Implementation of such patterns required a special device such as a liquid crystal on silicon (LCoS) display, which made the imaging system costly and prone to noise. To address this problem, we propose the use of a binary aperture pattern rotating along time, which can be implemented with a rotating plate with a hole. We demonstrate that although using such a pattern limits the design space, our method can still achieve a high reconstruction quality comparable to the original method.

Distributed detection techniques of multiple-input multiple-output (MIMO) spatially multiplexed signals are studied in this paper. This system considered employs multiple mobile stations (MSs) to receive signals from a base station, and then share their received signal waveforms with collaborating MSs. In order to reduce the amount of traffic over the collaborating wireless links, distributed detection techniques are proposed, in which multiple MSs are in charge of detection by making use of both the shared signal waveforms and its own received waveform. Selection combining schemes of detected bit sequences are studied to finalize the decisions. Residual error coefficients in iterative MIMO equalization and detection are utilized in this selection. The error-ratio performance is elucidated not only by computer simulations, but also by offline processing using experimental signals recorded in a measurement campaign.

During the detection of maritime targets, the jitter of the shipborne camera usually causes the video instability and the false or missed detection of targets. Aimed at tackling this problem, a novel algorithm for maritime target detection based on the electronic image stabilization technology is proposed in this study. The algorithm mainly includes three models, namely the points line model (PLM), the points classification model (PCM), and the image classification model (ICM). The feature points (FPs) are firstly classified by the PLM, and stable videos as well as target contours are obtained by the PCM. Then the smallest bounding rectangles of the target contours generated as the candidate bounding boxes (bboxes) are sent to the ICM for classification. In the experiments, the ICM, which is constructed based on the convolutional neural network (CNN), is trained and its effectiveness is verified. Our experimental results demonstrate that the proposed algorithm outperformed the benchmark models in all the common metrics including the mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and mean average precision (mAP) by at least -47.87%, 8.66%, 6.94%, and 5.75%, respectively. The proposed algorithm is superior to the state-of-the-art techniques in both the image stabilization and target ship detection, which provides reliable technical support for the visual development of unmanned ships.

In this study, we present a novel method for removing smoke from videos based on a single image sequence. Smoke is a significant artifact in images or videos because it can reduce the visibility in disaster scenes. Our proposed method for removing smoke involves two main processes: (1) the development of a smoke imaging model and (2) smoke removal using spatio-temporal pixel compensation. First, we model the optical phenomena in natural scenes including smoke, which is called a smoke imaging model. Our smoke imaging model is developed by extending conventional haze imaging models. We then remove the smoke from a video in a frame-by-frame manner based on the smoke imaging model. Next, we refine the appearance of the smoke-free video by spatio-temporal pixel compensation, where we align the smoke-free frames using the corresponding pixels. To obtain the corresponding pixels, we use SIFT and color features with distance constraints. Finally, in order to obtain a clear video, we refine the pixel values based on the spatio-temporal weightings of the corresponding pixels in the smoke-free frames. We used simulated and actual smoke videos in our validation experiments. The experimental results demonstrated that our method can obtain effective smoke removal results from dynamic scenes. We also quantitatively assessed our method based on a temporal coherence measure.