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Diamond Light Source is a publicly funded 3rd generation national synchrotron which will soon operate 39 state-of-the-art instruments covering a wide range of physical and life science applications. Realization of such instruments poses many challenges from initial scientific concept, to final user experience. To get best efficiency, Diamond operates a modular approach for engineering and software systems support, usually with custom hardware or software component coming together on the final instrument in-situ. To facilitate cross-group collaboration, prototyping, integrated development and testing of the full instrument including scientific case before the final implementation, an optical prototyping setup has been developed which has an identical backend to real beamline instruments. We present detail of the software and hardware components of this environment and how these have been used to develop functionality for the new operational instruments. We present several high impact examples of such integrated prototyping development including the instrumentation for DIAD (integrated Dual Imaging And Diffraction) and the J08 beamline for: soft X-ray ptychography end-station., Proceedings of the 17th International Conference on Accelerator and Large Experimental Physics Control Systems, ICALEPCS2019, New York, NY, USA

Automated target recognition (ATR) using a computer vision system is a problem of extremely high complexity. A 3-D object recognition scheme involves many image analysis and enhancement techniques, including image processing, image segmentation, image registration, and object modeling and projection. This dissertation addresses the problem of 3-D object recognition using five distinct methods of matching image data with model projection-derived data. In analyzing each digitized video image, a variety of techniques, including an optimal gray level map for correlating binary line drawings with gradient images, were used to enhance the visibility of particular features and to increase signal to noise ratios in images. The shapes extracted from these enhanced images were then analyzed in a number of fashions, including the statistical descriptors of Karhunen-Loeve transformation. The first of the five object identification methods which were tested compared descriptors of the object to be analyzed with those of model projections meeting certain criteria. The second compared the object descriptors to those of a precalculated series of model projections. The third method used the descriptions of the second method as a starting point for a neural network, and then proceeded to learn the differences between these model projections and actual data. The neural net as realized demonstrated a great reduction in training time over conventional implementations. Calibration difficulties of other methods were greatly reduced by the learning capability of the neural net. The fourth method cross-correlated the optimally mapped gradient of the object image with a series of model projections. Finally, ways of combining these methods to utilize the strengths of each were investigated. Superior accuracy was obtained for cross-correlation. Optimal techniques which significantly reduced the number of required correlations and hence the computational load were also found to give very accurate results.

Due to the prediction loop, instability is a major difficulty in the design of a predictive quantizer. The conventional open-loop method enjoys good stability but suffers from poor performance. In contrast, the closed-loop method may be able to perform better but it can not guarantee a complete convergence and so it is unstable. Recently, the asymptotic closed-loop approach was proposed to benefit from the stability of open-loop design while asymptotically optimizing the actual closed-loop system. In this work, all of the above three design algorithms for predictive quantization are discussed and applied to image coding. Based on the analysis of simulation results, modifications to closed-loop design and asymptotic closed-loop design are proposed for further improvement in design quality and reliability.

In pattern recognition problems, a feature is defined as any property that may be useful in differentiating among classes of inputs. A classic example entails using color and shape as features to sort apples and bananas into their respective classes. Both the generation and assessment of features has become an essential part of modern machine learning and pattern classification endeavors, particularly with the continued growth of feature spaces in recent years. In support of such problems, many feature selection algorithms with distinct benefits and drawbacks have been devised. Despite these advancements in the field, questions remain about feature subset selection and performance in certain scenarios. Many classes, such as some found in the medical domain, are not reliably described by a single defining feature. In these heterogeneous classes, detecting feature synergies is essential to high performance classification. A confounding issue occurs when sample sizes for heterogeneous classes are small. Small sample size is often unavoidable for various reasons, and such situations may present challenges to commonly employed techniques of feature selection, such as statistical methods. In this dissertation, a sequential forward floating search (SFFS) algorithm is investigated as a feature subset selection technique on heterogeneous data sets of small sample size. The objective is to assess SFFS performance against statistical selection techniques that are common in the literature. To this end, an exemplar data set from the neuroimaging domain is used to represent a typical heterogeneous data set. This data set is analyzed using both traditional approaches and with the SFFS algorithm. The findings of this investigation are then used to inform synthetic data simulations to establish the ground-truth performance of SFFS with respect to such heterogeneous data. Results of this investigation show SFFS to be a sensitive technique for feature subset selection in data sets. In many cases, SFFS is shown to outperform the t-test in individual feature detectability. However, potential outliers and increased variability in small sample size data remain a concern for successful assessment of classification results in specific problems.

This research project investigates the use of finite-state transition representations for path planning, cyber security, and closed-loop control of networked industrial control systems. In particular, we consider systems consisting of physical components that are controlled by networked digital devices, such as programmable logic controllers (PLCs). The physical components are typically described in continuous time using differential equations, while the communication and control components operate in discrete time. Such combinations of continuous and discrete elements are called "hybrid systems." Although there is a substantial body of literature for analyzing either continuous or discrete systems, the techniques applicable to one are generally not compatible with the other, making analysis and control of hybrid systems a challenging task. The approach taken here is to approximate the continuous dynamics by finitestate, discrete-time transition models. These can be naturally integrated with the digital control and communication infrastructure, and the resulting unified finite-state transition models can be analyzed using powerful algorithmic tools. In this work, the tasks of interest are 1) planning open-loop set-point changes of the system state, 2) detecting and neutralizing harmful control actions, and 3) implementing closed-loop control for uncertainty and disturbance rejection. We first extend existing methods for finite-state transition modeling to include multiple equilibrium points with both stable and unstable points. This extension is demonstrated on the classic inverted pendulum on a cart (IPC). We show the path planning and closed-loop control capabilities by swinging up the IPC to its unstable, upward-pointing position and stabilizing it there. Finally, we extend the current state-of-the-art in cyber security for networked industrial control systems, by using the finite-state transition model to create a state-aware packet monitoring system to interpret control inputs in the context of the current system configuration.

Deep learning recently attracted a lot of attention in image processing and signal processing. It shows great potential in downsampling high dimension data while abstracts key information inside these data. This characteristic makes deep learning very powerful in content-based image retrieval (CBIR) and speech enhancement (SE) because both of them need high quality and low dimension semantic features. By using the code layer in deep autoencoder (DAE), which is a fully connected deep learning model, the CBIR and SE system can get decent results. For the CBIR, our newly designed multiple input multiple task DAE (MIMT-DAE) using wavelet coefficients can even get better performance than the single input single task DAE using less trainable parameters. However, for image processing, the fully connected structure shows limitation and a locally connected structure named convolutional neural network (CNN) and a hybrid structure is proposed in this dissertation. The CNN works as a preprocessing stage for the autoencoder can provide better input features than the raw images because its locally connected weights. The hybrid structure boosts the retrieval performance substantially in both grayscale and color image retrieval. For the SE system, the fully connected DAE trained only on mask approximation (MA) function does not present desired performance. We design a multiple task structure adding a signal approximation (SA) function during training for the SE system to reduce false positive. Training on both cost functions simultaneously gives much better performance than trained only on MA function or even the latest method that fined tuned on SA function. We also explored the long-short term memory structure and propose it as the future work.

Augmented Reality (AR) is a technology that augments user reality with computer-generated data such as GPS, audio, video or 3D objects. In comparison with virtual reality (VR), where the user’s view is totally replaced with a computer-generated environment, Augmented Reality focus on enhancing user’s reality experience with superimposed information. The application of Augmented Reality not only focuses on military and advertisement applications but also on education and medicine. This thesis focuses on development of an Augmented Reality application on the Android platform to view computerized axial tomography (CAT) scan images and 3D atlas models superimposed over the physical world, displayed on the device screen in real time. The developed application is based on AndAR framework, a Java API that provides a foundation to develop Augmented Reality projects on the Android platform.

After Nikola Tesla’s initial research in the field of Wireless Power transmission not many improvements or developments were done in this field, until now when there is increased popularity of Wireless technology. In a step ahead in the application of wireless power transmission- Near field communication, research was done on Non-contact human interaction using this technology. With moving hand or finger position and gesture, the change in coil inductance was observed and using this this change desired output operations were performed. Initially research was conducted to improve upon the sensitivity of a differential structure based on oscillators and mixers and to reduce the circuitry used to achieve the desired result of using the wireless power transmission coil to not just transfer power but act as a sensor. Later on, application was developed and bringing this to reality and demonstrating how the WPT coils sense the presence of hand or finger and interact with humans. Although the sensitivity of the coil is not much, but it was successfully tested as a sensor and was able to detect the presence of hand and display different pictures depending on where the hand is placed. Also, by looking at the less reliable apple iPhone home buttons and constant presence of home button icon on the screen of the phone, application was developed to replace this with the coil sensor and perform the task same as the home button getting rid of the icon on the screen.

High-performance low power arithmetic circuit with reduced area is essential for advanced arithmetic processes. This thesis proposes a modification of arithmetic units using a new design technique which is called Complimentary Based Logic Design (CBLD). CBLD is the design technique which minimizes the number of gates hence improves the area efficiency of the overall circuit. This new design approach can be issued in an arithmetic unit in which multiple blocks perform conditional operations in parallel at one stage or in series with dependency. As multiple module performs in parallel only one module functions and others stays idle. CBLD reduces such idle modules by compressing all modules with a single module. The functionality of CBLD can be verified by implementing it on an optimized module of BCD adder and BCD adder/subtractor module. Comparison of CBLD design with its’ older counterpart shows significant area power and delay efficiency. Second Part of this thesis proposes a novel design of Carry select adder implementing CBLD. Proposed design of conditional BEC-CSLA or modified ripple carry adder is compared with recently published Area power and time efficient Carry select adders.

Integration of wind power plant into the power systems requires long distance transmission lines to transmit wind power to the market. Low frequency interarea oscillation modes are an important issue faced in long distance transmission of power system which can limit the electric power transfer between the areas. Insufficient damping of interarea oscillation modes can lead to rotor angle separation and blackouts. With this assertion, the present study is aimed at designing the systematic methodology to enhance the damping of interarea oscillation modes of a large interconnected power system integrated with wind power plant. A lead-lag Power Oscillation Damping (POD) controller is evaluated on two-area test system for single interarea mode. In addition, an observer-based POD controller is proposed for large interconnected power system with multiple interarea modes. Transient energy function is proposed to determine the Critical Clearing Time (CCT) of the power system integrated with wind power plant. This technique will enable us to easily define the CCT in comparison with the conventional time-consuming numerical analysis. Modern power system integrated with synchrophasors including Phasor Measurement Units (PMUs) and Phasor Data Concentrator (PDCs) will allow us to collect system level information in real time. In future electrical power systems, the wide use of PMUs is inevitable and thus raises the importance of cyber security. Conventional model-based methodologies are used to identify false data injection. A signal-based technique using Discrete Wavelet Transform (DWT) is proposed to detect the anomalies. The Hardware-in-the-loop simulation of the power system is demonstrated at Smart Grid Energy Center Lab at Texas Tech University. This technique will allow us to detect the malicious activities in real time with various resolutions.