Mel Siegel

Bio: Mel Siegel is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Stereoscopy & Sensor fusion. The author has an hindex of 28, co-authored 113 publications receiving 2758 citations.

Physically Based Simulation Model for Acoustic Sensor Robot Navigation

Abstract: A computer model is described that combines concepts from the fields of acoustics, linear system theory, and digital signal processing to simulate an acoustic sensor navigation system using time-of-flight ranging. By separating the transmitter/receiver into separate components and assuming mirror-like reflectors, closed-form solutions for the reflections from corners, edges, and walls are determined as a function of transducer size, location, and orientation. A floor plan consisting of corners, walls, and edges is efficiently encoded to indicate which of these elements contribute to a particular pulse-echo response. Sonar maps produced by transducers having different resonant frequencies and transmitted pulse waveforms can then be simulated efficiently. Examples of simulated sonar maps of two floor plans illustrate the performance of the model. Actual sonar maps are presented to verify the simulation results.

Sensor Fusion Using Dempster-Shafer Theory

Abstract: Context-sensing for context-aware HCI challenges the traditional sensor fusion methods with dynamic sensor configuration and measurement requirements commensurate with human perception. The Dempster-Shafer theory of evidence has uncertainty management and inference mechanisms analogous to our human reasoning process. Our Sensor Fusion for Context-aware Computing Project aims to build a generalizable sensor fusion architecture in a systematic way. This naturally leads us to choose the Dempster-Shafer approach as our first sensor fusion implementation algorithm. This paper discusses the relationship between Dempster-Shafer theory and the classical Bayesian method, describes our sensor fusion research work using Dempster-Shafer theory in comparison with the weighted sum of probability method. The experimental approach is to track a user’s focus of attention from multiple cues. Our experiments show promising, thought-provoking results encouraging further research.

Vehicle sound signature recognition by frequency vector principal component analysis

Abstract: The sound (engine, noise, etc.) of a working vehicle provides an important clue, e.g., for surveillance mission robots, to recognize the vehicle type. In this paper, we introduce the "eigenfaces method", originally used in human face recognition, to model the sound frequency distribution features. We show that it can be a simple and reliable acoustic identification method if the training samples can be properly chosen and classified. We treat the frequency spectra of about 200 ms of sound (a "frame") as a vector in a high-dimensional frequency feature space. In this space, we study the vector distribution for each kind of vehicle sound produced under similar working conditions. A collection of typical sound samples is used as the training data set. The mean frequency vector of the training set is first calculated, and subtracted from each vector in the set. To capture the frequency vectors' variation within the training set, we then calculate the eigenvectors of the covariance matrix of the zero-mean-adjusted sample data set. These eigenvectors represent the principal components of the vector distribution: for each such eigenvector, its corresponding eigenvalue indicates its importance in capturing the variation distribution, with the largest eigenvalues accounting for the most variance within this data set. Thus for each set of training data, its mean vector and its moat important eigenvectors together characterize its sound signature. When a new frame (not in the training set) is tested, its spectrum vector is compared against the mean vector; the difference vector is then projected into the principal component directions, and the residual is found. The coefficients of the unknown vector, in the training set eigenvector basis subspace, identify the unknown vehicle noise in terms of the classes represented in the training set. The magnitude of the residual vector measures the extent to which the unknown vehicle sound cannot be well characterized by the vehicle sounds included in the training set.

Sensor fusion using Dempster-Shafer theory [for context-aware HCI]

Abstract: Context-sensing for context-aware HCI challenges the traditional sensor fusion methods with dynamic sensor configuration and measurement requirements commensurate with human perception. The Dempster-Shafer theory of evidence has uncertainty management and inference mechanisms analogous to our human reasoning process. Our Sensor Fusion for Context-aware Computing Project aims to build a generalizable sensor fusion architecture in a systematic way. This naturally leads us to choose the Dempster-Shafer approach as our first sensor fusion implementation algorithm This paper discusses the relationship between Dempster-Shafer theory and the classical Bayesian method, describes our sensor fusion research work using Dempster-Shafer theory in comparison with the weighted sum of probability method The experimental approach is to track a user's focus of attention from multiple cues. Our experiments show promising, thought-provoking results encouraging further research.

Sensor data fusion for context-aware computing using dempster-shafer theory

Abstract: Towards having computers understand human users' “context” information, this dissertation proposes a systematic context-sensing implementation methodology that can easily combine sensor outputs with subjective judgments. The feasibility of this idea is demonstrated via a meeting-participant's focus-of-attention analysis case study with several simulated sensors using prerecorded experimental data and artificially generated sensor outputs distributed over a LAN network. The methodology advocates a top-down approach: (1) For a given application, a context information structure is defined; all lower-level sensor fusion is done locally. (2) Using the context information architecture as a guide, a context sensing system with layered and modularized structure is developed using the Georgia Tech Context Toolkit system, enhanced with sensor fusion modules, as its building-blocks. (3) Higher-level context outputs are combined through “sensor fusion mediator” widgets, and the results populate the context database. The key contribution of this thesis is introducing the Dempster-Shafer theory of evidence as a generalizable sensor fusion solution to overcome the typical context-sensing difficulties, wherein some of the available information items are subjective, sensor observations' probability (objective chance) distribution is not known accurately, and the sensor set is dynamic in content and configuration. In the sensor fusion implementation, this method is further extended in two directions: (1) weight factors are introduced to adjust each sensor's voting influence, thus providing an “objective” sensor performance justification; and (2) when the ground truth becomes available, it is used to dynamically adjust the sensors' voting weights. The effectiveness of the improved Dempster-Shafer method is demonstrated with both the prerecorded experimental data and the simulated data.

A survey of medical image registration.

Abstract: The purpose of this paper is to present a survey of recent (published in 1993 or later) publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods. The statistics of the classification show definite trends in the evolving registration techniques, which will be discussed. At this moment, the bulk of interesting intrinsic methods is based on either segmented points or surfaces, or on techniques endeavouring to use the full information content of the images involved.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Robot vision

Abstract: A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

Abstract: Deep-learning (DL) algorithms, which learn the representative and discriminative features in a hierarchical manner from the data, have recently become a hotspot in the machine-learning area and have been introduced into the geoscience and remote sensing (RS) community for RS big data analysis. Considering the low-level features (e.g., spectral and texture) as the bottom level, the output feature representation from the top level of the network can be directly fed into a subsequent classifier for pixel-based classification. As a matter of fact, by carefully addressing the practical demands in RS applications and designing the input?output levels of the whole network, we have found that DL is actually everywhere in RS data analysis: from the traditional topics of image preprocessing, pixel-based classification, and target recognition, to the recent challenging tasks of high-level semantic feature extraction and RS scene understanding.