Landmine detection using semi-supervised learning.

Landmine detection is imperative for the preservation of both military and civilian lives. While landmines are easy to place, they are relatively difficult to remove. The classic method of detecting landmines was by using metal-detectors. However, many present-day landmines are composed of little to no metal, necessitating the use of additional technologies. One of the most successful and widely employed technologies is Ground Penetrating Radar (GPR). In order to maximize efficiency of GPR-based landmine detection and minimize wasted effort caused by false alarms, intelligent detection methods such as machine learning are used. Many sophisticated algorithms are developed and employed to accomplish this. One such successful algorithm is K Nearest Neighbors (KNN) classification. Most of these algorithms, including KNN, are based on supervised learning, which requires labeling of known data. This process can be tedious. Semi-supervised learning leverages both labeled and unlabeled data in the training process, alleviating over-dependency on labeling. Semi-supervised learning has several advantages over supervised learning. For example, it applies well to large datasets because it uses the topology of unlabeled data to classify test data. Also, by allowing unlabeled data to influence classification, one set of training data can be adopted into varying test environments. In this thesis, we explore a graph-based learning method known as Label Propagation as an alternative classifier to KNN classification, and validate its use on vehicle-mounted and handheld GPR systems

Theoretical Developments in Electromagnetic Induction Geophysics with Selected Applications in the Near Surface

Near-surface applied electromagnetic geophysics is experiencing an explosive period of growth with many innovative techniques and applications presently emergent and others certain to be forthcoming. An attempt is made here to bring together and describe some of the most notable advances. This is a difficult task since papers describing electromagnetic induction methods are widely dispersed throughout the scientific literature. The traditional topics discussed herein include modeling, inversion, heterogeneity, anisotropy, target recognition, logging, and airborne electromagnetics (EM). Several new or emerging techniques are introduced including landmine detection, biogeophysics, interferometry, shallow-water electromagnetics, radiomagnetotellurics, and airborne unexploded ordnance (UXO) discrimination. Representative case histories that illustrate the range of exciting new geoscience that has been enabled by the developing techniques are presented from important application areas such as hydrogeology, contamination, UXO and landmines, soils and agriculture, archeology, and hazards and climat

Ensemble learning method for hidden markov models.

For complex classification systems, data are gathered from various sources and potentially have different representations. Thus, data may have large intra-class variations. In fact, modeling each data class with a single model might lead to poor generalization. The classification error can be more severe for temporal data where each sample is represented by a sequence of observations. Thus, there is a need for building a classification system that takes into account the variations within each class in the data. This dissertation introduces an ensemble learning method for temporal data that uses a mixture of Hidden Markov Model (HMM) classifiers. We hypothesize that the data are generated by K models, each of which reacts a particular trend in the data. Model identification could be achieved through clustering in the feature space or in the parameters space. However, this approach is inappropriate in the context of sequential data. The proposed approach is based on clustering in the log-likelihood space, and has two main steps. First, one HMM is fit to each of the N individual sequences. For each fitted model, we evaluate the log-likelihood of each sequence. This will result in an N-by-N log-likelihood distance matrix that will be partitioned into K groups using a relational clustering algorithm. In the second step, we learn the parameters of one HMM per group. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we investigate the maximum likelihood (ML), the minimum classification error (MCE) based discriminative, and the Variational Bayesian (VB) training approaches. Finally, to test a new sequence, its likelihood is computed in all the models and a final confidence value is assigned by combining the multiple models outputs using a decision level fusion method such as an artificial neural network or a hierarchical mixture of experts. Our approach was evaluated on two real-world applications: (1) identification of Cardio-Pulmonary Resuscitation (CPR) scenes in video simulating medical crises; and (2) landmine detection using Ground Penetrating Radar (GPR). Results on both applications show that the proposed method can identify meaningful and coherent HMM mixture components that describe different properties of the data. Each HMM mixture component models a group of data that share common attributes. The results indicate that the proposed method outperforms the baseline HMM that uses one model for each class in the data

Probabilistic Framework for Sensor Management

A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions

Selected Papers from the 9th World Congress on Industrial Process Tomography

Industrial process tomography (IPT) is becoming an important tool for Industry 4.0. It consists of multidimensional sensor technologies and methods that aim to provide unparalleled internal information on industrial processes used in many sectors. This book showcases a selection of papers at the forefront of the latest developments in such technologies

Object detection in dual-band infrared

Dual-Band Infrared (DBIR) offers the advantage of combining Mid-Wave Infrared (MWIR) and Long-Wave Infrared (LWIR) within a single field-of-view (FoV). This provides additional information for each spectral band. DBIR camera systems find applications in both military and civilian contexts. This work introduces a novel labeled DBIR dataset that includes civilian vehicles, aircraft, birds, and people. The dataset is designed for utilization in object detection and tracking algorithms. It comprises 233 objects with tracks spanning up to 1,300 frames, encompassing images in both MW and LW. This research reviews pertinent literature related to object detection, object detection in the infrared spectrum, and data fusion. Two sets of experiments were conducted using this DBIR dataset: Motion Detection and CNNbased object detection. For motion detection, a parallel implementation of the Visual Background Extractor (ViBe) was developed, employing ConnectedComponents analysis to generate bounding boxes. To assess these bounding boxes, Intersection-over-Union (IoU) calculations were performed. The results demonstrate that DBIR enhances the IoU of bounding boxes in 6.11% of cases within sequences where the camera’s field of view remains stationary. A size analysis reveals ViBe’s effectiveness in detecting small and dim objects within this dataset. A subsequent experiment employed You Only Look Once (YOLO) versions 4 and 7 to conduct inference on this dataset, following image preprocessing. The inference models were trained using visible spectrum MS COCO data. The findings confirm that YOLOv4/7 effectively detect objects within the infrared spectrum in this dataset. An assessment of these CNNs’ performance relative to the size of the detected object highlights the significance of object size in detection capabilities. Notably, DBIR substantially enhances detection capabilities in both YOLOv4 and YOLOv7; however, in the latter case, the number of False Positive detections increases. Consequently, while DBIR improves the recall of YOLOv4/7, the introduction of DBIR information reduces the precision of YOLOv7. This study also demonstrates the complementary nature of ViBe and YOLO in their detection capabilities based on object size in this data set. Though this is known prior art, an approach using these two approaches in a hybridized configuration is discussed. ViBe excels in detecting small, distant objects, while YOLO excels in detecting larger, closer objects. The research underscores that DBIR offers multiple advantages over MW or LW alone in modern computer vision algorithms, warranting further research investment

Estimation sous contraintes de communication (algorithmes et performances asymptotiques)

L'essor des nouvelles technologies de télécommunication et de conception des capteurs a fait apparaître un nouveau domaine du traitement du signal : les réseaux de capteurs. Une application clé de ce nouveau domaine est l'estimation à distance : les capteurs acquièrent de l'information et la transmettent à un point distant où l'estimation est faite. Pour relever les nouveaux défis apportés par cette nouvelle approche (contraintes d'énergie, de bande et de complexité), la quantification des mesures est une solution. Ce contexte nous amène à étudier l'estimation à partir de mesures quantifiées. Nous nous concentrons principalement sur le problème d'estimation d'un paramètre de centrage scalaire. Le paramètre est considéré soit constant, soit variable dans le temps et modélisé par un processus de Wiener lent. Nous présentons des algorithmes d'estimation pour résoudre ce problème et, en se basant sur l'analyse de performance, nous montrons l'importance de l'adaptativité de la dynamique de quantification pour l'obtention d'une performance optimale. Nous proposons un schéma adaptatif de faible complexité qui, conjointement, estime le paramètre et met à jour les seuils du quantifieur. L'estimateur atteint de cette façon la performance asymptotique optimale. Avec 4 ou 5 bits de résolution, nous montrons que la performance optimale pour la quantification uniforme est très proche des performances d'estimation à partir de mesures continues. Finalement, nous proposons une approche à haute résolution pour obtenir les seuils de quantification non-uniformes optimaux ainsi qu'une approximation analytique des performances d'estimation.With recent advances in sensing and communication technology, sensor networks have emerged as a new field in signal processing. One of the applications of his new field is remote estimation, where the sensors gather information and send it to some distant point where estimation is carried out. For overcoming the new design challenges brought by this approach (constrained energy, bandwidth and complexity), quantization of the measurements can be considered. Based on this context, we study the problem of estimation based on quantized measurements. We focus mainly on the scalar location parameter estimation problem, the parameter is considered to be either constant or varying according to a slow Wiener process model. We present estimation algorithms to solve this problem and, based on performance analysis, we show the importance of quantizer range adaptiveness for obtaining optimal performance. We propose a low complexity adaptive scheme that jointly estimates the parameter and updates the quantizer thresholds, achieving in this way asymptotically optimal performance. With only 4 or 5 bits of resolution, the asymptotically optimal performance for uniform quantization is shown to be very close to the continuous measurement estimation performance. Finally, we propose a high resolution approach to obtain an approximation of the optimal nonuniform quantization thresholds for parameter estimation and also to obtain an analytical approximation of the estimation performance based on quantized measurements.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Nonlinear state and parameter estimation of spatially distributed systems

In this thesis two probabilistic model-based estimators are introduced that allow the reconstruction and identification of space-time continuous physical systems. The Sliced Gaussian Mixture Filter (SGMF) exploits linear substructures in mixed linear/nonlinear systems, and thus is well-suited for identifying various model parameters. The Covariance Bounds Filter (CBF) allows the efficient estimation of widely distributed systems in a decentralized fashion