Reviewerly/exHarmony huggingface dataset:
High-speed implementation of privacy amplification for continuous-variable quantum-key distribution with Toeplitz matrix based on FPGA

For a high-speed and secure continuous-variable quantum key distribution (CV-QKD) system, privacy amplification (PA) plays an important role. To reduce the finite size effect, the input length of PA should be at least on the order of 10^8, 10^9, 10^10 when the transmission distance is about 50km, 80km, 100km, respectively. This leads to high computation complexity and large storage demand of the data, which is unfriendly to field programmable gate array (FPGA) implementation for its limited resource. In addition, the limited IO speed of Double Data Rate Synchronous Dynamic Random Access Memory (DDR SDRAM) restricts the implementation performance of PA. In this paper, we propose an effective way to access data based on DDR to improve the performance of PA. As a result, the reading time from DDR can be reduced, and it can eliminate the effect of the limited IO speed of DDR, so that PA can perform with multiple code-words. This can make full use of the resource of FPGA and increase the execution speed of PA. Besides, combining with the proposed method, an easier algorithm is used to decrease the complexity of calculations. Based on these methods, we realize PA with Toeplitz matrix based on FPGA and the experimental throughput is about 288Mbps when the input length is about 100Mbits.
5-bit all free space optical quantum random number generator

Random numbers play an important role in many fields, such as computation, simulation and cryptography. The randomness inherent in quantum mechanics is a perfect source of entropy for a true random number generator. Nevertheless, many QRNG schemes require post-processing work to ensure randomness. On the other hand, the bit number of the QRNG is limited, which hamper its further applications. Here, we demonstrate a novel 5-bit all free space optical quantum random number generator based on a time-division-multiplexed degenerate optical parametric oscillator (DOPO), which does not need a complicated experimental environment and tedious post-processing work. By inserting a multi-pass cell into the optical parametric oscillator, the equivalent cavity length is calculated to be as long as 15 m, which corresponds to fifth of the separation times of pump pulses. As a result, 5 identical DOPO pulses are generated simultaneously. The randomness comes from phase responses of degenerate OPOs. Under threshold, the photons produced by optical parametric down conversion have random initial phase, while above threshold, stable oscillations occurs in one of the two possible phase states. Random number is reliably verified by measuring the relative phase states between DOPO output pulses and the original pump pulse using a fast response photodiode. All spatial structures have the advantages of higher stability and are less sensitive to temperature fluctuation. The bit number can be readily multiplied by increasing the cavity length of DOPO or the repetition rate of the pump laser source.
3D-scanning system based on monocular vision

Monocular vision-based 3D-scanning systems have revolutionized object and environment capture. The authors have developed a system that uses a rotating camera and an angle sensor to capture images from different angles. Through deep learning techniques, depth maps are extracted from the images. The resulting depth map is used to generate a 3D point cloud model, which is preprocessed to enhance efficiency. This research considered several methods to improve depth accuracy of panorama image, as well as to recalibrate depth maps and smooth 360-degree 3D models. Our method produces better results than the pre-trained model. The authors use a LIDAR to get distance and increase accuracy of model. To visualize the model, the web framework was developed. The rendered model can be accessed through a web browser, providing functionalities such as coordinate selection and distance calculation.
Research on unsupervised learning-based image stitching

Image stitching is a technology that combines multiple images taken by different cameras to create a larger field of view. It has wide applications in scenarios such as surveillance and virtual reality, making it an important topic in computer vision. This paper addresses the challenges associated with stitching ground images with inconspicuous features using traditional methods. These challenges include poor feature extraction capabilities and issues like misalignment, artifacts, and structural deformations introduced during stitching. The paper proposes the utilization of unsupervised learning techniques to enhance the quality of image stitching. The network primarily consists of two parts: image alignment and image reconstruction. In the image reconstruction part, deformation rules for image stitching are learned through both a low-resolution branch and a high-resolution branch. Finally, it evaluates the stitched images before and after improvement using image stitching evaluation metrics. Experimental results demonstrate that this approach successfully mitigates artifacts and distortions introduced during stitching, resulting in an improved image stitching quality.
Neural processor architecture design for CNN processing acceleration

The current work introduces a neural processor architecture that allows hardware acceleration in the processing of convolutional neural networks (CNNs). The purpose of this study is to design the architecture and microarchitecture of the neural processor, which can be used to solve the problems of environmental analysis and recognition of objects that are located in the scene in augmented reality systems, presented as energy-efficient and compact wearable devices. The proposed architecture provides the opportunity to adjust the variable parameters of blocks for data processing and storage to optimize the performance, energy consumption and resources used to implement the neural processor. The article offers variants of scaling of computing blocks and memory blocks in the architecture of a neural processor, which can be used for increasing the performance of the end product. The paper describes a tool that generates a neural processor based on given limitations on power consumption and performance and on the structure of convolutional neural networks that you need to use for data processing. The proposed tool can potentially become a valuable product in the field of designing hardware accelerators of convolutional neural networks, as it allows to increase the degree of automation of the process of synthesis of neural processors for further implementation in mixed reality systems, made as portable devices. In general, this thesis presents tools that allow the developer of software based on convolutional neural networks for mixed reality systems to synthesize energy-efficient processors to accelerate the processing of convolutional neural networks.
Two-phase single-image deraining method via sparse blind detection and image inpainting

This paper mainly focuses on the rain streak removal task from a single image. Based on the observation that the distribution of rain streak in an image is sparse. We propose a two- phase single image deraining method. Firstly, it detects the rain locations with a proposed anisotropic global gradient prior (AGGP) and generates a rain mask for rain streak removal. Then it recovers the information in rain distorted region with AGGP based multi-layer image inpainting model. Furthermore, to solve the multi-variable optimization problems, we develop an alternating half-quadratic algorithm by introducing alternating algorithm and the variable split method. Both experiments on synthesized and realworld images show the proposed method outperforms state-of-the-art methods in terms of rain streak removal and image multi-layer information preservation.
The vehicle color recognition based on enhanced Yolov5 neural network

Color features play a unique role in vehicle recognition. The color recognition algorithms based on deep learning neural networks are studied in this paper. A color recognition experiment is carried on to some typical deep learning neural networks, the result of the experiment proves that Yolov5 has faster training speed and higher accuracy for vehicle color recognition, so Yolov5 is chosen for the color recognition. The structure of yolov5 is optimized by adding C2f module replacing C3 module and adjusting the parameters of HSV color space when it is applied to identify 8 typical vehicle colors using BIT Vehicles data set. The modified Yolov5 make the accuracy of vehicle color recognition improved effectively to the complex color vehicles and part covered vehicles comparing with original yolov5 network.
Reading between the lines: interpolation as it impacts ringing suppression

Hard apertures in imaging systems create artifacts at discontinuous points in an image. When we zoom in on such an image, Gibbs ringing can be a factor in the confusion of small features with artifacts, or in errors in measurements made of the features. In previous works, we have systemically analysed certain algorithms for suppressing artifacts, and developed metrics and procedures to make a quantifiable comparison between algorithms in that field. A common assumption in the literature is that zero-padding in the Fourier domain (sinc interpolation) is used for the zoom algorithm. Other algorithms make use of linear interpolation for a similar reason. In this paper, we analyse several interpolation formulae and compare them with sinc interpolation in a quantitative fashion. We further consider the interaction of the interpolation formulae with filtered Fourier reconstruction. Our results are foundational to establishing a quantitative evidence base for preferring certain ringing suppression algorithms over others, and provide grounds for revisiting assumptions that have long stood in commercial imaging pipelines.
Aberration compensation-enabled large-scale antiferromagnetic spatial photonic Ising machine

The spatial photonic Ising machine (SPIM) is a simple and elegant architecture capable of expanding the spin scale with ease, and thus this optical architecture is attractive for its potential in solving large-scale combinatorial optimization problems. Here, we propose a scheme for aberration compensation to improve the computing accuracy of the SPIM based on a spatial light modulator (SLM), resulting in good scalability. First, the gamma curve of the SLM is corrected with the help of double-hole interference fringes. Then, an enhanced phase shift interferometry is used to measure the aberration of the entire optical system, and this aberration is compensated by loading the compensation phase on the SLM. Moreover, an iterative algorithm using self-adaptive cluster flipping is proposed for accelerating the convergence of SPIM. During the converging process, the number of flipped spins within the cluster is updated dynamically according to the convergence speed and accuracy, which makes it possible to achieve a good balance between high speed and high accuracy. Further, a proof-of-principle experimental set-up of the optimized SPIM is implemented, and the ground-state search of an antiferromagnetic Ising model is demonstrated. As an example, this optimized Ising machine is used for solving the number partitioning problem. It is shown that the partition of 40,000 random numbers is well demonstrated, and the final partitioning accuracy is as high as 99.88%.
Visible watermarks inpainting using deep learning for multimedia application

In recent decades, with the development of digital technologies and the spread of the Internet, photography has become an integral part of our daily lives. Often, images with objects superimposed in text, symbols, or drawings. These superimposed objects can serve a variety of purposes. Adding text or graphics can highlight specific details in an image or convey important information, such as the event's date, location, or specifics. Adding graphic elements may be used commercially to promote products, services, or a brand. Another area of application of such objects is the field of intellectual property protection or verification of the authenticity of a digital image. The problem with such images is that objects can completely overlap the image, leading to a distortion of the perceived information. In this article, we propose an effective convolutional autoencoder model for removing watermarking and restoring an image's usable part. This network can perfectly remove the embedded, visible watermark, restoring the image. Experimental results demonstrate the effectiveness and superiority of our scheme.
Rotating machinery speed extraction through smartphone video acquisition from a radial viewpoint

This article investigates the viability of using a smartphone as a cost-effective tool for rotational speed extraction. The proposed method exploits the geometrical image deformations induced by a smartphone’s rolling shutter camera, which reads pixel lines sequentially with a delay equal to the rolling shutter period. This characteristic period is the basis for the developed methodology and allows measuring constant and varying speeds above the Nyquist limit related to the camera’s frame rate. With respect to the rotating shaft, the smartphone is pointed to the side surface of the shaft (radial viewpoint). A method is proposed to measure the speed from the deformation of a zebra pattern as a consequence of the sequential readout of a rolling shutter camera. A mathematical model, which also takes into account a possible misalignment between the shaft and the camera’s rolling shutter reading direction, is developed and validated on an in-house test rig. It is shown that the proposed method is robust against changing lighting conditions and a misalignment between the shaft and the smartphone camera. Normalized Root-Mean-Square Errors (NRMSE) of about 3% (or less) are reached for the smartphone aligned with the shaft with peaks in the percentage error of about 5% to 8%. Nevertheless, even for a misalignment of 60°, the NRMSE is only 4% with variations in the percentage error to about 10%. The proposed method shows promising results with room for improvements, making it possible to develop a smartphone application to measure a shaft’s rotational speed in the future.
Whole slide image representation in bone marrow cytology

One of the goals of AI-based computational pathology is to generate compact representations of whole slide images (WSIs) that capture the essential information needed for diagnosis. While such approaches have been applied to histopathology, few applications have been reported in cytology. Bone marrow aspirate cytology is the basis for key clinical decisions in hematology. However, visual inspection of aspirate specimens is a tedious and complex process subject to variation in interpretation, and hematopathology expertise is scarce. The ability to generate a compact representation of an aspirate specimen may form the basis for clinical decision-support tools in hematology. In this study, we leverage our previously published end-to-end AI-based system for counting and classifying cells from bone marrow aspirate WSIs, which enables the direct use of individual cells as inputs rather than WSI patches. We then construct bags of individual cell features from each WSI, and apply multiple instance learning to extract their vector representations. To evaluate the quality of our representations, we conducted WSI retrieval and classification tasks. Our results show that we achieved a mAP@10 of 0.58 ±0.02 in WSI-level image retrieval, surpassing the random-retrieval baseline of 0.39 ±0.1. Furthermore, we predicted five diagnostic labels for individual aspirate WSIs with a weighted-average F1 score of 0.57 ±0.03 using a k-nearest-neighbors (k-NN) model, outperforming guessing using empirical class prior probabilities (0.26 ±0.02). We present the first example of exploring trainable mechanisms to generate compact, slide-level representations in bone marrow cytology with deep learning. This method has the potential to summarize complex semantic information in WSIs toward improved diagnostics in hematology, and may eventually support AI-assisted computational pathology approaches.
Feature mixing and self-training for source-free active domain adaptation

Recently, active domain adaptation (ADA) has become an emerging paradigm, as it significantly boosts the performance of the model with additional supervision. Despite the impressive performance of ADA methods, they are assumed that the source domain can be available during model adaptation. In many scenarios, the source domain can be unavailable due to issues such as data privacy. Consequently, ADA methods fail to achieve the expected results or even become invalid. To solve the problem, we design an effective framework named Feature Mixing and Self-Training (FMAS) for source-free active domain adaptation. Specifically, we adopt Clustering-based Alignment to make the classification boundary clearer to be beneficial to the subsequent sample selection, which can also achieve initial domain adaptation. When reaching active sample selection, we utilize feature mixing to explore the uncertainty and diversity of the sample. We construct the sample candidate pool by Mixup-based Uncertainty Selection and then consider the diversity to overcome the redundancy among the pool by Entropy-based Diversity Selection. Finally, for the remaining unlabeled samples, we adopt the self-training framework, which screens out high-confidence samples to explore the information of unlabeled samples. Thorough experiments show that FMAS improves average accuracy by at least 1% compared to other methods on benchmark datasets OfficeHome and MiniDomainNet. And FMAS can also achieve better results with ADA methods without the source domain data, which demonstrates FMAS can outperform previous ADA methods.
Back projection deep unrolling network for handwritten text image super resolution

Current super-resolution (SR) methods have demonstrated exceptional advancements in the domain of natural image processing. Nevertheless, these approaches do not fully address the open issues of edge blurring and distortion. In order to address the aforementioned challenges, we propose a novel deep unrolling model, coined back projection deep unrolling network (BPDUN), for super-resolution of handwritten text images leveraging the Algorithm Unrolling paradigm. We design the network model of BPDUN by unfolding and truncating the traditional iterative back projection algorithm (IBPA). We unroll IBPA into a cascade operation of three building blocks (deep denoising module, low-frequency reconstruction module, and residual projection module). BPDUN inherits the interpretability of the iterative optimization algorithm and is also designed to make the reconstructed text image more realistic and natural. Moreover, we propose a new benchmark dataset to address challenging SR problems of handwritten text image (HDT300). Extensive experiments show that BPDUN obtains an enhanced balance between the performance (quantified by PSNR and SSIM) and the cost (as measured by network parameters). Notably, BPDUN sets new benchmarks on the HDT300 dataset, surpassing previous state-of-the-art approaches by achieving up to 0.2 dB gains in PSNR.
An assessment of blockchain academia and news developments: a bibliometric and text-mining analysis

Purpose This study aims to provide a more complete picture of blockchain development by combining numerous methodologies with diverse data sources, such as academic papers and news articles. This study displays the developmental status of each subject based on the interrelationships of each topic cluster by analyzing high-frequency keywords extracted from the collected data. Moreover, applying above methodologies will help understanding top research topics, authors, venues, institutes and countries. The differences of blockchain research and new are identified. Design/methodology/approach To identify and find blockchain development linkages, researchers have used search terms such as co-occurrence, bibliographic coupling, co-citation and co-authorship to help us understand the top research topics, authors, venues, institutes and countries. This study also used text mining analysis to identify blockchain articles' primary concepts and semantic structures. Findings The findings show the fundamental topics based on each topic cluster's links. While “technology”, “transaction”, “privacy and security”, “environment” and “consensus” were most strongly associated with blockchain in research, “platform”, “big data and cloud”, “network”, “healthcare and business” and “authentication” were closely tied to blockchain news. This article classifies blockchain principles into five patterns: hardware and infrastructure, data, networking, applications and consensus. These statistics helped the authors comprehend the top research topics, authors, venues, publication institutes and countries. Research limitations/implications Since Web of Science (WoS) and LexisNexis Academic data are used, the study has few sources. Others advise merging foreign datasets. WoS is one of the world's largest and most-used databases for assessing scientific papers. Originality/value This study has several uses and benefits. First, key concept discoveries can help academics understand blockchain research trends so they can prioritize research initiatives. Second, bibliographic coupling links academic papers on blockchain. It helps information seekers search and classify the material. Co-citation analysis results can help researchers identify potential partners and leaders in their field. The network's key organizations or countries should be proactive in discovering, proposing and creating new relationships with other organizations or countries, especially those from the journal network's border, to make the overall network more integrated and linked. Prominent members help recruit new authors to organizations or countries and link them to the co-authorship network. This study also used concept-linking analysis to identify blockchain articles' primary concepts and semantic structures. This may lead to new authors developing research ideas or subjects in primary disciplines of inquiry.