Software Layer Research Articles

Software-Defined Platform for Global Navigation Satellite System Antenna Array Development and Testing

With the increasing demand for accurate and robust positioning solutions, the use of GNSS antenna arrays has gained significant attention. However, their development and testing are frequently constrained by the inflexibility of traditional hardware platforms, often requiring extensive reconfiguration throughout the development cycle. This paper presents a platform based on a system on chip to develop a highly flexible software-controlled system that is capable of directly sampling up to 16 antenna elements. Multibeam digital beamforming is implemented using the available FPGA resources and the resulting signal is reproduced by the integrated DAC and can be connected to any conventional single antenna GNSS receiver. This paper presents the architecture of the platform, detailing its components and capabilities. Our experimental results demonstrate that the system can phase shift every channel with errors of less than 0.5° and can reconfigure 4 simultaneous beams of a 16-antenna array at speeds of 1.2 kHz, and 20 beams at around 400 Hz. The average delay introduced by each channel of the system is around 381 ns with a maximum deviation of 1.05 ns. The delay was also measured for the implementation using 4 beams, which achieves a slightly bigger average delay of 384.6 ns while keeping the variation to 5 to 6 ns. This system is intended to be used as the backbone for the development of antenna arrays and beamforming algorithms. Given its flexibility, it is not necessary to develop new hardware between development iterations or even for different systems, as only the software layer needs to be modified. Consequently, it is possible to expedite the development stage before producing dedicated solutions for industrial applications.

Cb-WoFS: Migrating the Warn-on-Forecast System to the Cloud

Abstract This article provides a brief, technical narrative of the WoFS journey to a cloud-based high-performance computing (HPC) system, including some of the technological challenges encountered and solutions found. Also, discussed are a few new components that are in development for cloud-based Warn-on-Forecast System (cb-WoFS), such as the cloud infrastructure project for managing resources, as well as a new web application that manages cb-WoFS runs. An important initial step in our cloud journey is containerizing all of the compiled applications, such as WRF, GSI, and EnKF and their dependencies like netCDF and MPI. With these applications compiled within an Apptainer container, WoFS can run on any local or cloud-based HPC cluster that supports MPI. Furthermore, an additional software layer was developed that creates and manages cloud vendor resources. This layer, which is referred to as the WoFS framework, contains the workflow required to run cb-WoFS, as well as management for other aspects of cb-WoFS (including but not limited to creation of HPC pools in the end-to-end workflow, runtime notifications, and database management). This additional layer was developed to separate the WoFS business logic from vendor-specific API calls. The WoFS framework exposes features through its service library, which is then referenced by the newly developed cb-WoFS web application and other cloud applications. This makes WoFS a complete end-to-end cloud-based application, where an administrator can launch a model run, manage resources, and view output all within a single web app.

Adaptive Middleware to Analyze and Confirm Data Centric Parameters for Data Aggregation in Federated Cloud Environment

In the last decade rapid data evolution has contributed to tremendous data movement. The efficient data movement from origin to cloud storage and vice versa leads to the significant performance of application. A software layer situated between user and cloud storage called middleware plays major role in handling data to full fill user goals and requirements. In this paper, proposed a development of middleware frameworks from generic to context aware framework. Explicit importance is given to the adaptation feature of the middleware. A preliminary background is presented to list out importance and details of adaptability of the middleware and curious differentiation in static and self-adaptive adaptation features of the middleware. The literature started with the description of many generic middleware which can be used for building applications in variety of domains and further the limitations imposed are exposed. The issues like multidisciplinary data, vendor lock-in, disparity of service and interoperability related to federated cloud services serving to the fast-developing technology are highlighted. Along with that the use of context aware data management techniques for efficient handling of federated cloud system are defined. An extensive and comprehensive analysis of context-aware middleware is presented. For skilful data management processes need of context aware application with self-adaptation capability is presented especially in response to dynamic situations in federated cloud environment. At the end three techniques to design and develop context-aware middleware are presented. Alongside, four different perspectives of realizing adaptive middleware are also detailed. The presented work concludes with the highlighted need of context aware middleware framework to enhance the performance of federated cloud system.

SLOPE: Safety LOg PEripherals implementation and software drivers for a safe RISC-V microcontroller unit

The focus of this manuscript is related to the main safety issues regarding a mixed criticality system running multiple concurrent tasks. Our concerns are related to the guarantee of Freedom of Interference between concurrent partitions, and to the respect of the Worst Case Execution Time for tasks. Moreover, we are interested in the evaluation of resources budgeting and the study of system behavior in case of occurring random hardware failures. In this paper we present a set of Safety LOg PEripherals (SLOPE): Performance Monitoring Unit (PMU), Execution Tracing Unit (ETU), Error Management Unit (EMU), Time Management Unit (TMU) and Data Log Unit (DLU); then, an implementation of SLOPE on a single core RISC-V architecture is proposed. Such peripherals are able to collect software and hardware information about execution, and eventually trigger recovery actions to mitigate a possible dangerous misbehavior. We show results of the hardware implementation and software testing of the units with a dedicated software library. For the PMU we standardized the software layer according to embedded Performance Application Programming Interface (ePAPI), and compared its functionality with a bare-metal use of the library. To test the ETU we compared the hardware simulation results with software ones, to understand if overflow may occur in internal hardware buffers during tracing. In conclusion, designed devices introduce new instruments for system investigation for RISC-V technologies and can generate an execution profile for safety related tasks.

Towards Applications on the Decentralized Web using Hypermedia-driven Query Engines

The Web is facing unprecedented challenges related to the control and ownership of data. Due to recent privacy and manipulation scandals caused by the increasing centralization of data on the Web into increasingly fewer large data silos, there is a growing demand for the re-decentralization of the Web. Enforced by user-empowering legislature such as GDPR and CCPA, decentralization initiatives such as Solid are being designed that aim to break personal data out of these silos and give back control to the users. While the ability to choose where and how data is stored provides significant value to users, it leads to major technical challenges due to the massive distribution of data across the Web. Since we cannot expect application developers to take up this burden of coping with all the complexities of handling this data distribution, there is a need for a software layer that abstracts away these complexities, and makes this decentralized data feel as being centralized. Concretely, we propose personal query engines to play the role of such an abstraction layer. In this article, we discuss what the requirements are for such a query-based abstraction layer, what the state of the art is in this area, and what future research is required. Even though many challenges remain to achieve a developer-friendly abstraction for interacting with decentralized data on the Web, the pursuit of it is highly valuable for both end-users that want to be in control of their data and its processing and businesses wanting to enable this at a low development cost.

SMART LOCK SYSTEM USING IOT AND SOLANA BLOCKCHAIN

This project explores the integration of Solana blockchain technology with smart locking systems to revolutionize access control and security. Leveraging the decentralized and immutable nature of the Solana blockchain, the intelligent system provides a powerful platform to manage access permissions and monitor lock transactions in real-time. With integrated management and remote control, users can grant or revoke access from any mobile or web application. The scalability and efficiency of the Solana blockchain enable the system to process large volumes of transactions without sacrificing performance, making it suitable for widespread use in the field, workplace, and office. Overall, the integration of the Solana blockchain into smart registration systems represents a significant advance in access control, providing a secure, transparent and user-friendly solution to the problems of the digital age. This project presents the design and implementation of a smart lock system empowered by Solana blockchain technology, aimed at enhancing security, transparency, and efficiency in access control. Leveraging a distributed ledger framework, the system facilitates decentralized access management, allowing users to remotely authorize and monitor door access through a user-friendly mobile/web interface. The integration of Solana blockchain ensures immutability and transparency in access permissions, while also offering scalability and low-latency transaction processing capabilities. Hardware components, including smart lock mechanisms and gateway devices, are seamlessly integrated with software modules such as firmware, gateway software, and blockchain integration layers to enable secure communication and interoperability. Robust security measures, including encryption protocols and cryptographic algorithms, safeguard data integrity and privacy. Through meticulous testing, deployment, and documentation processes, this project delivers a reliable and future-ready smart lock solution poised to redefine access management paradigms in various settings, from residential homes to commercial establishments. Key Words: Solana, Smart locking systems, Security, Blockchain, Immutability

Centralized smart energy monitoring system for legacy home appliances

The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants.

COER: A Network Interface Offloading Architecture for RDMA and Congestion Control Protocol Codesign

RDMA (Remote Direct Memory Access) networks require efficient congestion control to maintain their high throughput and low latency characteristics. However, congestion control protocols deployed at the software layer suffer from slow response times due to the communication overhead between host hardware and software. This limitation has hindered their ability to meet the demands of high-speed networks and applications. Harnessing the capabilities of rapidly advancing Network Interface Card (NIC) can drive progress in congestion control. Some simple congestion control protocols have been offloaded to RDMA NIC to enable faster detection and processing of congestion. However, offloading congestion control to the RDMA NIC faces a significant challenge in integrating the RDMA transport protocol with advanced congestion control protocols that involve complex mechanisms. We have observed that reservation-based proactive congestion control protocols share strong similarities with RDMA transport protocols, allowing them to integrate seamlessly and combine the functionalities of the transport layer and network layer. In this paper, we present COER, an RDMA NIC architecture that leverages the functional components of RDMA to perform reservations and completes the scheduling of congestion control during the scheduling process of the RDMA protocol. COER facilitates the streamlined development of offload strategies for congestion control techniques, specifically proactive congestion control, on RDMA NIC. We use COER to design offloading schemes for eleven congestion control protocols, which we implement and evaluate using a network emulator with a cycle-accurate RDMA NIC model that can load MPI programs. The evaluation results demonstrate that the architecture of COER does not compromise the original characteristics of the congestion control protocols. Compared to a layered protocol stack approach, COER enables the performance of RDMA networks to reach new heights.

Dynamic scheduling mechanism for intelligent workshop with deep reinforcement learning method based on multi-agent system architecture

With the development and changes of industry and market demand, the personalized customization production mode with small batch and multiple batches has gradually become a new production mode. This makes production environment become more complex and dynamic. However, traditional production workshops cannot effectively adapt to this environment. Combining with new technologies, transforming traditional workshops into intelligent workshop to cope with new production mode become an urgent problem. Therefore, this paper proposes a multi-agent manufacturing system based on IoT for intelligent workshop. Meanwhile, this paper takes flexible job shop scheduling problem (FJSP) as a specific production scenario and establishes relevant mathematics model. To build the agent in intelligent workshop, this paper proposes a data-based with combination of virtual and physical agent (DB-VPA) which has information layer, software layer and physical layer. Then, based on the manufacturing system, this paper designs a dynamic scheduling mechanism with deep reinforcement learning (DRL) for intelligent workshop. This method contains three aspects: (1) Modeling production process based on Markov decision process (MDP). (2) Designing communication mechanism for DB-VPAs. (3) Designing scheduling model combining with improved genetic programming and proximal policy optimization (IGP-PPO) which is a DRL method. Finally, relevant experiments are executed in a prototype experiment platform. The experiments indicate that the proposed method has superiority and generality in solving scheduling problem with dynamic events.

Cyber security Vulnerabilities and Remediation Through Cloud Security Tools

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.

Cyber security Vulnerabilities and Remediation Through Cloud Security Tools

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.

Cyber security Vulnerabilities and Remediation Through Cloud Security Tools

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.

Comparison of openEHR open-source servers

Medical information systems could benefit from electronic health records management using openEHR. On the other hand, such a standard adds an additional software layer to the system, which might impact performance. In this article, we present an in-depth comparison of open-source openEHR servers and propose tools for testing them. Load tests for selected opensource servers were prepared using Apache JMeter. Statistics of elapsed time of requests and throughput of each solution were calculated. Results show that open-source openEHR servers significantly differ in performance and stability and prove that load testing should be a crucial part of a development process.

Alya toward exascale: algorithmic scalability using PSCToolkit

In this paper, we describe an upgrade of the Alya code with up-to-date parallel linear solvers capable of achieving reliability, efficiency and scalability in the computation of the pressure field at each time step of the numerical procedure for solving a Large Eddy Simulation formulation of the incompressible Navier–Stokes equations. We developed a software module in the Alya’s kernel to interface the libraries included in the current version of PSCToolkit, a framework for the iterative solution of sparse linear systems, on parallel distributed-memory computers, by Krylov methods coupled to Algebraic MultiGrid preconditioners. The Toolkit has undergone various extensions within the EoCoE-II project with the primary goal of facing the exascale challenge. Results on a realistic benchmark for airflow simulations in wind farm applications show that the PSCToolkit solvers significantly outperform the original versions of the Conjugate Gradient method available in the Alya’s kernel in terms of scalability and parallel efficiency and represent a very promising software layer to move the Alya code toward exascale.

General-purpose programmable photonic processor for advanced radiofrequency applications

A general-purpose photonic processor can be built integrating a silicon photonic programmable core in a technology stack comprising an electronic monitoring and controlling layer and a software layer for resource control and programming. This processor can leverage the unique properties of photonics in terms of ultra-high bandwidth, high-speed operation, and low power consumption while operating in a complementary and synergistic way with electronic processors. These features are key in applications such as next-generation 5/6 G wireless systems where reconfigurable filtering, frequency conversion, arbitrary waveform generation, and beamforming are currently provided by microwave photonic subsystems that cannot be scaled down. Here we report the first general-purpose programmable processor with the remarkable capability to implement all the required basic functionalities of a microwave photonic system by suitable programming of its resources. The processor is fabricated in silicon photonics and incorporates the full photonic/electronic and software stack.

Qibolab: an open-source hybrid quantum operating system

We present Qibolab, an open-source software library for quantum hardware control integrated with the Qibo quantum computing middleware framework. Qibolab provides the software layer required to automatically execute circuit-based algorithms on custom self-hosted quantum hardware platforms. We introduce a set of objects designed to provide programmatic access to quantum control through pulses-oriented drivers for instruments, transpilers and optimization algorithms. Qibolab enables experimentalists and developers to delegate all complex aspects of hardware implementation to the library so they can standardize the deployment of quantum computing algorithms in a extensible hardware-agnostic way, using superconducting qubits as the first officially supported quantum technology. We first describe the status of all components of the library, then we show examples of control setup for superconducting qubits platforms. Finally, we present successful application results related to circuit-based algorithms.

An in-depth investigation of lead-free KGeCl3 perovskite solar cells employing optoelectronic, thermomechanical, and photovoltaic properties: DFT and SCAPS-1D frameworks.

Potassium germanium chloride (KGeCl3) has emerged as a promising contender for use as an absorber material for lead-free perovskite solar cells (PSCs), offering significant potential in this domain. In this study, we conducted a density functional theory (DFT) investigation to analyze and assess the structural, electronic, thermomechanical, and optical characteristics of the cubic KGeCl3 absorber. The positive phonon dispersion curve confirmed the dynamical stability of KGeCl3. The elastic constant satisfied the Born criteria, validating the mechanical stability and ductility of solid KGeCl3. The electronic band structure and density of states (DOS) confirmed that the KGeCl3 material is a semiconductor with a direct band gap of 0.754 eV (GGA) and 0.803 eV (mGGA-RSCAN). The study identified key optical parameters, including absorption, conductivity, reflectivity, dielectric function, refractive index, and loss function, revealing the potential suitability of KGeCl3 for solar applications. The Helmholtz free energy (F), internal energy (E), entropy (S), and specific heat capacity (Cv) are computed based on the phonon density of states. Additionally, we investigated twenty-four configurations comprising different combinations of electron transport layers (ETLs) and hole transport layers (HTLs) in SCAPS-1D software. For this purpose, ETLs such as Ws2, ZnSe, PCBM, and C60 and HTLs such as CBTS, CdTe, CFTS, Cu2O, P3HT, and PEDOT:PSS are employed. The highlighted structure, ITO/CBTS/KGeCl3/Ws2/Ni, demonstrates remarkable performance with an efficiency of 22.01%, a Voc of 0.6799 V, a Jsc of 41.439 mA cm-2, and a FF of 78.12%. To analyze photovoltaic (PV) performance, we chose the top four solar cell (SC) configurations. Moreover, a comprehensive examination was conducted to assess the impact of various factors, including the thickness of different layers, capacitance, Mott-Schottky behavior, series and shunt resistance, temperature, and generation-recombination rates, as well as J-V (current-voltage density) and quantum efficiency (QE) characteristics.

First implementation and results of the Analysis Grand Challenge with a fully Pythonic RDataFrame

First implementation and results of the Analysis Grand Challenge with a fully Pythonic RDataFrame

Layered software development model applied to the creation of the ECJP Virtual Legal Office, from the Software Development Technology program

The layered software development model is a technique used for building web applications, enabling a hierarchical organization of software layers. This article presents a layered software development model applied in a virtual legal clinic system at a higher education institution. The software architecture proposed in this model is based on defining layers from the highest level to the lowest, which allows for easy integration of new functionalities and efficient project management. Furthermore, the layered architecture also enables abstraction of the application across any software or hardware platform, both on the client and server sides. Each defined layer can be replaced without the need to rewrite the others, making system maintenance easier. Additionally, the UNAD law program will have a robust and user-friendly system for all its users.

Smart Manufacturing Implementation of a Continuous Downstream Precipitation and Filtration Process for Antibody Purification

Abstract Recently, continuous bioprocessing has gained momentum in biomanufacturing and can alleviate many of the hurdles faced in batch or semi-batch operations. Moreover, the parallel development of smart manufacturing (SM) allows the rapid small-scale prototyping and large-scale implementation of continuous bioprocesses. With this background, this paper presents the laboratory-scale implementation of a continuous precipitation-filtration process that can ultimately be used for therapeutic protein capture purification. The experimental setup includes four static mixers, four peristaltic pumps, one hollow fiber dewatering filtration module, and multiple pressure sensors and weigh scales. The system also includes an in-line advanced microscopic particle imaging probe that provides real-time images and derived metrics of the precipitate particle morphologies and a fiber optic 880 nm optical absorbance probe. A polyclonal human serum antibody mixture (hIgG) (10 g/L) was used as a stand-in for a monoclonal antibody therapeutic along with 7 % w/v polyethylene glycol (PEG, volume exclusion agent) and 10 mM zinc chloride (cross-linking agent) as the precipitants to demonstrate the principles of operation and control of a precipitation-based process using SM technology. An integrated input/output (I/O) system was used to acquire pressure, flow rate, and weigh scale data and also to communicate with the pumps to change flow rates in real-time. Edge computers communicate with the I/O system and the imaging probe and host the software layer. The software layer enables real-time data acquisition, data-driven and first-principles model predictions, closed-loop control of precipitate particle morphology using pump flow rate of PEG, and cloud communications with the Clean Energy Smart Manufacturing Innovation Institute Smart Manufacturing Innovation Platform. The paper presents the initial results obtained with this integrated system, demonstrating the potential of SM strategies to enhance the production of life-saving biopharmaceutical products.