Proposed Software Framework Research Articles

A Software Platform for Quadruped Robots with Advanced Manipulation Capabilities.

Recently, a diverse range of robots with various functionalities have become a part of our daily lives. However, these robots either lack an arm or have less capable arms, mainly used for gestures. Another characteristic of the robots is that they are wheeled-type robots, restricting their operation to even surfaces. Several software platforms proposed in prior research have often focused on quadrupedal robots equipped with manipulators. However, many of these platforms lacked a comprehensive system combining perception, navigation, locomotion, and manipulation. This research introduces a software framework for clearing household objects with a quadrupedal robot. The proposed software framework utilizes the perception of the robot's environment through sensor inputs and organizes household objects to their designated locations. The proposed framework was verified by experiments within a simulation environment resembling the conditions of the RoboCup@Home 2021-virtual competition involving variations in objects and poses, where outcomes demonstrate promising performance.

Assessing water resources systems’ dynamic resilience under hazardous events via a genetic fuzzy rule-based system

AbstractIn this paper, the use of a novel genetic fuzzy rule-based system (FRBS) is proposed for assessing the resilience of a water resources system to hazards. The proposed software framework generates a set of highly interpretable rules that transparently represent the causal relationships of hazardous events, their timings, and intensities that can lead to the system's failure. This is achieved automatically through an evolutionary learning procedure that is applied to the data acquired from system dynamics (SD) and hazard simulations. The proposed framework for generating an explainable predictive model of water resources system resilience is applied to the Pirot water resources system in the Republic of Serbia. The results indicate that our approach extracted high-level knowledge from the large datasets derived from multi-model simulations. The rule-based knowledge structure facilitates its common-sense interpretation. The presented approach is suitable for identifying scenario components that lead to increased system vulnerability, which are very hard to detect from massive raw data. The fuzzy model also proves to be a satisfying fuzzy classifier, exhibiting precisions of 0.97 and 0.96 in the prediction of low resilience and high rapidity, respectively.

A Real-Time Automated Visual Inspection System for Printed Circuit Boards Missing Footprints Detection

Visual inspection systems (VIS) are vital for recognizing and assessing parts in mass-produced products at the fabricating lines. In the past, item review was carried out physically, which made finding imperfections repetitive, moderate, and prone to error. VIS may be a strategy to abbreviate preparing times, boost item quality, and increment fabricating competitiveness. For the reason of reviewing lost components on uncovered printed circuit sheets, a visual inspection framework is required. The assessment assignment has become more challenging to accomplish the specified quality due to the more compact and complex surface of structured electronic components. This study proposes a real-time visual inspection system to assess lost impressions on Printed Circuit Boards (PCB). This system is composed of hardware and software frameworks. The main contribution of this study is the proposed software framework. The software framework consists of components region analysis and missing detection using image processing, cross-correlation, and production rules. Experimental results show the viability and achievability of the proposed system for PCB missing component detection.

Move cultural heritage knowledge graphs in everyone’s pocket

Last years witnessed a shift from the potential utility in digitisation to a crucial need to enjoy activities virtually. In fact, before 2019, data curators recognised the utility of performing data digitisation, while during the lockdown caused by the COVID-19, investing in virtual and remote activities to make culture survive became crucial as no one could enjoy Cultural Heritage in person. The Cultural Heritage community heavily invested in digitisation campaigns, mainly modelling data as Knowledge Graphs by becoming one of the most successful Semantic Web technologies application domains. Despite the vast investment in Cultural Heritage Knowledge Graphs, the syntactic complexity of RDF query languages, e.g., SPARQL, negatively affects and threatens data exploitation, risking leaving this enormous potential untapped. Thus, we aim to support the Cultural Heritage community (and everyone interested in Cultural Heritage) in querying Knowledge Graphs without requiring technical competencies in Semantic Web technologies. We propose an engaging exploitation tool accessible to all without losing sight of developers’ technological challenges. Engagement is achieved by letting the Cultural Heritage community leave the passive position of the visitor and actively create their Virtual Assistant extensions to exploit proprietary or public Knowledge Graphs in question-answering. By accessible to all, we mean that the proposed software framework is freely available on GitHub and Zenodo with an open-source license. We do not lose sight of developers’ technical challenges, which are carefully considered in the design and evaluation phases. This article first analyses the effort invested in publishing Cultural Heritage Knowledge Graphs to quantify data developers can rely on in designing and implementing data exploitation tools in this domain. Moreover, we point out challenges developers may face in exploiting them in automatic approaches. Second, it presents a domain-agnostic Knowledge Graph exploitation approach based on virtual assistants as they naturally enable question-answering features where users formulate questions in natural language directly by their smartphones. Then, we discuss the design and implementation of this approach within an automatic community-shared software framework (a.k.a. generator) of virtual assistant extensions and its evaluation in terms of performance and perceived utility according to end-users. Finally, according to a taxonomy of the Cultural Heritage field, we present a use case for each category to show the applicability of the proposed approach in the Cultural Heritage domain. In overviewing our analysis and the proposed approach, we point out challenges that a developer may face in designing virtual assistant extensions to query Knowledge Graphs, and we show the effect of these challenges in practice.

A New Algorithmic Trading Approach Based on Ensemble Learning and Candlestick Pattern Recognition in Financial Assets

Financial assets considered as time series are chaotic in nature. The main goal of investors is to take a position at the right time and in the right direction by making predictions about the future of this chaotic series. These time series consist of the opening, low, high, and closing prices of a certain period. The approaches used to make predictions about trend direction and strength using moving averages and indicators based on them have noise and lag problems as they are obtained statistically. Candlestick charts, on the other hand, reflect the price-based psychology of bear and bull investors, and facilitate the interpretation of price movements by consolidating the said opening, closing, lowest and highest prices in a single image. It is known that it was applied to Japanese rice markets for the first time in history and there are more than 100 candle patterns. In this study, an extensible architecture software framework using factory patterns and an object-oriented approach is proposed for defining candlestick patterns and developing intelligent learning algorithms based on them. In the studies carried out for financial assets, the profit factor, which shows the portfolio gain of the strategy, is used. It is desirable that this number of wins be greater than 1. When the proposed approach is tested for 5 major financial assets, this value was obtained as greater than 1 for all assets. The proposed software framework can also be used in the development of new robotic approaches in terms of being applicable to all kinds of financial assets in every period.

Bridging the Gap in Technology Transfer for Advanced Process Control with Industrial Applications.

In the present paper, a software framework comprising the implementation of Model Predictive Control—a popular industrial control method—is presented. The framework is versatile and can be run on a variety of target systems including programmable logic controllers and distributed control system implementations. However, the main attractive property of the framework stems from the goal of achieving smooth technology transfer from the academic setting to real industrial applications. Technology transfer is, in general, difficult to achieve, because of the apparent disconnect between academic studies and actual industry. The proposed software framework aims at bridging this gap for model predictive control—a powerful control technique which can result in substantial performance improvement of industrial control loops, thus adhering to modern trends for reducing energy waste and fulfilling sustainable development goals. In the paper, the proposed solution is motivated and described, and experimental evidence of its successful deployment is provided using a real industrial plant.

A Novel Tissue Identification Framework in Cataract Surgery Using an Integrated Bioimpedance-Based Probe and Machine Learning Algorithms.

The objective of this work was to develop and experimentally validate a bioimpedance-based framework to identify tissues in contact with the surgical instrument during cataract surgery. This work introduces an integrated hardware and software solution based on the unique bioimpedance of different intraocular tissues. The developed hardware can be readily integrated with commonly used surgical instruments. The proposed software framework, which encompasses data acquisition and a machine-learning classifier, is fast enough to be deployed in real-time surgical interventions. The experimental protocol included bioimpedance data collected from 31 ex vivo pig eyes targeting four intraocular tissues: Iris, Cornea, Lens, and Vitreous. A classifier based on a support vector machine exhibited an overall accuracy of 91% across all trials. The algorithm provided substantial performance in detecting the intraocular tissues with 100% reliability and 95% sensitivity for the lens, along with 88% reliability and 94% sensitivity for the vitreous. The developed impedance-based framework demonstrated successful intraocular tissue identification. Clinical implications include the ability to ensure safe operations by detecting posterior capsule rapture with 94% probability and improving surgical efficacy through lens detection with 100% reliability.

PIMCaffe: Functional Evaluation of a Machine Learning Framework for In-Memory Neural Processing Unit

The large amount of memory usage in recent machine learning applications imposes a significant system burden with respect to power and processing speed. To cope with such a problem, Processing-In-Memory (PIM) techniques can be applied as an alternative solution. Especially, the recommendation system, which is one of the major machine learning applications used in data centers, requires a large memory capacity and therefore represents a suitable candidate application that could be helped by the PIM technique. In this paper, we introduce a machine learning framework, PIMCaffe, designed for in-memory neural processing units and its evaluation environment. PIMCaffe consists of two components: a Caffe2-based deep learning framework that supports PIM acceleration and a PIM-emulating hardware platform. We develop a suite of functions, libraries, application programming interfaces, and a device driver to support the framework. In addition, we implement a prototype Neural Processing Unit (NPU) in PIMCaffe to evaluate the performance of our platform with machine learning applications. Our prototype NPU design includes a vector processor for parallel vector processing and a systolic array unit for matrix multiplication. Using the proposed software framework, we perform a detailed analysis of the in-memory neural processing unit. PIMCaffe supports evaluations of recommendation systems and various convolutional neural network models on the in-memory neural processing unit. PIMCaffe with the NPU shows up to 2.26×, 5.99×, and 1.71× speedup, compared to the ARM Cortex-A53 CPU, for the recommendation system, AlexNet, and ResNet-50, respectively.

A Highly Modular Software Framework for Reducing Software Development Time of Nanosatellites

The standardization of the physical aspects of nanosatellites (also known as CubeSats) and their wide adoption in academia and industry has made the mass production and availability of off-the-shelf components possible. While this has led to a significant reduction in satellite development time, the fact remains that a considerable amount of mission development time and effort continues to be spent on flight software development. The CubeSat’s agile development environment makes it challenging to utilize the advantages of existing software frameworks. Such an adoption is not straightforward due to the added complexity characterized by a steep learning curve. A well-designed flight software architecture mitigates possible sources of failure and increases mission success rate while maintaining moderate complexity. This paper presents a novel approach to a flight software framework developed specifically for nanosatellites. The software framework is characterized by simplicity, reliability, modularity, portability, and real-time capability. The main features of the proposed framework include providing a standardized and explicit skeleton for each module to simplify their construction, offering standardized interfaces for all modules to simplify communication, and providing a collection of ready-to-use common services open for further enhancement by CubeSat software developers. The framework efficiency was demonstrated through a software developed for the MeznSat mission that was successfully launched into Low Earth Orbit in September 2020. The proposed software framework proved to simplify software development for the application developer while significantly enhancing software modularity.

A microcontroller-based software framework for controlling a mechatronic system

The proposed software framework is presented and an Application Programming Interface (API) is developed based on the Arduino Mega 2560. The API processes external commands that follow the operational logic of a gel electrophoresis device. The API acts as an intermediary layer between the gel electrophoresis mechatronic system’s microcontroller and the motors’ controllers. The microcontroller enables the basic functionalities of the gel electrophoresis system while the use of 2 axis (X, Z) motor controllers is necessary for controlling the moving parts of the mechatronic system. We control the movement’s direction, position, speed, and acceleration. The developed API controls the stepper motors drive axles and the DC motors for opening and closing the drawers and other moving parts of the mechatronic system.

Optimizing the performance of optimization in the cloud environment–An intelligent auto-scaling approach

Abstract The cloud computing paradigm has gained wide acceptance in the scientific community, taking a significant share from fields previously reserved exclusively for High Performance Computing (HPC). On-demand access to a large amount of computing resources provided by Cloud makes it ideal for executing large-scale optimizations using evolutionary algorithms without the need for owning any computing infrastructure. In this regard, we extended WoBinGO, an existing parallel software framework for genetic algorithm based optimization, to be used in Cloud. With these extensions, the framework is capable of elastically and frugally utilizing the underlying cloud computing infrastructure for performing computationally expensive fitness evaluations. We studied two issues that are pertinent when dealing with large-scale optimization in the elastic cloud environment: the computing instance launching overhead and the price of engaging Cloud for solving optimization problems, in terms of the instances’ cumulative uptime. To explain the usability limits of WoBinGO framework running in the IaaS environment, a comprehensive analysis of the framework’s performance was given. Optimization of both total optimization time and total cumulative uptime, leads to minimizing the cost of cloud resources utilization. In this way, we are proposing an intelligent decision support engine based on artificial neural networks and metaheuristics to provide the user with an assessment of the framework’s behavior on the underlying infrastructure in terms of optimization duration and the cost of resource consumption. According to a given assessment, the user can decide upon faster delivery of results or lower infrastructure costs. The proposed software framework has been used to solve a complex real-world optimization problem of a subsurface rock mass model calibration. The results obtained from the private OpenStack deployment show that by using the proposed decision support engine, significant savings can be achieved in both optimization time and optimization cost.

A Software Framework for Heterogeneous Wireless Sensor Network Towards Environmental Monitoring

A wireless sensor network (WSN) is typically composed of spatially distributed miniature sensors that help collect large amounts of real-time environmental data from buildings. These environmental data (e.g., temperature, humidity, CO2 concentration) can assist a series of heating, ventilation, and air conditioning (HVAC) equipment to increase the building energy efficiency. From a system design perspective, heterogeneous wireless sensor networks need to address two challenges. First, sensor data acquisition, conversion, fusion, and packaging involve a series of software processing. Since each type of environmental sensor typically has unique processing requirements, it is difficult to develop an efficient software framework to combine the processing of multiple heterogeneous sensors. Second, during normal operation of a heterogeneous wireless sensor network, if users insert or remove some environmental sensors, the entire WSN system should operate normally. In this work, in order to solve the above two system design challenges, we have developed a low-power, low-cost, small form-factor WSN development platform, and its software framework can perform efficient data acquisition, conversion, fusion, and packaging for multiple heterogeneous sensors. Our proposed software framework enables easy and rapid WSN system deployment without affecting the overall functionality of each node. The proposed design differs from existing WSN platforms in that it emphasizes advanced high-level usability and reduces time to market without sacrificing low-level features. The proposed WSN system has been implemented and tested in an office building for indoor fire hazard detection. The experimental results show that our software framework can successfully complete data acquisition, conversion, fusion, and packaging tasks for three heterogeneous environmental sensors. In addition, we have verified that our software framework supports robust system operation when inserting or removing sensors from an existing heterogeneous WSN system.

A decision support tool to enhance agricultural growth in the Mékrou river basin (West Africa)

We describe in this paper the implementation of E-Water, an open software Decision Support System (DSS), designed to help local managers assess the Water Energy Food Environment (WEFE) nexus. E-Water aims at providing optimal management solutions to enhance food crop production at river basin level. The DSS was applied in the transboundary Mékrou river basin, shared among Benin, Burkina Faso and Niger. The primary sector for local economy in the region is agriculture, contributing significantly to income generation and job creation. Fostering the productivity of regional agricultural requires the intensification of farming practices, promoting additional inputs (mainly nutrient fertilizers and water irrigation) but, also, a more efficient allocation of cropland.In order to cope with the heterogeneity of data, and the analyses and issues required by the WEFE nexus approach, our DSS integrates the following modules: (1) the EPIC biophysical agricultural model; (2) a simplified regression metamodel, linking crop production with external inputs; (3) a linear programming and a multiobjective genetic algorithm optimization routines for finding efficient agricultural strategies; and (4) a user-friendly interface for input/output analysis and visualization.To test the main features of the DSS, we apply it to various real and hypothetical scenarios in the Mékrou river basin. The results obtained show how food unavailability due to insufficient local production could be reduced by, approximately, one third by enhancing the application and optimal distribution of fertilizers and irrigation. That would also affect the total income of the farming sector, eventually doubling it in the best case scenario. Furthermore, the combination of optimal agricultural strategies and modified optimal cropland allocation across the basin would bring additional moderate increases in food self-sufficiency, and more substantial gains in the total agricultural income.The proposed software framework proves to be effective, enabling decision makers to identify efficient and site-specific agronomic management strategies for nutrients and water. Such practices would augment crop productivity, which, in turn, would allow to cope with increasing future food demands, and find a balanced use of natural resources, also taking other economic sectors—like livestock, urban or energy—into account.

SoEasy: A Software Framework for Easy Hardware Control Programming for Diverse IoT Platforms.

Many Internet of Things (IoT) applications are emerging and evolving rapidly thanks to widespread open-source hardware platforms. Most of the high-end open-source IoT platforms include built-in peripherals, such as the universal asynchronous receiver and transmitter (UART), pulse width modulation (PWM), general purpose input output (GPIO) ports and timers, and have enough computation power to run embedded operating systems such as Linux. However, each IoT platform has its own way of configuring peripherals, and it is difficult for programmers or users to configure the same peripheral on a different platform. Although diverse open-source IoT platforms are widespread, the difficulty in programming those platforms hinders the growth of IoT applications. Therefore, we propose an easy and convenient way to program and configure the operation of each peripheral using a user-friendly Web-based software framework. Through the implementation of the software framework and the real mobile robot application development along with it, we show the feasibility of the proposed software framework, named SoEasy.

Optimization of patch antennas via multithreaded simulated annealing based design exploration

Abstract In this paper, we present a new software framework for the optimization of the design of microstrip patch antennas. The proposed simulation and optimization framework implements a simulated annealing algorithm to perform design space exploration in order to identify the optimal patch antenna design. During each iteration of the optimization loop, we employ the popular MEEP simulation tool to evaluate explored design solutions. To speed up the design space exploration, the software framework is developed to run multiple MEEP simulations concurrently. This is achieved using multithreading to implement a manager-workers execution strategy. The number of worker threads is the same as the number of cores of the computer that is utilized. Thus, the computational runtime of the proposed software framework enables effective design space exploration. Simulations demonstrate the effectiveness of the proposed software framework. Highlights A software framework for the optimization of the design of microstrip patch antennas. A simulated annealing algorithm to perform the design space exploration. The popular MEEP simulator is employed to evaluate explored solutions for accuracy. Multithreading is used as a technique to speed-up the proposed tool.

Edge Mesh: A New Paradigm to Enable Distributed Intelligence in Internet of Things

In recent years, there has been a paradigm shift in Internet of Things (IoT) from centralized cloud computing to edge computing (or fog computing). Developments in ICT have resulted in the significant increment of communication and computation capabilities of embedded devices and this will continue to increase in coming years. However, existing paradigms do not utilize low-level devices for any decision-making process. In fact, gateway devices are also utilized mostly for communication interoperability and some low-level processing. In this paper, we have proposed a new computing paradigm, named Edge Mesh, which distributes the decision-making tasks among edge devices within the network instead of sending all the data to a centralized server. All the computation tasks and data are shared using a mesh network of edge devices and routers. Edge Mesh provides many benefits, including distributed processing, low latency, fault tolerance, better scalability, better security, and privacy. These benefits are useful for critical applications, which require higher reliability, real-time processing, mobility support, and context awareness. We first give an overview of existing computing paradigms to establish the motivation behind Edge Mesh. Then, we describe in detail about the Edge Mesh computing paradigm, including the proposed software framework, research challenges, and benefits of Edge Mesh. We have also described the task management framework and done a preliminary study on task allocation problem in Edge Mesh. Different application scenarios, including smart home, intelligent transportation system, and healthcare, are presented to illustrate the significance of Edge Mesh computing paradigm.

Software Design Framework for Healthcare Systems

is costing lot of money and resources all over the world in all countries; it consumes almost 30-40 percent of their budget in the healthcare industry. The only solution is to make this health care industry automated and online. In this paper we are proposing a software design framework for health care systems which will be based on agent technologies. The proposed system is based on Artificial Intelligence techniques that can support the user for selection and making choices. The proposed software framework suggested that Multi-Agent Systems (MAS) is the most suitable technique for designing such systems. MAS collaborate intelligently for solving this complex problem. Patients can be supported remotely using the proposed framework so that it can reduce the patient load on hospitals, the proposed software framework operate on real time framework. Computing can help in improving the communication process between follow-up doctors and nurses with patients by making appointments the process easier, according to patient preference with a reminder on necessary actions such as taking scheduled prescribed medicine, engaging in exercises, avoiding some kinds of food and harmful habits such as smoking before and after patient.

Toward a Multi-level Parallel Framework on GPU Cluster with PetSC-CUDA for PDE-based Optical Flow Computation

In this work we present a multi-level parallel framework for the Optical Flow computation on a GPUs cluster, equipped with a scientific computing middleware (the PetSc library). Starting from a flow-driven isotropic method, which models the optical flow problem through a parabolic partial differential equation (PDE), we have designed a parallel algorithm and its software implementation that is suitable for heterogeneous computing environments (multiprocessor, single GPU and cluster of GPUs). The proposed software has been tested on real SAR images sequences. Numerical experiments highlight the performance of the proposed software framework, which can reach a gain of about 95% with respect to the sequential implementation.

${\rm HCI}^{\wedge}2$ Framework: A Software Framework for Multimodal Human-Computer Interaction Systems

This paper presents a novel software framework for the development and research in the area of multimodal human-computer interface (MHCI) systems. The proposed software framework, which is called the HCI∧2 Framework, is built upon publish/subscribe (P/S) architecture. It implements a shared-memory-based data transport protocol for message delivery and a TCP-based system management protocol. The latter ensures that the integrity of system structure is maintained at runtime. With the inclusion of bridging modules, the HCI∧2 Framework is interoperable with other software frameworks including Psyclone and ActiveMQ. In addition to the core communication middleware, we also present the integrated development environment (IDE) of the HCI∧2 Framework. It provides a complete graphical environment to support every step in a typical MHCI system development process, including module development, debugging, packaging, and management, as well as the whole system management and testing. The quantitative evaluation indicates that our framework outperforms other similar tools in terms of average message latency and maximum data throughput under a typical single PC scenario. To demonstrate HCI∧2 Framework's capabilities in integrating heterogeneous modules, we present several example modules working with a variety of hardware and software. We also present an example of a full system developed using the proposed HCI∧2 Framework, which is called the CamGame system and represents a computer game based on hand-held marker(s) and low-cost camera(s).

URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries.

BackgroundExperiments in silico using stochastic reaction-diffusion models have emerged as an important tool in molecular systems biology. Designing computational software for such applications poses several challenges. Firstly, realistic lattice-based modeling for biological applications requires a consistent way of handling complex geometries, including curved inner- and outer boundaries. Secondly, spatiotemporal stochastic simulations are computationally expensive due to the fast time scales of individual reaction- and diffusion events when compared to the biological phenomena of actual interest. We therefore argue that simulation software needs to be both computationally efficient, employing sophisticated algorithms, yet in the same time flexible in order to meet present and future needs of increasingly complex biological modeling.ResultsWe have developed URDME, a flexible software framework for general stochastic reaction-transport modeling and simulation. URDME uses Unstructured triangular and tetrahedral meshes to resolve general geometries, and relies on the Reaction-Diffusion Master Equation formalism to model the processes under study. An interface to a mature geometry and mesh handling external software (Comsol Multiphysics) provides for a stable and interactive environment for model construction. The core simulation routines are logically separated from the model building interface and written in a low-level language for computational efficiency. The connection to the geometry handling software is realized via a Matlab interface which facilitates script computing, data management, and post-processing. For practitioners, the software therefore behaves much as an interactive Matlab toolbox. At the same time, it is possible to modify and extend URDME with newly developed simulation routines. Since the overall design effectively hides the complexity of managing the geometry and meshes, this means that newly developed methods may be tested in a realistic setting already at an early stage of development.ConclusionsIn this paper we demonstrate, in a series of examples with high relevance to the molecular systems biology community, that the proposed software framework is a useful tool for both practitioners and developers of spatial stochastic simulation algorithms. Through the combined efforts of algorithm development and improved modeling accuracy, increasingly complex biological models become feasible to study through computational methods. URDME is freely available at http://www.urdme.org.