Accelerating Smith-Waterman Algorithm for Faster Sequence Alignment using Graphical Processing Unit

In bioinformatics, sequence alignment is very important task to compare and find similarity between biological sequences. Smith Waterman algorithm is most widely used for alignment process but it has quadratic time complexity. This algorithm is using sequential approach so if the no. of biological sequences is increasing then it takes too much time to align sequences. In this paper, parallel approach of Smith Waterman algorithm is proposed and implemented according to the architecture of graphic processing unit using CUDA in which features of GPU is combined with CPU in such a way that alignment process is three times faster than sequential implementation of Smith Waterman algorithm and helps in accelerating the performance of sequence alignment using GPU. This paper describes the parallel implementation of sequence alignment using GPU and this intra-task parallelization strategy reduces the execution time. The results show significant runtime savings on GPU.

WAREHOUSE OPERATIONS OF PASSENGER SERVICE CAR DEPOT WITH LOGISTICS ELEMENTS

The article discusses the issues of rational organization of the warehouse process of storing inventory, the sequential implementation of warehouse operations, the improvement of labor organization and technological solutions, the effective use of various equipment involved in the performance of technological operations at the warehouse of the carriage depot of the Joint Stock Company «Uzpasstrans». The issues of creating a database for storing goods, an effective system for organizing their account are considered. The ER-diagram of the database of warehouse inventory of spare parts and components has been developed. The functions and procedures for working with database data on components and spare parts located in the warehouse of the carriage depot have been determined.

Safer and More Efficient Parallel Cryptographic Algorithm and its Implementation in the GPU

In the digital world, the demand for data security during communication has increased. Hash functions are one of the cryptographic algorithms that provide data security in terms of data authenticity and integrity. Nowadays, most online applications require user authentication. These authentications are done on the server-side, which he must manage. As the number of applications increases, building a one-way function will be faster for calculating a hash value for small data such as passwords. In this paper, we will present a sequential cryptographic algorithm and its parallel implementation. We performed security analyses, executed comparisons for different amounts of data, and provided steps for further developing this algorithm. With the construction of this one-way function, we have provided the calculation of hash value in a shorter time for data in small quantities, which speeds up the authentication process on the server and thus speeds up the online services provided by the respective applications. A comparison was made between sequential implementation, parallel implementation on the CPU, and parallel implementation on the GPU using CUDA (Computer Unified Device Architecture) platform.

Development of the method of operational forecasting of fire in the premises of objects under real conditions

A method for operational forecasting of fires is proposed that enables the sequential implementation of five procedures. The method development is necessary to predict early fires in premises in order to take measures to prevent them from escalating into an uncontrolled combustion phase ‒ a fire. As a result of research, it was found that a short-term forecast of the recurrence of increments of the air conditions by one step, based on the current measure of recurrence, is an effective indicator of early fires in premises. At the same time, it was found that before the moment of ignition of the material, the state of the air environment is characterized by dynamic stability, which is described by an irregular and time-dependent random change in the recurrence of the states of the vector of current increments of the state of the air environment. The values of the indicated levels of recurrence of the state increments are determined by the probability levels of 0.67 and 0.1, respectively. The probability of recurrence of state increments of 0.67 is characteristic of a larger number of measured states. When the material is ignited, the dynamics of the probability of recurrence of state increments change abruptly. There is a transition from two to one level of recurrence, close to zero probability ‒ the loss of dynamic stability (in the region of count 250). Further dynamics are characterized by the appearance of separate random recurrent increments corresponding to the instability of the air environment in the premises. In the course of the experiment, it was found that the accuracy of predicting a fire by the proposed method ranges from 4.48 % to 12.79 %, which generally indicates its efficiency. The obtained data prove useful in the development of new systems that early warn of fire in premises, as well as in the modernization of existing systems and means of fire protection of premises

Adaptive based Sequential Implementation Resampling Particle Filter (ASIRPF) for Real Time Geophysical Applications

The services of geophysical application in the obstructedarea can be extremely challenging, especially if theGlobal Navigation Satellite System (GNSS) signal isunavailable or weak. Usually, the integration with theinertial sensor in the smartphone was used for assistingbetter navigation. Nonetheless, the usage of particle filtermodule in optimizing data from positioning sensor willcontribute to the low particle sample size or known assample impoverishment phenomenon, and finallyincreasing the location positioning error in a certainperiod. Adaptations towards to particle sample size andnoise, must be made, to make the particle filter moreintelligent, reliable and robust in a long time it isrunning. In this paper, we propose a new algorithm ofsequential implementation resampling particle filter byadapting the particle sample size and sensor noisemeasurement. This adaptation will be used to counteractin a different situation. As the results, the paper showsthe proposed solution can achieve an averageimprovement of 24.78% by reducing RMSE of stateestimation compared to previous algorithm. In the future,it is expected to contribute to the modernization ofgeophysical applications.

Parallelization of the K-Means++ Clustering Algorithm

K-means++ is the clustering algorithm that is created to improve the process of getting initial clusters in the K-means algorithm. The k-means++ algorithm selects initial k-centroids arbitrarily dependent on a probability that is proportional to each data-point distance to the existing centroids. The most noteworthy problem of this algorithm is when running happens in sequential mode, as this reduces the speed of clustering. In this paper, we develop a new parallel k-means++ algorithm using the graphics processing units (GPU) where the Open Computing Language (OpenCL) platform is used as the programming environment to perform the data assignment phase in parallel while the Streaming SIMD Extension (SSE) technology is used to perform the initialization step to select the initial centroids in parallel on CPU. The focus is on optimizations directly targeted to this architecture to exploit the most of the available computing capabilities. Our objective is to minimize runtime while keeping the quality of the serial implementation. Our outcomes demonstrate that the implementation of targeting hybrid parallel architectures (CPU & GPU) is the most appropriate for large data. We have been able to achieve a 152 times higher throughput than that of the sequential implementation of k-means ++.

METHOD OF REPLACEMENT OF SPARE PARTS FOR MAINTAINING AND ENSURING OPERATING STATE OF MOTOR TRANSPORT

A method based on the sequential implementation of a formalized description of the process of consuming spare parts, a formalized description of the process of determining the state of the el-ements of a market system, a formalized description of the process of determining a rational route for transporting the necessary material and technical means is presented.

PARALLELIZATION OF RSA CRYPTOGRAPHIC ALGORITHM BASED ON CUDA TECHNOLOGIES

The paper examines the efficiency of the application of CUDA technologies for the parallelization of the cryptographic algorithm with the public key. The speed of execution of several implementations of the algorithm is compared: sequential implementation on the CPU and two parallel implementations – on the CPU and GPU. A description of the public key algorithm is presented, as well as properties that allow it to be parallelized. The advantages and disadvantages of parallel implementations are analyzed. It is shown that each of them can be suitable for different scenarios. The software was developed and several numerical experiments were performed. The reliability of the obtained results of encryption and decryption is confirmed. To eliminate the influence of external factors at the time of execution the algorithm was tested ten times in a row and the average value was calculated. Acceleration coefficients for message encryption and decryption algorithms were estimated based on OpenMP and CUDA technology. The proposed approach focuses on the possibility of further optimization through the prospects of developing a multi-core architecture of computer systems and graphic processors.

INFORMATION SECURITY AUDITOR BASED ON ARTIFICIAL INTELLIGENCE-PRACTICAL IMPLEMENTATION EXPERIENCE

Искусственный интеллект (Artificial Intelligence - AI) в настоящее время это уже не просто модное название и словосочетание, а система мышления (когнитивная система), моделирующая работу головного мозга, мышления и принятия им целого спектра решений, в том числе и управленческих. Искусственный интеллект - реализуемая с помощью программно-аппаратного обеспечения и использующая разнообразный набор типов интерфейсов и их сочетаний, позволяющих осуществлять практически полноценную замену традиционно используемых человеческих мыслительных и управленческих функций для решения как теоретических, так и практических задач, начиная от простых, легко структурируемых задач и вплоть до задач, характеризующихся неопределенностью. В статье рассмотрены этапы последовательной реализации искусственного интеллекта. Приведен пример создания навыка для голосового ассистента на языке PHP. Artificial intelligence (AI) is now not just a fashionable name and phrase, but a thinking system (cognitive system) that simulates the work of the brain, thinking and making a whole range of decisions, including managerial ones. Artificial intelligence - implemented with the help of software and hardware and using a diverse set of interface types and their combinations, allowing for almost complete replacement of traditionally used human thinking and management functions for solving both theoretical and practical tasks, ranging from simple, easily structured tasks and up to tasks characterized by uncertainty. The article discusses the stages of the sequential implementation of artificial intelligence. An example of creating a skill for a voice assistant in PHP is given.

Sequential implementation of DSC-MR perfusion and dynamic [18F]FET PET allows efficient differentiation of glioma progression from treatment-related changes

Purpose Perfusion-weighted MRI (PWI) and O-(2-[18F]fluoroethyl-)-l-tyrosine ([18F]FET) PET are both applied to discriminate tumor progression (TP) from treatment-related changes (TRC) in patients with suspected recurrent glioma. While the combination of both methods has been reported to improve the diagnostic accuracy, the performance of a sequential implementation has not been further investigated. Therefore, we retrospectively analyzed the diagnostic value of consecutive PWI and [18F]FET PET. Methods We evaluated 104 patients with WHO grade II–IV glioma and suspected TP on conventional MRI using PWI and dynamic [18F]FET PET. Leakage corrected maximum relative cerebral blood volumes (rCBVmax) were obtained from dynamic susceptibility contrast PWI. Furthermore, we calculated static (i.e., maximum tumor to brain ratios; TBRmax) and dynamic [18F]FET PET parameters (i.e., Slope). Definitive diagnoses were based on histopathology (n = 42) or clinico-radiological follow-up (n = 62). The diagnostic performance of PWI and [18F]FET PET parameters to differentiate TP from TRC was evaluated by analyzing receiver operating characteristic and area under the curve (AUC). Results Across all patients, the differentiation of TP from TRC using rCBVmax or [18F]FET PET parameters was moderate (AUC = 0.69–0.75; p < 0.01). A rCBVmax cutoff > 2.85 had a positive predictive value for TP of 100%, enabling a correct TP diagnosis in 44 patients. In the remaining 60 patients, combined static and dynamic [18F]FET PET parameters (TBRmax, Slope) correctly discriminated TP and TRC in a significant 78% of patients, increasing the overall accuracy to 87%. A subgroup analysis of isocitrate dehydrogenase (IDH) mutant tumors indicated a superior performance of PWI to [18F]FET PET (AUC = 0.8/< 0.62, p < 0.01/≥ 0.3). Conclusion While marked hyperperfusion on PWI indicated TP, [18F]FET PET proved beneficial to discriminate TP from TRC when PWI remained inconclusive. Thus, our results highlight the clinical value of sequential use of PWI and [18F]FET PET, allowing an economical use of diagnostic methods. The impact of an IDH mutation needs further investigation.