Implementation Of Different Algorithms Research Articles

Implementation of the Cluster Counting and Timing technique on FPGA for the reduction of transferred data and stored information

Ultra-low mass and high granularity Drift Chambers fulfill the requirements of tracking systems for modern High Energy Physics experiments at future high luminosity accelerators (FCC or CEPC). The application of the Cluster Counting/Timing (CCT) technique adds a valuable particle identification capabilities with resolutions outperforming the traditional dE/dx technique. By measuring the arrival times of each individual ionization electron to the sense wire and by using suitable statistical tools it is possible to perform a bias free estimate of the impact parameter and a precise particle identification in drift chamber operating in a Helium based gas mixtures. The CCT technique consisting in identifying pulses due to different ionization electrons and in associating them in clusters according to their relative time delays, therefore it is necessary to have read-out interfaces capable of processing such high speed signals. This requires a data acquisition chain, able to manage the low amplitude signals from the sense wires (a ∼few mV) with a high bandwidth (∼1 GHz). Requirements on the drift chamber performance impose analog-to-digital conversions by a fast ADC at sample rates of at least 2 GS/s with 14-bit resolution. These constraints, together with maximum drift times and many readout channels, impose some sizeable data reduction strategy, while preserving all relevant information. Measuring both the amplitude and the arrival time of each peak in the signal associated to each ionization electron is the minimum requirement on the data transfer for storage to prevent any data loss. An electronic board including a Fast ADC and an FPGA for real-time processing of drift chamber signals is presented. The implementation of different algorithms for peaks finding are compared.

GenPop-An Online Tool to Analyze Human Population Genetic Data.

GenPop is a web based online cross platform tool developed to help Geneticist and Epidemiologist to deal with association studies in analyzing human population genetic data. The tool features include descriptive analysis such as Hardy-Weinberg equilibrium test, chi-square p-value and analysis of single nucleotide polymorphisms (SNPs) with multiple inheritance models such as dominant, recessive, allelic, genotype, odd's ratio and relative risk at 95% confidence interval and analysis of multiple SNPs including haplotype frequencies and linkage disequilibrium for a pair of biallelic markers. This is a user-driven human population genetic data analysis tool that is easily scalable and acceptable with multiple implementations of different algorithms. GenPop has been developed using PHP, JavaScript and with PHPExcel library to analyse the genetic data for case control studies.

A Semantic Approach for Outlier Detection in Big Data Streams

In recent years, the world faced a big revolution in data generation and collection technologies. The volume, velocity and veracity of data have changed drastically and led to new types of challenges related to data analysis, modeling and prediction. One of the key challenges is related to the semantic analysis of textual data especially in big data streams settings. The existing solutions focus on either topic analysis or the sentiment analysis. Moreover, the semantic outlier detection over data streams as one of the key problems in data mining and data analysis fields has less focus. In this paper, we introduce a new concept of semantic outlier through which the topic of the textual data is considered as the primary content of the data stream while the sentiment is considered as the context in which the data has been generated and affected. Also, we propose a framework for semantic outlier detection in big data streams which incorporates the contextual detection concepts. The advantage of the proposed concept is that it incorporates both topic and sentiment analysis into one single process; while at the same time the framework enables the implementation of different algorithms and approaches for semantic analysis.

A Centralized Model for Establishing End-to-End Communication Services via Management Agents

This paper presents a centralized approach for establishing end-to-end communication services via management agents. The main proposal is the modular architecture of the third-party based Service Establishment Agent (SEA). The SEA manages inter-provider service negotiation process with per-domain management agents through an appropriate signaling agent. It also receives and interprets end-toend service requests, selects inter-domain paths, performs mapping of service classes among domains on the path, and evaluates conformance of the offered service level with the required one. It allows implementation of different algorithms for the aforementioned functions as well as their selection and combination according to the predefined management policies. Simulation results show that the proposed model significantly outperforms the distributed model in terms of service negotiation times. In the prototype development process, a policy-based solution for mapping of service classes was implemented. The performance evaluation shows that processing requirements for handling multiple service requests are modest, while benefit of the SEA approach is the lack of need to build long-term consensus among providers about technical choices for achieving network interconnection. The SEA architecture is completely independent of the quality of service mechanisms available in particular domains.

Real Time Anomaly Detection in Massive Data Streams with ELK Stack

Real time anomaly detection is very popular topic nowadays this because the number of data generated every day is larger and larger. Facing with the phenomena of Big Data is not an easy task. The main aim of this research is to fine appropriate architecture for real-time big data analytic and its main task is to detect anomalies in this real-time data. In this paper we show the implementation of anomaly detection algorithm in real time infrastructure in order to find anomalies as soon as possible. We have proposed architecture for real time anomaly detection by adding some new components and the main part of the infrastructure is Timelion which enable implementation of different algorithms for anomaly detection. The research is focused to develop infrastructure to monitor e-dnevnik (education national system in Macedonia) application server and to detect errors in order to scale up the performance.

Developing a system for detecting the same content within the UBT academic institution, including special characters

One of the most acute problems in the academic world is plagiarism (prevention and detection). Based on this fact, numerous software systems have been developed in line with the growing demand from educational institutions. In principle, systems use string sort algorithms (matching algorithms) to detect possible cases of plagiarism. Each of them, although the original solution is prone to being sensitive in terms of security. These software platforms have some problems mainly in terms of the incompatibility of the character with the control system. This allows two identical documents (plagiarism and original) even though the same content is not detected by the system since they are written in a different alphabet (for the same characters). Plagiarism detection system addresses this problem by "translating" all data into a format where all characters are readable, exposing any document that is plagiarized (data). This is achieved by giving each character a unique value, regardless of the alphabet in which it is written in the document. This is accomplished through the implementation of different algorithms for sorting. This enables the system to face eventual errors where the client "cheats" the system and forces the system to compare documents in the database, written in an unsupported alphabet.

Implementation and Comparison of Text-Based Image Retrieval Schemes

Search engines, i.e., Google, Yahoo pro-vide various libraries and API’s to assist programmers and researchers in easier and efficient access to their collected data. When a user generates a search query, the dedicated Application Programming Interface (API) returns the JavaScript Object Notation (JSON) file which contains the desired data. Scraping techniques help image descriptors to separate the image’s URL and web host’s URL in different documents for easier implementation of different algorithms. The aim of this paper is to propose a novel approach to effectively filter out the desired image(s) from the retrieved data. More specifically, this work primarily focuses on applying simple yet efficient techniques to achieve accurate image retrieval. We compare two algorithms, i.e., Cosine similarity and Sequence Matcher, to obtain the accuracy with a minimum of irrelevance. Obtained results prove Cosine similarity more accurate than its counterpart in finding the maximum relevant image(s).

Reflection-coefficient experimental extraction from S21- parameter for radar oil-spill detection application

Oil spill in sea water is one of the main accidents that affect significantly the maritime environment over a long period of time. Knowing the severe influence of oil spills on the ecosystem, it is crucial to have oil spill detecting and monitoring systems for quick intervention and danger containment. In our project, we propose the usage of drones as an oil spill detection system. The drones will be implementing different previously developed multi-frequency approaches for the detection. The effectiveness of such techniques is based on the accuracy of the data collected and their match to the theory. This journal presents a method for the remote extraction of reflection coefficients from multilayer structure modeling an oil spill in sea water. The experimental results for the reflectivity extraction validate the theoretical calculations and allow the implementation of different algorithms based on the statistical information taken directly from the site.

New Synchronization Method for Transmission Systems with Variable Length of Bits

Abstract Based on the Spartan 3E evaluation module, a flexible platform for the implementation of different algorithms for A/D conversion was developed. The aim of presented work was to improve the concept of the sampling rate adaptation to the input signal rate of change in terms of practical issues including synchronization of delta codecs. The new, original synchronization method, useful in systems dedicated for transmission of variable duration of bits was proposed and experimentally verified. Performed measures and observations have shown elimination of the synchronization lose phenomenon

Development and Comparative Evaluation of Ramp Metering Algorithms Using Microscopic Traffic Simulation

Freeway ramp metering is an efficient freeway control that can ameliorate freeway congestion by limiting the number of vehicles entering the freeway. This study presents development and comparative evaluation of five ramp controls including no control, time-of-day plan, and three well-known ramp metering algorithms; ALINEA, FLOW and Stratified Zone through the use of microscopic traffic simulation. In this paper, ramp controls were developed and evaluated using Microscopic Traffic Simulation, AIMSUN NG. The simulation model of the Pacific Motorway in Queensland, Australia was used as a test-bed model. The results from the study indicated that ramp metering was basically found to be able to improve network performances up to 40% compared to no control. In terms of network and freeway mainline performance, ALINEA was superior to other algorithms under both normal and high traffic demand, whereas, Stratified Zone was the best algorithm for on-ramp performance. In terms of on-ramp performance, ALINEA was found to generate the lowest on-ramp performance. FLOW was found to be slightly superior to Stratified Zone. However, the trend was opposite for on-ramp performance. Gini coefficient was applied to measure the road user equity under the implementation of different algorithms. The results showed that FLOW was the most equitable algorithm, whereas, ALINEA was the worst. The results also indicated that network performance and on-ramp performance were trade-off in the presence of ramp metering operation.

Flaw detection improvement of digitised radiographs by morphological transformations

Radiographic inspection is one of the most appreciated techniques among non-destructive testing methods due to the production of a film which acts as a unique fingerprint record. Converting radiographs to a digital format and further digital image processing is the best method of enhancing the image quality and assisting the interpreter on his evaluation. In this research, different algorithms were used for image enhancement and radiograph interpretation in MATLAB environment. A graphical user interface (GUI) was created for implementation of different algorithms. Both spatial and frequency domains techniques were used for image enhancement. The main enhancement technique was a spatial technique based on morphological transformation (top-hat and bottom-hat transforms). The wavelet filtering was implemented as the frequency domain technique. A third technique, based on pseudo-colouring, was also used for a better comparison between different methods. Wavelet-based techniques are used very often nowadays. However, we show that wavelet applications are more complicated and a supervised and intelligent usage has to be carried out. A wisely established spatial technique can result in reasonably proper results. The diagnostic test reliability was carried out for the quantitative evaluation of the results with the help of industrial radiography interpreters. The obtained results indicated that the defect recognition probability can be increased effectively in both domains. However, it was concluded that better results can be achieved using a morphological method.

SNPAnalyzer: a web-based integrated workbench for single-nucleotide polymorphism analysis

SNPAnalyzer is a software that performs four essential statistical analyses of SNPs in a common computational environment. It is composed of three main modules: (i) data manipulation, (ii) analysis and (iii) visualization. The data manipulation module is responsible for data input and output, and handles genotype, phenotype and genetic distance data. To ensure user convenience, the data format is simple. The analysis module performs statistical calculations and consists of four subcomponents: (i) Hardy–Weinberg equilibrium, (ii) Haplotype Estimation, (iii) linkage disequilibrium (LD) and (iv) quantitative trait locus analysis. The main feature of the analysis module is multiple implementations of different algorithms and indices for haplotype estimation and for LD analysis. This enables users to compare separate results generated by different algorithms, which help to avoid biased results acquired by applying a single statistical algorithm. The performance of all implemented algorithms has been validated using experimentally proven datasets. The visualization module presents most of the analyzed results as figures, rather than as simple text, which aids in the intuitive understanding of complex data. The SNPAnalyzer has been developed using C and C++ and is available at .

Energy Metric for Software Systems

Acknowledging the intense requirement for low power operation in most portable computing systems, this paper introduces the notion of energy efficient software design and proposes metrics, for evaluating software systems in terms of their energy consumption. Considering the sources of power consumption in every digital circuit, and the fact that power is primarily dependent on the executing software, appropriate energy measures are derived, which can be extracted from the flowgraph of a program. The proposed measures are computed by applying rules common to the existing hierarchical measures of other internal software attributes, and form the basis for the definition of a software energy metric. This metric can be used in order to determine the level of energy consumption of any software system more efficiently than existing assembly-parsing techniques, with only a limited penalty in accuracy. Application to different implementations of algorithms, drawn from matrix algebra and multimedia, demonstrates the efficiency of the proposed energy metric for comparison purposes, and as an indicator for quality improvement.

Operational standards for CASA instruments.

Computer-aided sperm analysis (CASA) technology is 7 years old. Over 120 papers have been written that verify the technology or apply it in basic and clinical studies. Most of the technical problems with CASA, such as the dependence of velocity on video frame rate, inaccuracy of count and percent motility for low- and high-concentration specimens, parameter dependence on the number of frames analyzed, sensitivity of the subjective threshold setting, confusion over the presence of debris, and different implementations of algorithms across instruments, still persist. A critical review of the literature reveals that no standard practices are followed within or across instruments. Moreover, no standards have been embraced or recommended by professional societies. Despite its potential to provide objective measurements of specimen and individual sperm parameters, and to automate the laboratory semen analysis, the promise of CASA has not been fulfilled. Unless laboratory medicine defines instrument performance and laboratory standards and co-operates with industry to achieve these goals, CASA technology may remain a research curiosity. This outcome is especially worrisome in the context of increasing requirements for laboratory accuracy, precision, standardization, and accreditation under the Clinical Laboratory Improvement Act of 1988.