Software Implementation
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2022 ◽  
Vol 12 (2) ◽  
pp. 762
Damjan Vavpotič ◽  
Diana Kalibatiene ◽  
Olegas Vasilecas ◽  
Tomaž Hovelja

Today, businesses need to continuously adjust to a dynamic environment. Enterprises have to deal with global competition and technological advances, meet government regulations, and keep their expenses under control. Under these pressures, enterprises need to implement and improve software that supports and helps to evolve their business. However, as practice shows, software implementation projects are complex, and a considerable percentage of them do not meet business requirements. Therefore, a business needs to manage software implementation properly. Existing research shows that using business rules (BR) in software implementation projects helps to ensure its success. The purpose of our study is to advance the understanding of how BR affect software implementation success, namely, which key characteristics of BR are the most important. To achieve this goal, the top thousand enterprises in Slovenia, by added value, facing typical software implementation projects were surveyed. The obtained results show that BR that are specifically prepared for a particular project and easy to understand have a statistically significant positive effect on software implementation project success.

Micromachines ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 98
Eugeny Ryndin ◽  
Natalia Andreeva ◽  
Victor Luchinin

The article presents the results of the development and study of a combined circuitry (compact) model of thin metal oxide films based memristive elements, which makes it possible to simulate both bipolar switching processes and multilevel tuning of the memristor conductivity taking into account the statistical variability of parameters for both device-to-device and cycle-to-cycle switching. The equivalent circuit of the memristive element and the equation system of the proposed model are considered. The software implementation of the model in the MATLAB has been made. The results of modeling static current-voltage characteristics and transient processes during bipolar switching and multilevel turning of the conductivity of memristive elements are obtained. A good agreement between the simulation results and the measured current-voltage characteristics of memristors based on TiOx films (30 nm) and bilayer TiO2/Al2O3 structures (60 nm/5 nm) is demonstrated.

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 348
Francisco de Melo ◽  
Horácio C. Neto ◽  
Hugo Plácido da Silva

Biometric identification systems are a fundamental building block of modern security. However, conventional biometric methods cannot easily cope with their intrinsic security liabilities, as they can be affected by environmental factors, can be easily “fooled” by artificial replicas, among other caveats. This has lead researchers to explore other modalities, in particular based on physiological signals. Electrocardiography (ECG) has seen a growing interest, and many ECG-enabled security identification devices have been proposed in recent years, as electrocardiography signals are, in particular, a very appealing solution for today’s demanding security systems—mainly due to the intrinsic aliveness detection advantages. These Electrocardiography (ECG)-enabled devices often need to meet small size, low throughput, and power constraints (e.g., battery-powered), thus needing to be both resource and energy-efficient. However, to date little attention has been given to the computational performance, in particular targeting the deployment with edge processing in limited resource devices. As such, this work proposes an implementation of an Artificial Intelligence (AI)-enabled ECG-based identification embedded system, composed of a RISC-V based System-on-a-Chip (SoC). A Binary Convolutional Neural Network (BCNN) was implemented in our SoC’s hardware accelerator that, when compared to a software implementation of a conventional, non-binarized, Convolutional Neural Network (CNN) version of our network, achieves a 176,270× speedup, arguably outperforming all the current state-of-the-art CNN-based ECG identification methods.

Robotics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 7
Yannick Roberts ◽  
Amirhossein Jabalameli ◽  
Aman Behal

Motivated by grasp planning applications within cluttered environments, this paper presents a novel approach to performing real-time surface segmentations of never-before-seen objects scattered across a given scene. This approach utilizes an input 2D depth map, where a first principles-based algorithm is utilized to exploit the fact that continuous surfaces are bounded by contours of high gradient. From these regions, the associated object surfaces can be isolated and further adapted for grasp planning. This paper also provides details for extracting the six-DOF pose for an isolated surface and presents the case of leveraging such a pose to execute planar grasping to achieve both force and torque closure. As a consequence of the highly parallel software implementation, the algorithm is shown to outperform prior approaches across all notable metrics and is also shown to be invariant to object rotation, scale, orientation relative to other objects, clutter, and varying degree of noise. This allows for a robust set of operations that could be applied to many areas of robotics research. The algorithm is faster than real time in the sense that it is nearly two times faster than the sensor rate of 30 fps.

Nikolay Sergienko ◽  
Pavel Kalinin ◽  
Magomedemin Gasanov ◽  
Natalia Pavlova ◽  
Vitaliy Svidlo ◽  

The global trend of improving vehicle and traction transmissions is associated with the use of robotic preselector gearboxes, an important component of which is the friction clutch. The review of existing designs and analysis of clutch development concepts allows determining the directions of their improvement, in particular, the improvement of dry double clutches, which is the subject of research in this paper. A significant disadvantage of existing dual-clutch designs is the use of additional special (mostly hydraulic or combined) systems for on-off friction pairs, so the structural cost and energy costs for control are increased, and the design, maintenance, and repair are complicated. The proposed design of the drive to control the original dry dual-clutch involves a lever mechanism with the use of rotary stops, which are made in the form of mobile carriages with rollers, this provides switching clutches in a short time and actually without interruption of power flow, so the acceleration dynamics of vehicles will be improved, the clutch design will be simplified and energy consumption for its control during the start and gear shifting will be reduced. The mathematical model of the proposed clutch drive on the basis of which the control mechanism operation is simulated and influence of its design parameters on operational indicators of the clutch drive, in particular, on kinematic characteristics of the drive, ranges of kinematic and power gear ratios, the power interaction of the mechanism links, the power of the actuator when each clutch is turning on. It is established that the switching of clutches requires less energy, the movement of the mechanism elements is different, and the compression force of the friction pairs when starting the first and second clutches is different. The simulation results confirm that the proposed original lever design of the clutch control mechanism is effective. The obtained results allow us to reasonably determine the parameters of a clutch, but the choice of optimal-rational parameters of its control mechanism requires further research, for which appropriate methods, search algorithms, and their software implementation have been developed.

2021 ◽  
Vol 15 (4) ◽  
pp. 7-21
Eugene Korobov ◽  
Yulia Semernina ◽  
Alina Usmanova ◽  
Kristina Odinokova

The modern global debt market features historically low average interest rates, convergence of yields on bonds with different maturities, an increase of yield curve inversion emergence frequency and a large-scale trend to automate financial decision making. The researchers’ attention in these fields is mainly focused on designing models that describe the state of the debt market as whole or its individual instruments in particular, as well as on risk management methods. At the same time, the specialized literature offers very few works concerning the topic of computer algorithms for bond portfolio selection based on traditional or advanced investment strategies. The aim of the present research is to create a modification of the existing algorithm of riding the yield curve strategy application, employing, first, average bond yield over the holding period instead of traditional bond yield to maturity; second, a developed algorithm for calculating the market spread on bonds; and, third, alternative risk evaluation indicators (compensation coefficients), which allow us to measure objectively price risk, liquidity risk, transaction costs risk and a general risk. The modification and the development of the algorithm for calculating the market spread were carried out using the direct measurement of the result technique, which entails application of the strategy to the data on bond issues received through the Moscow Exchange API. The selection of financial instruments was conducted in all sectors of the Russian debt market: public bonds, sub-federal and municipal bonds, corporate bonds. The modified algorithm enabled us to get extra yield for each selected bond issue, thereby proving the high effectiveness of the technique compared to the traditional strategy. Software implementation of the algorithm can be integrated into any robotized or semi-robotized stock exchange trading application.

Liliia Bodnar ◽  
Kateryna Shulakova ◽  
Liudmyla Gryzun

This work is devoted to the analysis of algorithmic support of multimedia content recommender systems and the development of a web service toincrease the efficiency of learning foreign languages using a recommender system that personalized the selection of educational content for the user.To form a list of necessary multimedia content, the main criteria of the recommender system were selected, the basic needs of users were identified,which the system should solve, since increasing the efficiency of learning a foreign language is achieved not only by choosing teaching methods, butalso by watching multimedia content, namely news, films, educational videos, clips, etc. Therefore, in order to form a list of the necessary multimediacontent, the main criteria of the recommender system were formed, the main needs of users were identified, which the system must solve. From theside of the method for implementing algorithmic support, various types of data filtering were considered, from modern technical methods to librariesto ensure the functionality of the system, and the algorithm based on hybrid filtering was chosen, in which known user ratings are used to predict thepreferences of another user. Functional requirements have been developed and a web service has been proposed that allows a comprehensive impact onuser learning when learning a foreign language, software implementation of which is made using Java Script, Python and additional libraries. Thisimplementation allows you to build a process for tracking changes in user requirements and transfer information to the database (DB) and, afteranalyzing the input data, change the proposed multimedia content to the user. In the course of further research, it is planned to conduct practicalexperiments, taking into account the specifics of certain methods of teaching foreign languages and the use of statistical data to assess the effectivenessof the algorithm of the proposed recommender system.

Joanna Stanisz ◽  
Konrad Lis ◽  
Marek Gorgon

AbstractIn this paper, we present a hardware-software implementation of a deep neural network for object detection based on a point cloud obtained by a LiDAR sensor. The PointPillars network was used in the research, as it is a reasonable compromise between detection accuracy and calculation complexity. The Brevitas / PyTorch tools were used for network quantisation (described in our previous paper) and the FINN tool for hardware implementation in the reprogrammable Zynq UltraScale+ MPSoC device. The obtained results show that quite a significant computation precision limitation along with a few network architecture simplifications allows the solution to be implemented on a heterogeneous embedded platform with maximum 19% AP loss in 3D, maximum 8% AP loss in BEV and execution time 375ms (the FPGA part takes 262ms). We have also compared our solution in terms of inference speed with a Vitis AI implementation proposed by Xilinx (19 Hz frame rate). Especially, we have thoroughly investigated the fundamental causes of differences in the frame rate of both solutions. The code is available at

2021 ◽  
Vol 12 (5-2021) ◽  
pp. 117-127
Svetlana N. Malygina ◽  
Elena O. Neupokoeva ◽  

The article discusses the development of a simulation model of a production cluster by means of the Anylogic University 8.7 software tool system using agent-based and system-dynamic approaches. The authors focus on isualization tools and software implementation of algorithms for the movement of labor resources within the cluster enterprises, taking into account various restrictions.

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261431
Fakir Sharif Hossain ◽  
Taiyeb Hasan Sakib ◽  
Muhammad Ashar ◽  
Rian Ferdian

Advanced Encryption Standard (AES) is the most secured ciphertext algorithm that is unbreakable in a software platform’s reasonable time. AES has been proved to be the most robust symmetric encryption algorithm declared by the USA Government. Its hardware implementation offers much higher speed and physical security than that of its software implementation. The testability and hardware Trojans are two significant concerns that make the AES chip complex and vulnerable. The problem of testability in the complex AES chip is not addressed yet, and also, the hardware Trojan insertion into the chip may be a significant security threat by leaking information to the intruder. The proposed method is a dual-mode self-test architecture that can detect the hardware Trojans at the manufacturing test and perform an online parametric test to identify parametric chip defects. This work contributes to partitioning the AES circuit into small blocks and comparing adjacent blocks to ensure self-referencing. The detection accuracy is sharpened by a comparative power ratio threshold, determined by process variations and the accuracy of the built-in current sensors. This architecture can reduce the delay, power consumption, and area overhead compared to other works.

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