{"title":"Parallel Decision Fusion with Local Constraints","authors":"Weiqiang Dong, Moshe Kam","doi":"10.1109/CISS50987.2021.9400263","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400263","url":null,"abstract":"Motivated by an example by Tenney and Sandell (1981), we discuss the trade-off between performance of local detectors (LDs) and the combined LD/Data Fusion Center system in parallel decision fusion architectures. In these architectures the LDs make observations, translate these observations to local decisions, and send these local decisions forward to a Data Fusion Center (DFC). The DFC uses the local decisions to synthesize a global decision (in our context both local and global decisions are binary and pertain to binary hypothesis testing based on the LD observations; in other words, both LDs and DFC decide whether to accept or reject a hypothesis). The original example demonstrated how the minimization of a global performance index by the combined system may yield an alignment of the local detectors that avoids a high value of the performance index, but otherwise have no value at the LD level (the LDs are directed to make constant decisions that are almost independent of the observations, in order to avoid a local-decision combination that would incur a high penalty). If we require that the global performance index be minimized while the LDs are also allowed to minimize a local performance index (or have constraints on their error probabilities), a trade-off emerges between the local and global performances. In this paper we provide an example similar in nature to the Tenney-Sandell example, and proceed to analyze the impact of performance constraints on the LDs on the design and performance of the parallel decision fusion architecture. If we provide reasonable constraints on the performance of the LDs, a compromise can be established between the global performance index and the local LD performances.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122827102","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}
{"title":"An Ensemble of Deep Semantic Representation for Medical X-ray Image Classification","authors":"M. Zare, Mehdi Mehtarizadeh","doi":"10.1109/CISS50987.2021.9400268","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400268","url":null,"abstract":"An efficient medical image classification system has gained high interest in the scientific community. This paper presents a classification algorithm that aims to gain a high accuracy rate by addressing some of the typical challenges involved in classification of large medical datasets. In this paper, the convolutional neural networks (CNNs) are employed together with probabilistic latent semantic analysis (PLSA) which are capable of mining hidden semantics of images. This high-level semantic representation of the images is then fed into a discriminative support vector machine (SVM) to build a classification model. An ensemble of machine learning models is also employed to utilize the capability of classification models created from different sets of data. The evaluation is based on a medical image dataset consisting of 11,000 X-ray images from 116 distinct categories. The classification accuracy rate obtained by the proposed classification model is 94.5 %. The results show that the proposed classification model outperformed the methods in the literature evaluated on the same benchmark dataset.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"194 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127564493","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}
{"title":"Kronecker Compressive Sensing for OFDM Channel Estimation in Millimeter Wave Channels","authors":"John Franklin, A. Cooper","doi":"10.1109/CISS50987.2021.9400223","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400223","url":null,"abstract":"We develop a channel estimation approach utilizing the joint sparsity in the virtual channel for OFDM channel estimation in a frequency selective millimeter wave channel. Our approach addresses possible non-uniformly spaced base station antenna arrays by representing them as a sampling of a larger virtual antenna array with uniform spacing. A virtual channel representation is developed associated with the virtual array where the channel is presumed sparse. A Kronecker Compressive Sensing approach is adopted to estimate the virtual channel represented by receive angle of arrivals and delays. This approach is shown to out perform the least squares channel estimate and newly proposed single antenna channel estimators implemented on a per antenna basis.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"89 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132536210","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}
L. Z. Ladeira, Pedro Eduardo Baird, Filipe E. S. P. Palma
{"title":"Spectrometry for Light Bulb Classification","authors":"L. Z. Ladeira, Pedro Eduardo Baird, Filipe E. S. P. Palma","doi":"10.1109/CISS50987.2021.9400295","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400295","url":null,"abstract":"Light bulbs energy consumption are paid by city halls to electrical companies in Brazil. These electrical companies do not have a guaranteed aspect of each light bulb's characteristics. Light bulbs with distinct power and steam type have different energy consumption. Most of the time, to guarantee the light bulbs characteristics a technician has to climb and verify each light pole. It is clear that this task is exhaustive and has to be repeated many times a year. In this work, a system is proposed capable of measuring the light bulb's characteristics easily. The system utilizes machine learning models to identify the required characteristics using as input the light spectrum. The results show that the trained models are able to identify correctly steam type, power, and brand.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132932021","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}
{"title":"Improvements to Sanov and PAC Sublevel-set Bounds for Discrete Random Variables","authors":"M. A. Tope, Joel M. Morris","doi":"10.1109/CISS50987.2021.9400225","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400225","url":null,"abstract":"We derive an improvement for probably approximately correct (PAC) sublevel-set bounds for the multinomial distributed discrete random variables. Previous bounds (including Sanov's Theorem) show that the Kullback Leibler (KL) divergence between the empirical probability mass function (pmf) and the true PMF converges with rate O(log(N)/N), where $N$ is the number of independent and identically distributed (i.i.d.) samples used to compute the empirical pmf. We interpret the KL divergence as bounding the probability that a multinomial distributed random variable (RV) deviates into a halfspace and construct improved uniform PAC sublevel-set bounds that converge with rates $O$(log (log (N)) / N). These results bound the worst case performance for a number of machine learning algorithms. Finally, the ‘halfspace bound’ methodology suggests further improvements are possible for non-uniform bounds. In this paper, we derive an improvement (on the convergence rate) for various Probably Approximately Correct (PAC) bounds (including Sanov's Theorem) for multinomially distributed discrete random variables.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134503157","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}
{"title":"Autonomy's Hierarchy of Needs: Smart City Ecosystems for Autonomous Space Habitats","authors":"Gregory Falco","doi":"10.1109/CISS50987.2021.9400218","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400218","url":null,"abstract":"Space habitats such as NASA's proposed Artemis Base Camp will house both astronauts and autonomous systems. The Artemis Base Camp's infrastructure could provide supporting services to its tenants to optimize their function. This calls for a smart city ecosystem. Maslow's Hierarchy of Needs has been engaged as a framework to inform human-centric smart city design and feature prioritization; however, autonomous systems have different needs to humans. This paper aligns the needs of humans and autonomous systems in a framework called Autonomy's Hierarchy of Needs, which provides a smart city ecosystem design framework for the Artemis Base Camp.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130380504","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}
{"title":"Energy-balancing AC and DC grid-forming control for power converters","authors":"Dominic Gross","doi":"10.1109/CISS50987.2021.9400298","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400298","url":null,"abstract":"Control strategies for grid-connected power converters can be broadly categorized into (i) grid-forming strategies that form a stable AC voltage (i.e., magnitude and frequency) at the converter terminal but assume that the DC voltage is stabilized by a fully controllable power source, and (ii) grid-following controls that form a stable DC voltage but assume that the AC voltage is stabilized by other devices in the grid. Consequently, grid-following is often fragile and frequently fails when the power system is under stress. In contrast, grid-forming power converters are commonly seen as a robust solution that is envisioned to replace synchronous machines as the cornerstone of future power systems. However, requiring a stable DC voltage is a significant obstacle in several application scenarios such as highvoltage DC transmission, low-frequency AC networks with converter-based frequency conversion, flywheel energy storage systems, and grid-connected renewable generation with limited flexibility. Instead, we propose control strategies that simultaneously form the converter's DC side and AC side voltage while ensuring power balance between the two sides and discuss theoretical results and applications in power systems consisting of interconnected AC and DC grids and grid-connected renewable generation.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115647871","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}
Daniel R. Mendat, Jonah P. Sengupta, Drake K. Foreman, A. Andreou
{"title":"Parallel Computation of Event-Based Visual Features Using Relational Graphs","authors":"Daniel R. Mendat, Jonah P. Sengupta, Drake K. Foreman, A. Andreou","doi":"10.1109/CISS50987.2021.9400272","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400272","url":null,"abstract":"A graphical framework to approximate visual features from spike-based sensor data has been demonstrated. An event-based camera or dynamic vision sensor (DVS) provides the sensory input into the network which computes the intrascene optical flow, spatial gradient, and absolute intensity. The network uses the sparse, event-based input along with fundamental relations in parallel to converge upon quantities via incremental optimization. An event-based algorithm to compute optical flow was used to provide another stream of input into the network to aid convergence. A full network has been deployed in Python and parallelized to demonstrate its potential deployment on specialized hardware.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123030797","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}
{"title":"Quickest Change Detection in the Presence of Transient Adversarial Attacks","authors":"Thirupathaiah Vasantam, D. Towsley, V. Veeravalli","doi":"10.1109/CISS50987.2021.9400287","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400287","url":null,"abstract":"We study a monitoring system in which the distributions of sensors' observations change from a nominal distribution to an abnormal distribution in response to an adversary's presence. The system uses the quickest change detection procedure, the Shewhart rule, to detect the adversary that uses its resources to affect the abnormal distribution, so as to hide its presence. The metric of interest is the probability of missed detection within a predefined number of time-slots after the changepoint. Assuming that the adversary's resource constraints are known to the detector, we find the number of required sensors to make the worst-case probability of missed detection less than an acceptable level. The distributions of observations are assumed to be Gaussian, and the presence of the adversary affects their mean. We also provide simulation results to support our analysis.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122464378","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}
Hendrik Bernd Petersen, S. Agarwal, P. Jung, B. Bah
{"title":"Improving the Reliability of Pooled Testing with Combinatorial Decoding and Compressed Sensing","authors":"Hendrik Bernd Petersen, S. Agarwal, P. Jung, B. Bah","doi":"10.1109/CISS50987.2021.9400261","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400261","url":null,"abstract":"The problem of detecting few viral infections in a possibly large group with as few as possible tests can be modeled as either a group testing (GT) or a compressed sensing (CS) problem. CS approaches also allow to recover the viral load, but the underlying noise models are not common in CS and not well understood. Therefore, we study hybrid approaches that combine methods from CS and GT on various noise models. We compare the performance of such approaches with classical decoders from CS and GT. Our results show that combined strategies can improve the error rates and provide viral load estimation.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128588231","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}