Defense + Commercial Sensing最新文献_第5页

A systems theoretic perspective on open architectures for learning systems 从系统论角度看学习系统的开放式架构

Defense + Commercial Sensing Pub Date : 2024-06-06 DOI: 10.1117/12.3012391

Tyler Cody, Peter A. Beling

{"title":"A systems theoretic perspective on open architectures for learning systems","authors":"Tyler Cody, Peter A. Beling","doi":"10.1117/12.3012391","DOIUrl":"https://doi.org/10.1117/12.3012391","url":null,"abstract":"The advancement of open architecture ecosystems is fundamentally dependent on the interoperability, scalability, and adaptability of their constituent elements. As Machine Learning (ML) systems become increasingly integral to these ecosystems, the need for a systematic approach to engineer, deploy, and re-engineer them grows. This paper presents a novel modeling approach based on recently published, formal, systems-theoretic models of learning systems. These models serve dual purposes: first, they give a theoretical grounding to standards that govern the architecture, functionality, and performance criteria for ML systems; second, they allow for requirements to be specified at various levels of abstraction to ensure the systems are intrinsically aligned with the overall objectives of the open architecture ecosystem they belong to. Through the proposed modeling approach, we demonstrate how the adoption of standardized models can significantly enhance interoperability between disparate machine learning systems and other architectural components. Further, we relate our framework to on-going efforts such as Open Neural Network Exchange (ONNX). We identify how our approach can be used to address limitations in government acquisition processes for ML systems. The proposed systems-theoretic framework provides a structured methodology that contributes to the foundational building blocks for open architecture ecosystems for ML systems, thereby advancing the state-of-the-art in complex system integration.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"62 s284","pages":"1305808 - 1305808-6"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of uncertainties in above-water radiometric measurements from hyperspectral and polarimetric imaging 通过高光谱和偏振成像估算水上辐射测量的不确定性

Defense + Commercial Sensing Pub Date : 2024-06-06 DOI: 10.1117/12.3014923

M. Malinowski, Eder Herrera-Estrella, Robert Foster, Jacopo Agagliate, Alex Gilerson

{"title":"Estimation of uncertainties in above-water radiometric measurements from hyperspectral and polarimetric imaging","authors":"M. Malinowski, Eder Herrera-Estrella, Robert Foster, Jacopo Agagliate, Alex Gilerson","doi":"10.1117/12.3014923","DOIUrl":"https://doi.org/10.1117/12.3014923","url":null,"abstract":"Uncertainties in radiance above the ocean surface are mostly determined by the skylight reflected from the air-water interface. Their accurate characterization is important for the accurate measurements of the water-leaving radiance as well as for the estimation of the impact of these uncertainties on the atmospheric correction of satellite and airborne ocean observations. Uncertainties are affected by the state of the ocean surface dependent on the wind speed and the corresponding reflection coefficient, which can be calculated based on Cox-Munk relationships. These uncertainties were estimated in the hyperspectral mode from shipborne measurements by the Hyperspectral Imager ULTRIS X20 (Cubert, Germany), with a 400-1000 nm wavelength range and a 410x410 pixel resolution. Measurements were taken during a VIIRS Cal/Val cruise in Hawaii area in a broad range of wind speeds 0-10 m/s and at viewing angles 20-60 degrees. In addition, airborne measurements from a helicopter at four different altitudes of 60, 150, 450, and 750 meters were carried out in different parts of Chesapeake Bay to establish a relationship between uncertainties and altitude. For these, a Teledyne DALSA M2450 polarized camera with a filter wheel containing several filters at different spectral bands was used together with the imager to characterize wave slope statistics and to determine uncertainties in measurements of the Stokes vector components and the degree of linear polarization (DoLP). Measurement uncertainties are further compared with simulations.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"37 1","pages":"1306103 - 1306103-13"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancements in pathogen detection: the integration of SERS and LSPR technologies in handheld clinical diagnostics 病原体检测的进展：将 SERS 和 LSPR 技术整合到手持式临床诊断中

Defense + Commercial Sensing Pub Date : 2024-06-06 DOI: 10.1117/12.3022222

Sebastian Huelck

引用次数: 0

Bridging the AI/ML gap with explainable symbolic causal models using information theory 利用信息论建立可解释的符号因果模型，弥合人工智能与人工智能之间的差距

Defense + Commercial Sensing Pub Date : 2024-06-06 DOI: 10.1117/12.3014447

Stuart W. Card

{"title":"Bridging the AI/ML gap with explainable symbolic causal models using information theory","authors":"Stuart W. Card","doi":"10.1117/12.3014447","DOIUrl":"https://doi.org/10.1117/12.3014447","url":null,"abstract":"We report favorable preliminary findings of work in progress bridging the Artificial Intelligence (AI) gap between bottom-up data-driven Machine Learning (ML) and top-down conceptually driven symbolic reasoning. Our overall goal is automatic generation, maintenance and utilization of explainable, parsimonious, plausibly causal, probably approximately correct, hybrid symbolic/numeric models of the world, the self and other agents, for prediction, what-if (counter-factual) analysis and control. Our old Evolutionary Learning with Information Theoretic Evaluation of Ensembles (ELITE2) techniques quantify strengths of arbitrary multivariate nonlinear statistical dependencies, prior to discovering forms by which observed variables may drive others. We extend these to apply Granger causality, in terms of conditional Mutual Information (MI), to distinguish causal relationships and find their directions. As MI can reflect one observable driving a second directly or via a mediator, two being driven by a common cause, etc., to untangle the causal graph we will apply Pearl causality with its back- and front-door adjustments and criteria. Initial efforts verified that our information theoretic indices detect causality in noise corrupted data despite complex relationships among hidden variables with chaotic dynamics disturbed by process noise, The next step is to apply these information theoretic filters in Genetic Programming (GP) to reduce the population of discovered statistical dependencies to plausibly causal relationships, represented symbolically for use by a reasoning engine in a cognitive architecture. Success could bring broader generalization, using not just learned patterns but learned general principles, enabling AI/ML based systems to autonomously navigate complex unknown environments and handle “black swans”.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"23 25","pages":"1305802 - 1305802-4"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed edge computing for cooperative augmented reality: enhancing mobile sensing capabilities 用于合作增强现实的分布式边缘计算：增强移动传感能力

Defense + Commercial Sensing Pub Date : 2024-06-06 DOI: 10.1117/12.3021841

Cheng-Yu Cheng, Qi Zhao, Cheng-Ying Wu, Yuchen Yang, Muhammad Qureshi, Hang Liu, Genshe Chen

{"title":"Distributed edge computing for cooperative augmented reality: enhancing mobile sensing capabilities","authors":"Cheng-Yu Cheng, Qi Zhao, Cheng-Ying Wu, Yuchen Yang, Muhammad Qureshi, Hang Liu, Genshe Chen","doi":"10.1117/12.3021841","DOIUrl":"https://doi.org/10.1117/12.3021841","url":null,"abstract":"Cooperative Augmented Reality (AR) can provide real-time, immersive, and context-aware situational awareness while enhancing mobile sensing capabilities and benefiting various applications. Distributed edge computing has emerged as an essential paradigm to facilitate cooperative AR. We designed and implemented a distributed system to enable fast, reliable, and scalable cooperative AR. In this paper, we present a novel approach and architecture that integrates advanced sensing, communications, and processing techniques to create such a cooperative AR system and demonstrate its capability with HoloLens and edge servers connected over a wireless network. Our research addresses the challenges of implementing a distributed cooperative AR system capable of capturing data from a multitude of sensors on HoloLens, performing fusion and accurate object recognition, and seamlessly projecting the reconstructed 3D model into the wearer’s field of view. The paper delves into the intricate architecture of the proposed cooperative AR system, detailing its distributed sensing and edge computing components, and the Apache Storm-integrated platform. The implementation encompasses data collection, aggregation, analysis, object recognition, and rendering of 3D models on the HoloLens, all in real-time. The proposed system enhances the AR experience while showcasing the vast potential of distributed edge computing. Our findings illustrate the feasibility and advantages of merging distributed cooperative sensing and edge computing to offer dynamic, immersive AR experiences, paving the way for new applications.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"55 1","pages":"130620O - 130620O-11"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantization to accelerate inference in multimodal 3D object detection 量化加速多模态三维物体检测推理

Defense + Commercial Sensing Pub Date : 2024-06-06 DOI: 10.1117/12.3013702

Billy E. Geerhart, Venkateswara Dasari, Brian Rapp, Peng Wang, Ju Wang, Christopher X. Payne

引用次数: 0

A lightweight deep learning model for rapid detection of fabricated ENF signals from audio sources 轻量级深度学习模型，用于快速检测音频源中的伪造 ENF 信号

Defense + Commercial Sensing Pub Date : 2024-06-06 DOI: 10.1117/12.3013456

Adilet Pazylkarim, Deeraj Nagothu, Yu Chen

{"title":"A lightweight deep learning model for rapid detection of fabricated ENF signals from audio sources","authors":"Adilet Pazylkarim, Deeraj Nagothu, Yu Chen","doi":"10.1117/12.3013456","DOIUrl":"https://doi.org/10.1117/12.3013456","url":null,"abstract":"The rapid advancement of multimedia content editing software tools has made it increasingly easy for malicious actors to manipulate real-time multimedia data streams, encompassing audio and video. Among the notorious cybercrimes, replay attacks have gained widespread prevalence, necessitating the development of more efficient authentication methods for detection. A cutting-edge authentication technique leverages Electrical Network Frequency (ENF) signals embedded within multimedia content. ENF signals offer a range of advantageous attributes, including uniqueness, unpredictability, and total randomness, rendering them highly effective for detecting replay attacks. To counter potential attackers who may seek to deceive detection systems by embedding fake ENF signals, this study harnesses the growing accessibility of deep Convolutional Neural Networks (CNNs). These CNNs are not only deployable on platforms with limited computational resources, such as Single-Board Computers (SBCs), but they also exhibit the capacity to swiftly identify interference within a signal by learning distinctive spatio-temporal patterns. In this paper, we explore applying a Computationally Efficient Deep Learning Model (CEDM) as a powerful tool for rapidly detecting potential fabrications within ENF signals originating from diverse audio sources. Our experimental study validates the effectiveness of the proposed method.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"18 23","pages":"1305811 - 1305811-13"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative AI agile assistant 生成式人工智能敏捷助手

Defense + Commercial Sensing Pub Date : 2024-06-06 DOI: 10.1117/12.3011173

Darrell L. Young, Perry Boyette, James Moreland, Jason Teske

引用次数: 0

Active detection for fish species recognition in underwater environments 水下环境中鱼类物种识别的主动探测

Defense + Commercial Sensing Pub Date : 2024-06-06 DOI: 10.1117/12.3013344

Chiranjibi Shah, M. M. Nabi, S. Y. Alaba, Ryan Caillouet, Jack H. Prior, Matthew Campbell, Matthew D. Grossi, Farron Wallace, John E. Ball, Robert J. Moorhead

{"title":"Active detection for fish species recognition in underwater environments","authors":"Chiranjibi Shah, M. M. Nabi, S. Y. Alaba, Ryan Caillouet, Jack H. Prior, Matthew Campbell, Matthew D. Grossi, Farron Wallace, John E. Ball, Robert J. Moorhead","doi":"10.1117/12.3013344","DOIUrl":"https://doi.org/10.1117/12.3013344","url":null,"abstract":"Fish species must be identified for stock assessments, ecosystem monitoring, production management, and the conservation of endangered species. Implementing algorithms for fish species detection in underwater settings like the Gulf of Mexico poses a formidable challenge. Active learning, a method that efficiently identifies informative samples for annotation while staying within a budget, has demonstrated its effectiveness in the context of object detection in recent times. In this study, we present an active detection model designed for fish species recognition in underwater environments. This model can be employed as an object detection system to effectively lower the expense associated with manual annotation. It uses epistemic uncertainty with Evidential Deep Learning (EDL) and proposes a novel module denoted as Model Evidence Head (MEH) for fish species detection in underwater environments. It employs Hierarchical Uncertainty Aggregation (HUA) to obtain the informativeness of an image. We conducted experiments using a fine-grained and extensive dataset of reef fish collected from the Gulf of Mexico, specifically the Southeast Area Monitoring and Assessment Program Dataset 2021 (SEAMAPD21). The experimental results demonstrate that an active detection framework achieves better detection performance on the SEAMAPD21 dataset demonstrating a favorable balance between performance and data efficiency for underwater fish species recognition.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"5 15","pages":"130610D - 130610D-10"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application specificity of data for pre-training in computer vision 计算机视觉预训练数据的应用特殊性

Defense + Commercial Sensing Pub Date : 2024-06-06 DOI: 10.1117/12.3013088

Gabriel Peters, Scott Couwenhoven, Derek Walvoord, Carl Salvaggio

{"title":"Application specificity of data for pre-training in computer vision","authors":"Gabriel Peters, Scott Couwenhoven, Derek Walvoord, Carl Salvaggio","doi":"10.1117/12.3013088","DOIUrl":"https://doi.org/10.1117/12.3013088","url":null,"abstract":"In an era of immense data generation, unlocking the full potential of Machine Learning (ML) hinges on overcoming the limitations posed by the scarcity of labeled data. In Computer Vision (CV) research, algorithm design must consider this shift and focus instead on the abundance of unlabeled imagery. In recent years, there has been a notable trend within the community toward Self-Supervised Learning (SSL) methods that can leverage this untapped data pool. ML practice promotes self-supervised pre-training for generalized feature extraction on a diverse unlabeled dataset followed by supervised transfer learning on a smaller set of labeled, application-specific images. This shift in learning methods elicits conversation about the importance of pre-training data composition for optimizing downstream performance. We evaluate models with varying measures of similarity between pre-training and transfer learning data compositions. Our findings indicate that front-end embeddings sufficiently generalize learned image features independent of data composition, leaving transfer learning to inject the majority of application-specific understanding into the model. Composition may be irrelevant in self-supervised pre-training, suggesting target data is a primary driver of application specificity. Thus, pre-training deep learning models with application-specific data, which is often difficult to acquire, is not necessary for reaching competitive downstream performance. The capability to pre-train on more accessible datasets invites more flexibility in practical deep learning.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"3 13","pages":"1305803 - 1305803-14"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0