A Novel Approach Based on PCNNs Template for Fingerprint Image Thinning

A PCNNs-based square-and-triangle-template method for binary fingerprint image thinning is proposed. The algorithm is iterative, in which a combined sequential and parallel processing is employed to accelerate execution. When a neuron satisfies the square template, the pixel corresponding to this neuron will be noted during the process and be deleted until the end of the iteration; on the other hand, if a neuron meets a triangle template, it will be removed directly. In addition, this proposed algorithm can be effective for fingerprint thinning without considering the direction. The results showed that, with combined sequential and parallel conditions for border pixels removal, the algorithm could not only speed up the fingerprint thinning process, but also be applied to other common images. Furthermore, this algorithm might be applied to fingerprint identification systems to save the time for identifying and eliminating spurious minutia

Vision technology/algorithms for space robotics applications

The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed

Single-imager occupant detection based on surface reconstruction

This thesis introduces a novel framework for a real-time occupant detection system capable of extracting both two- and three-dimensional information using a single imager with active illumination. The primary objective of this thesis is to demonstrate the feasibility of such a low-cost classification system with comparable performance to multi-camera based stereo vision systems. Severe illumination conditions characterised by a frequent and wide illumination fluctuation are also challenging problems addressed in this work. The proposed system is designed to solve a problem of classifying three occupant classes being an adult, a forward-facing child seat, and a rear-facing child seat. DoubleFlash is employed to eliminate the influence of ambient illumination and to compress the optical dynamic range of target scenes. The idea underlying this technique is to subtract images flashed by different illumination power levels. The extension of this active illumination technique leads to the development of a novel shadow removal technique, called ShadowFlash. By simulating an artificial infinite illuminating plane over the field of view, the technique produces a shadowless scene without losing image details by composing multiple images illuminated from different directions. The ShadowFlash technique is then extended to the temporal domain by employing the sliding n-tuple strategy, which is introduced to avoid the reduction of the original frame rate. A modified active contour model, facilitated by morphological operations, extracts the boundary of the target object from the shadow-free scenes produced by the ShadowFlash. Based on the brightness information of the image triplet generated by the DoubleFlash, the orientations of the object surface at pixel points are estimated by the photometric stereo method and integrated into the 3D surface by means of global minimisation. The boundary information is used to specify the region of interest to reconstruct. Investigating both the two- and three-dimensional properties of vehicle occupants, 29 features are defined for the training of a neural network. The system is tested on a database of over 84,000 frames collected from a wide range of objects in various illumination conditions. A classification accuracy of 98.9% was achieved within the decision-time limit of three seconds

Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs

14th International Conference on Turbochargers and Turbocharging

14th International Conference on Turbochargers and Turbocharging addresses current and novel turbocharging system choices and components with a renewed emphasis to address the challenges posed by emission regulations and market trends. The contributions focus on the development of air management solutions and waste heat recovery ideas to support thermal propulsion systems leading to high thermal efficiency and low exhaust emissions. These can be in the form of internal combustion engines or other propulsion technologies (eg. Fuel cell) in both direct drive and hybridised configuration. 14th International Conference on Turbochargers and Turbocharging also provides a particular focus on turbochargers, superchargers, waste heat recovery turbines and related air managements components in both electrical and mechanical forms

Quantum Computation with Superconducting Parametric Cavity

Multimode superconducting parametric cavity is a flexible platform that has been used to study a variety of topics in microwave quantum optics ranging from parametric amplification, entanglement generation to higher order spontaneous parametric downconversion (SPDC). Leveraging the extensive toolbox of interactions available in this system, we can look to explore exciting applications in quantum computation and simulation. In this thesis, we study the use of the parametric cavity to realize continuous variable (CV) quantum computation. We propose and examine in detail the scheme to compute with the microwave photons in the orthogonal frequency modes of the cavity via successive application of parametric pump pulses or cavity drives. The family of all Gaussian transformations can be accomplished easily with interactions already demonstrated in this system. From recent results and proposals involving higher order SPDC, there are also clear pathways towards realizing the non-Gaussian resources necessary for universal computation. Common measurements on the system are accomplished with standard measurement techniques on the output state of the cavity and additional useful measurements may be implemented using available parametric interactions or new device designs involving a qubit as a nonlinear probe. Using the parametric cavity, we experimentally implemented a hybrid quantum-classical machine learning algorithm called the Quantum Kitchen Sinks (QKS) as the first step towards developing this platform for quantum computation. The algorithm is studied over two sets of experiments starting from partial experimental implementation of the quantum variational circuits to fully experimental implementation using multiple simultaneous continuous wave (CW) pumps. In both cases, we find that the quantum part of the algorithm implemented in the parametric cavity improved the classification accuracy on a difficult synthetic data set up to 90.1% and 99.5% respectively when compared to a classical linear machine learning algorithm