Comparison of MSP-EXP432P401R Program Performances by Using Different Development Environments

In this paper, a photovoltaic (PV) system based on modified duty cycle sweeping (MDCS) has been proposed to achieve the maximum power point tracking (MPPT). The disadvantages of perturb and observe (P&O), such as diverging tracking directions and the inability to detect the global peak during partial shading (PS), are intended to be overcome by this method (PS). An intelligent double identification and tracking method consistently tracks the global peak under partial shading and the MPP under rapid irradiance fluctuations. Strict dynamic irradiance and partial shading tests are imposed in MATLAB/Simulink@ and simulated to validate the suggested concept. Additionally, a laboratory prototype MPPT standalone PV system supported by Texas Instruments’ Code Composer Studio is operated by a SEPIC converter in conjunction with the C2000 real-time microcontroller in order to conduct an experimental validation study. The effectiveness of the method is compared with the other well-known MPPT techniques, conventional P&O. The suggested method successfully follows the global peak under various patterns of partial shading as compared to the conventional algorithms. The algorithm’s efficiency has been preserved at around 95-100%.

The searching denotes a process that aims to find a specific item in the data set. After performing the process of searching, the desired items are likely found or not. This kind of investigations was actually demonstrated by some researchers. For each search algorithm, the previous studies just focused to obtain the search time but did not obtain the number of passed-nodes. Furthermore, the results obtained with regards to the search time were not reported systematically. Hence, this study investigates how fast the desired items were found and how few passed-nodes were obtained when searching desired items by using three search algorithms. These algorithms were linear, binary, and interpolation algorithms. By using search algorithms, one hundred twenty desired items were searched in a collection of active-student data (3946 records in total). The search time needed to find a specific item and the number of passed nodes were recorded. The results showed that the searching process with linear search algorithm required more time and more passed-nodes than binary and interpolation search algorithms. Following this, searching time and the number of passed-nodes for desired items were significantly smaller for binary search than for the interpolation search algorithm. Taken together, the present results suggested that the binary search could be considered as a solid algorithm in searching a specific item in the data set utilized by the current study.

In Indonesia, English is a compulsory subject for students. This course is an uninteresting subject and tends to be difficult for students to understand. Meanwhile, on the other hand, international issues are often discussed, namely English which is Students must know and be fluent in reading, writing and communication. The basic idea of research objectives, comparing the sorting process using two different algorithms, namely bubble sort and Merge Sort. The basic model of the research method Comparative Analysis of the Speed of the Shorting Method on Google Translate Indonesian-English Using Binary Search. Sorting, Sorted (ordered according to certain rules/rules), and the data is presented in sorted form, as said in dictionaries, and files in a directory. The algorithm used for sorting is bubble sort, which is an element comparison operation that is exchanged for other elements until the end of the data series is reached, until no more elements are swapped. Results for find out how well the performance speed of the bubble sort algorithm is in sorting data.

Research on sorting algorithms is a very core research field in computer science. Today, any advancement in sorting algorithms would lead to less time and space required to perform the sorting task. This paper discusses a newly proposed algorithm—‘ledger-based sorting algorithm’. It is basically an advancement to ‘counting sort’, but not exactly counting sort. The purpose of this paper is to reduce the time and space required for sorting when the numbers given to you belong to a particular range (0–n) and all of them are distinct. This paper also compares the newly proposed algorithm with insertion sort, bubble sort, selection sort, quick sort, merge sort and mainly counting sort through graphical time analysis of these algorithms. The paper especially compares counting sort algorithm with the proposed algorithm in terms of both time and space complexity.

An alternative taxonomy (to that of Knuth and others) of sorting algorithms is proposed. It emerges naturally out of a top-down approach to the derivation of sorting algorithms. Work done in automatic program synthesis has produced interesting results about sorting algorithms that suggest this approach. In particular, all sorts are divided into two categories: hardsplit/easyjoin and easysplit/hardjoin. Quicksort and merge sort, respectively, are the canonical examples in these categories. Insertion sort and selection sort are seen to be instances of merge sort and quicksort, respectively, and sinking sort and bubble sort are in-place versions of insertion sort and selection sort. Such an organization introduces new insights into the connections and symmetries among sorting algorithms, and is based on a higher level, more abstract, and conceptually simple basis. It is proposed as an alternative way of understanding, describing, and teaching sorting algorithms.

Sorting is the most fundamental operation in database system. There are many classical sorting algorithms and among them the most commonly‐used sorting algorithm in modern database system is merge sort. Merge sort is an efficient, general‐purpose, comparison‐based sorting algorithm. As merge sort is based on a divide and conquer model, it has found wide use in parallel computing algorithms. In this study, the authors present an adaptive sorting algorithm based on merge sort which is called array sort. Array sort not only takes advantages of merge sort, but also simplify the merge step by using a tag array. The proposed implementation consists of three phases: tag array creation phase, tag array split phase and list merge phase. Instead of just evaluating array sort in a single thread environment, the authors also run their experiments on multi‐thread to test how array sort performs.

The aim of this paper is to compare the complexity of three simple sort algorithm; selection sort algorithm, bubble sort algorithm, and insertion sort algorithm. The measurement of the complexity is based on control flow structure and time processing. The control flow structure is illustrated into flowgraph and measured by number of out-degree and number of in-degree. The algorithm is coded in C programming language. The sorting experiment uses four data sets: 100,000 random words, 50,000 random words, 100,000 random integer numbers, and 50,000 random integer numbers to measure the time processing. The result of the measurement explains that the bubble sort algorithm has the complexity more than others.

This book focuses on embedded image processing and, more specifically, on the efficient implementation of image processing algorithms on the TMS320C6000 family of digital signal processors (DSPs) from Texas Instruments (TI). The text is divided into six chapters: "Introduction," "Tools," "Spatial Processing Techniques," "Image Filtering," "Edge Detection and Segmentation," and "Wavelets." The text is supported by two appendices, as well as a CD-ROM. The CD-ROM includes all source code and project files as well as many test images and one text video clip. There is no better way to learn than by example, and the book offers plenty of them. While the book is intended for signal and image processing practitioners or software developers that plan to use a TI DSP, most of the programming techniques demonstrated in the book can easily be applied to other embedded platforms. The book is well written and succeeds in filling a big void in image processing literature, tackling how to efficiently implement signal and image processing algorithms using embedded processors.

The wide usage of credit scoring is to determine credit, credit analysis cost reduction, and faster decision making. However, traditional credit scoring models are not the factor for the influence of noises. There are several objectives of this study: 1) to develop Random Binary Search algorithm-based feature selection in Mahalanobis Taguchi System (MTS), 2) to validate the techniques of feature selection problems which are computationally efficient, and 3) to apply Random Binary Search algorithm in solving credit problems. In this study, Random Binary Search (RBS) algorithm was proposed, which was incorporated between MTS. The purpose of this was to optimize the procedures for determining the most useful variables. Besides being a relatively new statistical methodology where various mathematical concepts are integrated, there is an involvement of MTS in the field of diagnosis and classification in multidimensional systems. Besides, it is a highly effective method and has been implemented to a range of disciplines such as engineering, medical, financial, and others. As for the main problem of this study, data sets of credit scoring were used.

Search is considered to be an important functionality in a computational system. Search techniques are applied in file retrievals and indexing. Though there exists various search techniques, binary search is widely used in many applications due to its advantage over other search techniques namely linear and hash search. Binary search is easy to implement and is used to search for an element in a large search space. The worst case time complexity of binary search is O (log 2 n) where n is the number of elements (search space) in the array. However, in binary search, searching is performed on the entire search space. The complexity of binary search may be further reduced if the search space is reduced. This paper proposes an Ant Colony Optimization based Binary Search (ACOBS) algorithm to find an optimal search space for binary search. ACOBS algorithm categorizes the search space and the key element is searched only in a specific category where the key element can exist thereby reducing the search space. The time complexity of ACOBS algorithm is O (log 2 c) where c is the number of elements in the reduced search space and c < n. The proposal is best suited for real time applications where searching is performed on a large domain.KeywordsAnt colony optimizationBinary searchOptimal search space

It is well known that n integers in the range [1,n c ] can be sorted in O(n) time in the RAM model using radix sorting. More generally, integers in any range [1,U] can be sorted in $O(n\sqrt{\log\log n})$ time [5]. However, these algorithms use O(n) words of extra memory. Is this necessary? We present a simple, stable, integer sorting algorithm for words of size O(logn), which works in O(n) time and uses only O(1) words of extra memory on a RAM model. This is the integer sorting case most useful in practice. We extend this result with same bounds to the case when the keys are read-only, which is of theoretical interest. Another interesting question is the case of arbitrary c. Here we present a black-box transformation from any RAM sorting algorithm to a sorting algorithm which uses only O(1) extra space and has the same running time. This settles the complexity of in-place sorting in terms of the complexity of sorting.

Linear search algorithms have been shown to be particularly effective for solving partial Maximum Satisfiability (MaxSAT) problem instances. These algorithms start by adding a new relaxation variable to each soft clause and solving the resulting formula with a SAT solver. Whenever a Model is found, a new constraint on the relaxation variables is added such that Models with a greater or equal value are excluded. However, if the problem instance has a large number of relaxation variables, then adding a new constraint over these variables can lead to the exploration of a much larger search space. This article proposes new algorithms to improve the performance of linear search algorithms for MaxSAT by using Models found by the SAT solver to partition the relaxation variables. These algorithms add a new constraint on a subset of relaxation variables, thus intensifying the search on that subspace. In addition, the proposed algorithms are further enhanced by using incremental approaches where learned clauses are kept between calls to the SAT solver. Experimental results show that Model-based algorithms can outperform a classic linear search algorithm in several problem instances. Moreover, incremental approaches further improve the performance of linear search and Model-based algorithms for MaxSAT. Overall, Model-based algorithms are competitive with state-of-the-art MaxSAT solvers, and can improve their performance when solving problem instances with a large number of soft clauses.

We implement a promising algorithm for sparse-matrix sparse-vector multiplication (SpMSpV) on the GPU. An efficient k-way merge lies at the heart of finding a fast parallel SpMSpV algorithm. We examine the scalability of three approaches -- no sorting, merge sorting, and radix sorting -- in solving this problem. For breadth-first search (BFS), we achieve a 1.26x speedup over state-of-the-art sparse-matrix dense-vector (SpMV) implementations. The algorithm seems generalize able for single-source shortest path (SSSP) and sparse-matrix sparse-matrix multiplication, and other core graph primitives such as maximal independent set and bipartite matching.

In the past, pseudo code guidelines and intricate blackboard visuals were used to teach methods. This makes visualization technology a useful teaching tool for both students and teachers. In this assignment, we developed an algorithm visualizer. A sorting visualizer and a route finder are also provided. This project intends to provide a platform that could be used to understand better these ideas as well as to illustrate the uses of these various methodologies. As a foundation for learning algorithm design and analysis, Dijkstra's, A*, Deep First Search, Broadness First Search, Rapid Sort, Stack Sort, Sorting Algorithm, Bubble Sort, and other fundamental algorithms are commonly used. Google Street view and web searches are only two examples of software that uses these techniques and that we are using on a daily basis. As a consequence, we made an effort to create a tool that makes use of HTML, CSS, Express's, and other tools to graphically depict how these algorithms work. Our objective is to simplify the algorithm such that basic computer science students may comprehend it via a sequence of graphical stages.

This paper is intended to develop a bubble sort algorithm visualization for an Algorithm and Programming course in computer education. This learning model should encourage student's ability to learn the variety of sorting, such as selection sort, merge sort. etc. This research consists of 6 steps in visualization development such as need assessment, front-end analysis, design, development, implementation, and evaluation. During the testing step, the application is run and checked to confirm that it performs exactly what the author has intended and the students can learn bubble sorting algorithm by studying the visualization. Research findings indicated that the students increased their motivation and ability to create sorting visualitation application. Based on the data of process and product competencies taken from the evaluation, 85 per cent of the students were competent in sorting visualization programming and there were 15 per cent of the students were not competent.