Audio-Visual CNN using Transfer Learning for TV Commercial Break Detection

The TV commercial detection problem is a hard challenge due to the variety of programs and TV channels. The usage of deep learning methods to solve this problem has shown good results. However, it takes a long time with many training epochs to get high accuracy.     This research uses transfer learning techniques to reduce training time and limits the number of training epochs to 20. From video data, the audio feature is extracted with Mel-spectrogram representation, and the visual features are picked from a video frame. The datasets were gathered by recording programs from various TV channels in Indonesia. Pre-trained CNN models such as MobileNetV2, InceptionV3, and DenseNet169 are re-trained and are used to detect commercials at the shot level. We do post-processing to cluster the shots into segments of commercials and non-commercials.     The best result is shown by Audio-Visual CNN using transfer learning with an accuracy of 93.26% with only 20 training epochs. It is faster and better than the CNN model without using transfer learning with an accuracy of 88.17% and 77 training epochs. The result by adding post-processing increases the accuracy of Audio-Visual CNN using transfer learning to 96.42%

NASA Tech Briefs Index, 1976

Abstracts of new technology derived from the research and development activities of the National Aeronautics and Space Administration are presented. Emphasis is placed on information considered likely to be transferrable across industrial, regional, or disciplinary lines. Subject matter covered includes: electronic components and circuits; electronic systems; physical sciences; materials; life sciences; mechanics; machinery; fabrication technology; and mathematics and information sciences

VLSI Design

This book provides some recent advances in design nanometer VLSI chips. The selected topics try to present some open problems and challenges with important topics ranging from design tools, new post-silicon devices, GPU-based parallel computing, emerging 3D integration, and antenna design. The book consists of two parts, with chapters such as: VLSI design for multi-sensor smart systems on a chip, Three-dimensional integrated circuits design for thousand-core processors, Parallel symbolic analysis of large analog circuits on GPU platforms, Algorithms for CAD tools VLSI design, A multilevel memetic algorithm for large SAT-encoded problems, etc

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 272)

This bibliography lists 360 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1985

Exploring protein fitness landscapes with new high-throughput technologies

The concept of a protein’s fitness landscape – an abstract space in which related sequences are close together and matched with their fitness – is a useful tool to visualize core principles of protein evolution. Acquiring a new function, for example the laboratory evolution of an enzyme to convert an industrially relevant substrate, can be understood as a stepwise climb through a fitness landscape, reaching higher fitness (or activity) with each step (or mutation). The valleys of such a space relate to the starting points of protein engineering campaigns. Understanding this area could enlighten principles of how proteins quickly adapt in nature and help to identify starting points with a high potential for evolution, a high ‘evolvability’, speeding up protein engineering. In this study, high-throughput technologies will be developed that enable the read-out of directed evolution on a large scale, tracking the exploration of the valley of a fitness landscape: the conversion of an amino acid- to amine dehydrogenase will be investigated as a model of enzyme evolvability with a drastic change of substrate specificity. A sensitive high-throughput screening assay as well as a comprehensive sequencing read-out will be required to establish the identity of selected variants during evolution. I will first generate and characterize three different but related starting points and test their initial evolvability. Stabilizing the starting point results in increased mutational robustness, broadening the range of accepted mutations. However, increased initial stability does not necessarily correlate to higher functional improvement, hinting at a nuanced view of evolvability. A sensitive high-throughput assay is necessary to verify the full potential of the starting points and study the early steps of evolution comprehensively. Broadly applicable ultrahigh-throughput assays of enzyme function, such as absorbance-activated droplet sorting, currently lack the sensitivity of more specific fluorescence-based or low-throughput counterparts. A universal approach to increase detectability in single cell-lysate microfluidic enzyme assays is established by amplifying the enzyme content per droplet more than 10-fold via homogeneous clonal cell growth. Clonal amplification enables the sensitive and precise detection of newly introduced amine dehydrogenase activities, a feat restricted in conventional assays by low initial activity and stability. To generate a truly complete view of directed evolution in a fitness landscape, however, an equally powerful sequencing read-out is necessary to identify all selected variants. Here, unique molecular identifiers are used to increase the accuracy of nanopore sequencing to levels that can reliably distinguish point mutations. I establish an inexpensive and straightforward long read amplicon sequencing workflow which is then applied to map the trajectories of two comparative long-term directed evolution campaigns. In the parallel evolution campaigns, initial beneficial mutations are exclusive to each starting point and lead to incompatible trajectories. Beneficial mutations are scarce and large improvements are unavailable until recombination occurs and a jump through the fitness landscape is realized. The recombined variant holds high evolvability and quickly evolves to take over the population and form the most successful lineages, indicating the power of recombination as a means to innovation in protein evolution. The tools established in this thesis can help protein engineers explore fitness landscapes more economically and comprehensively. Their application to mapping full trajectories of early adaptation uncovers differences in the evolvability of homologs, potentially aiding the identification of evolvable starting points as well as strategies to increase evolvability for efficient protein engineering in the future

Portal Imaging Using a CSI (TL) Scintillator Coupled to a Cooled CCD Camera

The purpose of this research was to design a high performance digital portal imaging system, using a transparent x-ray scintillator coupled to a cooled CCD camera. Theoretical analysis using Monte Carlo simulation was performed to calculate the QDE, SNR and DQE of the system. A prototype electronic portal imaging device (EPID) was built, using a 12.7 mm thick, 20.32 cm diameter, CsI (Tl) scintillator, coupled to an Astromed ® liquid nitrogen cooled CCD TV camera. The system geometry of the prototype EPID was optimized to achieve high spatial resolution. Experimental evaluation of the prototype EPID was performed, by determining its spatial resolution, contrast resolution, depth of focus and light scatter. Images of phantoms, animals and human subjects were acquired using the prototype EPID and were compared with those obtained using conventional and high contrast portal film and a commercial EPID. An image processing protocol was developed. The protocol was comprised of preprocessing, noise removal and image enhancement algorithms. An adaptive median filter algorithm for the removal of impulse noise was developed, analyzed and incorporated into the image processing protocol. Results from the theoretical analysis and experimental evaluation have indicated that the performance of the CsI (Tl) - CCD system is comparable or superior to that of current commercial and experimental portal imaging technologies, such as high contrast portal film, commercial TV camera based EPIDs, and amorphous silicon based flat panel EPIDs

Computational Methods for Image Acquisition and Analysis with Applications in Optical Coherence Tomography

The computational approach to image acquisition and analysis plays an important role in medical imaging and optical coherence tomography (OCT). This thesis is dedicated to the development and evaluation of algorithmic solutions for better image acquisition and analysis with a focus on OCT retinal imaging. For image acquisition, we first developed, implemented, and systematically evaluated a compressive sensing approach for image/signal acquisition for single-pixel camera architectures and an OCT system. Our evaluation outcome provides a detailed insight into implementing compressive data acquisition of those imaging systems. We further proposed a convolutional neural network model, LSHR-Net, as the first deep-learning imaging solution for the single-pixel camera. This method can achieve better accuracy, hardware-efficient image acquisition and reconstruction than the conventional compressive sensing algorithm. Three image analysis methods were proposed to achieve retinal OCT image analysis with high accuracy and robustness. We first proposed a framework for healthy retinal layer segmentation. Our framework consists of several image processing algorithms specifically aimed at segmenting a total of 12 thin retinal cell layers, outperforming other segmentation methods. Furthermore, we proposed two deep-learning-based models to segment retinal oedema lesions in OCT images, with particular attention on processing small-scale datasets. The first model leverages transfer learning to implement oedema segmentation and achieves better accuracy than comparable methods. Based on the meta-learning concept, a second model was designed to be a solution for general medical image segmentation. The results of this work indicate that our model can be applied to retinal OCT images and other small-scale medical image data, such as skin cancer, demonstrated in this thesis

LASER Tech Briefs, Spring 1994

Topics in this Laser Tech Brief include: Electronic Components and Circuits. Electronic Systems, Physical Sciences, Materials, Mechanics, Fabrication Technology, and books and reports