Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Super-resolution imaging of cell-surface Sonic hedgehog multimolecular signalling complexes

Sonic hedgehog is a fascinating protein with great responsibility over the formation and upkeep of our bodies. It is widely studied, not least because dysregulation of the Shh signalling pathway leads to repercussions on human health, such as contraction of cancer. Gaining an understanding of its signalling mechanism is central to inventing preventative measures and treatments against this disease. This thesis focuses on the study of the spatial organisation of Shh multimolecular signalling complexes on the surface of producing cells, and those dispatched in the vicinity of those cells, using high-resolution optical imaging beyond the diffraction limit. With un-precedented resolution, the differences in organisation of Shh pre- and post-release from the surface were characterised, and the influence of the lipid modifications of Shh, namely choles-terol and palmitate, investigated. The main findings were that both lipid adducts are necessary for large-scale multimerisation, but not for the formation of small, sub-diffraction limit oligomers. Together with data I collected about the profile of the clusters’ size distributions, I find that electrostatic interactions between the molecules may be the engine driving the multimerisation process. Furthermore, the role of lipid modifications may, at least in part, be to retain Shh on the surface while multimerisation proceeding according to the law of mass action builds upon the small oligomer nucleation sites prepared presumably by the electrostatic interactions in the first place. Other, more indirect lines of evidence again based on the profile of the multimer size distribution insinuated that Shh complexes may not undergo any proteolytic modifications prior to release – contrary to some reports in the literature. The results presented in this thesis are the fruits of a completely fresh and innovative approach to examining Shh, which for the first time delivers concrete dimensional details about the elusive structure of the Shh multimer.Open Acces

Holistic Optimization of Embedded Computer Vision Systems

Despite strong interest in embedded computer vision, the computational demands of Convolutional Neural Network (CNN) inference far exceed the resources available in embedded devices. Thankfully, the typical embedded device has a number of desirable properties that can be leveraged to significantly reduce the time and energy required for CNN inference. This thesis presents three independent and synergistic methods for optimizing embedded computer vision: 1) Reducing the time and energy needed to capture and preprocess input images by optimizing the image capture pipeline for the needs of CNNs rather than humans. 2) Exploiting temporal redundancy within incoming video streams to perform computationally cheap motion estimation and compensation in lieu of full CNN inference for the majority of frames. 3) Leveraging the sparsity of CNN activations within the frequency domain to significantly reduce the number of operations needed for inference. Collectively these techniques significantly reduce the time and energy needed for computer vision at the edge, enabling a wide variety of exciting new applications

Connected Attribute Filtering Based on Contour Smoothness

A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform