Improved Closed-loop Detection Algorithm Based on Dynamic Increase or Decrease Mechanism

This study addresses the challenges of robotic localization and navigation in visually degraded environments, such as low illumination and adverse weather conditions, by proposing a novel thermal infrared visual SLAM (Simultaneous Localization and Mapping) algorithm. The research introduces a new infrared visual odometry that integrates feature-based methods with optical flow techniques, enhancing image processing capabilities to mitigate the issues of high time overhead and cumulative errors in traditional feature-based odometry. Additionally, an improved bag-of-words model is employed to develop a novel loop closure detection method that addresses the challenge of scale drift. The purpose of this paper is to address the shortcomings in robustness and accuracy encountered by existing visual SLAM algorithms when processing low-texture thermal infrared images. Experimental validation using the JPL, Airey, and ViViD++ thermal infrared datasets demonstrates that the proposed algorithm exhibits superior real-time performance and robustness across various environments. Compared to mainstream thermal infrared visual SLAM algorithms, WTI-SLAM significantly improves the robot localization accuracy in weak-texture thermal infrared image scenarios, reducing the localization error by approximately 46%. This research offers an innovative and effective solution for achieving stable SLAM systems for robots operating in complex and visually degraded environments.

8 - Real-time fault-tolerant control systems

Large Scale Multiple Input Multiple Output (MIMO) technology is a promising technology in wireless communications, and it is already at the heart of many wireless standards. MIMO technologies provide significant performance improvements in terms of data transfer rate and reduction the interference. However, MIMO techniques face large-scale linear dynamic problems such as system stability and it will be possible to overcome this problem by tuning the proportional integral derivative (PID) in continuous systems. The aim of this paper is to design an efficient model for MIMO based on Adaptive Neural Inference System (ANFIS) controller and compare it with a traditional PID controller. and evaluated by objective function as integral time absolute error (ITAE). ANFIS is used to train fuzzy logic systems according to the hybrid learning algorithm. The training involves the fuzzy logic parameters through simulating the validation data to represent a model to know the correctness and effectiveness of the system. It is optimizes the system performance in real time, however, to avoid potential problems such as easy local optimality. In the proposed approach stability is guaranteed as the initial steady-state scheme. ITAE is combined with ANFIS to minimize the steady-state transient time responses between the high-order initial pattern and unit amplitude response. The proposed ANFIS self-tuning controller is evaluated by comparing with the conventional PID. MATLAB simulink is used to illustrate the results and demonstrate the possibility of adopting ANFIS controller. The simulation results showed that the performance of ANFIS controller is better than the PID controller in terms of settling time, undershoot and overshoot time.

This paper describes a way to get more flexibility in a real-time product and its base platform (real-time operating system and hardware). Industrial hardware and software platforms are due to change and in some cases a new platform is needed after five to ten years, if not earlier. This is costly and there is a need to be able to make the product grow in performance without changing the platform. The ongoing work that is described in this paper is performed in cooperation with industry and the attempt is to convert a single processor software application to a multiprocessor application. By changing the platform to a flexible multiprocessor real-time problem, flexibility and performance is increased, resulting in a more optimized platform for different configurations of the application.

The core idea in this project is to have two operating systems for both cores of dual core processor individually. General purpose operating system on one core and Real time operating system (RTOS) on second core. Implementation of same will be done by making one of core independent from General purpose operating system and install RTOS in that core so that both will work as one system i.e. General purpose operating system will look after the non-real time applications and RTOS will ensure that all real time application should not miss their deadline and executed as fast as they can. As by installing general purpose operating system and RTOS in both the cores of dual core processors of system and running them in parallel way, dual core processors is being used to its maximum efficiency. The advantage is that if one of OS gets crashed, whole system will not crash instead one of OS which didn't crash hold the system to safe mode till damaged OS is recovered.

To address the challenges of visual SLAM algorithms in unmanned surface vehicles (USVs) during nearshore navigation or docking, where dynamic feature points degrade localization accuracy and dynamic objects impede static dense mapping, this study proposes an improved visual SLAM algorithm that removes dynamic feature points. Building upon the ORB-SLAM3 framework, the improved SLAM algorithm integrates a shore segmentation module and a dynamic region elimination module, while enabling static dense point cloud mapping. The system first implements shore segmentation based on Otsu’s method to generate masks covering water and sky regions, ensuring the SLAM system avoids extracting interfering feature points from these areas. Secondly, the deep learning network YOLOv8n-seg is employed to detect priori dynamic objects, with the motion consistency check method to identify non-priori dynamic feature points, collectively removing dynamic feature points. Additionally, the ELAS algorithm computes disparity maps, integrating depth information and dynamic object information to construct a static dense map. Experimental results demonstrate that, compared to the original ORB-SLAM3, the improved SLAM algorithm achieves superior localization accuracy in dynamic nearshore environments, significantly reduces the impact of dynamic objects on pose estimation, and successfully constructs ghosting-free static dense point cloud maps.

X-by-Wire system is a typical automotive network control system. The control of x-by-wire system depends on communication network (like CAN, TTCAN or FlexRay), it must meet not only the control performance, but also the real-time performance and reliability requirement. Network optimization in x-by-wire system is great significant for achieving these targets. In this paper, first task graph was designed for the x-by-wire system, and different control links were distinguished from the task graph. Then the network optimization algorithm was designed to implement message slot arrangement coordinated with task execution time in the control links, and the principle of the network optimization algorithm design is reducing the control link response time for the best effort. Through a typical car x-by-wire control system, we can see that the network optimization algorithm research has important guidance for the optimization of the system message transmission, and can improve the system's real-time performance.

Mathematical models are often required in process modeling, control, and evaluation. To meet the increasing demand of production system real-time performance evaluation and improvement, in this paper: 1) an innovative event-based, data-driven mathematical model is established for network structured manufacturing systems; 2) important properties of network structured manufacturing systems are obtained through the concept development of a virtual ideal clean system; and 3) a system performance diagnostic method is developed based on the mathematical model and system properties, as well as available sensor data. The mathematical model and system performance identification methodology are studied analytically and validated by simulation studies. This data-driven mathematical framework and system real-time performance diagnostic methodology are invaluable for real-time production control to improve system responsiveness and efficiency. Note to Practitioners —For production systems with complex networked structure layouts, the dependence relationship between machines and buffers is more complicated compared with serial production lines. Therefore, it is more difficult to analyze and evaluate their real-time production performance such as permanent production loss. Such information is critical for real-time production management and control to achieve higher system responsiveness and efficiency. This paper establishes an event-based data-driven mathematical model to describe the real-time dynamic behavior of manufacturing systems with complex networked structures. Furthermore, an analysis method for networked manufacturing system properties and a data-driven system performance diagnostic method are proposed. The methods provide a quantitative solution to evaluate production capacity and production constraints of complex networked manufacturing systems, and to evaluate the system real-time performance such as permanent production loss and its attribution to each disruption event and machine.

The time of delivering the embedded system solution to market is very important, because of the highly competitive market and demand of the consumer needs. In Real Time Operating System (RTOS) based embedded systems, the overall correctness of the embedded system depends on logically correct output within the specified time. Developing, testing and debugging of an RTOS based embedded system takes lots of time. The virtual platform provides the great advantage in developing, testing and debugging the RTOS based embedded system. This work describes the design and development of a reconfigurable virtual platform for real-time kernel. Eclipse IDE platform, NXP LPC-2129 target, OpenOCD have been chosen to develop this reconfigurable virtual platform for real-time kernels. For the selected target, GNU Toolchain has been configured as a plug-in in Eclipse IDE for both Windows and Linux to get own virtual platform. The μC/OS-II Board Support Package and the virtual timer for gdb debugger to support selected target has been developed and added in this virtual platform. This virtual platform facilitates the reconfiguration of the various RTOS such as μC/OS-II, DrRTOS and AVX. From the implementation of this design framework on real system, it can be concluded that the virtual platform increases the efficiency of the RTOS based applications. It gives the single solution for the developers to develop, execute, test, debug and verify the applications for various RTOS in a shorter span of time and with substantial reduction of developmental cost.

The sonar systems developing is a very particular branch of engineering, it requires full-scale validation considering all possible factors characterizing oceanological characteristics and weather conditions. This is either impractical or requires significant resources (time and expenses), therefore simulation is used to accomplish the complexity of the systems. Since the hydroacoustic systems operate in real time, it is necessary to maintain the real-time simulation performance in order to obtain an adequate representation. As soon as MATLAB modelling on personal computer is not appropriate for this task, the calculations are perfomed on a modular electronic instrumentation platform with real-time operating system. In this paper authors proposed a method of simulation of the signal of boundary hydroacoustic reverberation using the Hartley basis and its implementation using PXI platform by National Instruments with LabVIEW RealTime OS. The experimental processing of simulated signals has been carried out with the onboard control system of hydracoustic complex designed in Bauman Moscow State Technical University.

Traditionally, performance of telecommunications networks has been undertaken on a Network Element (NE) basis, often in an uncoordinated manner and requiring sophisticated human expertise for relatively trivial tasks. However, as networks become increasingly diverse and complex, and the demands upon them increase, this approach is more and more inefficient. Additionally, the advent of distributed processing networks, such as mobile cellular, IN (Intelligent Networks) and TINA (Telecommunication Information Networking Architecture), means that local performance will no longer be sufficient and co-ordination of these activities across the network is now required. The problems with centralised performance of networks are the volume of data, the complexity of the network, and the resultant latencies in response times to network problems, A real time performance system for telecommunications networks is described, which can form the basis of real time decentralised control of future telecommunications networks. The proposed performance measurement system provides open interfaces for subscription to real time performance information at varying degrees of abstraction, corresponding to the required view. The measurement system takes an event-based approach to delivering firm real time performance information. The measurement enables real time and near real time control systems to be decentralised within the network, both at a local level and at a regional level. A hierarchical approach both reduces the volume and complexity of performance information and facilitates the development of a management by delegation paradigm in the network.

The increasing demand for real-time language translation in multilingual societies highlights the need for efficient speech translation systems. This research proposes the development of an English-to-Marathi speech translation framework leveraging a dictionary-based dataset to enhance linguistic accuracy and contextual understanding. Marathi, being a morphologically rich language with unique syntactic and semantic structures, poses significant challenges when paired with English, a structurally divergent language. The proposed system will integrate automatic speech recognition (ASR), machine translation (MT), and text-to-speech synthesis (TTS) to achieve seamless speech-to-speech conversion. A dictionary-based dataset will serve as the foundation, enriched with linguistic rules and context-aware mappings to address lexical and syntactic divergences. By incorporating rule-based techniques alongside advanced neural network models, such as sequence-to- sequence transformers and attention mechanisms, the system aims to capture the nuances of Marathi grammar, idiomatic expressions, and gendered verb forms. The hybrid approach will ensure both robustness and adaptability across diverse linguistic inputs. The research will evaluate translation accuracy, fluency, and contextual relevance using metrics such as BLEU and TER. This study aspires to contribute to the growing field of low-resource language translation by enabling effective communication between English and Marathi speakers, thus fostering inclusivity in education, commerce, and digital communication. In the proposed research work the focus will be given to translation of English language video to Marathi language. Considering the strong grammar and variety of word dictionaries of the Marathi language, a strong algorithm based on deep learning techniques is required. Speech to Speech machine translation using deep learning focuses on the language conversion from English to Marathi which allows for seamless communication between people who speak Marathi languages. Speech-to speech translation solves this problem by translating spoken words in real time, allowing people who speak Marathi languages to communicate with one another.

INTEX is a linguistic development environment that includes large-coverage dictionaries and grammars, and parses texts of several million words in real time. INTEX has tools to create and maintain large-coverage lexical resources as well as morphological and syntactic grammars. Dictionaries and grammars are applied to texts in order to locate morphological, lexical and syntactic patterns, remove ambiguities, and tag simple and compound words. INTEX can build lemmatized concordances and indices of large texts with respect to all types of Finite State patterns. INTEX is used as a corpus processor, to analyze literary, journalistic and technical texts. I describe here the subset of tools used to perform advanced search requests on large texts.

Time resolved imaging techniques, such as MEG and EEG, are unique in their ability to reveal the rich dynamic spatiotemporal patterning of neural activities. Here we propose a technique based on spatiotemporal searchlight Representational Similarity Analysis (RSA) of combined MEG and EEG (EMEG) data to directly analyze the multivariate pattern of information flow across the brain. This novel technique can recognize fine-grained dynamic neural computations both in space and in time. A prime example of such neural computations is our ability to understand spoken words in real time. A computational approach to these processes is suggested by the Cohort Model of spoken-word recognition. Here we show how spatiotemporal searchlight RSA applied to source estimations of EMEG data can provide insights into the neural correlates of the cohort model within bilateral front temporal brain regions.

A new single-chip microprocessor for speech recognition, the SRP, has been developed, utilizing a multiprocessor architecture and a pipelined structure. It can recognize up to 340 isolated words or 40 connected words in real time. The SRP contains a vector distance calculator, a DP-equation calculator, and an I/O controller operating in a pipelined manner. Algorithm variations and operation parameters are user programmable, and the total size of the SRP program for a typical speech recognition system is about 700 words. The device has been fabricated with n-channel Si-gate E/D MOS technology with 2.5 μm design rules and employs 7296 three-transistor dynamic RAM cells for a total of more than 40 000 transistors.