The main aim of the course recommendation system is to build a course recommendation path for students to help them plan courses to successfully graduate on time. The Model-View-Controller (MVC) architecture is used to isolate the user interface (UI) design from the business logic. The front-end of the application develops the UI using AngularJS. The front-end design is done by gathering the functionality system requirements -- input controls, navigational components, informational components and containers and usability testing. The back-end of the application involves setting up the database and server-side routing. Server-side routing is done using Express JS.

Due to the rapidly increasing complexity in hardware designs and competitive time to market trends in the industry, there is an inherent need to move designs to a higher level of abstraction. Behavioral Synthesis is the process of automatically compiling such Electronic System Level (ESL) designs written in high-level languages such as C, C++ or SystemC into Register-Transfer Level (RTL) implementation in hardware description languages such as Verilog or VHDL. However, the adoption of this flow is dependent on designers' faith in the correctness of behavioral synthesis tools. Loop pipelining is a critical transformation employed in behavioral synthesis process, and ubiquitous in commercial and academic behavioral synthesis tools. It improves the throughput and reduces the latency of the synthesized hardware. It is complex and error-prone, and a small bug can result in faulty hardware with expensive ramifications. Therefore, it is critical to certify the loop pipelining transformation so that designers can trust the behaviorally synthesized pipelined designs. Certifying a loop pipelining transformation is however, a major research challenge because there is a huge semantic gap between the input sequential design and the output pipelined implementation, making it infeasible to verify their equivalence with automated sequential equivalence checking (SEC) techniques. Complex loop pipelining transformations can be certified by a combination of theorem proving and SEC: (1) creating a certified pipelining algorithm which generates a reference pipeline model by exploiting pipeline generation information from the synthesis flow (e.g. the iteration interval of a generated pipeline) and (2) conduct SEC between the synthesized pipeline and this reference model. However, a key and arguably, the most complex component of this approach is the development of a formal, mechanically verifiable loop pipelining algorithm. We show how to systematically construct such an algorithm, and carry out its verification using the ACL2 theorem prover. We propose a framework of certified pipelining primitives which are essential for designing pipelining algorithms. Using our framework, we build a certified loop pipelining algorithm. We also propose a key invariant in certifying this algorithm, which links sequential loops with their pipelined counterparts. This is unlike other invariants that have been used in proofs of microprocessor pipelines so far. This dissertation provides a framework for creating certified pipelining algorithms utilizing a mechanical theorem prover. Using this framework, we have developed a certified loop pipelining algorithm. This certified algorithm is essential in the overall approach to certify behaviorally synthesized pipelined designs. We demonstrate the scalability and robustness of our algorithm on several ESL designs across various domains.

Biological data derived from high-throughput microarrays can be transformed into finite, simple, undirected graphs and analyzed using tools first introduced by the Langston Lab at the University of Tennessee. Transforming raw data can be broken down into three main tasks: data normalization, generation of similarity metrics, and threshold selection. The choice of methods used in each of these steps effect the final outcome of the graph, with respect to size, density, and structure. A number of different algorithms are examined and analyzed to illustrate the magnitude of the effects. Graph-based tools are then used to extract putative gene networks. These tools are loosely based on the concept of clique, which generates clusters optimized for density. Innovative additions to the paraclique algorithm, developed at the Langston Lab, are introduced to generate results that have highest average correlation or highest density. A new suite of algorithms is then presented that exploits the use of a priori gene interactions. Aptly named the anchored analysis toolkit, these algorithms use known interactions as anchor points for generating subgraphs, which are then analyzed for their graph structure. This results in clusters that might have otherwise been lost in noise. A main product of this thesis is a novel collection of algorithms to generate exact solutions to the maximum clique problem for graphs that are too large to fit within core memory. No other algorithms are currently known that produce exact solutions to this problem for extremely large graphs. A combination of in-core and out-of-core techniques is used in conjunction with a distributed-memory programming model. These algorithms take into consideration such pitfalls as external disk I/O and hardware failure and recovery. Finally, a web-based tool is described that provides researchers access the aforementioned algorithms. The Graph Algorithms Pipeline for Pathway Analysis tool, GrAPPA, was previously developed by the Langston Lab and provides the software needed to take raw microarray data as input and preprocess, analyze, and post-process it in a single package. GrAPPA also provides access to high-performance computing resources, via the TeraGrid.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Electrical and Computer Engineering, 2009. / Includes bibliographical references.

Thesis (Ph. D.)--Carnegie Mellon University, 2003. / Title from PDF title page (viewed Dec. 18, 2009). Includes bibliographical references (p. 49-52).

Thesis (M.S.)--Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.

The following thesis outlines the goal and process of algorithmic musical key detection as well as the underlying music theory. This includes a discussion of signal-processing techniques intended to most accurately detect musical pitch, as well as a detailed description of the Krumhansl-Shmuckler (KS) key-finding algorithm. It also describes the Java based implementation and testing process of a musical key-finding program based on the KS algorithm. This thesis provides an analysis of the results and a comparison with the original algorithm, ending with a discussion of the recommended direction of further development.

Contour extraction of Drosophila embryos is an important step to build a computational system for pattern matching of embryonic images which aids in the discovery of genes. Automatic contour extraction of embryos is challenging due to several image variations such as size, shape, orientation and neigh- boring embryos such as touching and non-touching embryos. In this thesis, we introduce a framework for contour extraction based on the connected components in the gaussian scale space of an embryonic image. The active contour model is applied on the images to refine embryo contours. Data cleaning methods are applied to smooth the jaggy contours caused by blurred embryo boundaries. The scale space theory is applied to improve the performance of the result. The active contour adjusts better to the object for finer scales. The proposed framework contains three components. In the first component, we find the connected components of the image. The second component is to find the largest component of the image. Finally, we analyze the largest component across scales by selecting the optimal scale corresponding to the largest component having largest area. The optimal scale at which maximum area is attained is assumed to give information about the feature being extracted. We tested the proposed framework on BDGP images, and the results achieved promising accuracy in extracting the targeting embryo.

Thesis (M. Sc.)--Carleton University, 1993. / Includes bibliographical references. Also available in electronic format on the Internet.

Thesis (Ph. D.)--University of Alabama at Birmingham, 2006. / Description based on contents viewed Jan. 26, 2007; title from title screen. Includes bibliographical references (p. 119-125).