Integrated Computer-Aided Engineering

We propose a novel method for 3D head reconstruction and view-invariant recognition from single 2D images. We employ a deterministic Shape From Shading (SFS) method with initial conditions estimated by Hybrid Principal Component Analysis (HPCA) and multi-level global optimization with error-dependent smoothness and integrability constraints. Our HPCA algorithm provides good initial estimates of 3D range mapping for the SFS optimization and yields much improved 3D head reconstruction. The paper also presents novel approaches to global optimization. It also describes a novel method in SFS handling of variable and unknown surface albedo, a problem with unsatisfactory solutions by prevalent SFS methods. In the experiments, we reconstruct 3D head range images from 2D single images in different views. The 3D reconstructions are then used to recognize stored model persons. This enables one to recognize faces in wide range of views. Empirical results show that our HPCA based SFS method provides 3D head reconstructions that notably improve the accuracy compared to other approaches. 3D reconstructions derived from images of 40 persons are tested against 80 3D head models and a recognition rate of over 90% is achieved. Such a capability was not demonstrated by any other method for view-invariant face recognition.

The design and development of a data acquisition unit for an implantable multi-channel optical glucose sensor is described. The sensing technology involves sampling of the interstitial fluid in a micro-fabricated chamber and measurement of the absorbance of the fluid in a non-destructive and reagent free manner. The glucose levels are estimated based on the absorbance data. This new technology relies on the unique optical characteristics of glucose in the near infrared spectrum. The sensor element is intended for implantation in the subcutaneous tissues of the human body. The data acquisition unit acquires optical data from the sensor and converts it into spectral data for processing. This new technology will be used as the sensing technology in a feedback controlled insulin delivery system for the in situ treatment of diabetes.

Images captured in dark or bright environments are usually characterized of low contrast. It is important to preprocess these images to make them suitable for other image processing applications. The histogram equalization (HE) algorithm is widely used for this purpose due to its simplicity and effectiveness. However, it can result in a significant change in the mean brightness and produce undesirable visual artifacts. This paper introduces the Constrained Variational Histogram Equalization (CVHE) algorithm which basically extends the variational definition of the HE algorithm by adding a mean brightness constraint to formulate a functional optimization problem, the solution of which defines a new graylevel transformation function for contrast enhancement. Preserving the mean brightness is expected to add more control on histogram stretching, thus reducing the artifacts and change in brightness. We also develop two variants of the CVHE algorithm. The first variant is the Constrained Variational Local Histogram Equalization (CVLHE) algorithm which works in a similar manner to the popular local histogram equalization (LHE) algorithm; however it uses the CVHE transformation function. This variant achieves better performance than the CVHE algorithm but with higher computational requirements. The second variant is the Accelerated CVLHE (ACVLHE) algorithm which uses a modified nonoverlapped block processing approach to reduce the CVLHE computations. The ACVLHE strikes a balance between the speed of the CVHE and the performance of the CVLHE. The choice between the CVHE algorithm and its two local variants is a tradeoff between speed and desired enhancement levels. Visual and quantitative evaluation involving benchmark images show our algorithms to be better than their HE counterparts.

This paper presents a receding horizon methodology for trajectory tracking with safe collision conflict resolution for multiple autonomous vehicles. The proposed decentralized scheme is formulated in discrete-time domain where each vehicle's objective function represents deviations from its desired trajectory. The safety constraints penalizing if two or more vehicles get closer than a prescribed safety distance are incorporated using avoidance functions. These avoidance functions are added to the objective functions to be minimized by each vehicle. They also represent the coupling elements in the decentralized scheme allowing for implicit coordination among the vehicles in the case of a possible collision. Vehicles are modeled by the unicycle model subject to bounds on both velocity and angular velocity. The optimization scheme performed by each vehicle uses a sequential quadratic programming method which is well suited for minimization of a scalar nonlinear function subject to nonlinear equality constraints (dynamic model) and multiple inequality constraints (velocity constraints and safety conditions). Outputs of the optimization process are kinematic control inputs for each vehicle. For symmetric cases where the gradient based methods are known to perform poorly (such as singular cases) a limit cycle method is implemented to modify segments of the desired trajectories leading to feasible solutions in terms of the optimization process performed by each vehicle.

Most of the existing noise analysis techniques in analog integrated circuits apply to only single noise source, and cannot take into account the evolving reality of multiple noise sources interacting with each other. Again the individual and relative impacts of various noise sources will determine what types of remedial steps can be taken. This paper proposes the concept of analyzing the impacts of multiple concurrent noise sources in a circuit network, and applies blind source separation (BSS) technique to analyze the characteristics of this compound noise effect in analog integrated circuits. The proposed algorithm can effectively extracts the time characteristics of individual noise sources from observed noises at circuit nodes. The estimated noise sources can aid in timing and spectral analysis and yield better design techniques.

The amount of the data storage in signal processing systems, whose behavior is described by loop-organized algorithmic specifications, has an important impact on the overall energy consumption, chip area, as well as system performance. This paper presents a methodology based on lattices [25] which can be used to address several memory management tasks for applications with high-level specifications, where the main data structures are multidimensional arrays. This methodology was used in the past for the exact computation of the minimum data storage in applications with procedural, affine specifications [2]. The paper discusses two applications of that technique in the memory management of data-dominated signal processing systems: (1) the evaluation of the impact of loop transformations on the data storage, and (2) the assessment and efficient implementation of models of mapping multidimensional signals into the physical memory.

Fuzzy cognitive maps are a qualitative tool that can capture the extensive cause/effect relationships that an expert believes exist within a complicated system such as an electronic circuit. In addition to predictive capabilities when inputs are applied and propagated through the model, the topology of the map itself can be used in diagnosing failures by identifying causes for nodes of interest. But simply tagging a node as a cause of another is inadequate because this identification does not indicate whether this node is a sole cause or whether other nodes must also be present. The identification of combinations of nodes is key to the determination of a strategy for locating the system problem. An algorithm is given in this paper that uses a modified version of the reachability matrix to identify multi-node combinations that can cause a node of interest.