NAND Flash-based Solid State Disks have many attractive technical merits, such as low power consumption, light weight, shock resistance, sustainability of hotter operation regimes, and extraordinarily high performance for random read access, which makes SSDs immensely popular and be widely employed in different types of environments including portable devices, personal computers, large data centers, and distributed data systems. However, current SSDs still suffer from several critical inherent limitations, such as the inability of in-place-update, asymmetric read and write performance, slow garbage collection processes, limited endurance, and degraded write performance with the adoption of MLC and TLC techniques. To alleviate these limitations, we propose optimizations from both specific outside applications layer and SSDs' internal layer. Since SSDs are good compromise between the performance and price, so SSDs are widely deployed as second layer caches sitting between DRAMs and hard disks to boost the system performance. Due to the special properties of SSDs such as the internal garbage collection processes and limited lifetime, traditional cache devices like DRAM and SRAM based optimizations might not work consistently for SSD-based cache. Therefore, for the outside applications layer, our work focus on integrating the special properties of SSDs into the optimizations of SSD caches. Moreover, our work also involves the alleviation of the increased Flash write latency and ECC complexity due to the adoption of MLC and TLC technologies by analyzing the real work workloads.

The fast capacity growth of cheap storage devices presents an ever-growing problem of scale for digital forensic investigations. One aspect of scale problem in the forensic process is the need for new approaches to visually presenting and analyzing large amounts of data. Current generation of tools universally employ three basic GUI components—trees, tables, and viewers—to present all relevant information. This approach is not scalable as increasing the size of the input data leads to a proportional increase in the amount of data presented to the analyst. We present an alternative approach, which leverages data visualization techniques to provide a more intuitive interface to explore the forensic target. We use tree visualization techniques to give the analyst both a high-level view of the file system and an efficient means to drill down into the details. Further, we provide means to search for keywords and filter the data by time period.

Geographic Information Systems (GIS) and related applications of geospatial data were once a small software niche; today nearly all Internet and mobile users utilize some sort of mapping or location-aware software. This widespread use reaches beyond mere consumption of geodata; projects like OpenStreetMap (OSM) represent a new source of geodata production, sometimes dubbed “Volunteered Geographic Information.” The volume of geodata produced and the user demand for geodata will surely continue to grow, so the storage and query techniques for geospatial data must evolve accordingly. This thesis compares three paradigms for systems that manage vector data. Over the past few decades these methodologies have fallen in and out of favor. Today, some are considered new and experimental (distributed), others nearly forgotten (flat file), and others are the workhorse of present-day GIS (relational database). Each is well-suited to some use cases, and poorly-suited to others. This thesis investigates exemplars of each paradigm.

Data collecting and preparing is generally considered a crucial process in data science projects. Especially for image data, adding semantic attributes when preparing image data provides much more insights for data scientists. In this project, we aim to implement a general-purpose central image data repository that allows image researchers to collect data with semantic properties as well as data query. One of our researchers has come up with the specific challenge of collecting images with weight data of infants in least developed countries with limited internet access. The rationale is to predict infant weights based on image data by applying Machine Learning techniques. To address the data collecting issue, I implemented a mobile application which features online and offline image and annotation upload and a web application which features image query functionality. This work is derived and partly decoupled from the previous project – ImageSfERe (Image Sharing for Epilepsy Research), which is a web-based platform to collect and share epilepsy patient imaging.

Given mobile devices’ weak computational power, game programmers must learn to create games with simple graphics that are engaging and easy to play. Though seldom created for phones and tablets, puzzle games are a perfect fit. In recent years, the genre has gained a following and even won some acclaim. Games like Myst, The Seventh Guest and Portal all engage gamers with challenging puzzles and then reward them with story components upon task fulfillment. Few such games have been created for mobile devices, in part due to the difficulty of developing for devices with different operating systems. Android, WebIOS and Windows Phone all have different software development kits that produce a final product incompatible with operating systems other than what it was developed for. One promising solution is to use browser technology to deliver games since all devices are geared to interact with the Web through browsers such as Internet Explorer, Mozilla Firefox, and Google Chrome. The aim of this project was to build a puzzle game that can be run on any digital device. The project can be accessed without any plug-ins and was created by using web technologies such as JQuery, Touch Punch, local storage, and WebGL. JQuery allows drag and drop functionality and Touch Punch allows the JQuery functionality intended for a mouse to work on a touch interface. Local storage provides storage on a user’s device, as opposed to a server, and WebGL enables graphics processing on a user’s tablet or phone through web commands.

Hospitals are the largest and most complex organizations where health care is provided. Safe and effective patient care services in hospitals depend on the efficient decisions made by hospital executives. The main task of hospital executives is to ensure the hospital can provide high quality patient care and services. “Android Mobile Application For Hospital Executives” is an Android application used for displaying hospital performance metrics on a daily basis. This application allows hospital executives to review and monitor hospital operational data with ease of access and in a portable manner. Thus, reducing the effort of the hospital executives to perform their tasks.

At California State University San Bernardino, students can ordinarily register online and join waiting lists when a course is full. However, the system does not support waiting lists when a course has associated laboratory sections. This project addresses this problem.

In a big organization there are many events organized every day. To know about the events, we typically need to check an events page, rely on flyers or on distributed pamphlets or through word of mouth. To register for an event a user now a days typically does this online which involves inputting user details. At the event, the user either signs a sheet of paper or enters credentials in a web page loaded on a tablet or other electronic device. Typically, this is a time-consuming process with many redundancies like entering user details every time the user wants to register for a new event and re-entering the details at the event. This project designs a system that eliminates these redundancies and improves event management.

Modern main memory is primarily built using dynamic random access memory (DRAM) chips. As DRAM chip scales to higher density, there are mainly three problems that impede DRAM scalability and performance improvement. First, DRAM refresh overhead grows from negligible to severe, which limits DRAM scalability and causes performance degradation. Second, although memory capacity has increased dramatically in past decade, memory bandwidth has not kept pace with CPU performance scaling, which has led to the memory wall problem. Third, DRAM dissipates considerable power and has been reported to account for as much as 40% of the total system energy and this problem exacerbates as DRAM scales up. To address these problems, 1) we propose Rank-level Piggyback Caching (RPC) to alleviate DRAM refresh overhead by servicing memory requests and refresh operations in parallel; 2) we propose a high performance and bandwidth efficient approach, called SELF, to breaking the memory bandwidth wall by exploiting die-stacked DRAM as a part of memory; 3) we propose a cost-effective and energy-efficient architecture for hybrid memory systems composed of high bandwidth memory (HBM) and phase change memory (PCM), called Dual Role HBM (DR-HBM). In DR-HBM, hot pages are tracked at a cost-effective way and migrated to the HBM to improve performance, while cold pages are stored at the PCM to save energy.

Fast Block Matching (FBM) algorithms for video compression are well suited for acceleration using parallel data-path architectures on Field Programmable Gate Arrays (FPGAs). However, designing an efficient on-chip memory subsystem to provide the required throughput to this parallel data-path architecture is a complex problem. This thesis presents a memory architecture template that can be parameterized for a given FBM algorithm, number of parallel Processing Elements (PEs), and block size. The template can be parameterized with well known exploration techniques to design efficient on-chip memory subsystems. The memory subsystems are derived for two existing FBM algorithms and are implemented on a Xilinx Virtex 4 family of FPGAs. Results show that the derived memory subsystem in the best case supports up to 27 more parallel PEs than the three existing subsystems and processes integer pixels in a 1080p video sequence up to a rate of 73 frames per second. The speculative execution of an FBM algorithm for the same number of PEs increases the number of frames processed per second by 49%.