2019 53rd Asilomar Conference on Signals, Systems, and Computers最新文献_第8页

Average Age of Information in Multi-Source Self-Preemptive Status Update Systems with Packet Delivery Errors 具有包传递错误的多源自抢占状态更新系统中信息的平均年龄

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048914

S. Farazi, A. G. Klein, D. Brown

{"title":"Average Age of Information in Multi-Source Self-Preemptive Status Update Systems with Packet Delivery Errors","authors":"S. Farazi, A. G. Klein, D. Brown","doi":"10.1109/IEEECONF44664.2019.9048914","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048914","url":null,"abstract":"This paper studies the \"age of information\" (AoI) in a multi-source status update system where multiple sources send updates of their process to a monitor through a last-come first-served server with preemption in service and packet delivery errors. Arrival times of the status updates from the sources are assumed to be random according to independent Poisson processes. Service times are also assumed to be exponentially distributed and independent of the status arrivals. If the server is idle, any arriving packet immediately enters service. When the server is busy, if the arriving packet and the packet in service are from the same source, the packet in service is preempted and the new packet immediately enters service. Otherwise, any arriving packet is discarded. A closed-form expression for the average AoI of each source as a function of the system parameters is derived and, for the case without packet delivery errors, is compared to the average AoI in the \"source agnostic\" preemption setting considered by Yates and Kaul where any source can preempt any other source. The results show that source agnostic preemption in service results in better average AoI than self preemption in service for all sources.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"8 1","pages":"396-400"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87098146","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: 20

Modeling Variability in Brain Architecture with Deep Feature Learning 基于深度特征学习的脑结构变异性建模

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048805

Aishwarya H. Balwani, Eva L. Dyer

{"title":"Modeling Variability in Brain Architecture with Deep Feature Learning","authors":"Aishwarya H. Balwani, Eva L. Dyer","doi":"10.1109/IEEECONF44664.2019.9048805","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048805","url":null,"abstract":"The brain has long been divided into distinct areas based upon its local microstructure, or patterned composition of cells, genes, and proteins. While this taxonomy is incredibly useful and provides an essential roadmap for comparing two brains, there is also immense anatomical variability within areas that must be incorporated into models of brain architecture. In this work we leverage the expressive power of deep neural networks to create a data-driven model of intra- and inter-brain area variability. To this end, we train a convolutional neural network that learns relevant microstructural features directly from brain imagery. We then extract features from the network and fit a simple classifier to them, thus creating a simple, robust, and interpretable model of brain architecture. We further propose and show preliminary results for the use of features from deep neural networks in conjunction with unsupervised learning techniques to find fine-grained structure within brain areas. We apply our methods to micron-scale X-ray microtomography images spanning multiple regions in the mouse brain and demonstrate that our deep feature-based model can reliably discriminate between brain areas, is robust to noise, and can be used to reveal anatomically relevant patterns in neural architecture that the network wasn’t trained to find.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"2 1","pages":"1186-1191"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87136699","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: 3

Approximated Canonical Signed Digit for Error Resilient Intelligent Computation 误差弹性智能计算的近似正则有符号数

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9049051

G. Cardarilli, L. Nunzio, R. Fazzolari, A. Nannarelli, M. Re

引用次数: 1

Low-Rank Modeling of Local Sinogram Neighborhoods with Tomographic Applications 层析成像应用于局部正弦图邻域的低秩建模

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048651

Rodrigo A. Lobos, R. Leahy, J. Haldar

引用次数: 0

Deep Learning for Musculoskeletal Image Analysis 肌肉骨骼图像分析的深度学习

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048671

I. Irmakci, S. Anwar, D. Torigian, Ulas Bagci

{"title":"Deep Learning for Musculoskeletal Image Analysis","authors":"I. Irmakci, S. Anwar, D. Torigian, Ulas Bagci","doi":"10.1109/IEEECONF44664.2019.9048671","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048671","url":null,"abstract":"The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging (MRI), and ultrasound) and their precise analysis by expert radiologists. Radiology scans can also help assessment of metabolic health, aging, and diabetes. This study presents how machine learning, specifically deep learning methods, can be used for rapid and accurate image analysis of MRI scans, an unmet clinical need in MSK radiology. As a challenging example, we focus on automatic analysis of knee images from MRI scans and study machine learning classification of various abnormalities including meniscus and anterior cruciate ligament tears. Using widely used convolutional neural network (CNN) based architectures, we comparatively evaluated the knee abnormality classification performances of different neural network architectures under limited imaging data regime and compared single and multi-view imaging when classifying the abnormalities. Promising results indicated the potential use of multi-view deep learning based classification of MSK abnormalities in routine clinical assessment.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"43 1","pages":"1481-1485"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85747915","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: 14

Optimal Adaptive Sampling for Boundary Estimation with Mobile Sensors 移动传感器边界估计的最优自适应采样

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048986

P. Kearns, J. Lipor, B. Jedynak

引用次数: 2

Perceptually Driven Conditional GAN for Fourier Ptychography 用于傅立叶平面摄影的感知驱动条件GAN

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9049029

Abhinau K. Venkataramanan, Shashank Gupta, Sumohana S. Channappayya

引用次数: 0

Distributed empirical risk minimization over directed graphs 有向图上的分布式经验风险最小化

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9049065

Ran Xin, Anit Kumar Sahu, S. Kar, U. Khan

引用次数: 2

Radar Beampattern Design for a Drone Swarm 无人机群雷达波束设计

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048820

M. Alaee-Kerahroodi, K. Mishra, B. Shankar

引用次数: 1

Reducing the Complexity of Fingerprinting-Based Positioning using Locality-Sensitive Hashing 利用位置敏感哈希降低基于指纹定位的复杂性

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048657

Larry L Tang, Ramina Ghods, Christoph Studer

引用次数: 2