The EMMA2 multiprocessor operating system

The central theme of this paper is information extraction from remote sensing data with the aid of software developed in the fields of image processing, pattern recognition and artificial intelligence, more specifically of expert systems.

The topic of Artificial Intelligence (AI)-Empowered Intelligent Transportation Systems (ITS) has drawn more attention recently, with the rapid development of ubiquitous networks and smart vehicles. Researchers around the world have been working on new automotive applications to create a comfortable and safer driving environment. Current challenges include: how to run computing-intensive applications on vehicles; how to enable real-time feedback between vehicles and the traffic management server based on the current Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication modes; and how to provide efficient computing capabilities for resource-consumption applications and reasonable resource allocation for vehicles and infrastructures. Recently, AI has made remarkable achievements in many fields such as image processing, pattern recognition, and natural language processing. It is also involved in computing-intensive applications, such as autopilot and real-time navigation through V2V or V2I. However, AI-Empowered ITS is still in its infancy. How can AI be integrated with ITS and function well in dynamic vehicular network scenarios? In addition, there are still questions on how to design more efficient AI solutions for resource management and coordination in ITS.

This chapter explores the nature of intelligence itself, conceptualizing it as a massive operating system that functions on biological technology as human intelligence (HI), or digital technology as artificial intelligence (AI). Through the lens of biological technology, the operating system contains many different biological individuals, each with an evolved range of dimensional “capabilities” needed to exist in the world environment. The operating system itself can be considered a hyperselfish intelligence (HYPERINT) development system that is relentlessly evolving, and exploitatively optimizing the development of the highest performing technologies within the world environment, regardless of the needs of individuals or a species, in order to ruthlessly maximize its capabilities. This chapter argues that HYPERINT is driving humans towards a situation where AI technologies could rapidly lead to the development of artificial general intelligence (AGI) with super intelligence that vastly exceeds HI and human controllability. Strong global regulation of AI is needed to protect human civilization from the most extreme risks of AI and AGI. In conclusion, the chapter argues that the global community must make a transition out of Industry 4.0, which focuses on progressive total automation of human work, and move towards the goals of Industry 5.0, which are more human-centric and globally sustainable.

This article presents an overview of computer operating systems (OS) and emerging trends. OS is simply defined as an interface between computer hardware and the user. The objective of the study is to investigate the emerging trends of OS and to find out the direction of OS for modern computing systems. To achieve this goal the paper looks at the concepts of OS, its underlying architectures and evolution. The papers also outline the components of the OS provide knowledge on security issues with OS architectures and provide best practices to secure OS. The paper found out that the current trends in OS include IoT OS, Cloud OS, AI-powered OS, Blockchain OS, Hybrid OS and Container OS. The paper compares the strengths and weaknesses of the major OS. The Paper proposes a double-layer security approach where the OS is hardened with security policies and embedding security protocols in the Hardware architecture of the OS. It was discovered that every technology comes with its unique architecture and OS. This makes it worrisome for engineers to crack their brains to develop such a system. The paper further proposes the development of a universal OS for all architectures leaving room for further expansion while mitigating power consumption issues by incorporating green computing technology in the design architecture.

Today’s complex applications must face the distribution of data and code among different network nodes. Computation in distributed contexts is demanding increasingly powerful languages and execution environments, able to provide programmers with appropriate abstractions and tools. Java is a wide-spread language that allows developers to build complex software, even distributed, but it cannot handle the migration of computations (i.e. threads), due to intrinsic limitations of many traditional JVMs. After analyzing the approaches in the literature, this paper presents our thread migration framework (called Mobile JikesRVM), implemented on top of the IBM Jikes Research Virtual Machine (RVM): exploiting some of the innovative techniques in the JikesRVM, we implemented an extension of its scheduler that allows applications to easily capture the state of a running thread and makes it possible to restore it elsewhere (i.e. on a different hardware architecture or operating system), but still with a version of the framework installed). Our thread serialization mechanism provides support for both proactive and reactive migration, available also for multi-threaded Java applications, and tools to deal with the problems of resource relocation management. With respect to previous approaches, we implemented Mobile JikesRVM without recompiling its JVM (Java Virtual Machine) source code, but simply extending JikesRVM functionalities with a full Java package to be imported when thread migration is needed.

The rapid advancement in artificial intelligence, semiconductor and electronic technology, software engineering and programming techniques as well as hardware interfaces and communication has raised the demand for advancement in operating system. In this article we have studied the classical and current operating systems and have proposed the advanced components such as new user interfaces, advanced human-to-machine interfaces, communication, artificial intelligence and advanced hardware technology etc. which are necessary for next generation operating systems.

Recent progress in artificial intelligence has reshaped the design philosophy of modern operating systems, with Large Language Models (LLMs) emerging as a key enabling technology. Existing operating systems primarily rely on predefined graphical and command-line interactions, which limits their ability to adapt to user preferences, understand context, and respond to complex intent. Early natural language–based interfaces attempted to address these limitations; however, they lacked robust contextual reasoning and system-level intelligence required for dynamic computing environments. As workloads and user expectations continue to grow in complexity, there is an increasing need for operating systems capable of understanding and acting upon natural language instructions in a reliable and transparent manner. This paper examines operating system architectures that embed LLMs within core system components to facilitate intent-driven interaction and intelligent automation. The study surveys representative frameworks such as AIOS, PEROS, Compressor–Retriever architectures, and Herding LLaMaS, emphasizing how these designs translate linguistic input into executable operating system functions. Key capabilities explored include continuous context retention, dynamic allocation of computational resources, and semantic coordination between software services and underlying hardware. These design choices aim to enhance usability while reducing the cognitive burden placed on end users. The discussion further addresses essential system considerations, including interpretability of model-driven decisions, user-specific adaptation, protection of sensitive data, and scalability across diverse computing platforms. Emphasis is placed on explainable reasoning mechanisms that help users and developers understand system behavior, particularly in safety- and security-critical scenarios. By integrating adaptable intelligence with transparent control logic, LLMenhanced operating systems can improve trust and operational efficiency.

Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.

Moving target detection and recognition methods are the foundation and key of modern intelligent video recognition systems. It combines advanced technologies in many fields such as image processing, pattern recognition, and artificial intelligence, and is a research hotspot in computer vision technology. Therefore, it is of great significance to study moving target detection and recognition algorithms. The non-local image extraction algorithm proposed in this paper uses an adaptive clustering method to perform fine cluster analysis on non-local image blocks with different feature types. Through the step-by-step principal component approximation method, we carefully find the features in each class. This progressive principal component approximation implements singular value hard threshold processing based on the Marchenko-Pastur (MP) theorem to select the main part of the feature, and uses special soft thresholds in the principal component transform domain to further improve the extraction performance. The Lower Bound-Based Within-Class Maximum Division (LBWCMD) is proposed, and this method is used as a preprocessing step of robust principal component analysis in moving target detection. This article applies LBWCMD to the video frame set based on the position information of the moving target, and the obtained frame subset meets the signal requirements of Robust Principle Component Analysis (RPCA) to the greatest extent. On this basis, we add frames with smaller motion amplitudes to each frame subset to increase the proportion of background pixels in each subset. Frame set division and low-rank decomposition realize the detection of moving targets under a unified framework. The detection rate of the proposed method is higher than that of the current popular methods in sports video data sets, and the detection accuracy is improved compared with the original RPCA method.

Image analysis and computer vision: 1991

Industrial morphology combines the dominant paradigms of machine vision, image processing and pattern recognition, into a cohesive framework of algebraic fundamentals and operational systems. Machine vision systems based on the principles of industrial morphology represent a logical advancement in machine vision technology, combining the flexibility and speed of special parallel architectures for image processing, high level languages for application programming, and statistical classification and learning for interactive, menu driven calibration and setup.

Modern high speed local area networks offer great potential for communication intensive applications, but their performance is limited by the use of traditional communication protocols, such as TCP/IP. In most cases, these protocols require that all network access be through the operating system, which adds significant overhead to both the transmission path (typically a system call and data copy) and the receive path (typically an interrupt, a system call, and a data copy). To address this performance problem, several user level communication architectures have been developed that remove the operating system from the critical communication path. The article describes six important issues to consider in designing communication protocols for user level architectures. The issues discussed focus on the performance and semantics of a communication system. These issues include data transfer, address translation, protection, and control transfer mechanisms, as well as the issues of reliability and multicast. To provide a basis for analyzing these issues, the authors present a simple network interface protocol for Myricom's Myrinet network, which has a programmable network interface. Researchers can thus explore many protocol design options, and several groups have designed communication systems for Myrinet. The authors refer to 11 such systems, all of which differ significantly in how they resolve these design issues but all of which aim for high performance and provide a lean, low level, and more or less generic communication facility.

In this paper we present briefly the highlights of the projects undertaken at the National Nodal Centre for the Knowledge-Based Computer Science at ISI, Calcutta and the related sub-problems being tackled in order to make meaningful application of knowledge-based approach on some image processing, pattern recognition and computer vision to some real life problems. Application areas are interpretation of remotely sensed images for mineral location & targetting and hydro-carbon exploration problems, industrial inspection and verification system, such as cold rolling steel inspection, computer vision-based extraction and validation of PCB lay out, expert CAD system for electronic board inspection and test and medical imaging and expert systems. In relation to the above areas of applications there are several theoretical and experimental problems of fundamental importance which are being investigated such as 2D and 3D shape analysis recognition and positioning of partially occluded object, binary contour coding scheme, KBCS approach to cluster analysis, neural modelling & simulation and learning algorithms, semantic network and architectural mapping, connectivity vertification scheme and mathematical morphology.

Studies have been made on computer systems based on the user model reflecting the behavior of the users, aiming at the human interface which is easy to use. Recently, it has been proposed that individual models be constructed for the users, rather than a model where the only one “typical” human model represents the user in general. A case has not been observed, however, where the individual model is utilized fully in the interactive computer system which actually requires such an approach. From such a viewpoint, this paper proposes a generation of suitable messages utilizing the individual user model, by restricting the problem to the generation of the messages from the computer side in the construction of a program in the interactive programming environment. The features of the user model which will be useful in such a problem, are pointed out. The points in this study are that the skill of the user concerning the system is separated into the general understanding level and the knowledge of the individual functions; utilization of the information obtained by the operating system and for the language processor; and object‐oriented user model whose contents are always updated. A human interface was implemented experimentally which indicates the individual user, the error message from Lisp interpreter according to each situation. We have conducted an experiment by using the interface. The usefulness of our method was demonstrated by the result of experiment.

The DBMS research is moving towards formalization of its problems and solutions. The DBMS researcher can learn and use the techniques that have been developed in formalizing the concepts in Operating systems, Artificial Intelligence, Adaptive systems and Pattern Recognition, and Programming Languages. The panel members are currently involved in research oriented towards cross fertilization of DBMS and various disciplines of computer science. The panel will try to identify commonality of problems, approach and solutions. We hope the discussion will stimulate research in the database area that can fully exploit what we have learned elsewhere.