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Learning with Errors: A Lattice-Based Keystone of Post-Quantum Cryptography 在错误中学习:基于网格的后量子密码学基石
Pub Date : 2024-04-13 DOI: 10.3390/signals5020012
Maria E. Sabani, I. Savvas, Georgia Garani
The swift advancement of quantum computing devices holds the potential to create robust machines that can tackle an extensive array of issues beyond the scope of conventional computers. Consequently, quantum computing machines create new risks at a velocity and scale never seen before, especially with regard to encryption. Lattice-based cryptography is regarded as post-quantum cryptography’s future and a competitor to a quantum computer attack. Thus, there are several advantages to lattice-based cryptographic protocols, including security, effectiveness, reduced energy usage and speed. In this work, we study the learning with errors (LWE) problem and the cryptosystems that are based on the LWE problem and, in addition, we present a new efficient variant of LWE cryptographic scheme.
量子计算设备的迅猛发展有可能创造出强大的机器,可以解决传统计算机所无法解决的大量问题。因此,量子计算机器会以前所未有的速度和规模带来新的风险,尤其是在加密方面。基于晶格的加密技术被认为是后量子加密技术的未来,也是量子计算机攻击的竞争对手。因此,基于网格的加密协议有几个优势,包括安全性、有效性、降低能耗和速度。在这项工作中,我们研究了带错误学习(LWE)问题和基于 LWE 问题的密码系统,此外,我们还提出了一种新的高效 LWE 密码方案变体。
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引用次数: 0
Optimizing Dynamic Mode Decomposition for Video Denoising via Plug-and-Play Alternating Direction Method of Multipliers 通过即插即用交替方向乘法优化视频去噪的动态模式分解
Pub Date : 2024-04-01 DOI: 10.3390/signals5020011
Hyoga Yamamoto, Shunki Anami, Ryo Matsuoka
Dynamic mode decomposition (DMD) is a powerful tool for separating the background and foreground in videos. This algorithm decomposes a video into dynamic modes, called DMD modes, to facilitate the extraction of the near-zero mode, which represents the stationary background. Simultaneously, it captures the evolving motion in the remaining modes, which correspond to the moving foreground components. However, when applied to noisy video, this separation leads to degradation of the background and foreground components, primarily due to the noise-induced degradation of the DMD mode. This paper introduces a novel noise removal method for the DMD mode in noisy videos. Specifically, we formulate a minimization problem that reduces the noise in the DMD mode and the reconstructed video. The proposed problem is solved using an algorithm based on the plug-and-play alternating direction method of multipliers (PnP-ADMM). We applied the proposed method to several video datasets with different levels of artificially added Gaussian noise in the experiment. Our method consistently yielded superior results in quantitative evaluations using peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared to naive noise removal methods. In addition, qualitative comparisons confirmed that our method can restore higher-quality videos than the naive methods.
动态模式分解(DMD)是分离视频中背景和前景的有力工具。该算法将视频分解为动态模式(称为 DMD 模式),以方便提取代表静止背景的近零模式。同时,它还能捕捉到其余模式中不断变化的运动,这些模式对应的是运动的前景成分。然而,当应用于噪声视频时,这种分离会导致背景和前景成分的劣化,这主要是由于噪声引起的 DMD 模式劣化。本文针对嘈杂视频中的 DMD 模式介绍了一种新的去噪方法。具体来说,我们提出了一个最小化问题,以减少 DMD 模式和重建视频中的噪声。我们使用基于即插即用交替方向乘法(PnP-ADMM)的算法来解决所提出的问题。我们将所提出的方法应用于多个视频数据集,并在实验中人为添加了不同程度的高斯噪声。在使用峰值信噪比(PSNR)和结构相似性(SSIM)进行定量评估时,我们的方法与传统的去噪方法相比始终取得优异的结果。此外,定性比较也证实了我们的方法能比传统方法还原出更高质量的视频。
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引用次数: 0
Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis 基于大语言模型的 X 射线光电子能谱数据分析
Pub Date : 2024-03-27 DOI: 10.3390/signals5020010
J. de Curtò, I. de Zarzà, Gemma Roig, C. T. Calafate
X-ray photoelectron spectroscopy (XPS) remains a fundamental technique in materials science, offering invaluable insights into the chemical states and electronic structure of a material. However, the interpretation of XPS spectra can be complex, requiring deep expertise and often sophisticated curve-fitting methods. In this study, we present a novel approach to the analysis of XPS data, integrating the utilization of large language models (LLMs), specifically OpenAI’s GPT-3.5/4 Turbo to provide insightful guidance during the data analysis process. Working in the framework of the CIRCE-NAPP beamline at the CELLS ALBA Synchrotron facility where data are obtained using ambient pressure X-ray photoelectron spectroscopy (APXPS), we implement robust curve-fitting techniques on APXPS spectra, highlighting complex cases including overlapping peaks, diverse chemical states, and noise presence. Post curve fitting, we engage the LLM to facilitate the interpretation of the fitted parameters, leaning on its extensive training data to simulate an interaction corresponding to expert consultation. The manuscript presents also a real use case utilizing GPT-4 and Meta’s LLaMA-2 and describes the integration of the functionality into the TANGO control system. Our methodology not only offers a fresh perspective on XPS data analysis, but also introduces a new dimension of artificial intelligence (AI) integration into scientific research. It showcases the power of LLMs in enhancing the interpretative process, particularly in scenarios wherein expert knowledge may not be immediately available. Despite the inherent limitations of LLMs, their potential in the realm of materials science research is promising, opening doors to a future wherein AI assists in the transformation of raw data into meaningful scientific knowledge.
X 射线光电子能谱(XPS)仍然是材料科学中的一项基础技术,能为了解材料的化学状态和电子结构提供宝贵的信息。然而,XPS 光谱的解读可能非常复杂,需要深厚的专业知识和复杂的曲线拟合方法。在本研究中,我们提出了一种分析 XPS 数据的新方法,综合利用大型语言模型 (LLM),特别是 OpenAI 的 GPT-3.5/4 Turbo,在数据分析过程中提供具有洞察力的指导。在利用环境压力 X 射线光电子能谱(APXPS)获取数据的 CELLS ALBA 同步加速器设施的 CIRCE-NAPP 光束线框架内,我们对 APXPS 光谱实施了强大的曲线拟合技术,突出了包括重叠峰、不同化学状态和噪声存在在内的复杂情况。曲线拟合后,我们利用 LLM 的大量训练数据来模拟与专家咨询相对应的互动,从而促进对拟合参数的解释。手稿还介绍了一个利用 GPT-4 和 Meta 的 LLaMA-2 的实际案例,并描述了将该功能集成到 TANGO 控制系统中的情况。我们的方法不仅为 XPS 数据分析提供了一个全新的视角,还为人工智能(AI)融入科学研究引入了一个新的维度。它展示了 LLM 在增强解释过程中的威力,尤其是在无法立即获得专家知识的情况下。尽管 LLMs 存在固有的局限性,但其在材料科学研究领域的潜力令人期待,为人工智能协助将原始数据转化为有意义的科学知识的未来打开了大门。
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引用次数: 0
A Review of Recent Advancements in Knock Detection in Spark Ignition Engines 火花点火发动机爆震检测最新进展综述
Pub Date : 2024-03-21 DOI: 10.3390/signals5010009
Vikram Mittal
In gasoline engines, the combustion process involves a flame’s propagation from the spark plug to the cylinder walls, resulting in the localized heating and pressurization of the cylinder content ahead of the flame, which can lead to the autoignition of the gasoline and air. The energy release from the autoignition event causes the engine cylinder to resonate, causing an unpleasant noise and eventual engine damage. This process is termed as knock. Avoiding knock has resulted in limiting the maximum engine pressures, and hence limiting the maximum efficiencies of the engine. Modern engines employ knock sensors to detect resonances, adjusting the spark plug timing to reduce pressures and temperatures, albeit at the expense of engine performance. This paper sets out to review the different signals that can be measured from an engine to detect the start of knock. These signals traditionally consist of the in-cylinder pressure, the vibrations of the engine block, and acoustic noise. This paper reviews each of these techniques, with a focus on recent advances. A number of novel methods are also presented, including identifying perturbations in the engine speed or exhaust temperature; measuring the ion charge across the spark plug leads; and using artificial intelligence to build models based on engine conditions. Each of these approaches is also reviewed and compared to the more traditional approaches. This review finds that in-cylinder pressure measurements remain as the most accurate for detecting knock in modern engines; however, their usage is limited to research settings. Meanwhile, new sensors and processing techniques for vibration measurements will more accurately detect knock in modern vehicles in the short term. Acoustic measurements and other novel approaches are showing promise in the long term.
在汽油发动机中,燃烧过程涉及火焰从火花塞向气缸壁的传播,导致火焰前方气缸内容物的局部加热和增压,从而导致汽油和空气的自燃。自燃事件释放的能量会引起发动机气缸共振,从而产生难听的噪音,最终导致发动机损坏。这一过程被称为爆震。避免爆震的结果是限制发动机的最大压力,从而限制发动机的最大效率。现代发动机采用爆震传感器来检测共振,调整火花塞正时以降低压力和温度,但这是以牺牲发动机性能为代价的。本文旨在回顾可从发动机测量到的不同信号,以检测爆震的开始。这些信号传统上包括缸内压力、发动机缸体振动和声学噪声。本文回顾了上述每种技术,并重点介绍了最新进展。文中还介绍了一些新方法,包括识别发动机转速或排气温度的扰动;测量火花塞引线上的离子电荷;以及使用人工智能根据发动机工况建立模型。本文还对上述每种方法进行了综述,并将其与更传统的方法进行了比较。本综述发现,缸内压力测量仍然是检测现代发动机爆震的最准确方法,但其使用仅限于研究环境。同时,用于振动测量的新型传感器和处理技术将在短期内更准确地检测现代车辆的爆震。从长远来看,声学测量和其他新型方法将大有可为。
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引用次数: 0
ApeTI: A Thermal Image Dataset for Face and Nose Segmentation with Apes ApeTI:人猿人脸和鼻子分割热图像数据集
Pub Date : 2024-03-15 DOI: 10.3390/signals5010008
Pierre-Etienne Martin
The ApeTI dataset was built with the aim of retrieving physiological signals such as heart rate, breath rate, and cognitive load from thermal images of great apes. We want to develop computer vision tools that psychologists and animal behavior researchers can use to retrieve physiological signals noninvasively. Our goal is to increase the use of a thermal imaging modality in the community and avoid using more invasive recording methods to answer research questions. The first step to retrieving physiological signals from thermal imaging is their spatial segmentation to then analyze the time series of the regions of interest. For this purpose, we present a thermal imaging dataset based on recordings of chimpanzees with their face and nose annotated using a bounding box and nine landmarks. The face and landmarks’ locations can then be used to extract physiological signals. The dataset was acquired using a thermal camera at the Leipzig Zoo. Juice was provided in the vicinity of the camera to encourage the chimpanzee to approach and have a good view of the face. Several computer vision methods are presented and evaluated on this dataset. We reach mAPs of 0.74 for face detection and 0.98 for landmark estimation using our proposed combination of the Tifa and Tina models inspired by the HRNet models. A proof of concept of the model is presented for physiological signal retrieval but requires further investigation to be evaluated. The dataset and the implementation of the Tina and Tifa models are available to the scientific community for performance comparison or further applications.
建立 ApeTI 数据集的目的是从类人猿的热图像中检索心率、呼吸频率和认知负荷等生理信号。我们希望开发出计算机视觉工具,供心理学家和动物行为研究人员用于非侵入式检索生理信号。我们的目标是增加热成像模式在社区中的使用,避免使用更具侵入性的记录方法来回答研究问题。从热成像中获取生理信号的第一步是对其进行空间分割,然后分析感兴趣区域的时间序列。为此,我们展示了一个基于黑猩猩记录的热成像数据集,该数据集使用边界框和九个地标标注了黑猩猩的脸部和鼻子。脸部和地标的位置可用于提取生理信号。数据集是使用莱比锡动物园的热像仪采集的。在摄像头附近提供了果汁,以鼓励黑猩猩靠近并看清黑猩猩的脸。在此数据集上介绍并评估了几种计算机视觉方法。使用我们受 HRNet 模型启发而提出的 Tifa 和 Tina 模型组合,人脸检测的 mAP 达到 0.74,地标估计的 mAP 达到 0.98。该模型的概念验证已用于生理信号检索,但还需要进一步研究才能进行评估。数据集以及 Tina 和 Tifa 模型的实现可供科学界进行性能比较或进一步应用。
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引用次数: 0
A Complete Pipeline for Heart Rate Extraction from Infant ECGs 从婴儿心电图提取心率的完整管道
Pub Date : 2024-03-13 DOI: 10.3390/signals5010007
Harry T. Mason, A. P. Martinez-Cedillo, Q. Vuong, Maria Carmen Garcia-de-Soria, Stephen Smith, Elena Geangu, Marina I. Knight
Infant electrocardiograms (ECGs) and heart rates (HRs) are very useful biosignals for psychological research and clinical work, but can be hard to analyse properly, particularly longform (≥5 min) recordings taken in naturalistic environments. Infant HRs are typically much faster than adult HRs, and so some of the underlying frequency assumptions made about adult ECGs may not hold for infants. However, the bulk of publicly available ECG approaches focus on adult data. Here, existing open source ECG approaches are tested on infant datasets. The best-performing open source method is then modified to maximise its performance on infant data (e.g., including a 15 Hz high-pass filter, adding local peak correction). The HR signal is then subsequently analysed, developing an approach for cleaning data with separate sets of parameters for the analysis of cleaner and noisier HRs. A Signal Quality Index (SQI) for HR is also developed, providing insights into where a signal is recoverable and where it is not, allowing for more confidence in the analysis performed on naturalistic recordings. The tools developed and reported in this paper provide a base for the future analysis of infant ECGs and related biophysical characteristics. Of particular importance, the proposed solutions outlined here can be efficiently applied to real-world, large datasets.
婴儿心电图(ECGs)和心率(HRs)是对心理研究和临床工作非常有用的生物信号,但却很难进行正确分析,尤其是在自然环境中进行的长格式(≥5 分钟)记录。婴儿的心率通常比成人快得多,因此对成人心电图的一些基本频率假设可能对婴儿不适用。然而,大部分公开的心电图方法都集中在成人数据上。在此,我们在婴儿数据集上测试了现有的开源心电图方法。然后对表现最好的开源方法进行修改,以最大限度地提高其在婴儿数据上的表现(例如,加入 15 Hz 高通滤波器,增加局部峰值校正)。随后对心率信号进行分析,开发出一种清理数据的方法,并为分析较干净和较嘈杂的心率信号分别设置了不同的参数。此外,还开发了心率信号质量指数(SQI),可深入了解哪些信号可恢复,哪些不可恢复,从而提高对自然记录分析的信心。本文开发和报告的工具为今后分析婴儿心电图和相关生物物理特征奠定了基础。尤其重要的是,本文提出的解决方案可有效地应用于现实世界的大型数据集。
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引用次数: 0
Study of Time–Frequency Domain of Acoustic Emission Precursors in Rock Failure during Uniaxial Compression 单轴压缩过程中岩石破坏声发射前兆的时频域研究
Pub Date : 2024-02-29 DOI: 10.3390/signals5010006
Gang Jing, Pedro Marin Montanari, Giuseppe Lacidogna
Predicting rock bursts is essential for maintaining worker safety and the long-term growth of subsurface infrastructure. The purpose of this study is to investigate the precursor reactions and processes of rock instability. To determine the degree of rock damage, the research examines the time-varying acoustic emission (AE) features that occur when rocks are compressed uniaxially and introduces AE parameters such as the b-value, γ-value, and βt-value. The findings suggest that the evolution of rock damage during loading is adequately reflected by the b-value, γ-value, and βt-value. The relationships between b-value, γ-value, and βt-value are studied, as well as the possibility of using these three metrics as early-warning systems for rock failure.
预测岩爆对于维护工人安全和地下基础设施的长期发展至关重要。本研究的目的是调查岩石失稳的前兆反应和过程。为了确定岩石的破坏程度,研究人员检查了岩石受到单轴压缩时发生的时变声发射(AE)特征,并引入了 b 值、γ 值和βt 值等 AE 参数。研究结果表明,b 值、γ 值和βt 值能够充分反映加载过程中岩石损伤的演变。研究了 b 值、γ 值和βt 值之间的关系,以及使用这三个指标作为岩石破坏预警系统的可能性。
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引用次数: 0
Object Detection with Hyperparameter and Image Enhancement Optimisation for a Smart and Lean Pick-and-Place Solution 利用超参数和图像增强优化进行物体检测,打造智能、精益的取放解决方案
Pub Date : 2024-02-26 DOI: 10.3390/signals5010005
Elven Kee, Jun Jie Chong, Zi Jie Choong, Michael Lau
Pick-and-place operations are an integral part of robotic automation and smart manufacturing. By utilizing deep learning techniques on resource-constraint embedded devices, the pick-and-place operations can be made more accurate, efficient, and sustainable, compared to the high-powered computer solution. In this study, we propose a new technique for object detection on an embedded system using SSD Mobilenet V2 FPN Lite with the optimisation of the hyperparameter and image enhancement. By increasing the Red Green Blue (RGB) saturation level of the images, we gain a 7% increase in mean Average Precision (mAP) when compared to the control group and a 20% increase in mAP when compared to the COCO 2017 validation dataset. Using a Learning Rate of 0.08 with an Edge Tensor Processing Unit (TPU), we obtain high real-time detection scores of 97%. The high detection scores are important to the control algorithm, which uses the bounding box to send a signal to the collaborative robot for pick-and-place operation.
拾放操作是机器人自动化和智能制造不可或缺的一部分。通过在资源受限的嵌入式设备上利用深度学习技术,与大功率计算机解决方案相比,拾放操作可以更加精确、高效和可持续。在本研究中,我们利用 SSD Mobilenet V2 FPN Lite,通过优化超参数和图像增强,提出了一种在嵌入式系统上进行物体检测的新技术。通过提高图像的红绿蓝(RGB)饱和度,与对照组相比,我们的平均精度(mAP)提高了 7%,与 COCO 2017 验证数据集相比,mAP 提高了 20%。使用 0.08 的学习率和边缘张量处理单元(TPU),我们获得了 97% 的高实时检测分数。高检测分数对控制算法非常重要,该算法使用边界框向协作机器人发送信号,以进行拾放操作。
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引用次数: 0
Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series 非静态时间序列异常的在线检测和模糊聚类
Pub Date : 2024-01-24 DOI: 10.3390/signals5010003
Changjiang He, David S. Leslie, James A. Grant
We consider the challenge of detecting and clustering point and collective anomalies in streaming data that exhibit significant nonlinearities and seasonal structures. The challenge is motivated by detecting problems in a communications network, where we can measure the throughput of nodes, and wish to rapidly detect anomalous traffic behaviour. Our approach is to train a neural network-based nonlinear autoregressive exogenous model on initial training data, then to use the sequential collective and point anomaly framework to identify anomalies in the residuals generated by comparing one-step-ahead predictions of the fitted model with the observations, and finally, we cluster the detected anomalies with fuzzy c-means clustering using empirical cumulative distribution functions. The autoregressive model is sufficiently general and robust such that it provides the nearly (locally) stationary residuals required by the anomaly detection procedure. The combined methods are successfully implemented to create an adaptive, robust, computational framework that can be used to cluster point and collective anomalies in streaming data. We validate the method on both data from the core of the UK’s national communications network and the multivariate Skoltech anomaly benchmark and find that the proposed method succeeds in dealing with different forms of anomalies within the nonlinear signals and outperforms conventional methods for anomaly detection and clustering.
我们所面临的挑战是,如何在表现出显著非线性和季节性结构的流数据中检测并聚类点状和集体异常。这一挑战的动机是检测通信网络中的问题,我们可以测量节点的吞吐量,并希望快速检测异常流量行为。我们的方法是在初始训练数据上训练一个基于神经网络的非线性自回归外生模型,然后使用序列集合和点异常框架来识别通过比较拟合模型的一步预测值和观测值而产生的残差中的异常,最后,我们使用经验累积分布函数对检测到的异常进行模糊 c-means 聚类。自回归模型具有足够的通用性和鲁棒性,可以提供异常检测程序所需的接近(局部)静止的残差。我们成功地实施了这些组合方法,创建了一个自适应、稳健的计算框架,可用于对流数据中的点和集合异常进行聚类。我们在英国国家通信网络核心数据和多变量 Skoltech 异常基准上验证了该方法,发现所提出的方法能成功处理非线性信号中不同形式的异常,并优于异常检测和聚类的传统方法。
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引用次数: 0
The Effect of Jittered Stimulus Onset Interval on Electrophysiological Markers of Attention in a Brain–Computer Interface Rapid Serial Visual Presentation Paradigm 在脑机接口快速串行视觉呈现范式中,抖动刺激起始间隔对注意力电生理标记的影响
Pub Date : 2024-01-09 DOI: 10.3390/signals5010002
Daniel Klee, Tab Memmott, Barry Oken
Brain responses to discrete stimuli are modulated when multiple stimuli are presented in sequence. These alterations are especially pronounced when the time course of an evoked response overlaps with responses to subsequent stimuli, such as in a rapid serial visual presentation (RSVP) paradigm used to control a brain–computer interface (BCI). The present study explored whether the measurement or classification of select brain responses during RSVP would improve through application of an established technique for dealing with overlapping stimulus presentations, known as irregular or “jittered” stimulus onset interval (SOI). EEG data were collected from 24 healthy adult participants across multiple rounds of RSVP calibration and copy phrase tasks with varying degrees of SOI jitter. Analyses measured three separate brain signals sensitive to attention: N200, P300, and occipitoparietal alpha attenuation. Presentation jitter visibly reduced intrusion of the SSVEP, but in general, it did not positively or negatively affect attention effects, classification, or system performance. Though it remains unclear whether stimulus overlap is detrimental to BCI performance overall, the present study demonstrates that single-trial classification approaches may be resilient to rhythmic intrusions like SSVEP that appear in the averaged EEG.
当多个刺激依次呈现时,大脑对离散刺激的反应会受到调节。当诱发反应的时间过程与对后续刺激的反应重叠时,这些改变尤其明显,例如在用于控制脑机接口(BCI)的快速序列视觉呈现(RSVP)范例中。本研究探讨了在 RSVP 过程中,应用一种处理重叠刺激呈现的成熟技术(即不规则或 "抖动 "刺激起始间隔 (SOI))是否会改善对选定大脑反应的测量或分类。研究人员收集了 24 名健康成年参与者的脑电图数据,这些数据来自多轮 RSVP 校准和具有不同程度 SOI 抖动的复制短语任务。分析测量了三种对注意力敏感的独立大脑信号:N200、P300 和枕顶阿尔法衰减。呈现抖动明显减少了 SSVEP 的侵入,但总的来说,它对注意效应、分类或系统性能没有积极或消极的影响。虽然目前还不清楚刺激重叠是否会对 BCI 的整体性能造成损害,但本研究表明,单次试验分类方法可能对平均脑电图中出现的 SSVEP 等节律性侵入具有抵抗力。
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引用次数: 0
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Signals
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