维普期刊资源整合服务平台（V6.5）

维普中文期刊产品整合服务

7篇 您的检索式：作者名="Pasiliao"

选中清除导出查看参考文献查看引证文献引用追踪共1页首页上一页第1页下一页末页 /1 跳转

		题名	作者	年代	出处	被引量
	1	Decreased colonic motility in persons with chronic spinal cord injury显示文摘	Noel R Fajardo Ronnie-vic Pasiliao Roberta Modeste-Duncan Graham Creasey William A Bauman Mark A Korsten	2003	The American Journal of Gastroenterology2003，，1：	1
	2	Minimum vertex cover problem for interdependent networks with cascading failures 显示文摘	Veremyev A Sorokin A Boginski V Pasiliao E L	2014	European Journal of Operational Research2014，232，49：	1
	3	Decreased colonic motility in persons with chronic spinal cord injury显示文摘	Fajardo NR Pasiliao R Duncan RM	2003	Am Gastroenterol2003，98，：	1
	4	Decreased colonic motility in persons with chronic spinal cord injury 显示文摘	Fajardo NR Pasiliao RV Modeste DR	2003	Am J Gastroenterol2003，98，1：	1
	5	Decreased colonic motility in persons with chronic spinal cord injury 显示文摘	FAJARDO N R PASILIAO R ROBERTA M D	2003	Am J Gastroenterol2003，98，1：	1
	6	LoSI: Large Scale Location Inference Through FM Signal Integration and Estimation显示文摘In this paper we present a large scale, passive positioning system that can be used for approximate localization in Global Positioning System(GPS) denied/spoofed environments. This system can be used for detecting GPS spoofing as well as for initial position estimation for input to other GPS free positioning and navigation systems like Terrain Contour Matching(TERCOM). Our Location inference through Frequency Modulation(FM)Signal Integration and estimation(LoSI) system is based on broadcast FM radio signals and uses Received Signal Strength Indicator(RSSI) obtained using a Software Defined Radio(SDR). The RSSI thus obtained is used for indexing into an estimated model of expected FM spectrum for the entire United States. We show that with the hardware for data acquisition, a single point resolution of around 3 miles and associated algorithms, we are capable of positioning with errors as low as a single pixel(more precisely around 0.12 mile). The algorithm uses a largescale model estimation phase that computes the expected FM spectrum in small rectangular cells(realized using geohashes) across the Contiguous United States(CONUS). We define and use Dominant Channel Descriptor(DCD) features, which can be used for positioning using time varying models. Finally we use an algorithm based on Euclidean nearest neighbors in the DCD feature space for position estimation. The system first runs a DCD feature detector on the observed spectrum and then solves a subset query formulation to find Inference Candidates(IC).Finally, it uses a simple Euclidean nearest neighbor search on the ICs to localize the observation. We report results on 1500 points across Florida using data and model estimates from 2015 and 2017. We also provide a Bayesian decision theoretic justification for the nearest neighbor search.	Tathagata Mukherjee Piyush Kumar Debdeep Pati Erik Blasch Eduardo Pasiliao Liqin Xu	2019	Big Data Mining and Analytics2019，2，4：	0
	7	Deep Feature Learning for Intrinsic Signature Based Camera Discrimination显示文摘In this paper we consider the problem of“end-to-end”digital camera identification by considering sequence of images obtained from the cameras.The problem of digital camera identification is harder than the problem of identifying its analog counterpart since the process of analog to digital conversion smooths out the intrinsic noise in the analog signal.However it is known that identifying a digital camera is possible by analyzing the camera’s intrinsic sensor artifacts that are introduced into the images/videos during the process of photo/video capture.It is known that such methods are computationally intensive requiring expensive pre-processing steps.In this paper we propose an end-to-end deep feature learning framework for identifying cameras using images obtained from them.We conduct experiments using three custom datasets:the first containing two cameras in an indoor environment where each camera may observe different scenes having no overlapping features,the second containing images from four cameras in an outdoor setting but where each camera observes scenes having overlapping features and the third containing images from two cameras observing the same checkerboard pattern in an indoor setting.Our results show that it is possible to capture the intrinsic hardware signature of the cameras using deep feature representations in an end-to-end framework.These deep feature maps can in turn be used to disambiguate the cameras from each another.Our system is end-to-end,requires no complicated pre-processing steps and the trained model is computationally efficient during testing,paving a way to have near instantaneous decisions for the problem of digital camera identification in production environments.Finally we present comparisons against the current state-of-the-art in digital camera identification which clearly establishes the superiority of the end-to-end solution.	Chaity Banerjee Tharun Kumar Doppalapudi Eduardo Pasiliao Jr. Tathagata Mukherjee	2022	Big Data Mining and Analytics2022，5，3：	0

返回顶部每页显示：

共1页首页上一页第1页下一页末页 /1 跳转

goolge学术搜索-维普

网站首页 | 关于我们 | 联系我们 | 产品服务 | 客服中心 | 广告服务 | 版权声明 | 网站联盟 | 友情链接 | 售卡网点

版权所有© 渝B2-20050021-1　渝公网安备 50019002500403号　违法和不良信息举报中心

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费