This paper presents a new nonintrusive appliance load monitoring technique based on the integer programming. Nonintrusive appliance load monitoring is a problem to identify the operating conditions of the electric appliances in a house only by observing the overall load current and voltage. Since the overall load current is expressed as a superposition of each current of the operating appliance, the monitoring problem can be formulated as an integer quadratic programming problem by expressing the operating conditions as integer variables. This problem is solvable with sufficiently small computational burden thanks to the recent development of the commercial software. The proposed method does not require the relearning even when a new appliance is installed in the house. Furthermore, the proposed formulation is applicable to cases that some appliance has multiple modes, and cases that some same-type appliances operate simultaneously. Usefulness of the proposed technique is verified through some experimental results.

Nonintrusive appliance load monitoring based on integer programming

We consider a framework that learns human habitual behaviors from the data obtained by various kinds of sensors installed in an environment, so that the environment can interact with us based on those patterns. In this paper, we achieved extracting human behaviors by infrared sensor network as an initial step for the framework. Infrared sensor network is able to track us without putting an extra burden on us. Moreover it is able to collect long-term data. However, tracking with it has two problems, that is, link miss and incorrect link. In order to mitigate these problems, we propose the tracking method utilizing estimated "time distances" between sensors from movements' records. We have installed infrared sensor network in our laboratory, and validated the proposed tracking method by test courses. Afterwards, we confirmed that human behaviors can be extracted from longterm data.

Extracting Human Behaviors with Infrared Sensor Network

Many image processing techniques have been developed over the past two decades to help radiologists in diagnosing breast cancer. At the same time, many studies proven that an early diagnosis of breast cancer can increase the survival rate, thus making screening programmes a mandatory step for females. Radiologists have to examine a large number of images. Digital Mammogram has emerged as the most popular screening technique for early detection of Breast Cancer and other abnormalities. Raw digital mammograms are medical images that are difficult to interpret so we need to develop Computer Aided Diagnosis (CAD) systems that will improve detection of abnormalities in mammogram images. Extraction of the breast region by delineation of the breast contour and pectoral muscle allows the search for abnormalities to be limited to the region of the breast without undue influence from the background of the mammogram. We need to perform essential pre-processing steps to suppress artifacts, enhance the breast region and then extract breast region by the process of segmentation. In this paper we present a fully automated scheme for detection of abnormal masses by anatomical segmentation of Breast Region of Interest (ROI). We are using medio-lateral oblique (MLO) view of mammograms. We have proposed a new homogeneity enhancement process namely Binary Homogeneity Enhancement Algorithm (BHEA), followed by an innovative approach for edge detection (EDA). Then obtain the breast boundary by using our proposed Breast Boundary Detection Algorithm (BBDA). After we use our proposed Pectoral Muscle Detection Algorithm (PMDA) to suppress the pectoral muscle thus obtaining the breast ROI, we use our proposed Anatomical Segmentation of Breast ROI (ASB) algorithm to differentiate various regions within the breast. After segregating the different breast regions we use our proposed Seeded Region Growing Algorithm (SRGA) to isolate normal and abnormal regions in the breast tissue. If any abnormalities are present it gets accurately highlighted by this algorithm thus helping the radiologists to further investigate these regions. This composite method have been implemented and applied on all mammograms with abnormalities in mini-MIAS database. The algorithms proposed are fully autonomous, and are able to isolate different types of abnormalities, if present, a task very few existing mammogram segmentation algorithms can claim.

/pdf/automated-digital-mammogram-segmentation-for-detection-of-35mhp7vj51.pdf

Automated digital mammogram segmentation for detection of abnormal masses using binary homogeneity enhancement algorithm

This paper proposes a human localization method in sensor networks for monitoring elderly people. First, we explain the proposed intelligent sensor networks. Next, we apply a spiking neural network to extract feature points for human localization from a measurement data by sensor networks. Furthermore, we propose a learning method using spiking neural network based on the time series of measurement data. Finally, we discuss the effectiveness of proposed method through experimental results in a living room.

Human localization based on spiking neural network in intelligent sensor networks

Objectives: Our purpose was to evaluate the potential usefulness of the nearest neighbor case which was assumed to be the similar case in a CAD scheme for determining the histological classification of clustered microcalcifications. Methods: Our database consisted of current and previous magnification mammograms obtained from 93 patients before and after three-month follow-up examination. It included 11 invasive carcinomas, 19 noninvasive carcinomas of the comedo type, 25 noninvasive carcinomas of the noncomedo type, 23 mastopathies, and 15 fibroadenomas. Six objective features on clustered microcalcifications were first extracted from each of the current and the previous images. The nearest neighbor case was then identified by the Euclidean distance in the previous and current feature-space. The histological classification of an unknown new case in question was assumed to be the same as that of the nearest neighbor case which has the shortest Euclidean distance in our database. Results: The classification accuracies were 90.9% for invasive carcinoma, 89.5% for noninvasive carcinoma of the comedo type, 96.0% for noninvasive carcinoma of the noncomedo type, 82.6% for mastopathy, and 93.3% for fibroadenoma. These results were substantially higher than those with our previous CAD scheme. Conclusion: The nearest neighbor criterion was useful in a CAD scheme for determining the histological classification.

An improved computer-aided diagnosis scheme using the nearest neighbor criterion for determining histological classification of clustered microcalcifications.

In general, it is possible to find certain behavioral patterns in human daily activity. Such patterns are called as daily behavioral patterns. The purpose of this research is to learn and recognize behavioral patterns. In the previous methods, it is difficult to recognize in detail how a person acts in a room because the methods recognize only a sequence of existing position of human by using the information of infrared sensors or of switching on/off of electrical appliances. On the other hand, many have proposed the methods recognizing human motions from sequential images, in most of which motion models must be prepared in advance. In this paper, we propose a method for learning and recognizing motions of human without any motion models. In addition, we also propose perceptive methods of recognizing behavioral patterns by taking not only the sequence of position but also the sequence of motion into consideration. Experiments show that our approach is able to learn and recognize human behavior and confirm effectiveness of our method

/pdf/learning-and-recognizing-behavioral-patterns-using-position-odxuzp5q9k.pdf

Learning and recognizing behavioral patterns using position and posture of human

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Learning and recognizing behavioral patterns using position and posture of human