Low-level Programming Research Articles

A ROS2-Based Gateway for Modular Hardware Usage in Heterogeneous Environments

The rise of robotics and the Internet of Things (IoT) could potentially represent a significant shift towards a more integrated and automated future, where the physical and digital domains may merge. However, the integration of these technologies presents certain challenges, including compatibility issues with existing systems and the need for greater interoperability between different devices. It would seem that the rigidity of traditional robotic designs may inadvertently make these difficulties worse, which in turn highlights the potential benefits of modular solutions. Furthermore, the mastery of new technologies may introduce additional complexity due to the varying approaches taken by robot manufacturers. In order to address these issues, this research proposes a Robot Operating System (ROS2)-based middleware, called the “ROS2-based gateway”, which aims to simplify the integration of robots in different environments. By focusing on the payload layer and enabling external communication, this middleware has the potential to enhance modularity and interoperability, thus accelerating the integration process. It offers users the option of selecting payloads and communication methods via a shell interface, which the middleware then configures, ensuring adaptability. The solution proposed in this article, based on the gateway concept, offers users and programmers the flexibility to specify which payloads they want to activate depending on the task at hand and the high-level protocols they wish to use to interact with the activated payloads. This approach allows for the optimisation of hardware resources (only the necessary payloads are activated), as well as enabling the programmer/user to utilise high-level communication protocols (such as RESTful, Kafka, etc.) to interact with the activated payloads, rather than low-level programming.

Entrepreneurship Education Around the World: A Study on Member Universities of the Association to Advance Collegiate Schools of Business

It has long been debated whether people are born entrepreneurs or if it is a combination of skills and knowledge that can be acquired through education and training. While there are passionate scholars on both sides of the debate, the increasing number of studies and programs focusing on training people on how to become entrepreneurs perhaps shows that the balance is shifting in favour of the latter side. The purpose of this study is to examine how universities are performing the task of teaching entrepreneurship by focusing on member institutions of the Association to Advance Collegiate Schools of Business (AACSB). A total of 461 out of 1794 institutions that are members of the AACSB member directory were examined for the study. First, descriptive statistics were used to determine the number of bachelor's and master's degree programs that focus directly on entrepreneurship education along with lower-level programs such as minors and certificates. This was supplemented by examining additional entrepreneurship-related best practices employed by these institutions, such as entrepreneurship centres or certificate programs. All analyses were bifurcated by examining results based on criteria such as educational level, geographical location, and AACSB accreditation. The results show that although almost all business schools have included some form of entrepreneurship courses in their curricula, the number of programs or degrees directly dedicated to entrepreneurship remains a minority. In addition to classroom teachings, some of these institutions offer innovative practices related to entrepreneurship, such as entrepreneurship scorecards, entrepreneurship competitions, and certificate programs. All data collected and analysed were used to develop an overview of undergraduate and graduate level entrepreneurship education to encourage institutions and individuals to develop entrepreneurship programs that cover all theoretical and applied aspects of entrepreneurship education to support aspiring entrepreneurs along their journey.

Разработка кроссплатформенного приложения для управления робототехническим комплексом

A brief analysis of the approaches currently used to develop management programs is provided, their application features, advantages and disadvantages are identified. For the study, the development of a cross-platform appli-cation of control systems for robotic complexes (RTC) was chosen, using the example of RTC with MP-11 robotic manipulators. The structure of the control system includes a controller on the Arduino Mega 2560 Pro Mini board with a loaded low-level program and an upper-level program. The development is carried out on the platform.NET MAUI, which allows you to create an application for Windows, Android, and IOS operating systems on a single code base, i.e. the same code is automatically compiled for all operating systems, taking into account their features and applied elements. The control computer is connected via USB, mobile devices via Bluetooth, while data is programmatically transmitted via a serial port. The description of the modes of operation of the developed application, its functioning algorithms, indicating the features of execution for this type of programs and the appearance of forms with elements located on them is given. The program provides manual and automatic modes of operation, as well as control of not only the RTC, but also its model. The key features of this application are the use of a single code base and design for the implementation of desktop and mobile applications for various operating systems without using network resources, unlike web applications.

A Retina-inspired Sampling Method for Coding and Compression for Enhancement Image Based on Discrete Double Density Wavelet Transformer

The retina, a layer of light-sensitive tissue on the inner surface of the eye, is what allows for vision. That propose to design and analytically examine an image quantizer coding that is inspired by the way the retina manages and compresses visual features based on past studies. Retina-inspired coding is a new cipher algorithm which is very promising. Comparing the Double-Density Discrete Wavelet Transform (DDDWT) to earlier traditional approaches, it is more difficult but also produces excellent results. This efficient coding is credited to a streamlined account that, by the way, deals with 2-D and 3-D pictures, transformation matrices, and the conversion of the number DDWT as if via matrix multiplication. Naturally, this code makes a lot of interesting points of view available. In this proposal, we'll try to show how various theoretical model extensions can be applied to applications in video surveillance, information technology, and neuroscience. Therefore, that believe that using a multidisciplinary approach will help system achieve objectives. This strategy would apply signal processing methods with data from neurophysiology. There are hoping that our efforts will result in some innovative new coding algorithms. The proposed codes will be implemented in MATLAB for ease of experimentation. To implement this code more successfully, a low-level programming language is required. Index Terms— DDDWT, Retina-inspired, Coding.

Evaluation of Programming in C using WSM Method

Programming in C. The machine-oriented programming language C is mostly used to create many applications and operating systems, like Windows, as well as other complicated programmers, such the Oracle database, Kit, the Python interpreter, and games. Computer programmers and low-level programming applications are often written in the procedural or structured programming language C. Concatenation, data hiding, data compression, inheritance, and polymorphism are some extra aspects of C++ despite the fact that it is an object-oriented programming language. Multiple entities of the same type can be grouped together into a larger group using the C concept of an array. These entities or components may be user-defined data types or structures, such as integer, float, double, or float data types. C-written programmers compile and execute far more quickly than those written in other languages. This is because there are no additional processing overheads like garbage collection. As a result, when compared to other programming languages, the language is quick. An algorithm is a series of actions carried out in a preset order to address a challenge or finish a task. A function is a section of code that is called and carried out by other software elements. An operating system is made using it. The "C" programming language is used to create operating systems like Apple's OS X, Microsoft's Windows, and Symbian. It is utilized to create platforms for desktop and mobile devices. It is used to create compilers. One C feature that contains symbols for doing mathematical, relational, bitwise, conditional, or logical manipulations is the C operator. There are many built-in operators in the C programming language that can be used to carry out different tasks as needed by the application. C has an advantage over other dynamic languages since it is a statically typed programming language. Also, C is a compiler-based programming language, in contrast to Python and Java, which are interpreter-based. It expedites the compilation and execution of code. Weighted Sum works by multiplying the designated field values Indian Technical Institution or appraising the alternatives Analysis in Simply types, Enum types, Struct types, Nullable types. Evaluation parameters in Class types, Interface types, Array types, and Delegate types. Simply types, Enum types, Struct types, Nullable types. Class types, Interface types, Array types, and Delegate types. Nullable types got the first rank whereas Simply types have the lowest rank.

The Renoir Dataflow Platform: Efficient Data Processing without Complexity

Today, data analysis drives the decision-making process in virtually every human activity. This demands for software platforms that offer simple programming abstractions to express data analysis tasks and that can execute them in an efficient and scalable way. State-of-the-art solutions range from low-level programming primitives, which give control to the developer about communication and resource usage, but require significant effort to develop and optimize new algorithms, to high-level platforms that hide most of the complexities of parallel and distributed processing, but often at the cost of reduced efficiency.To reconcile these requirements, we developed Renoir, a novel distributed data processing platform written in Rust. Renoir provides a high-level dataflow programming model as mainstream data processing systems. It supports static and streaming data, it enables data transformations, grouping, aggregation, iterative computations, and time-based analytics, and it provides all these features incurring in a low overhead.In this paper, we present the programming model and the implementation details of Renoir. We evaluate it under heterogeneous workloads. We compare it with state-of-the-art solutions for data analysis and high-performance computing, as well as alternative research products, which offer different programming abstractions and implementation strategies. Renoir programs are compact and easy to write: developers need not care about low-level concerns such as resource usage, data serialization, concurrency control, and communication. At the same time, Renoir consistently presents comparable or better performance than competing solutions, by a large margin in several scenarios.We conclude that Renoir offers a good tradeoff between simplicity and performance, allowing developers to easily express complex data analysis tasks and achieve high performance and scalability.

SoC-FPGA Based Concept of Hardware Aided Quantum Simulation

Contemporary industry and science expectations towards technological solutions set the bar high. Current approaches to increasing the computing power of standard systems are reaching the limits of physics known to humankind. Fast, programmable systems with relatively low power consumption are a different concept for performing complex calculations. Highly parallel processing opens up a number of possibilities in the context of accelerating calculations. Application of SoC (System On Chip) with FPGA (Field-Programmable Gate Array) enables to delegate of a part of computations to the gates matrix, thereby expediting processing by using parallelization of hardware operations. This paper presents the general concept of using SoC FPGA systems to support CPU (Central Processing Unit) in many modern tasks. While some tasks might be really hard to implement on an FPGA in a reasonable time, the SoC FPGA platform allows for easy low-level interconnections, and with such virtualized access to the hardware computing resources, it is seen as making FPGAs, or hardware in general, more accessible to engineers accustomed to high-level solutions. The concept presented in the article takes into account the limited resources of cheaper educational platforms, which, however, still provide an interesting and alternative hybrid solution to the problem of parallelization and acceleration of data processing. This allows to overcome encountered limitations and maintain the flexibility known from high-level solutions and high performance achieved with low-level programming, without the need for a high financial background.

Extracting high-level activities from low-level program execution logs

Extracting high-level activities from low-level program execution logs

Technical Note: mzML and imzML Libraries for Processing Mass Spectrometry Data with the High-Performance Programming Language Julia.

Julia combines the virtues of high-level and low-level programming languages: The code is human-readable, and the performance of the created binaries competes with machine-orientated compilers. Thus, Julia is popular in "Big Data" sciences. Reading mass spectrometry (MS) data with Julia was impossible until now due to missing libraries. Here, we present a Julia library for importing mass spectrometry (MS) data in HUPO standard mzML and imzML formats and demonstrate its function with direct and ambient ionization MS, liquid chromatography-MS, and MS imaging data on standard platforms (Windows, Linux, and Mac OS). The processing speed of Julia for reading imzML MS imaging files was up to 214 times faster than the comparable code in R. Julia can remove bottlenecks for computationally demanding tasks in large-scale MS-Omics and MS imaging data processing workflows and supports their agile development. In addition, time-critical and complex data evaluation tasks become possible, such as following the real-time monitoring of biological processes and pattern recognition in large MS imaging projects. Our mzML/imzML libraries and code examples are available under the terms of the MIT license from https://github.com/CINVESTAV-LABI/julia_mzML_imzML.

Методика разработки web-приложения для управления робототехническими комплексами

The purpose of the study is to develop a web application for control systems of robotic complexes (RTK) using the example of RTK with MP-11 robotic manipulators. The structure of the control system includes a controller on the Arduino Mega 2560 Pro Mini board with a loaded low-level program and an upper-level program. A web application has been selected as a software component. This type of application consists of a server and a client part. Data is exchanged between them over the network using the HTTP protocol. The application is built on the Microsoft .NET platform and the ASP .NET WebForms framework. The web server is a computer connected via a USB cable to the controller and currently used for a desktop application. The description of the web pages of the developed web application and its operation, algorithms of functioning, indicating the specifics of execution for this type of program is given. The program provides manual and automatic operation modes, as well as control of not only the RTK, but also its model. This type of management system involves multi-user mode and network transmission, two types of user rights and authentication have been developed to ensure the security of the complex, as well as antivirus protection, network settings for protection against unauthorized access are used. Guest access allows you to control only the robot model, in order to ensure the safety of the equipment. The main advantages of a web application are its cross-platform nature, i.e. the ability to work on any devices and operating systems, as well as the absence of the need for installation.

SwCUDA: Auto parallel code translation framework from CUDA to ATHREAD for new generation sunway supercomputer

Since specific hardware characteristics and low-level programming model are adapted to both NVIDIA GPU and new generation Sunway architecture, automatically translating mature CUDA kernels to Sunway ATHREAD kernels are realistic but challenging work. To address this issue, swCUDA, an auto parallel code translation framework is proposed. To that end, we create scale affine translation to transform CUDA thread hierarchy to Sunway index, directive based memory hierarchy and data redirection optimization to assign optimal memory usage and data stride strategy, directive based grouping-calculation-asynchronous-reduction (GCAR) algorithm to provide general solution for random access issue. swCUDA utilizes code generator ANTLR as compiler frontend to parse CUDA kernel and integrate novel algorithms in the node of abstracted syntax tree (AST) depending on directives. Automatically translation is performed on the entire Polybench suite and NBody simulation benchmark. We get an average 40x speedup compared with baseline on the Sunway architecture, average speedup of 15x compared to x86 CPU and average 27 percentage higher than NVIDIA GPU. Further, swCUDA is implemented to translate major kernels of the real world application Gromacs. The translated version achieves up to 17x speedup.

DRLFluent: A distributed co-simulation framework coupling deep reinforcement learning with Ansys-Fluent on high-performance computing systems

For active flow control (AFC), several frameworks have been developed to enable dynamic interactions between deep reinforcement learning (DRL) agents and computational fluids dynamics (CFD) environments. However, the highly coupled modules in the existing frameworks require massive development efforts to be applied to different flow scenarios. Moreover, these frameworks do not support applications to run flexibly on multiple nodes in high-performance computing (HPC) systems. Herein, we propose a new framework named as DRLFluent coupling an open-source DRL package with a well-developed general CFD solver, Ansys-Fluent. The distributed HPC deployment of DRLFluent is achieved by a broker architecture which enables the DRL client and CFD server written in different languages to communicate across different nodes via a pair of standardized interfaces. We have evaluated DRLFluent along with two other representative frameworks using a benchmark case in which a laminar flow around a circular cylinder (Re=100) is controlled by two synthetic jets. The strategy discovered in DRLFluent is capable of neutralizing the small asymmetry of the shedding vortices, yielding a higher drag reduction than the other two frameworks. When the number of parallel environments exceeds 20, DRLFluent exhibits the largest speedup among the frameworks evaluated. Capable of distributing workload across multiple nodes, DRLFluent is suitable for implementing DRL for AFC of complex flow scenarios which requires hundreds of cores or more. Benefitting from a loose coupling between DRL and CFD, the standardized interfaces and the user-friendly CFD solver, DRLFluent is readily applicable to a wide range of flow scenarios without laborious low-level programming efforts.

Bit-Stealing Made Legal: Compilation for Custom Memory Representations of Algebraic Data Types

Initially present only in functional languages such as OCaml and Haskell, Algebraic Data Types (ADTs) have now become pervasive in mainstream languages, providing nice data abstractions and an elegant way to express functions through pattern matching. Unfortunately, ADTs remain seldom used in low-level programming. One reason is that their increased convenience comes at the cost of abstracting away the exact memory layout of values. Even Rust, which tries to optimize data layout, severely limits control over memory representation. In this article, we present a new approach to specify the data layout of rich data types based on a dual view: a source type, providing a high-level description available in the rest of the code, along with a memory type, providing full control over the memory layout. This dual view allows for better reasoning about memory layout, both for correctness, with dedicated validity criteria linking the two views, and for optimizations that manipulate the memory view. We then provide algorithms to compile constructors and destructors, including pattern matching, to their low-level memory representation. We prove our compilation algorithms correct, implement them in a tool called ribbit that compiles to LLVM IR, and show some early experimental results.

SEAL: Integrating Program Analysis and Repository Mining

Software projects are complex technical and organizational systems involving large numbers of artifacts and developers. To understand and tame software complexity, a wide variety of program analysis techniques have been developed for bug detection, program comprehension, verification, and more. At the same time, repository mining techniques aim at obtaining insights into the inner socio-technical workings of software projects at a larger scale. While both program analysis and repository mining have been successful on their own, they are largely isolated, which leaves considerable potential for synergies untapped. We present SEAL, the first integrated approach that combines low-level program analysis with high-level repository information. SEAL maps repository information, mined from the development history of a project, onto a low-level intermediate program representation, making it available for state-of-the-art program analysis. SEAL’s integrated approach allows us to efficiently address software engineering problems that span multiple levels of abstraction, from low-level data flow to high-level organizational information. To demonstrate its merits and practicality, we use SEAL to determine which code changes modify central parts of a given software project, how authors interact (indirectly) with each other through code, and we demonstrate that putting static analysis’ results into a socio-technical context improves their expressiveness and interpretability.

From low-level programming to full-fledged industrial model-based development: the story of the Rubus Component Model

Developing distributed real-time systems is a complex task that has historically entailed specialized handcraft. In this paper, we propose a retrospective on the (r)evolutionary changes that led to the transition from low-level programming to industrial full-fledged model-based development embodied by the Rubus Component Model and its tool-ecosystem. We focus on the needs, challenges, and solutions of a 15-year-long evolution journey of a software development approach that has gone from low-level and manual programming to a highly automated environment offering modeling, analysis, and development of vehicular software systems with multi-criticality for deployment on single- and multi-core platforms.

Automatic extension of a symbolic mobile manipulation skill set

Symbolic planning can provide an intuitive interface for non-expert users to operate autonomous robots by abstracting away much of the low-level programming. However, symbolic planners assume that the initially provided abstract domain and problem descriptions are closed and complete. This means that they are fundamentally unable to adapt to changes in the environment or tasks that are not captured by the initial description. We propose a method that allows an agent to automatically extend the abstract description of its skill set upon encountering such a situation. We introduce strategies for generalizing from previous experience, completing sequences of key actions and discovering preconditions to ensure computational efficiency. The resulting system is evaluated on a symbolic planning benchmark task and on object rearrangement tasks in simulation. Compared to a Monte Carlo Tree Search baseline, our strategies for efficient search have on average a 25% higher success rate at a 67% faster runtime. Code is available at https://github.com/ethz-asl/high_level_planning.

Pychastic: Precise Brownian dynamics using Taylor-Itō integrators in Python

In the last decade, Python-powered physics simulations ecosystem has been growing steadily, allowing greater interoperability, and becoming an important tool in numerical exploration of physical phenomena, particularly in soft matter systems. Driven by the need for fast and precise numerical integration in colloidal dynamics, here we formulate the problem of Brownian Dynamics (BD) in a mathematically consistent formalism of the Itō calculus, and develop a Python package to assist numerical computations. We show that, thanks to the automatic differentiation packages, the classical truncated Taylor-Itō integrators can be implemented without the burden of computing the derivatives of the coefficient functions beforehand. Furthermore, we show how to circumvent the difficulties of BD simulations such as calculations of the divergence of the mobility tensor in the diffusion equation and discontinuous trajectories encountered when working with dynamics on S^2S2 and SO(3)SO(3). The resulting Python package, Pychastic, is capable of performing BD simulations including hydrodynamic interactions at speeds comparable to dedicated implementations in lower-level programming languages, but with a much simpler end-user interface.

Codebase release 0.2 for Pychastic

In the last decade, Python-powered physics simulations ecosystem has been growing steadily, allowing greater interoperability, and becoming an important tool in numerical exploration of physical phenomena, particularly in soft matter systems. Driven by the need for fast and precise numerical integration in colloidal dynamics, here we formulate the problem of Brownian Dynamics (BD) in a mathematically consistent formalism of the Itō calculus, and develop a Python package to assist numerical computations. We show that, thanks to the automatic differentiation packages, the classical truncated Taylor-Itō integrators can be implemented without the burden of computing the derivatives of the coefficient functions beforehand. Furthermore, we show how to circumvent the difficulties of BD simulations such as calculations of the divergence of the mobility tensor in the diffusion equation and discontinuous trajectories encountered when working with dynamics on S^2S2 and SO(3)SO(3). The resulting Python package, Pychastic, is capable of performing BD simulations including hydrodynamic interactions at speeds comparable to dedicated implementations in lower-level programming languages, but with a much simpler end-user interface.

Demand Side Management and Peer-to-Peer Energy Trading for Industrial Users Using Two-Level Multi-Agent Reinforcement Learning

Over the past few years, with more concerns on energy equity, energy security and environmental sustainability, peer-to-peer (P2P) energy trading market has increasingly attracted attention to energy users. With the rising electricity price and the growing public doubt to unsustainable development, more efforts are given to the integration between P2P energy trading and industrial practice. To date, the current research work has emphasized electricity cost reduction using demandside management (DSM) and P2P energy trading market to promote energy sharing and create a win–win situation for all industrial market participants. However, less attention has been given to evaluating industrial production. With this attention, this paper formulates a cost minimization problem for multiple discrete manufacturers, where DSM and P2P energy trading market are integrated to optimize manufacturing productivity and energy consumption. Moreover, this paper proposes a two-level reinforcement learning (RL) method to obtain the optimal DSM and energy trading strategies for discrete manufacturers. Unlike the existing RL approaches, the proposed method depicts a twolevel learning approach, where the upper-level program suggests a production plan for the manufacturer to improve the cost return, while the lower-level program optimizes the net benefits. Further, to verify the performance of the proposed method, simulations are conducted based on four different case studies, including different weather and load conditions. Numerical results demonstrate that the proposed algorithm provides a better production plan with enhanced learning speed and economic benefits compared to the existing RL approaches.

Future Database Engine Development: You Will Only Need One Programming Language

Database systems must make good use of the hardware for high performance. This is usually done by implementing their core components (storage engine, optimizer and query execution engine) in a low-level programming language (PL) such as C/C++ that can directly "talk to the hardware." But these PLs traditionally lacked high-level abstractions, lowering DBMS developer productivity. Some systems [18, 16, 10] then mix different PLs to balance productivity and performance. For example, Presto [16] and Spark [18] originally used Java but are now replacing their query engines with new ones [12, 1] built in C++ for higher performance. However, doing so brings such non-trivial challenges as interacting with different PL runtimes [1].