LiMOX-A Point Cloud Lidar Model Toolbox Based on NVIDIA OptiX Ray Tracing Engine.

Virtual testing and validation are building blocks in the development of autonomous systems, in particular autonomous driving. Perception sensor models gained more attention to cover the entire tool chain of the sense-plan-act cycle, in a realistic test setup. In the literature or state-of-the-art software tools various kinds of lidar sensor models are available. We present a point cloud lidar sensor model, based on ray tracing, developed for a modular software architecture, which can be used stand-alone. The model is highly parametrizable and designed as a toolbox to simulate different kinds of lidar sensors. It is linked to an infrared material database to incorporate physical sensor effects introduced by the ray-surface interaction. The maximum detectable range depends on the material reflectivity, which can be covered with this approach. The angular dependence and maximum range for different Lambertian target materials are studied. Point clouds from a scene in an urban street environment are compared for different sensor parameters.

Entropy as a Measure of Consistency in Software Architecture.

In building software architectures, the relations between elements in different diagrams are often overlooked. The first stage of building IT systems is the use of ontology terminology, not software terminology, in the requirements engineering process. Then, when constructing software architecture, IT architects more or less consciously however introduce elements that represent the same classifier on different diagrams with similar names. These connections are called consistency rules and are usually not attached in any way in a modeling tool, and only a significant number of them in the models increase the quality of the software architecture. It is mathematically proved that the application of consistency rules increases the information content of software architecture. Authors show that increasing readability and ordering of software architecture by means of consistency rules have their mathematical rationale. In this article, we found proof of decreasing Shannon entropy while applying consistency rules in the construction of software architecture of IT systems. Therefore, it has been shown that marking selected elements in different diagrams with these same names is, therefore, an implicit way to increase the information content of software architecture while simultaneously improving its orderliness and readability. Moreover, this increase in the quality of the software architecture can be measured by entropy, which allows for checking whether the number of consistency rules is sufficient to compare different architectures, even of different sizes, thanks to entropy normalization, and checking during the development of the software architecture, what is the improvement in its orderliness and readability.

Mamba: a systematic software solution for beamline experiments at HEPS.

To cater for the diverse experiment requirements at the High Energy Photon Source (HEPS) with often limited human resources, Bluesky was chosen as the basis for our software framework, Mamba. In our attempt to address Bluesky's lack of integrated graphical user interfaces (GUIs), command injection with feedback was chosen as the main way for the GUIs to cooperate with the command line interface; a remote-procedure-call service is also provided, which covers functionalities unsuitable for command injection, as well as pushing of status updates. In order to fully support high-frequency applications like fly scans, Bluesky's support for asynchronous control is being improved; furthermore, to support high-throughput experiments, Mamba Data Worker is being developed to cover the complexity in asynchronous online data processing for these experiments. To systematically simplify the specification of metadata, scan parameters and data-processing graphs for each type of experiment, an experiment parameter generator will be developed; experiment-specific modules to automate preparation steps will also be made. The integration of off-the-shelf code in Mamba for domain-specific needs is under investigation, and Mamba GUI Studio is being developed to simplify the implementation and integration of GUIs.

Better automation of beamline control at HEPS.

At the High Energy Photon Source (HEPS; Beijing, People's Republic of China), where up to 90 beamlines can be provided in the future, minimization of workload for individual beamlines and maximization of knowledge about one beamline that can be applied to other beamlines is essential to minimize the total complexity in beamline control. Presented in this paper are our efforts to achieve these goals by composing relatively simple utilities and mechanisms to automate tasks, while always remembering to keep our automation solutions simple and clear. After an introduction to our choice of basic software in EPICS-based beamline control, the issues encountered in introducing package management to EPICS modules, as well as solutions to them, are presented; the design and implementation of the packaging system is concisely discussed. After a presentation of our efforts to reduce the need for self-built multi-device EPICS `input/output controller' (IOC) applications by providing reusable modular IOC executables, implementation of easily maintainable multi-IOC setups through the separation and minimization of each user's IOC configurations is given. The ongoing project of comprehensive beamline services at HEPS to further simplify configuration management on multiple scales, ranging from individual beamline devices to all beamlines at HEPS, is introduced.

Lightweight Software Architecture Evaluation for Industry: A Comprehensive Review.

Processes for evaluating software architecture (SA) help to investigate problems and potential risks in SA. It is derived from many studies that proposed a plethora of systematic SA evaluation methods, while industrial practitioners currently refrain from applying them since they are heavyweight. Nowadays, heterogeneous software architectures are organized based on the new infrastructure. Hardware and associated software allow different systems, such as embedded, sensor-based, modern AI, and cloud-based systems, to cooperate efficiently. It brings more complexities to SA evaluation. Alternatively, lightweight architectural evaluation methods have been proposed to satisfy the practitioner's concerns, but practitioners still do not adopt these methods. This study employs a systematic literature review with a text analysis of SA's definitions to propose a comparison framework for SA. It identifies lightweight features and factors to improve the architectural evaluation methods among industrial practitioners. The features are determined based on the practitioner's concerns by analyzing the architecture's definitions from stakeholders and reviewing architectural evaluation methods. The lightweight factors are acquired by studying the five most commonly used lightweight methods and the Architecture-based Tradeoff Analysis Method (ATAM), the most well-known heavyweight method. Subsequently, the research addresses these features and factors.

Composing Optimized Embedded Software Architectures for Physics-Based EKF-MPC Smart Sensor for Li-Ion Battery Cell Management.

Efficient battery technology is imperative for the adoption of clean energy automotive solutions. In addition, efficient battery technology extends the useful life of the battery as well as provides improved performance to fossil fuel technology. Model predictive control (MPC) is an effective way to operate battery management systems (BMS) at their maximum capability, while maintaining the safety requirements. Using the physics-based model (PBM) of the battery allows the control system to operate on the chemical and physical process of the battery. Since these processes are internal to the battery and are physically unobservable, the extended Kalman filter (EKF) serves as a virtual observer that can monitor the physical and chemical properties that are otherwise unobservable. These three methods (i.e., PBM, EKF, and MPC) together can prolong the useful life of the battery, especially for Li-ion batteries. This capability is not limited to the automotive industry: any real-world smart application can benefit from a portable/mobile efficient BMS, compelling these systems to be executed on resource-constrained embedded devices. Furthermore, the intrinsic adaptive control process of the PBM is uniquely suited for smart systems and smart technology. However, the sheer computational complexity of PBM for MPC and EKF prevents it from being realized on highly constrained embedded devices. In this research work, we introduce a novel, unique, and efficient embedded software architecture for a PB-EKF-MPC smart sensor for BMS, specifically on embedded devices, by addressing the computational complexity of PBM. Our proposed embedded software architecture is created in such a way to be executed on a 32-bit embedded microprocessor running at 100 MHz with a limited memory of 128 KB, and still obtains an average execution time of 4.8 ms.

ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems.

Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems' environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems' security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach's feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems' constituents to learn about security threats in their environments collaboratively.

Intensifying the search-based optimization of product line architectures with crossover operators.

The Product Line Architecture (PLA) is a crucial artifact for the development of Software Product Lines. However, PLA is a complex artifact to be designed due to its large size and the multiple conflicting properties that need to be considered to ensure its quality, requiring a great effort for the architect. PLA designing has been formulated as an optimization problem aiming at improving some architectural properties in order to maximize both the feature modularization and the relational cohesion, and to minimize the class coupling. This kind of problem was successfully solved by multi-objective evolutionary algorithm. Nevertheless, most of existing approaches optimize PLA designs without applying the crossover operator, one of the fundamental genetic operators. To overcome these limitations, this paper aims to intensify the search-based PLA design optimization by presenting three crossover operators. These operators were empirically evaluated in quantitative and qualitative studies using three well-studied PLA designs. The experiments were conducted with eight experimental configurations of NSGA-II in comparison with a baseline that uses only mutation operators. Empirical results showed that there are significant differences among the use of only mutation and mutation with crossover. Also, we observed that the crossover operators contributed to generate solutions with better feature modularization. Finally, we could see that the proposed operators complement each other, since the experiment that combines at least two of the proposed operators achieved better results.

Designing a reference architecture for health information systems.

BACKGROUND: Healthcare relies on health information systems (HISs) to support the care and receive reimbursement for the care provided. Healthcare providers experience many problems with their HISs due to improper architecture design. To support the design of a proper HIS architecture, a reference architecture (RA) can be used that meets the various stakeholder concerns of HISs. Therefore, the objective of this study is to develop and analyze an RA following well-established architecture design methods. METHODS: Domain analysis was performed to scope and model the domain of HISs. For the architecture design, we applied the views and beyond approach and designed the RA's views based on the stakeholders and features from the domain analysis. We evaluated the RA with a case study. RESULTS: We derived the following four architecture views for HISs: The context diagram, decomposition view, layered view, and deployment view. Each view shows the architecture of the HIS from a different angle, suitable for various stakeholders. Based on a Japanese hospital information system study, we applied the RA and derived the application architecture. CONCLUSION: We demonstrated that the methods of the software architecture design community could be used in the healthcare domain effectively and showed the applicability of the RA.

Modular Software Architecture for Local Smart Building Servers.

This paper presented the architecture and construction of a novel smart building system that could monitor and control buildings' use in a safe and optimal way. The system operates on a Raspberry local server, which could be connected via the cloud technology to a central platform. The local system includes nine modules that inter-communicate. The system detects sensor faults, and provides a friendly interface to occupants. The paper presented the software architecture IoT used for the building monitoring and the use of this system for the management of fifteen social housing units during a year. The system allowed the investigation of indoor comfort and both energy and hot water consumptions. Data analysis resulted in the detection of abnormal energy consumptions. The system could be easily used in buildings' management. It works in a plug-and-play mode.

A software architecture for mechanism-based social systems modelling in agent-based simulation models.

This paper introduces the MBSSM (Mechanism-Based Social Systems Modelling) software architecture that is designed for expressing mechanisms of social theories with individual behaviour components in a unified way and implementing these mechanisms in an agent-based simulation model. The MBSSM architecture is based on a middle-range theory approach most recently expounded by analytical sociology and is designed in the object-oriented programming paradigm with Unified Modelling Language diagrams. This paper presents two worked examples of using the architecture for modelling individual behaviour mechanisms that give rise to the dynamics of population-level alcohol use: a single-theory model of norm theory and a multi-theory model that combines norm theory with role theory. The MBSSM architecture provides a computational environment within which theories based on social mechanisms can be represented, compared, and integrated. The architecture plays a fundamental enabling role within a wider simulation model-based framework of abductive reasoning in which families of theories are tested for their ability to explain concrete social phenomena.

Analyzing the performance of a blockchain-based personal health record implementation.

BACKGROUND: The Personal Health Record (PHR) and Electronic Health Record (EHR) play a key role in more efficient access to health records by health professionals and patients. It is hard, however, to obtain a unified view of health data that is distributed across different health providers. In particular, health records are commonly scattered in multiple places and are not integrated. OBJECTIVE: This article presents the implementation and evaluation of a PHR model that integrates distributed health records using blockchain technology and the openEHR interoperability standard. We thus follow OmniPHR architecture model, which describes an infrastructure that supports the implementation of a distributed and interoperable PHR. METHODS: Our method involves implementing a prototype and then evaluating the integration and performance of medical records from different production databases. In addition to evaluating the unified view of records, our evaluation criteria also focused on non-functional performance requirements, such as response time, CPU usage, memory occupation, disk, and network usage. RESULTS: We evaluated our model implementation using the data set of more than 40 thousand adult patients anonymized from two hospital databases. We tested the distribution and reintegration of the data to compose a single view of health records. Moreover, we profiled the model by evaluating a scenario with 10 superpeers and thousands of competing sessions transacting operations on health records simultaneously, resulting in an average response time below 500 ms. The blockchain implemented in our prototype achieved 98% availability. CONCLUSION: Our performance results indicated that data distributed via a blockchain could be recovered with low average response time and high availability in the scenarios we tested. Our study also demonstrated how OmniPHR model implementation can integrate distributed data into a unified view of health records.

General Architecture for Development of Virtual Coaches for Healthy Habits Monitoring and Encouragement.

Good health is the result of a healthy lifestyle, where caring about physical activity and nutrition are key concerns. However, in today's society, nutritional disorders are becoming increasingly frequent, affecting children, adults, and elderly people, mainly due to limited nutrition knowledge and the lack of a healthy lifestyle. A commonly adopted therapy to these imbalances is to monitor physical activity and daily habits, such as recording exercise or creating custom meal plans to count the amount of macronutrients and micronutrients acquired in each meal. Nowadays, many health tracking applications (HTA) have been developed that, for instance, record energy intake as well as users' physiological parameters, or measure the physical activity during the day. However, most existing HTA do not have a uniform architectural design on top of which to build other applications and services. In this manuscript, we present system architecture intended to serve as a reference architecture for building HTA solutions. In order to validate the proposed architecture, we performed a preliminary evaluation with 15 well recognized experts in systems and software architecture from different entities around world and who have estimated that our proposal can generate architecture for HTA that is adequate, reliable, secure, modifiable, portable, functional, and with high conceptual integrity. In order to show the applicability of the architecture in different HTA, we developed two telemonitoring systems based on it, targeted to different tasks: nutritional coaching (Food4Living) and physical exercise coaching (TrainME). The purpose was to illustrate the kind of end-user monitoring applications that could be developed.

Enhanced Flexibility and Reusability through State Machine-Based Architectures for Multisensor Intelligent Robotics.

This paper presents a state machine-based architecture, which enhances the flexibility and reusability of industrial robots, more concretely dual-arm multisensor robots. The proposed architecture, in addition to allowing absolute control of the execution, eases the programming of new applications by increasing the reusability of the developed modules. Through an easy-to-use graphical user interface, operators are able to create, modify, reuse and maintain industrial processes, increasing the flexibility of the cell. Moreover, the proposed approach is applied in a real use case in order to demonstrate its capabilities and feasibility in industrial environments. A comparative analysis is presented for evaluating the presented approach versus traditional robot programming techniques.

Behavioral Reference Model for Pervasive Healthcare Systems.

The emergence of mobile healthcare systems is an important outcome of application of pervasive computing concepts for medical care purposes. These systems provide the facilities and infrastructure required for automatic and ubiquitous sharing of medical information. Healthcare systems have a dynamic structure and configuration, therefore having an architecture is essential for future development of these systems. The need for increased response rate, problem limited storage, accelerated processing and etc. the tendency toward creating a new generation of healthcare system architecture highlight the need for further focus on cloud-based solutions for transfer data and data processing challenges. Integrity and reliability of healthcare systems are of critical importance, as even the slightest error may put the patients' lives in danger; therefore acquiring a behavioral model for these systems and developing the tools required to model their behaviors are of significant importance. The high-level designs may contain some flaws, therefor the system must be fully examined for different scenarios and conditions. This paper presents a software architecture for development of healthcare systems based on pervasive computing concepts, and then models the behavior of described system. A set of solutions are then proposed to improve the design's qualitative characteristics including, availability, interoperability and performance.

Software to Facilitate Remote Sensing Data Access for Disease Early Warning Systems.

Satellite remote sensing produces an abundance of environmental data that can be used in the study of human health. To support the development of early warning systems for mosquito-borne diseases, we developed an open-source, client based software application to enable the Epidemiological Applications of Spatial Technologies (EASTWeb). Two major design decisions were full automation of the discovery, retrieval and processing of remote sensing data from multiple sources, and making the system easily modifiable in response to changes in data availability and user needs. Key innovations that helped to achieve these goals were the implementation of a software framework for data downloading and the design of a scheduler that tracks the complex dependencies among multiple data processing tasks and makes the system resilient to external errors. EASTWeb has been successfully applied to support forecasting of West Nile virus outbreaks in the United States and malaria epidemics in the Ethiopian highlands.

Task-Motion Planning System for Socially Viable Service Robots Based on Object Manipulation.

This paper presents a software architecture to implement a task-motion planning system that can improve human-robot interactions by including social behavior when social robots provide services related to object manipulation to users. The proposed system incorporates four main modules: knowledge reasoning, perception, task planning, and motion planning for autonomous service. This system adds constraints to the robot motions based on the recognition of the object affordance from the perception module and environment states from the knowledge reasoning module. Thus, the system performs task planning by adjusting the goal of the task to be performed, and motion planning based on the functional aspects of the object, enabling the robot to execute actions consistent with social behavior to respond to the user's intent and the task environment. The system is verified through simulated experiments consisting of several object manipulation services such as handover and delivery. The results show that, by using the proposed system, the robot can provide different services depending on the situation, even if it performs the same tasks. In addition, the system demonstrates a modular structure that enables the expansion of the available services by defining additional actions and diverse planning modules.

Autonomous System for Tumor Resection (ASTR) - Dual-Arm Robotic Midline Partial Glossectomy.

Head and neck cancers are the seventh most common cancers worldwide, with squamous cell carcinoma being the most prevalent histologic subtype. Surgical resection is a primary treatment modality for many patients with head and neck squamous cell carcinoma, and accurately identifying tumor boundaries and ensuring sufficient resection margins are critical for optimizing oncologic outcomes. This study presents an innovative autonomous system for tumor resection (ASTR) and conducts a feasibility study by performing supervised autonomous midline partial glossectomy for pseudotumor with millimeter accuracy. The proposed ASTR system consists of a dual-camera vision system, an electrosurgical instrument, a newly developed vacuum grasping instrument, two 6-DOF manipulators, and a novel autonomous control system. The letter introduces an ontology-based research framework for creating and implementing a complex autonomous surgical workflow, using the glossectomy as a case study. Porcine tongue tissues are used in this study, and marked using color inks and near-infrared fluorescent (NIRF) markers to indicate the pseudotumor. ASTR actively monitors the NIRF markers and gathers spatial and color data from the samples, enabling planning and execution of robot trajectories in accordance with the proposed glossectomy workflow. The system successfully performs six consecutive supervised autonomous pseudotumor resections on porcine specimens. The average surface and depth resection errors measure 0.73±0.60 mm and 1.89±0.54 mm, respectively, with no positive tumor margins detected in any of the six resections. The resection accuracy is demonstrated to be on par with manual pseudotumor glossectomy performed by an experienced otolaryngologist.

LPMSAEF: Lightweight process mining-based software architecture evaluation framework for security and performance analysis.

The article discusses the need for a lightweight software architecture evaluation framework that can address practitioners' concerns. Specifically, the proposed framework uses process mining and Petri nets to analyze security and performance in software development's early and late stages. Moreover, the framework has been implemented in six case studies, and the results show that it is a feasible and effective solution that can detect security and performance issues in complex and heterogeneous architecture with less time and effort. Furthermore, the article provides a detailed explanation of the framework's features, factors, and evaluation criteria. Additionally, this article discusses the challenges associated with traditional software architecture documentation methods using Unified Modeling Language diagrams and the limitations of code alone for creating comprehensive Software Architecture models. Various methods have been developed to extract implicit Software Architecture from code artifacts, but they tend to produce code-oriented diagrams instead of Software Architecture diagrams. Therefore, to bridge the model-code gap, the article proposes a framework that considers existing Software Architecture in the source code as architectural components and focuses on Software Architecture behaviors for analyzing performance and security. The proposed framework also suggests comparing Software Architecture extracted by different Process Mining algorithms to achieve consensus on architecture descriptions, using visualizations to understand differences and similarities. Finally, the article suggests that analyzing the previous version of a system's Software Architecture can lead to improvements and deviations from planned Software Architecture can be detected using traceability approaches to aid software architects in detecting inconsistencies.

Dataset of open-source software developers labeled by their experience level in the project and their associated software metrics.

Developers are extracted from 17 open-source projects from GitHub. Projects are chosen that use the java programming language, the Spring framework and Maven/Gradle build tools. Along with these developers, 24 software engineering metrics are extracted for each of them. These metrics are either calculated by analyzing the source code or relative to project management metadata. Each of these developers then are manually searched for in professional social media such as LinkedIn or Twitter to be labeled with their experience level in their project. Outliers are statistically detected and manually re-assigned when needed. The resulting dataset contains 703 anonymized developers qualified by their 24 project-related software engineering metrics and labeled for their experience. It is suitable for empirical software engineering studies that need to connect developers' level of experience to tangible software engineering metrics.