In this paper, we introduce a novel technique called Local Sense for detecting appliance usage events in a household by leveraging existing home power infrastructure. Specifically, the Local Sense technique works by purely analyzing the records taken from the main power meter. The main power meter is installed in a household to measure the overall real time aggregated power consumption in terms of wattage and voltage for the whole load of appliances in a household. The Local Sense technique requires no additional apparatuses being installed in existing household power infrastructure, which significantly reduces the cost for enabling the location-aware power usage information over existing techniques. The idea behind the Local Sense system is to reflect the fact that different outlets are with different power line impedances, which causes different power dissipation even for the same appliance usage. By identifying the additional power dissipation, detecting the appliance usage events at outlet levels is therefore possible. The proposed technique is validated in a research lab and preliminary results demonstrate the effectiveness of the proposed system.

Permutation arrays have found applications in powerline communication. One construction method for permutation arrays is to map good codes to permutations using a distance-preserving mappings (DPM). DPMs are mappings from the set of all q-ary vectors of a fixed length to the set of permutations of some fixed length (the same or longer) such that every two distinct vectors are mapped to permutations with the same or larger Hamming distance than that of the vectors. A DPM is called distance increasing (DIM) if the distances are strictly increased (except when the two vectors are equal). In this correspondence, we propose constructions of DPMs and DIMs from ternary vectors. The constructed DPMs and DIMs improve many lower bounds on the maximal size of permutation arrays.

In routine chest radiographs, the mediastinum is often underexposed and vascular structures are obscured by the diaphragm. We have developed a region-based processing algorithm to enhance contrast and brightness of selected anatomical regions, such as the mediastinum, without altering the other regions. We employ wavelet processing technique to segment a chest radiograph into different gray level regions and then apply different degrees of contrast enhancement procedure to them. The image enhancement is controlled by a proposed sigmoid function. The enhanced images showed improved visualization in the mediastinum area over the original unprocessed chest radiographs. Furthermore, the visibility of vascular structures which are obscured by the diaphragm and mediastinum is improved.

The authors have developed a neural-digital computer-aided diagnosis system, based on a parameterized two-level convolution neural network (CNN) architecture and on a special multilabel output encoding procedure. The developed architecture was trained, tested, and evaluated specifically on the problem of diagnosis of lung cancer nodules found on digitized chest radiographs. The system performs automatic "suspect" localization, feature extraction, and diagnosis of a particular pattern-class aimed at a high degree of "true-positive fraction" detection and low "false-positive fraction" detection. In this paper, the authors aim at the presentation of the two-level neural classification method in reducing false-positives in their system. They employed receiver operating characteristics (ROC) method with the area under the ROC curve (A(z)) as the performance index to evaluate all the simulation results. The two-level CNN showed superior performance (A(z)=0.93) to the single-level CNN (A(z)=0.85). The proposed two-level CNN architecture is proven to be promising and to be extensible, problem-independent, and therefore, applicable to other medical or difficult diagnostic tasks in two-dimensional (2-D) image environments.

We have developed several training methods in conjunction with a convolution neural network for general medical image pattern recognition. An unconventional method of using rotation and shift invariance is also proposed to enhance the neural net performance. The structure of the artificial neural network is a simplified network structure of the neocognitron. Two-dimensional local connection as a group is the fundamental architecture for the signal propagation in the convolution neural network. Weighting coefficients of convolution kernels are formed by the neural network through backpropagated training for this artificial neural net. In addition, radiologists' reading procedure was modelled in order to instruct the artificial neural network to recognize the predefined image patterns and those of interest to experts. Our training techniques involve (a) radiologists' rating for each suspected image area, (b) backpropagation of generalized distribution, (c) trainer imposed functions, (d) shift and rotation invariance of diagnosis interpretation, and (e) consistency of clinical input data using appropriate background reduction functions. We have tested these methods for detecting lung nodules on chest radiographs and microcalcications on mammograms. The performance studies have shown the potential use of this technique in a clinical environment. We also used a profile double-matching technique for initial nodule search and used a wavelet high pass filtering technique to enhance subtle clustered microcalcifications. We set searching parameters at a highly sensitive level to identify all potential disease areas. The artificial convolution neural network acts as a final detection classifier to determine whether a disease pattern is shown on the suspected image area.

We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation.

A hybrid neural-digital computer-aided diagnosis (N.CADx) system is proposed for early detection and classification of cancerous lung nodules of size 3-15 mm. Digital image processing techniques are used for noise reduction, image enhancement, and "suspect" search and localization. The neural classification concerns feature extraction and diagnosis of a particular pattern-class aimed at high degree of "true positive fraction" detection and low "false positive fraction". Applying fuzzy linguistic concepts, we have developed a multi-label output encoding procedure, for purposes of neural training and for interpretation of the activity distributions in the output neurons. Over five output neurons, that were used in the nodule diagnosis problem, the activity distribution is interpreted by the centroid as the normalized disease index-NDI, or as a nodule detection probability-NDP value associated with a confidence level factor. The proposed N.CADx system has great potential in many medical applications.>