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Automatic and Parallel Optimized Learning for Neural Networks performing MIMO Applications
FULGINEI, F. R.,LAUDANI, A.,SALVINI, A.,PARODI, M.
Advances in Electrical and Computer Engineering , 2013, DOI: 10.4316/aece.2013.01001
Abstract: An automatic and optimized approach based on multivariate functions decomposition is presented to face Multi-Input-Multi-Output (MIMO) applications by using Single-Input-Single-Output (SISO) feed-forward Neural Networks (NNs). Indeed, often the learning time and the computational costs are too large for an effective use of MIMO NNs. Since performing a MISO neural model by starting from a single MIMO NN is frequently adopted in literature, the proposed method introduces three other steps: 1) a further decomposition; 2) a learning optimization; 3) a parallel training to speed up the process. Starting from a MISO NN, a collection of SISO NNs can be obtained by means a multi-dimensional Single Value Decomposition (SVD). Then, a general approach for the learning optimization of SISO NNs is applied. It is based on the observation that the performances of SISO NNs improve in terms of generalization and robustness against noise under suitable learning conditions. Thus, each SISO NN is trained and optimized by using limited training data that allow a significant decrease of computational costs. Moreover, a parallel architecture can be easily implemented. Consequently, the presented approach allows to perform an automatic conversion of MIMO NN into a collection of parallel-optimized SISO NNs. Experimental results will be suitably shown.
Automatic Aircraft Target Recognition by ISAR Image Processing based on Neural Classifier
F Benedetto,F. Riganti Fulginei,A. Laudani,G. Albanese
International Journal of Advanced Computer Sciences and Applications , 2012,
Abstract: This work proposes a new automatic target classifier, based on a combined neural networks’ system, by ISAR image processing. The novelty introduced in our work is twofold. We first present a novel automatic classification procedure, and then we discuss an improved multimedia processing of ISAR images for automatic object detection. The classifier, composed by a combination of 20 feed-forward artificial neural networks, is used to recognize aircraft targets extracted from ISAR images. A multimedia processing by two recently introduced image processing techniques is exploited to improve the shape and features extraction process. Performance analysis is carried out in comparison with conventional multimedia techniques and standard detectors. Numerical results obtained from wide simulation trials evidence the efficiency of the proposed method for the application to automatic aircraft target recognition.
SVM versus MAP on Accelerometer Data to Distinguish among Locomotor Activities Executed at Different Speeds
Maurizio Schmid,Francesco Riganti-Fulginei,Ivan Bernabucci,Antonino Laudani,Daniele Bibbo,Rossana Muscillo,Alessandro Salvini,Silvia Conforto
Computational and Mathematical Methods in Medicine , 2013, DOI: 10.1155/2013/343084
Abstract: Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes’ classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25–35 yrs.). From each segmented activity epoch, sixteen features were chosen in the frequency and time domain. Dimension reduction was then performed through 2D Sammon’s mapping. An Artificial Neural Network (ANN) was trained to mimic Sammon’s mapping on the whole dataset. In the Bayes’ approach, the two features were then fed to a Bayes’ classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier. Bayes’ approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes’ scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities. 1. Introduction With the evolution of wireless communication technology, it is now possible to use inertial sensors (Inertial Measurement Units (IMU)) to gather and transmit over the air patterns associated with different activities performed by people moving in unconstrained environments [1]. IMUs allow to collect kinematic data through miniaturized accelerometers [2], gyroscopes [3], and possibly magnetometers [4]. Restricting the analysis to accelerometers, they are popular as fall detectors [5], as means to monitor physical activity [6], and also as tools to classify among different motor activities [7, 8]. They have also been shown as good predictors of the functional capacity in healthy adults [9] and elderly people [10] and of the level of energy expenditure [11, 12]. In these specific regards, since the accuracy in the prediction strongly depends on the kind of activity [13], classification of activities is often necessary as a preliminary step for energy expenditure estimation [14]. The utility of distinguishing between activities is also apparent when, for long term monitoring, the wearable device needs to transmit data in a compact way. Following this perspective, the general communication model of having raw data to be sent continuously from the sensing devices over the air,
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