Flexibility Of Software Research Articles

Speed vs. efficiency: A framework for high-frequency trading algorithms on FPGA using Zynq SoC platform

Software-based technical indicators have been widely used for the stock market forecasting, aiming to predict market direction. Even though many algorithms for the software based technical indicators are presented, there are almost no hardware implementations reported in the literature. In this paper, the hardware implementation is presented for three commonly used technical indicators: Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), and Aroon. Latency evaluation is conducted for Bitcoin and Ethereum within a single-day timeframe, utilizing the Xilinx Zynq-7000 programmable SoC XC7Z020-CLG484-1 platform.Additionally, various hardware/software (HW/SW) partitioning strategies are explored to leverage the flexibility of software alongside the performance advantages of hardware via the Zynq SoC platform. The results show that the best performing technical indicator is MACD with a speedup of 30 times over its software only counterpart. Furthermore, a hybrid design integrating multiple technical indicators is proposed, pairing MACD with RSI due to their competitive throughput values, differing by only 0.38 microseconds. This hybrid approach capitalizes on the parallel processing capabilities of hardware, enabling multiple systems to operate simultaneously.

CIPO: Efficient, lightweight and programmable packet scheduling

With the rapid development of network devices and increasingly high CPU workloads, packet scheduling will have to be offloaded to hardware. Programmable packet scheduling allows scheduling algorithms to be programmed into network devices without modifying the hardware. It not only retains the flexibility of software but also the scalability of hardware. In existing primitives, the most expressive Push-In-ExtractOut (PIEO) is prohibitively expensive to implement due to its complexity. While its variant, Push-In-Pick-Out (PIPO), offers some improvements, it suffers from insufficient scalability. In this paper, we propose the Classify-In-Push-Out (CIPO) primitive. The core idea of CIPO is to track the rank and predicate of recent packets through a sliding window, filter and classify packets using a prediction-based two-dimensional classification algorithm and a finite number of First-In-First-Out (FIFO) queues. Through theoretical analysis and evaluation with a range of real workloads, CIPO proves that it has a scheduling performance similar to the most expressive scheduling primitive. Importantly, CIPO requires fewer hardware resources while still providing sufficient expressiveness. Primitive on FPGA show that the CIPO-based scheduler achieves an average of 1.24× higher throughput than the PIEO-based scheduler but uses only an average of 26.2% of look-up tables (LUTs) and 12.2% of the block RAM of the latter.

Multifunction fluorescence open source in vivo/in vitro imaging system (openIVIS).

The widespread availability and diversity of open-source microcontrollers paired with off-the-shelf electronics and 3D printed technology has led to the creation of a wide range of low-cost scientific instruments, including microscopes, spectrometers, sensors, data loggers, and other tools that can be used for research, education, and experimentation. These devices can be used to explore a wide range of scientific topics, from biology and chemistry to physics and engineering. In this study, we designed and built a multifunction fluorescent open source in vivo/in vitro imaging system (openIVIS) system that integrates a Raspberry Pi with commercial cameras and LEDs with 3D printed structures combined with an acrylic housing. Our openIVIS provides three excitation wavelengths of 460 nm, 520 nm, and 630 nm integrated with Python control software to enable fluorescent measurements across the full visible light spectrum. To demonstrate the potential applications of our system, we tested its performance against a diverse set of experiments including laboratory assays (measuring fluorescent dyes, using optical nanosensors, and DNA gel electrophoresis) to potentially fieldable applications (plant and mineral imaging). We also tested the potential use for a high school biology environment by imaging small animals and tracking their development over the course of ten days. Our system demonstrated its ability to measure a wide dynamic range fluorescent response from millimolar to picomolar concentrations in the same sample while measuring responses across visible wavelengths. These results demonstrate the power and flexibility of open-source hardware and software and how it can be integrated with customizable manufacturing to create low-cost scientific instruments with a wide range of applications. Our study provides a promising model for the development of low-cost instruments that can be used in both research and education.

Access Interval Prediction by Partial Matching for Tightly Coupled Memory Systems

In embedded systems, tightly coupled memories (TCMs) are usually shared between multiple masters for the purpose of hardware efficiency and software flexibility. On the one hand, memory sharing improves area utilization, but on the other hand, this can lead to a performance degradation due to an increase in access conflicts. To mitigate the associated performance penalty, access interval prediction (AIP) has been proposed. In a similar fashion to branch prediction, AIP exploits program flow regularity to predict the cycle of the next memory access. We show that this structural similarity allows for adaption of state-of-the-art branch predictors, such as Prediction by Partial Matching (PPM) and the TAgged GEometric history length (TAGE) branch predictor. Our analysis on memory access traces reveals that PPM predicts 99 percent of memory accesses. As PPM does not lend itself to hardware implementation, we also present the PPM-based TAGE access interval predictor which attains an accuracy of over 97 percent outperforming all previously presented implementable AIP schemes.

РЕАЛИЗАЦИЯ ИНТЕЛЛЕКТУАЛЬНОЙ СИСТЕМЫ НЕПРЯМОГО ПРОГНОЗИРОВАНИЯ ВЫРАБАТЫВАЕМОЙ ЭЛЕКТРОЭНЕРГИИ СОЛНЕЧНОЙ ЭЛЕКТРОСТАНЦИИ КАК ПРОГРАММЫ ДЛЯ ЭВМ

The forecasting of electric power generated by a solar power plant enables effective and safe control over electric networks which integrate a cluster of solar power plants. Penalty rates for the purchase of solar power at the day-ahead market, which deviates by more than 5 % of the maximum capacity of solar power plants from the provided hourly model of the day-ahead market of solar power generation, update the accuracy of the day-ahead market model through effective intelligent systems for forecasting solar power generation. It has been found that there is no accessible software for successful forecasting of solar power generation; the advisability and relevance of designing such software with an intelligent system have been shown based on the fi ndings of the examined existing software. The study developed, tested and implemented an intelligent system of indirect forecasting of solar power generation in the form of computer software designed based on a modifi ed fuzzy neural network with an attention mechanism. A class diagram and a block-modular architecture for computer software that implements an intelligent system of indirect forecasting of solar power generation were developed in UML notes using the Microsoft Visio CASE tool. A block-modular architecture provides the fl exibility of computer software. The computer software implementing an intelligent system of indirect forecasting for solar power generation was tested for effectiveness, robust results, and the advisability of its application for building a day-ahead market model. The SCADA database of a solar power plant can be easily integrated with an intelligent system of indirect forecasting of solar power generation.

Sustainable manufacturing through application of reconfigurable and intelligent systems in production processes: a system perspective

Sustainable production aims at creating products from processes that minimize environmental impact, energy consumption and natural resources. Customers and markets are ever more leaning towards digital, custom, and flexible solutions with lower environmental impact. Hence, Industry 4.0 (I4.0) solutions are increasingly including social and environmental sustainability aspects. We focus on the realization of an infrastructure integrating industrially relevant application modules by combining system reconfigurability and artificial intelligence, towards sustainable production. To meet the final goal of sustainable production, we address four challenges considering flexibility and sustainability in production in a holistic way: (1) developing infrastructural and methodological tools to support companies to explore the potential of I4.0 towards sustainable production; (2) managing the configurability and customization possibilities of products; (3) effectively handling the flexibility provided by a production system with rapid reconfiguration capabilities; (4) integrating hardware and software flexibility by using reconfigurable robotics and machine learning methods. By developing and connecting different application modules, we obtain a physical demonstrator which represents on the one hand an exemplary scenario of reconfigurable and flexible production system; on the other, it enables new research activities and insights with a see, touch & feel approach for industrial and research realities.

Automated monitoring of pollen quality in Douglas fir using digital images: applications for handling and storage

Background: Conservation and breeding programs for forest species require efficient pollen quality monitoring strategies to maximize collection, storage and pollination efforts.
 Question: Can the ImageJ image analysis software be implemented to efficiently monitor pollen viability and germinability for quality testing?
 Studied species: Pseudotsuga menziesii (Mirb.) Franco
 Methods: Tetrazolium staining and germination on 10 % Brewback media was used to test Douglas-fir pollen quality. Two macros for automated counting were developed with ImageJ free software using digitized microscopic images, and were tested to evaluate provenances, germination in different media, dehydration/rehydration and freeze-storage treatments, and finally to compare pollen quality between mature trees and GA-induced strobili from juvenile trees.
 Results: Automated monitoring of pollen quality was feasible because the software suitably distinguishes viable from non-viable pollen grains due to differential tetrazolium staining that translates into pixel density, whereas germinability was assayed based on pollen size and circularity. This tool was used to guide the selection of appropriate pollen handling conditions and compare pollen production in adult and GA-induced juvenile trees. Pollen flash-freezing and storage at -80 °C showed recovery between 74 and 98 % of its initial germinability after rehydration; however, viability detected by tetrazolium is reduced, suggesting that handling and storage affect the reliability of the test.
 Conclusions: The flexibility of the Fiji software allowed the development of an efficient and accurate macro for monitoring P. menziesii pollen quality, reducing invested time and effort involved, which supports future research on its conservation physiology and its successful application for germplasm production.

Mixture of experts distributional regression: implementation using robust estimation with adaptive first-order methods

In this work, we propose an efficient implementation of mixtures of experts distributional regression models which exploits robust estimation by using stochastic first-order optimization techniques with adaptive learning rate schedulers. We take advantage of the flexibility and scalability of neural network software and implement the proposed framework in mixdistreg, an R software package that allows for the definition of mixtures of many different families, estimation in high-dimensional and large sample size settings and robust optimization based on TensorFlow. Numerical experiments with simulated and real-world data applications show that optimization is as reliable as estimation via classical approaches in many different settings and that results may be obtained for complicated scenarios where classical approaches consistently fail.

Innovation in Cross-Linking Mass Spectrometry Workflows: Toward a Comprehensive, Flexible, and Customizable Data Analysis Platform.

Cross-linking mass spectrometry (XL-MS) is widely used in the analysis of protein structure and protein-protein interactions (PPIs). Throughout the entire workflow, the utilization of cross-linkers and the interpretation of cross-linking data are the core steps. In recent years, the development of cross-linkers and analytical software mostly follow up on the classical models of non-cleavable cross-linkers such as BS3/DSS and MS-cleavable cross-linkers such as DSSO. Although such a paradigm promotes the maturity and robustness of the XL-MS field, it confines the innovation and flexibility of new cross-linkers and analytical software. This critical insight will discuss the classification, advantages, and disadvantages of existing data analysis search engines. Take the new platinum-based metal cross-linker as an example, potential pitfalls in characterization of cross-linked peptides using existing software are discussed. Finally, ideas on developing more flexible, comprehensive, and user-friendly cross-linkers and software tools are proposed.

Canadian manufacturer and technician perspectives on the design and use of augmentative and alternative communication technology

Purpose Device manufacturers and technicians (MaTs) of augmentative and alternative communication (AAC) systems play key roles in the design and successful uptake of communication devices. This study aims to investigate MaT perspectives on AAC device design and effective use. Materials and Methods To investigate their perspectives, a focus group of MaTs within Canada was conducted. Reflexive thematic analysis was used to analyze data. Findings Three major themes resulted from analysis, which reflect MaT’s views: AAC hardware and software flexibility, AAC knowledge and implementation, and social good versus financial resources. Conclusions This study provides insights into the complexities faced by MaTs in balancing technical support of system end-users and the financial resources necessary for that support. These insights indicate a need for increased financial resources and the expansion of individuals who qualify for AAC system candidacy. MaTs suggest that an increase in resources and candidacy could lead to more successful AAC implementation and a greater understanding of AAC for all stakeholders.

The yaq project: Standardized software enabling flexible instrumentation

Modern instrumentation development often involves the incorporation of many dissimilar hardware peripherals into a single unified instrument. The increasing availability of modular hardware has brought greater instrument complexity to small research groups. This complexity stretches the capability of traditional, monolithic orchestration software. In many cases, a lack of software flexibility leads creative researchers to feel frustrated, unable to perform experiments they envision. Herein, we describe Yet Another acQuisition (yaq), a software project defining a new standardized way of communicating with diverse hardware peripherals. yaq encourages a highly modular approach to experimental software development that is well suited to address the experimental flexibility needs of complex instruments. yaq is designed to overcome hardware communication barriers that challenge typical experimental software. A large number of hardware peripherals are already supported, with tooling available to expand support. The yaq standard enables collaboration among multiple research groups, increasing code quality while lowering development effort.

Spectroscopic Based Ammonia Measurement System

This article describes the design and implementation of a microcontroller-based system for determining ammonia concentrations in blood. By adding a blood sample to a test strip, the proposed system induces a series of reactions known as Berthelot's response, resulting in an accurate change in strip color. A current is then generated when the photodiode detects the 635 nm wavelength reflected off the strip. An op-amp is used to convert the current to voltage. Because of their ease of use and software flexibility, microcontrollers are recognized as important components in modern electronics. The concentration of (NH3) is determined by a microcontroller depending on the voltage value. The test results are shown on an LCD or recorded in data files. Any normal or abnormal outcomes are appropriately recorded by the system. A PC software program is written to accept and save data. The proposed system is initially designed using the concepts of Software Analysis and Design; the process of building the proposed systems begins with determining the system's requirements, working on the analysis, using the best design techniques according to the software Engineering process, and finally modeling it on an electronic board before being realized and implemented. It is used in biomedical measurements. It is also handy for monitoring and researching final circuits.

Operation extension in gas turbine-based advanced cycles with a surge prevention tool

This work aims to present the development and testing of an innovative tool for surge prevention in advanced gas turbine cycles. The presence of additional components, such as a saturator in humid cycles, a heat exchanger for an external combustor, a solar receiver or fuel cell stack in a hybrid system, implies the presence of larger size volumes between compressor outlet and recuperator or expander inlet. This large volume increases the risk of incurring in surge instability, especially during dynamic operations. For these reasons, at the University of Genoa, the Thermochemical Power Group (TPG) has implemented four surge precursors in a new diagnostic real-time software which can recognise a surge incipience condition comparing the precursor values with a set of moving thresholds. The most innovative aspects of this work are: (i) operational range extension and safer management of advanced gas turbine systems for energy generation, (ii) positive impact in energy efficiency due to this range extension of high efficiency systems, (iii) development of a new diagnostic tool for surge prevention using standard probes, (iv) small impact of this tool on the control and sensor costs, (v) software flexibility for adaptation to different conditions and machines. This very important final aspect is obtained with thresholds able to change automatically to adapt themselves to the plant and machine operational regime. From the cost point of view, the utilization of standard measurements is an essential requirement to equip commercial machines without significant impact on the capital costs. The software performance has been demonstrated using experimental data from a test rig composed of a T100 microturbine connected with a modular vessel, which permits to generate the effect of additional components (especially from the volume size point of view). Vibro-acoustic data, collected during machine transients from a stable operative condition to surge, were used to tune all the software parameters and to obtain a good surge predictivity.

Accelerated RISC-V for Post-Quantum SIKE

In this work, we present a fast and area-efficient software-hardware implementation of the supersingular isogeny key encapsulation (SIKE) mechanism. Our software-hardware design achieves both the flexibility of software as well as the efficient performance of intense computations of hardware. In particular, our implementation takes advantage of new and highly optimized hardware modules for addition, multiplication, and hardware-software control, targeted at Xilinx FPGAs. In conjunction with a small RISC-V processor, we can support all four SIKE parameter sets. On a Virtex-7 FPGA, this implementation occupies 3,492 slices, 78 DSPs, and 29 BRAMs, to perform encapsulation and decapsulation over SIKEp434, SIKEp503, SIKEp610, and SIKEp751 in 14.5, 19.2, 29.8, and 42.7 ms, respectively. Despite supporting all four parameter sets, this design has the best area-time product of all isogeny accelerators in the literature.

Biological Breakdown of Sports in Athletics Based on Multimedia Image Acquisition Techniques.

This paper provides an in-depth analysis and study of the decomposition of sports biology of athletics events through multimedia image acquisition techniques. The proposed design uses SOPC hardware-software collaboration technology, which makes full use of the parallelism of hardware as well as the flexibility of software to essentially improve the speed of image processing and greatly improve the efficiency of image processing. The high speed and parallelism of hardware are used to realize the image acquisition part with high timing requirements and the preprocessing algorithm with a large number of operations and strong repetition, in addition to the difficulties of many initialization configuration parameters of each peripheral. Additionally, the need for frequent adjustment of peripheral working parameters is solved by making full use of the scalability and flexibility of software, and the control signals output by software can coordinate the work of each hardware module to ensure that each module of the system cooperates. It is also possible to coordinate the work of each hardware module through the control signal output from the software to ensure that each module of the system cooperates and operates in an orderly and efficient manner and provides the possibility of realizing a higher level of the image processing algorithm. The system includes four main factors: swinging action, supporting action, vacating action, and speed rhythm. Based on this system, 14 key kinematic indicators were selected to reflect the technical status of the youth walkers. The landing angle decreased with the increase of speed, and the landing technique was insufficient; it had better control of the large and small arm angle, and their stride width was open, while the other four athletes showed increased tension in the hip joint and upper limb with the increase of walking speed, as well as the wrong action of forward and internal rotation of the large and small arms.

Calculating geodesics:

Introduction: The article is the product of the research “Connections on Semi-Riemannian Geometry and Christoffel Coefficients – Towards the study of the computational calculation of geodesics”, developed at the Pascual Bravo University Institution in the year 2021. Problem: Based on solutions of the Euler-Lagrange equations, the explicit calculation of geodesics on certain manifolds is possible. However, there are several cases in which it is impossible to continue calculating analytically and we have to resort to a numerical calculation. In this sense, several geometric and dynamic characteristics of geodesics, unexpectedly emerge. Objective: The objective of the research is to calculate geodesics of a Riemannian or semi-Riemannian manifold using SageMath as software to more easily go beyond what intuition provides. Methodology: First, some simple examples of characterizations of geodesics on certain manifolds, based on solutions of the Euler-Lagrange equations, are presented. Then, an ellipsoid is selected as a test subject with which to numerically calculate geodesics, observing how it changes depending on whether it is defined within a Spherical, Triaxial or Mercator coordinate system. Results: With the flexibility of software like SageMath, an explicit expression of the differential equations was made possible along with, from numeric solutions for these equations, their corresponding simulations depending on the selected parameters. Conclusion: These simulations confirm that great circles are not the only geodesics existing on the ellipsoid, but rather there are many other types of geodesic curves, some of which can be dense curves on the surface and others can be closed curves. At the same time, this shows a relationship between the existence of certain types of geodesic curves and the parameterization of the surface.

S4 Features and Artificial Intelligence for Designing a Robot against COVID-19—Robocov

Since the COVID-19 Pandemic began, there have been several efforts to create new technology to mitigate the impact of the COVID-19 Pandemic around the world. One of those efforts is to design a new task force, robots, to deal with fundamental goals such as public safety, clinical care, and continuity of work. However, those characteristics need new products based on features that create them more innovatively and creatively. Those products could be designed using the S4 concept (sensing, smart, sustainable, and social features) presented as a concept able to create a new generation of products. This paper presents a low-cost robot, Robocov, designed as a rapid response against the COVID-19 Pandemic at Tecnologico de Monterrey, Mexico, with implementations of artificial intelligence and the S4 concept for the design. Robocov can achieve numerous tasks using the S4 concept that provides flexibility in hardware and software. Thus, Robocov can impact positivity public safety, clinical care, continuity of work, quality of life, laboratory and supply chain automation, and non-hospital care. The mechanical structure and software development allow Robocov to complete support tasks effectively so Robocov can be integrated as a technological tool for achieving the new normality’s required conditions according to government regulations. Besides, the reconfiguration of the robot for moving from one task (robot for disinfecting) to another one (robot for detecting face masks) is an easy endeavor that only one operator could do. Robocov is a teleoperated system that transmits information by cameras and an ultrasonic sensor to the operator. In addition, pre-recorded paths can be executed autonomously. In terms of communication channels, Robocov includes a speaker and microphone. Moreover, a machine learning algorithm for detecting face masks and social distance is incorporated using a pre-trained model for the classification process. One of the most important contributions of this paper is to show how a reconfigurable robot can be designed under the S3 concept and integrate AI methodologies. Besides, it is important that this paper does not show specific details about each subsystem in the robot.

A monolithic algebraic multigrid framework for multiphysics applications with examples from resistive MHD

We consider monolithic algebraic multigrid (AMG) algorithms for the solution of block linear systems arising from multiphysics simulations. While the multigrid idea is applied directly to the entire linear system, AMG operators are constructed by leveraging the matrix block structure. In particular, each block corresponds to a set of physical unknowns and physical equations. Multigrid components are constructed by first applying existing AMG procedures to matrix sub-blocks. The resulting AMG sub-components are then composed together to define a monolithic AMG preconditioner. Given the problem-dependent nature of multiphysics systems, different blocking choices may work best in different situations, and so software flexibility is essential. We apply different blocking strategies to systems arising from resistive magnetohydrodynamics in order to demonstrate the associated trade-offs.

Usability of Emergency Department Information System Based on Users' Viewpoint; a Cross-Sectional Study.

The emergency department is of special importance due to its emergency and vital services, the high volume of referrals, and the patients' physical condition. Thus, it requires a well-designed information system with no usability problems. This study aimed to evaluate the usability of the emergency department information system from users' perspectives. This was a cross-sectional study. The research setting was the emergency department of 3 hospitals. The research instrument was a 37-item questionnaire adapted from the USE and ISO Metrics questionnaires, consisting of five dimensions measuring the usefulness of the system, ease of use, ease of learning, user satisfaction, and suitability for the task. The content validity of the questionnaire was examined using the content validity ratio and content validity index, and its reliability was assessed using Cronbach's alpha (α = 0.88). Fifty questionnaires were administered in the three hospitals, and the response rate was 80%. According to the findings, 55% of the respondents were female. The highest mean scores belonged to usefulness in emergency department information system (EDIS) A, ease of use in EDIS B, ease of learning in EDIS A, user satisfaction in EDIS C, and suitability for the task in EDIS A. According to the usability evaluation criteria, ease of learning (3.66 ± 0.74), usefulness (3.53 ± 0.87), and suitability for the task (3.47 ± 0.96) received the highest scores, and the lowest scores belonged to user satisfaction (3.29 ± 1.01) and ease of use (3.12 ± 1.00). In terms of usability criteria, the emergency department information system is at a relatively good level. The usability of these systems can be further enhanced by considering the users' working needs, improving software flexibility, customizing the software, using data visualization tools, observing consistency of features and standards, and increasing the quality of information and system services.

ReAAP: A Reconfigurable and Algorithm-Oriented Array Processor with Compiler-Architecture Co-Design

Parallelism and data reuse are the most critical issues for the design of hardware acceleration in a deep learning processor. Besides, abundant on-chip memories and precise data management are intrinsic design requirements because most of deep learning algorithms are data-driven and memory-bound. In this paper, we propose a compiler-architecture co-design scheme targeting a reconfigurable and algorithm-oriented array processor, named ReAAP. Given specific deep neural networks, the proposed co-design scheme is effective to perform parallelism and data reuse optimization on compute-intensive layers for guiding reconfigurable computing in hardware. Especially, the systemic optimization is performed in our proposed domain-specific compiler to deal with the intrinsic tensions between parallelism and data locality, for the purpose of automatically mapping diverse layer-level workloads onto our proposed reconfigurable array architecture. In this architecture, abundant on-chip memories are software-controlled and its massive data access is precisely handled by compiler-generated instructions. In our experiments, the ReAAP is implemented on an embedded FPGA platform. Experimental results demonstrate that our proposed co-design scheme is effective to integrate software flexibility with hardware parallelism for accelerating diverse deep learning workloads. As a whole system, ReAAP achieves a consistently high utilization of hardware resource for accelerating all the diverse compute-intensive layers in ResNet, MobileNet, and BERT.