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On capacity of cognitive radio networks with average interference power constraints

IEEE Transactions on Wireless Communications 2009年第4期8卷 1620-1625页

作者： Wang, Cheng-Xiang Hong, Xuemin Chen, Hsiao-Hwa Thompson, John Joint Research Institute for Signal and Image Processing School of Engineering and Physical Sciences Heriot-Watt University United Kingdom Department of Electrical and Computer Engineering University of Waterloo ON Canada Department of Engineering Science National Cheng Kung University Taiwan Joint Research Institute for Signal and Image Processing Institute for Digital Communications University of Edinburgh United Kingdom

Cognitive radio (CR) has been considered as a promising technology to improve the spectrum utilization. In this paper we analyze the capacity of a CR network with average received interference power constraints. Under the assumptions of uniform node placements and a simple power control scheme, the maximum transmit power of a target CR transmitter is characterized by its cumulative distribution function (CDF). We study two CR scenarios for future applications. The first scenario is called the CR based central access network, which aims at providing broadband access to CR devices. In the second scenario, the so-called CR assisted virtual multiple-input multiple-output (MIMO) network, CR is used to improve the access capability of a cellular system. The uplink ergodic channel capacities of both scenarios are derived and analyzed with an emphasis on understanding the impact of numbers of primary users and CR users on the capacity. Numerical and simulation results suggest that the CR based central access network is more suitable for less-populated rural areas where a relatively low density of primary receivers is expected;while the CR assisted virtual MIMO network performs better in urban environments with a dense population of mobile CR users. © 2006 IEEE.

关键词： Cognitive radio

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MS-UNIQUE: Multi-model and sharpness-weighted unsupervised image quality estimation 14

MS-UNIQUE: Multi-model and sharpness-weighted unsupervised i...

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image Quality and System Performance XIV, IQSP 2017

作者： Prabhushankar, Mohit Temel, Dogancan AlRegib, Ghassan Center for Signal and Information Processing School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

In this paper, we train independent linear decoder models to estimate the perceived quality of images. More specifically, we calculate the responses of individual non-overlapping image patches to each of the decoders and scale these responses based on the sharpness characteristics of filter set. We use multiple linear decoders to capture different abstraction levels of the image patches. Training each model is carried out on 100,000 image patches from the imageNet database in an unsupervised fashion. Color space selection and ZCA Whitening are performed over these patches to enhance the descriptiveness of the data. The proposed quality estimator is tested on the LIVE and the TID 2013 image quality assessment databases. Performance of the proposed method is compared against eleven other state of the art methods in terms of accuracy, consistency, linearity, and monotonic behavior. Based on experimental results, the proposed method is generally among the top performing quality estimators in all categories. © 2017, Society for Imaging Science and Technology.

关键词： Decoding

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Building hardware-based low-cost experimental DSP learning modules

Building hardware-based low-cost experimental DSP learning m...

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2008 ASEE Annual Conference and Exposition

作者： Uluagac, A. Williams, Douglas Georgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA United States Department of Undergraduate Affairs Center for Signal and Image Processing Arbutus Center for Distributed Engineering Education

Students often face difficulties in grasping and understanding fundamental digital signal processing (DSP) concepts in the classroom environment. Most DSP courses do not have labs associated with them, and the ones that do typically depend on the usage of text-based programming languages (e.g., MATLAB) that do not allow real-time external hardware interaction. Although the availability of external DSP platforms (e.g. National Instruments Speedy-33, Texas Instruments DSP Student Kits, etc.) for educational purposes has increased because of recent advances in embedded processors and sensor technology, they are rarely used in early DSP courses because of the programming sophistication needed for real-time processing. In this paper we discuss the development of low-cost experimental DSP learning modules for classes that do not usually have a lab component. These modules use National Instruments' Lab VIEW for their programming and development platform with the Speedy-33 DSP board and LEGO Mindstorms NXT Brick as the hardware platforms. Many of the modules can also be run completely on the host computer's sound card. Modules have been developed for examining different aspects of topics such as sampling, aliasing, and filtering, while working with data that has been captured and processed in real-time. Students are able to interact with the hardware and data through GUIs, thus obviating the need to first develop real-time programming skills. This paper will describe these modules and how they are designed to be used both in lectures and as part of homework assignments. © American Society for engineering Education, 2008.

关键词： Digital signal processing

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Using shape contexts method for registration of contra lateral breasts in thermal images

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World Journal of Clinical Oncology 2014年第5期5卷 1055-1059页

作者： Mahnaz Etehadtavakol Eddie Yin-Kwee Ng Niloofar Gheissari Medical Image and Signal Processing Research Center Isfahan University of Medical Sciences Isfahan University School of Mechanical and Aerospace Engineering College of Engineering Nanyang Technological University Electrical and Computer Engineering Department Isfahan University of Technology

AIM: To achieve symmetric boundaries for left and right breasts boundaries in thermal images by registration. METHODS: The proposed method for registration consists of two steps. In the first step, shape context, an approach as presented by Belongie and Malik was applied for registration of two breast boundaries. The shape context is an approach to measure shape similarity. Two sets of finite sample points from shape contours of two breasts are then presented. Consequently, the correspondences between the two shapes are found. By finding correspondences, the sample point which has the most similar shape context is obtained. RESULTS: In this study, a line up transformation which maps one shape onto the other has been estimated in order to complete shape. The used of a thin plate spline permitted good estimation of a plane transformation which has capability to map unselective points from one shape onto the other. The obtained aligningtransformation of boundaries points has been applied successfully to map the two breasts interior points. Some of advantages for using shape context method in this work are as follows:(1) no special land marks or key points are needed;(2) it is tolerant to all common shape deformation; and(3) although it is uncomplicated and straightforward to use, it gives remarkably powerful descriptor for point sets significantly upgrading point set registration. Results are very promising. The proposed algorithm was implemented for 32 cases. Boundary registration is done perfectly for 28 ***: We used shape contexts method that is simple and easy to implement to achieve symmetric boundaries for left and right breasts boundaries in thermal images.

关键词： Breast thermal images Shape contexts Registration Cancer detection Infrared

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A program for distributed laboratories in the ECE CURRICULUM

A program for distributed laboratories in the ECE CURRICULUM

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2008 ASEE Annual Conference and Exposition

作者： Ferri, Bonnie Auerbach, Jill Jackson, Joel Michaels, Jennifer Williams, Douglas Georgia Tech. Graduate Affairs in ECE Georgia Tech. School of Electrical and Computer Engineering Georgia Institute of Technology Center for Signal and Image Processing School of ECE Georgia Tech. Georgia Institute of Technology Faculty of the School of Electrical and Computer Engineering Georgia Tech.

This paper describes a project that seeks to improve undergraduate learning by developing a cohesive program where experiments are introduced into a wide selection of ECE courses that currently do not have labs. Most of the experiments are low cost and portable, which facilitates a decentralized laboratory environment where students perform the experiments at their homes or in the classroom rather than in dedicated laboratories. We will assess our work through the involvement of twelve faculty members teaching ten courses, some of which are offered in distance learning settings. © American Society for engineering Education, 2008.

关键词： Laboratories

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Targeting Negative Flips in Active Learning using Validation Sets

Targeting Negative Flips in Active Learning using Validation...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Benkert, Ryan Prabhushankar, Mohit Alregib, Ghassan Georgia Institute of Technology Olives The Center for Signal and Information Processing Csip School of Electrical and Computer Engineering AtlantaGA United States

ISBN: (纸本)9798350362480

The performance of active learning algorithms can be improved in two ways. The often used and intuitive way is by reducing the overall error rate within the test set. The second way is to ensure that correct predictions are not forgotten when the training set is increased in between rounds. The former is measured by the accuracy of the model and the latter is captured in negative flips between rounds. Negative flips are samples that are correctly predicted when trained with the previous/smaller dataset and incorrectly predicted after additional samples are labeled. In this paper, we discuss improving the performance of active learning algorithms both in terms of prediction accuracy and negative flips. The first observation we make in this paper is that negative flips and overall error rates are decoupled and reducing one does not necessarily imply that the other is reduced. Our observation is important as current active learning algorithms do not consider negative flips directly and implicitly assume the opposite. The second observation is that performing targeted active learning on subsets of the unlabeled pool has a significant impact on the behavior of the active learning algorithm and influences both negative flips and prediction accuracy. We then develop ROSE - a plug-in algorithm that utilizes a small labeled validation set to restrict arbitrary active learning acquisition functions to negative flips within the unlabeled pool. We show that integrating a validation set results in a significant performance boost in terms of accuracy, negative flip rate reduction, or both. © 2024 IEEE.

关键词： Active learning

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Enhanced Machine Learning Approaches for Diagnosing Building Systems

Enhanced Machine Learning Approaches for Diagnosing Building...

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2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

作者： Gharsellaoui, Sondes Mansouri, Majdi Refaat, Shady S. Abu-Rub, Haitham Messaoud, Hassani National Higher Engineering School of Tunis Electrical Engineering Department Monfleury Tunisia Electrical and Computer Engineering Program Texas AM University at Qatar Doha Qatar National Engineering School of Monastir Laboratory of Automatic Signal and Image Processing Monastir Tunisia

ISBN: (纸本)9781728151847

Fault Detection and Classification (FDC) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDC framework is the focus in this paper. The developed approach aims at reducing the energy needs for buildings and improving indoor environment quality. It merges the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers in order to improve the efficiency of FDC in heating systems. Firstly, a multiscale decomposition is used to extract the dynamics of the systems at different scales. The multiscale representation gives several advantages for monitoring heating systems generally driven by events in different time and frequency responses. Secondly, the multiscaled data-sets are then introduced into the PCA model to extract more efficient characteristics. Thirdly, the ML algorithms are applied to the extracted and selected characteristics to deal with the problem of fault diagnosis. The FDC efficiency of the developed technique is evaluated using a simulated data extracted from heating systems. © 2019 IEEE.

关键词： Fault detection

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Non-rigid registration of Fluorescein Angiography and Optical Coherence Tomography via scanning laser ophthalmoscope imaging 39

Non-rigid registration of Fluorescein Angiography and Optica...

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39th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2017

作者： Ghasemi Kamasi, Zeinab Mokhtari, Marzieh Rabbani, Hossein Department of Computer Science and Electrical Engineering WVU 26505 United States Department of Biomedical Engineering Medical Image and Signal Processing Research Center Isfahan University of Medical Sciences Isfahan Iran

ISBN: (纸本)9781509028092

Fluorescein Angiography (FA) imaging is the gold standard technique for neurovascular imaging regarding assessing neurovascular diseases such as Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME). On the other hand, as FA imaging is invasive and does not provide any depth information, Optical Coherence Tomography (OCT) imaging technique is a good complementary for it in diagnosis process. To correlate the information of both FA and OCT images, an image alignment/registration process is needed. In absence of an automatic registration software, the clinician should do intuitive comparison to integrate these data which is a subjective and time consuming process. In this paper, we demonstrate a non-rigid registration method called multi-step correlation-based registration algorithm to automatically register FA and OCT images together. Our algorithm consists of two steps including rigid/global and non-rigid/local registration. We evaluate our algorithm's performance by labeling Micro-Aneurysm (MA) spots -hallmarks of DR- on FA images and determining MA regions on OCT B-scans after registration. Our Results show that our algorithm performs accurately regarding registration of FA images and OCT B-scans. © 2017 IEEE.

关键词： Software algorithms Diabetes Retina image segmentation Standards Angiography

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COLLABORATIVE SYSTEM FOR signal processing EDUCATION

COLLABORATIVE SYSTEM FOR SIGNAL PROCESSING EDUCATION

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Gregory A. Krudysz James H. McClellan Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology

ISBN: (纸本)9781457705380

In this paper we present a collaborative system designed to develop problem solving skills in learners through problem-centric exercises. This system is part of a data collection study consisting of hundreds of students which has been incorporated into the curriculum of a signal processing course. The purpose of this study is to examine a large population of students learning about DSP as they work through concept-specific problems. By tracking student problem-solving sessions, we aim to data mine the results to model student comprehension and characterize learning preferences.

关键词： Intelligent Tutoring System Web-based Learning Tools Collaborative Filtering

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Hidden Markov models for face recognition

Hidden Markov models for face recognition

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1998 23rd IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 1998

作者： Nefian, Ara V. Hayes III, Monson H. Center for Signal and Image Process. School of Electrical and Computer Engineering Georgia Inst. of Technol. Atlanta GA 30332 United States

ISBN: (纸本)0780344286

The work presented in this paper focuses on the use of hidden Markov models for face recognition. A new method based on the extraction of 2D-DCT feature vectors is described, and the recognition results are compared with other face recognition approaches. The method introduced reduces significantly the computational complexity of previous HMM-based face recognition system, while preserving the same recognition rate. © 1998 IEEE.

关键词： Face recognition

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