Current presence-sensing technologies for energy-efficient lighting control and building optimisation are (i) catered to commercial and institutional environments, and (ii) focused on lamp technology and occupancy detection. They often ignore user behaviour characteristics, which significantly influence energy consumption. Therefore, this study aims to identify alternative sensing techniques as part of a lighting control system that can energy-efficiently support user's behavioural needs in mixed-function residential spaces. An exploratory study investigated the optimal placement of a non-wearable radar sensor to detect an imitated user's breathing frequency at varying pre-set horizontal distance positions, and the sensor's performance was validated with a spirometer. The procedure measured a balloon's radar-detected distance, radar-detected breathing frequency, and spirometer-registered breathing frequency at each pre-set position. The radar sensor detected all simulated breathing frequencies with almost 100% data accuracy but was not comparable in detecting all distances. The radar offers a less intrusive short-range presence-sensing for homes, accurately detecting breathing frequencies in a contactless way between 0.2m to 0.8m. Further investigations are recommended to develop radar sensing that could predict lighting options based on user's objective feedback.

Due to the considerable increase of images in everyday life, many applications require a study on their similarity. The main challenge is to find a simple and efficient method to compare and classify image pairs into similar and dissimilar classes. This study presents a new method to image pairs comparison and classification based on the modeling of the Local Dissimilarity Map (LDM). The LDM is a tool for locally measuring the dissimilarity between two binary or grayscale images. It is a measure of dissimilarities based on a modified version of the Hausdorff distance, which allows quantifying locally the dissimilarities between images. This measure is completely without parameters and generic. The image pairs classification (2-class classification) method is structured as follows. First, a statistical model for the LDM is proposed. The model parameters, used as descriptors, are relevant to discriminate similar and dissimilar image pairs. Second, classifiers are applied to compute the classification scores (2-class classification problem). In addition, this approach is robust with respect to geometric transformations such as translation compared to the state-of-the-art similarity measures. Although the main objective of this paper is to apply our approach to image pairs classification, it is also performed on a classification with more than two classes (multi-class classification). Experiments on the well-known image data sets ^∗NIST and on old print data set prove that the proposed method produces comparable, even better results than the state-of-the-art methods in terms of accuracy and F(1) score.

Considering the unavailability of labeled data sets in remote sensing change detection, this letter presents a novel and low complexity unsupervised change detection method based on the combination of similarity and dissimilarity measures: mutual information (MI), disjoint information (DI), and local dissimilarity map (LDM). MI and DI are calculated on sliding windows with a step of 1 pixel for each pair of channels of both images. The resulting scalar values, weighted by q and m coefficients, are multiplied by the values of the center pixels of the windows weighted by p to remove the textures on images. The changes are detected using, respectively, the grayscale LDM and color LDM. A sliding window is then used on the color LDM and each pixel is characterized by a two-parameter Weibull distribution. Binarized change maps can be obtained by using a k-means clustering on the model parameters. Experiments on optical aerial image data set show that the proposed method produces comparable, even better results, to the state-of-the-art methods in terms of recall, precision, and F-measure.

This paper presents a new change detection technique for images taken from the sentinel-2 satellite between 2015 and 2018 in different regions of the world. These images are widely used in recent years for change detection. This technique is based on two dissimilarity measures: the Disjoint Information and the Local Dissimilarity Map. The disjoint information quantifies the dissimilarities between textures and the Local Dissimilarity Map those between structures of images. Firstly, the disjoint information is computed across the blocks of the RGB image channels and the value is multiplied by the center value of the pixel of each block. Secondly, the Local Dissimilarity Maps over the pre-processed channels and the average of the pixel values on the Local Dissimilarity Maps are computed. Finally, an extension of the Gaussian OTSU's threshold is used to detect changes in images. Experimental results on the well-known Onera Satellite Change Detection (OSCD) dataset show the effectiveness of our proposed method compared to the state-of-the-art deep learning methods.

This work aims at exploring a general framework embedding techniques from classifiers, Time–Frequency Distributions (TFD) and dissimilarity measures for epileptic seizures detection. The proposed approach consists firstly in computing dissimilarities between TFD of electroencephalogram (EEG) signals and secondly in using them to define a decision rule. Compared to the existing approaches, the proposed one uses entire TFD of EEG signals and does not require arbitrary feature extraction. Several dissimilarity measures and TFDs have been compared to select the most appropriate for EEG signals. Classifiers, such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Linear Discriminate Analysis (LDA) and k-Nearest Neighbours (k-NN), have been combined with the proposed approach. In order to evaluate the proposed approach, 13 different classification problems (including 2, 3 and 5-class) pertaining to five types of EEG signals have been used. The comparison between results obtained with the proposed approach and results reported in the literature with the same database of epileptic EEG signals demonstrates the effectiveness of this approach for seizure detection. Experimental results show that this approach has achieved highest accuracy in the most studied classification problems. A high value of 98% is achieved for the 5-class problem. Further, in most classification problems with 2 and 3-class, it also yields a satisfactory accuracy of approximately 100%. The robustness of the proposed approach is evaluated with the addition of noise to the EEG signals at various signal-to-noise ratios (SNRs). The experimental results show that this approach has a good classification accuracy at low SNRs.

In previous works, the Local Dissimilarity Map (LDM) was proposed to compare two binary and grayscale images. This measure is based on a Hausdorff distance, which allows to quantify locally the dissimilarities between images. In this paper, we proposed the two-parameter Weibull distribution to model the LDM and the undersampled LDMs for two structural images. To classify structural image pairs, we used the two parameters of Weibull distribution for each LDM as descriptors. They are relevant to discriminate image pairs into similar and dissimilar classes. Experiments were made on the MNIST image dataset and in our own old print image dataset. The results shown our approach is more accurate than the other measures in the literature.

Accurate prostate cancer localization with noninvasive imaging can be used to guide biopsy, radiotherapy and surgery as well as to monitor disease progression [1]. Published studies have shown multispectral magnetic resonance imaging (MRI), i.e., a combination of multiple types of MR images, as a promising noninvasive imaging technique for the localization of prostate cancer.

This paper proposes a frequency-domain Magnetic Resonance (MR) spectral processing method based on sparse representation for accurate quantification of prostate spectra. Generally, an observed prostate spectrum can be considered as a mixture of resonances of interest, a baseline and noise. As the resonances of interest often overlap and the baseline is unknown, their separation and quantification can be difficult. In the proposed method, based on the commonly used signal model of prostate spectra and some a priori knowledge of nonlinear model parameters, a dictionary is constructed which can sparsely represent the resonances of interest as well as the baseline in an input spectrum. The estimation of the resonances of interest is achieved by finding their sparse representations with respect this dictionary. A linear pursuit algorithm based on regularized FOCUSS (Focal Underdetermined System Solver) algorithm is proposed to estimate these sparse representations. The robustness and accuracy of prostate spectrum quantification of the proposed method are improved compared with two classical spectral processing methods: model-based time domain fitting and frequency-domain analysis based on peak integration when tested on simulation data. Quantification on in vivo prostate spectra is also demonstrated and the results appear encouraging.

In this paper, a sparse representation method is proposed for magnetic resonance spectroscopy (MRS) quantification. An observed MR spectrum is composed of a set of metabolic spectra of interest, a baseline and a noise. To separate the spectra of interest, the a priori knowledge about these spectra, such as signal models, the peak frequencies, and linewidth ranges of different resonances, is first integrated to construct a dictionary. The separation of the spectra of interest is then performed by using a pursuit algorithm to find their sparse representations with respect to the dictionary. For the challenging baseline problem, a wavelet filter is proposed to filter the smooth and broad components of both the observed spectra and the basis functions in the dictionary. The computation of sparse representation can then be carried out by using the remaining data. Simulation results show the good performance of this wavelet filtering-based strategy in separating the overlapping components between the baselines and the spectra of interest, when no appropriate model function for the baseline is available. Quantifications of in vivo brain MR spectra from tumor patients in different stages of progression demonstrate the effectiveness of the proposed method.

This communication deals with finding the position of a reference shape in a given image. The proposed matcher is constructed from local dissimilarity maps. These maps allow to efficiently and robustly measure the differences between two images. It is shown an example that the matcher potentially returns less false-positives than a reference method (chamfer matching). This is possible as the local dissimilarity measure is symmetric, which makes it more robust to noise.We show that the proposed matcher is a generalization of the chamfer matching. It also allows fast computation times. A good robustness to noise is confirmed from presented simulations.