MR Brain Image Classification: A Comparative Study on Machine Learning Methods

The brain tissue classification from magnetic resonance images provides valuable insight in neurological research study. A significant number of computational methods have been developed for pixel classification of magnetic resonance brain images. Here, we have shown a comparative study of various machine learning methods for this. The results of the classifiers are evaluated through prediction error analysis and several other performance measures. It is noticed from the results that the Support Vector Machine outperformed other classifiers. The superiority of the results is also established through statistical tests called Friedman test

Data fusion of multi-sensor for IOT precise measurement based on improved PSO algorithms

AbstractThis work proposes an improved particle swarm optimization (PSO) method to increase the measurement precision of multi-sensors data fusion in the Internet of Things (IOT) system. Critical IOT technologies consist of a wireless sensor network, RFID, various sensors and an embedded system. For multi-sensor data fusion computing systems, data aggregation is a main concern and can be formulated as a multiple dimensional based on particle swarm optimization approaches. The proposed improved PSO method can locate the minimizing solution to the objective cost function in multiple dimensional assignment themes, which are considered in particle swarm initiation, cross rules and mutation rules. The optimum seclusion can be searched for efficiently with respect to reducing the search range through validated candidate measures. Experimental results demonstrate that the proposed improved PSO method for multi-sensor data fusion is highly feasible for IOT system applications

Distributed detection by a large team of sensors in tandem

Cover title. "This paper has been submitted for publication in the IEEE Transactions on Aerospace and Electronic Systems"--Cover.Includes bibliographical references (p. 17-19).Research was supported by the National Science Foundation (under a subcontract from the University of Connecticut) NSF/IRI-8902755 Research was supported by the Office of Naval Research. ONR/N00014-84-K-0519Jason D. Papastavrou and Michael Athans

Decentralized detection

Cover title. "To appear in Advances in Statistical Signal Processing, Vol. 2: Signal Detection, H.V. Poor and J.B. Thomas, Editors."--Cover.Includes bibliographical references (p. 40-43).Research supported by the ONR. N00014-84-K-0519 (NR 649-003) Research supported by the ARO. DAAL03-86-K-0171John N. Tsitsiklis

Une stratégie efficace d'approximation pour un test d'hypothèses multiples distribuées

In the general distributed detection problem a set of decision makers (DMs) receive observations of the environment and transmit finite-valued messages to other DMs according to prespecified communication protocols . A designated primary DM makes the final decision on one of the alternative hypotheses . All DMs make decisions so as to optimize a measure of organizational performance . Since the "quest for optimality" in problems in this framework is associated with great computational and inherent complexity, simple approximate solutions which take into consideration the specific characteristics of the problem should be employed . This approach is demonstrated by addressing the issues involved with reducing a complex Wary hypothesis testing problem into a sequence of simpler binary hypothesis testing subproblems . An approximate decision scheme is derived that is computationally easy to implement and performs very well by exploiting the structure of the alternative hypotheses of each particular problem.Dans le problèmne général de la détection distribuée, un ensemble de preneurs de décision reçoit des observations de l'environnement et transmet des messages prenant des valeurs finies à d'autres preneurs de décision selon des protocoles de communication préétablis. Un preneur de décision choisi comme primaire, prend la décision finale sur l'une des hypothèses possibles. Tous les preneurs de décision décident de manière à optimiser une mesure de performance d'organisation. Puisque la «quête d'optimalité» dans ce type de problèmes s'accompagne d'une grande complexité et d'une lourde charge de calcul, des solutions approximatives simples qui tiennent compte des caractéristiques spécifiques du problème devraient être employées. Nous présentons cette approche en convertissant les questions impliquées dans la réduction d'un problème complexe de test d'hypothèses M-aires en une suite de sous-problèmes de test d'hypothèses binaires. Nous dérivons une stratégie de décision approximative facile à implanter numériquement et qui est très efficace car elle utilise la structure des hypothèses possibles dans chaque problème particulie

On optimal distributed decision architectures in a hypothesis testing environment

Cover title.Includes bibliographical references (p. 35-37).Research supported by the National Science Foundation. NSF/IRI-8902755 Research supported by the Office of Naval Research. ONR/N00014-84-K-0519Jason D. Papastavrou and Michael Athans

Distributed detection, localization, and estimation in time-critical wireless sensor networks

In this thesis the problem of distributed detection, localization, and estimation (DDLE) of a stationary target in a fusion center (FC) based wireless sensor network (WSN) is considered. The communication process is subject to time-critical operation, restricted power and bandwidth (BW) resources operating over a shared communication channel Buffering from Rayleigh fading and phase noise. A novel algorithm is proposed to solve the DDLE problem consisting of two dependent stages: distributed detection and distributed estimation. The WSN performs distributed detection first and based on the global detection decision the distributed estimation stage is performed. The communication between the SNs and the FC occurs over a shared channel via a slotted Aloha MAC protocol to conserve BW. In distributed detection, hard decision fusion is adopted, using the counting rule (CR), and sensor censoring in order to save power and BW. The effect of Rayleigh fading on distributed detection is also considered and accounted for by using distributed diversity combining techniques where the diversity combining is among the sensor nodes (SNs) in lieu of having the processing done at the FC. Two distributed techniques are proposed: the distributed maximum ratio combining (dMRC) and the distributed Equal Gain Combining (dEGC). Both techniques show superior detection performance when compared to conventional diversity combining procedures that take place at the FC. In distributed estimation, the segmented distributed localization and estimation (SDLE) framework is proposed. The SDLE enables efficient power and BW processing. The SOLE hinges on the idea of introducing intermediate parameters that are estimated locally by the SNs and transmitted to the FC instead of the actual measurements. This concept decouples the main problem into a simpler set of local estimation problems solved at the SNs and a global estimation problem solved at the FC. Two algorithms are proposed for solving the local problem: a nonlinear least squares (NLS) algorithm using the variable projection (VP) method and a simpler gird search (GS) method. Also, Four algorithms are proposed to solve the global problem: NLS, GS, hyperspherical intersection method (HSI), and robust hyperspherical intersection (RHSI) method. Thus, the SDLE can be solved through local and global algorithm combinations. Five combinations are tied: NLS2 (NLS-NLS), NLS-HSI, NLS-RHSI, GS2, and GS-N LS. It turns out that the last algorithm combination delivers the best localization and estimation performance. In fact , the target can be localized with less than one meter error. The SNs send their local estimates to the FC over a shared channel using the slotted-Aloha MAC protocol, which suits WSNs since it requires only one channel. However, Aloha is known for its relatively high medium access or contention delay given the medium access probability is poorly chosen. This fact significantly hinders the time-critical operation of the system. Hence, multi-packet reception (MPR) is used with slotted Aloha protocol, in which several channels are used for contention. The contention delay is analyzed for slotted Aloha with and without MPR. More specifically, the mean and variance have been analytically computed and the contention delay distribution is approximated. Having theoretical expressions for the contention delay statistics enables optimizing both the medium access probability and the number of MPR channels in order to strike a trade-off between delay performance and complexity

Distributed Detection and Estimation in Wireless Sensor Networks

Wireless sensor networks (WSNs) are typically formed by a large number of densely deployed, spatially distributed sensors with limited sensing, computing, and communication capabilities that cooperate with each other to achieve a common goal. In this dissertation, we investigate the problem of distributed detection, classification, estimation, and localization in WSNs. In this context, the sensors observe the conditions of their surrounding environment, locally process their noisy observations, and send the processed data to a central entity, known as the fusion center (FC), through parallel communication channels corrupted by fading and additive noise. The FC will then combine the received information from the sensors to make a global inference about the underlying phenomenon, which can be either the detection or classification of a discrete variable or the estimation of a continuous one.;In the domain of distributed detection and classification, we propose a novel scheme that enables the FC to make a multi-hypothesis classification of an underlying hypothesis using only binary detections of spatially distributed sensors. This goal is achieved by exploiting the relationship between the influence fields characterizing different hypotheses and the accumulated noisy versions of local binary decisions as received by the FC, where the influence field of a hypothesis is defined as the spatial region in its surrounding in which it can be sensed using some sensing modality. In the realm of distributed estimation and localization, we make four main contributions: (a) We first formulate a general framework that estimates a vector of parameters associated with a deterministic function using spatially distributed noisy samples of the function for both analog and digital local processing schemes. ( b) We consider the estimation of a scalar, random signal at the FC and derive an optimal power-allocation scheme that assigns the optimal local amplification gains to the sensors performing analog local processing. The objective of this optimized power allocation is to minimize the L 2-norm of the vector of local transmission powers, given a maximum estimation distortion at the FC. We also propose a variant of this scheme that uses a limited-feedback strategy to eliminate the requirement of perfect feedback of the instantaneous channel fading coefficients from the FC to local sensors through infinite-rate, error-free links. ( c) We propose a linear spatial collaboration scheme in which sensors collaborate with each other by sharing their local noisy observations. We derive the optimal set of coefficients used to form linear combinations of the shared noisy observations at local sensors to minimize the total estimation distortion at the FC, given a constraint on the maximum average cumulative transmission power in the entire network. (d) Using a novel performance measure called the estimation outage, we analyze the effects of the spatial randomness of the location of the sensors on the quality and performance of localization algorithms by considering an energy-based source-localization scheme under the assumption that the sensors are positioned according to a uniform clustering process