Efficient Real-time Processing Research Articles

New obstacle detection method for a mobile robot based on improved disparity map processing

Stereovision is a crucial technique for vision-based obstacle detection as it provides accurate 3D information for analyzing scenes and detecting obstacles. Despite its effectiveness, existing methods often struggle with real-time performance and computational efficiency. This paper introduces a new stereovision-based obstacle detection system that integrates two novel approaches: Optimized Disparity Map Computation (ODMC), which enhances Hirschmüller’s method for faster disparity map generation, and Disparity Seeded Region Growing (D-SRG), an improved version of the SRG algorithm optimized for identifying obstacles in real-time. Our system is designed to address key challenges such as high memory consumption and slow processing speed, enabling efficient real-time processing from image acquisition to obstacle detection using a 3D camera. Experimental results show that:

Use of a hand-worn accelerometer to measure hand differences in Grooved Pegboard Test

Research on laterality, which pertains to the preference for using one hand over the other in daily activities, has recently been a growing topic of investigation. Studies demonstrate that performance differences between the right and left hands arise from distinct strategies in utilizing information for movement execution. To conduct a more comprehensive analysis of these differences, we aimed to investigate various measures using a wrist accelerometer during the execution of a traditional tool to assess manual performance. The proposed hypothesis suggested that tasks performed with the right hand would exhibit shorter movement times due to more efficient real-time information processing. In contrast, tasks performed with the left hand would show shorter reaction times due to movement planning proficiency. Furthermore, we hypothesized that increased task complexity would make these differences more pronounced. The study revealed that, in the less complex task, the right hand outperformed the left hand in execution speed, whereas the left hand demonstrated faster reaction times in the more complex task. The task complexity highlighted the differences, emphasizing the impact of task demands on hand specialization. Using an accelerometer provided valuable insights, indicating potential avenues for refining assessment tools and analyzing manual control. Keywords: motor control, hemispheric specialization, laterality, hand function, dexterity.

Real-time information processing via volatile resistance change in scalable protonic devices

Biological neural systems operate on multiple time scales, enabling real-time interaction with their environment. However, replicating this in electronic systems is challenging, as scalable devices that operate on similar time scales, particularly from seconds to minutes, are lacking. This study addresses this gap by exploiting proton dynamics to achieve volatile resistance changes over long time scales, and developed a neuromorphic system that can predict biomedical data, such as blood glucose levels, in real time. By applying a low voltage (below 1 V) to a two-terminal device, the Pd electrode hydrogenates or dehydrogenates the mixed conductor WO3, resulting in significant changes in electronic resistance. The device is scalable due to the uniform proton distribution, leading to high impedance and minimal power consumption. Utilizing these volatile protons for short-term memory in an Echo State Network (ESN), this approach demonstrates low-power, efficient real-time information processing, paving the way for future neuromorphic computing applications.

The investigation of traffic flow prediction and optimization based on Spark

Spark and flink have made great strides in big data processing in recent years. Although the current spark can process a large amount of data at the same time, it still has a lot of shortcomings in the transparency, credibility, and practicality of the model. This paper provides a comprehensive overview of how to tackle the performance bottlenecks, insufficient model interpretability, and lack of regional adaptability faced by Spark and Flink in big data processing. It discusses the introduction of interpretable algorithms such as SHAP and LIME to enhance the transparency and user trust of neural network models. Then it discusses how to combine time-aware transfer learning and Geographic Information Systems (GIS) technology to enhance the technology generalization and adaptability of Spark machine learning models. Time-aware transfer learning uses historical datas temporal evolution to ensure models perform well in new time periods or scenarios, while GIS technology enables more precise predictions and analyses based on geographical data, enhancing spatial adaptability. Lastly, the study explores hybrid processing strategies by integrating Apache Flink, Kafka Streams, and Spark batch processing frameworks. This approach not only facilitates efficient real-time data processing and detailed analysis but also enhances the models flexibility and processing capabilities in complex data scenarios. By integrating these techniques, it is possible to improve the efficiency and effectiveness of big data processing frameworks in addressing complex real-world challenges, thereby advancing technology and application development in related fields.

COTTON-YOLO: Enhancing Cotton Boll Detection and Counting in Complex Environmental Conditions Using an Advanced YOLO Model

This study aims to enhance the detection accuracy and efficiency of cotton bolls in complex natural environments. Addressing the limitations of traditional methods, we developed an automated detection system based on computer vision, designed to optimize performance under variable lighting and weather conditions. We introduced COTTON-YOLO, an improved model based on YOLOv8n, incorporating specific algorithmic optimizations and data augmentation techniques. Key innovations include the C2F-CBAM module to boost feature recognition capabilities, the Gold-YOLO neck structure for enhanced information flow and feature integration, and the WIoU loss function to improve bounding box precision. These advancements significantly enhance the model’s environmental adaptability and detection precision. Comparative experiments with the baseline YOLOv8 model demonstrated substantial performance improvements with COTTON-YOLO, particularly a 10.3% increase in the AP50 metric, validating its superiority in accuracy. Additionally, COTTON-YOLO showed efficient real-time processing capabilities and a low false detection rate in field tests. The model’s performance in static and dynamic counting scenarios was assessed, showing high accuracy in static cotton boll counting and effective tracking of cotton bolls in video sequences using the ByteTrack algorithm, maintaining low false detections and ID switch rates even in complex backgrounds.

Analysis of Real-time Processing Approaches for Large Data Volumes in Metering Infrastructure

Smart grid systems and communication technologies, such as Advanced Metering Infrastructure (AMI), have revolutionized utility service management and monitoring. AMI leverages smart meters equipped with advanced communication capabilities, facilitating bidirectional communication between utilities and consumers. The increasing deployment of smart meters and the adoption of sub-hourly data collection requirements by utility companies highlight significant data volume growth. Thus, there is a need for efficient real-time data processing solutions as existing approaches may not meet previously established Service-Level Agreements (SLAs) concerning performance, accuracy, and scalability metrics. This research aims to comprehensively review the latest publications relevant to distributed real-time data processing methods for smart grid applications and outline problems for further research. Specifically, the study delves into the effectiveness and application of reviewed approaches in managing the constant stream of data from smart meters and IoT devices within the smart grid context. By analysing existing methodologies and advancements, this study seeks to identify challenges and opportunities in real-time data processing for smart grid infrastructures, focusing on addressing the complexities of processing, managing, and storing large volumes of real-time data. The literature review revealed two primary applications of real-time data processing: optimization of data streaming performance and data analysis. The review encompasses various studies, each presenting distinct methodologies and technologies applied to address the challenges of processing large volumes of real- time data from smart meters and IoT devices. Future research should address the challenges and limitations discovered in this study.

Optimization research of handwritten digit recognition algorithm based on hardware neural networks

In the modern era of digitization, the widespread application of handwritten numerical data has led to an exponential increase in data volume, necessitating efficient real-time recognition and processing systems. This project is grounded in the foundational structure of neural networks, with a specific focus on integrating these networks into hardware through Very-Large-Scale Integration (VLSI). The primary objective is to achieve faster processing speeds, lower power consumption, and real-time operations. The central emphasis of this study is the design and implementation of a hardware neural network system tailored specifically for handwritten digit recognition. Drawing from neural network theory, we explore the construction of a hardware-based handwritten digit classifier. The fundamental model employed is the single-layer perceptron neuron model. Upon receiving 28x28 pixel grayscale images, the image classifier utilizes pre-trained weights, activation functions, and a maximum selector to compare and output recognition results for numerical digits. The concrete implementation is realized using the Verilog hardware description language, coupled with algorithm optimization strategies to enhance performance and efficiency. This research endeavor aims to provide an effective hardware solution for real-time handwritten digit recognition in the digital age.

Exploring Real-Time Sentiment Analysis Prototype for Retail Industry

The rise of social media platforms has revolutionized the way consumers interact with retailers and express their opinions on products and services. Online retailers particularly need to keep a close eye on customer sentiment in real-time to make informed decisions about their offerings and improve customer satisfaction. However, efficiently analysing large volumes of unstructured text data from social media in real-time poses a significant challenge. This research aimed to develop a scalable, real-time sentiment analysis system tailored for online retailers using Reddit as the data source. The system comprises three main components: a data extraction and streaming pipeline, a sentiment analysis model, and a web application with real-time analytics. To address the data extraction challenge, a job queue-based system was implemented using Node.js, ‘BullMQ’, and Redis to create and manage campaigns for data streaming from Reddit. The data was streamed using Kafka, a distributed streaming platform, to enable efficient real-time processing. The sentiment analysis model was developed using a Naive Bayes classifier after experimenting with other machine learning and deep learning techniques. In the conducted study, the sentiment analysis model's performance was evaluated using standard metrics tailored to the context of online retail sentiment analysis. An accuracy of 0.6737 was achieved, reflecting the model's ability to correctly classify approximately 67.37 per cent of the sentiments in the test data. Concurrently, an F1 score of 0.7894 was recorded and the Area Under the Curve (AUC) value on the test data was measured at 0.5468, a metric that, while acceptable, suggests room for further refinement in the model's discriminatory ability between classes. The integration of the Data Version Control (DVC) system provided a mechanism for fine-tuning the model according to specific data requirements of various tenants. These results, taken together, not only validate the feasibility of employing a Naive Bayes classifier for real-time sentiment analysis in the retail context, but also provide a baseline for future research aimed at enhancing both the accuracy and efficiency of sentiment classification. The project’s evaluation focused on the performance of the sentiment analysis model, the efficiency of the Kafka streaming and real-time Spark pre-processing pipeline, and the backend infrastructure, including the job queuing system and WebSocket implementation. Various evaluation techniques, such as graphs and literature comparisons, were used to assess the system’s performance. In conclusion, this project successfully demonstrated the feasibility of a scalable, real-time sentiment analysis system for online retailers using Reddit data. The system has the potential to help retailers better understand customer opinions and make data-driven decisions for their businesses. Future work could include exploring alternative data sources, experimenting with more advanced sentiment analysis techniques, and enhancing the web application’s user interface and analytics capabilities.

Tracking Small Wildlife With Minimal-Complexity Radio Frequency Transmitters: Near-Optimal Detection

In a range of IoT applications, edge nodes require minimal size, mass, and power/energy use. When node complexity is severely constrained, the burden of detection falls to the receiving system. In this paper we present a method of detection for RF signals motivated by wildlife radio telemetry, where miniature beacons emit VHF pulses that are used to detect and localize animals in the field. We address the challenges of limited transmit power and path losses due to terrain that lead to low received SNR. In addition, the pulse train structure requires joint frequency acquisition, time synchronization, and detection; our approach addresses the key challenge of large uncertainty in signal parameters. Our algorithm explicitly integrates frequency acquisition and time synchronization, using nearly phase-coherent matched filtering followed by non-coherent pulse combining and a multiple hypotheses test to provide near-optimal detection performance in low SNR scenarios. We provide quantitative results showing performance as a function of SNR and signal uncertainty. When compared to the single pulse detection case as used in existing systems, our multipulse combining approach pulses improves detection performance by 4 dB, even under the conditions of temporal and frequency/phase uncertainty resulting from beacon design and manufacturing. We also describe an efficient real-time signal processing implementation of the algorithm, including signal tracking that supports localization after initial signal detection.

Устройство детектирования ферромагнитных тел в подающем тракте кормоуборочных комбайнов на базе датчика Холла

The operation principle of metal detectors used in forage harvesting equipment operating in the harvested area is based on the registration and processing of a signal from sensors that respond to changes in the static magnetic field caused by the movement of ferromagnetic bodies in the area of the magnetic field. The systems based on measuring coils do not meet modern requirements for the level of sensitivity. The authors consider a possibility of using magnetic field sensors based on the Hall effect as sensitive elements. Taking into account the geometric features of the system, each sensing element must be positioned in such a way that the magnetic field generated by two adjacent magnets is perpendicular to the plane of the sensing element. Under this design, the sensitive elements should be located in planes oriented at different angles relative to the direction of the crop supply. When a ferromagnetic object passes through the detection field, it changes the magnetic field density on one or more sensing elements, and the Hall effect sensor outputs a voltage directly proportional to the magnitude of the magnetic field. This signal is processed, after which another signal is generated to stop the feed rolls. Efficient real-time signal processing has a positive effect on the speed of response to a foreign body in the machine feeder.

Low-Cost Self-Calibration Data Glove Based on Space-Division Multiplexed Flexible Optical Fiber Sensor.

Wearable devices such as data gloves have experienced tremendous growth over the past two decades. It is vital to develop flexible sensors with fast response, high sensitivity and high stability for intelligent data gloves. Therefore, a tractable low-cost flexible data glove with self-calibration function based on a space-division multiplexed flexible optical fiber sensor is proposed. A simple, stable and economical method was used to fabricate flexible silicone rubber fiber for a stretchable double-layered coaxial cylinder. The test results show that the fiber is not sensitive to the temperature range of (20~50 °C) and exhibits excellent flexibility and high stability under tensile, bending and torsional deformation. In addition, the signal detection part of the data glove enables compact and efficient real-time information acquisition and processing. Combined with a self-calibration function that can improve the accuracy of data acquisition, the data glove can be self-adaptive according to different hand sizes and bending habits. In a gesture capture test, it can accurately recognize and capture each gesture, and guide the manipulator to make the same action. The low-cost, fast-responding and structurally robust data glove has potential applications in areas such as sign language recognition, telemedicine and human–robot interaction.

Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data.

.Significance: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technique for measuring hemodynamic changes in the human cortex related to neural function. Due to its potential for miniaturization and relatively low cost, fNIRS has been proposed for applications, such as brain–computer interfaces (BCIs). The relatively large magnitude of the signals produced by the extracerebral physiology compared with the ones produced by evoked neural activity makes real-time fNIRS signal interpretation challenging. Regression techniques incorporating physiologically relevant auxiliary signals such as short separation channels are typically used to separate the cerebral hemodynamic response from the confounding components in the signal. However, the coupling of the extra-cerebral signals is often noninstantaneous, and it is necessary to find the proper delay to optimize nuisance removal. Aim: We propose an implementation of the Kalman filter with time-embedded canonical correlation analysis for the real-time regression of fNIRS signals with multivariate nuisance regressors that take multiple delays into consideration.Approach: We tested our proposed method on a previously acquired finger tapping dataset with the purpose of classifying the neural responses as left or right.Results: We demonstrate computationally efficient real-time processing of 24-channel fNIRS data (400 samples per second per channel) with a two order of selective magnitude decrease in cardiac signal power and up to sixfold increase in the contrast-to-noise ratio compared with the nonregressed signals.Conclusion: The method provides a way to obtain better distinction of brain from non-brain signals in real time for BCI application with fNIRS.

Intelligent Technologies and Robotic Machines for Garden Crops Cultivation

The existing models of industrial robots cannot perform technological processes of apple harvesting. It is noted that there is a need for developing special actuators, grippers and new control algorithms for harvesting horticulture products. (Research purpose) The research aimed to develop an intelligent control system for horticulture industrial technologies and robotic techniques for yield monitoring and fruit harvesting. (Materials and methods) The research methodology was based on such modern methods as computer modeling and programming. In particular, the following methods were applied: systems analysis, artificial neural networks theory, pattern recognition, digital signal processing. The development of software, hardware and software was carried out in accordance with the requirements of GOST technical standards. The following programming languages were used: (C / C ++)-based OpenCV library, Spyder Python Development Environment, PyTorch and Flask frameworks, and JavaScript. Image marking for training neural networks was carried out via VGG ImageAnnotator and in Labelbox. The design process was based on the finite element method, CAD SolidWorks software environment. (Results and discussion) An intelligent management system for horticulture industrial technologies has been created based the on the «Agrointellect VIM» hardware and software complex. The concept of the system is shown to be implemented via computer and communication technology, robotic machines, the software for collecting, organizing, analyzing and storing data. The gripper proves to fix an apple gently and holds it securely. Depending on the size, the fruit fixation time is 1.5-2.0 seconds, the fruit maximum size is 85 per 80 millimeters , and its maximum weight is 500 grams. (Conclusions) The developed intelligent control system for industrial technologies based on «Agrointellect VIM» hardware and software complex ensures the efficient real-time processing of information necessary for the design of intelligent agricultural technologies using robotic machines and artificial intelligence systems.

Architecture and Architectural Patterns for Mobile Augmented Reality

A software architecture codifies the design choices of software developers, which defines a modularorganizational spine for the design of a software artefact. Different architectures may bespecified for different types of artefacts, a real-time interactive artefact, for example, wouldhave markedly different requirements to those of a batch based transactional system. The use ofsoftware architecture becomes increasingly important as the complexity ofartefacts increases. Augmented Reality blends the real world observed through a computerinterface, with a computer generated virtual world. With the advent ofpowerful mobile devices, Mobile Augmented Reality (MAR)applications have become increasingly feasible, however the increased power hasled to increased complexity. Most MAR research has been directed towardstechnologies and not design resulting in a dearth of architecture and design literature for MAR. This research is targeted at addressing this void. The main requirement that a MAR architecture must meet isidentified as being the efficient real-time processing of data streams such asvideo frames and sensor data. A set of highly parallelised architecturalpatterns are documented within the context of MAR that meet thisrequirement. The contribution of this research is a software architecture, codifiedas architectural patterns, for MAR.

Distributed-Reasoning for Task Scheduling through Distributed Internet of Things Controller

Abstract The adoption of distributed reasoning through ubiquitous instrumentation in the distributed Internet of Things (IoT) leads to outstanding improvements in real-time monitoring, optimization, fault tolerance, traffic, healthcare, etc. Using a ubiquitous controller to interconnect devices in the IoT is still in the embryonic stage. However, it has the potential to create distributed-intelligent IoT solutions that are more efficient and secure than centric intelligence. It is essential to take a new direction to design a distributed intelligent controller for task scheduling that can firstly dynamically interact with a smart environment in efficient real-time data processing and secondly respond to flexible changes. To address these issues, we outline a two-level intelligence scheme that leverages edge computing to improve distributed IoT. The edge scheme shifts the capability of streaming processing from the cloud to edge devices to alleviate latency, support better reliable streaming analytics, and improve smart IoT applications’ performance. In this work, to enable better, reliable, and flexible streaming analytics and overcome the data uncertainties, we proposed an IoT gateway controller that provides low-level intelligence by using a fuzzy abductive reasoner. Numerical simulations support the feasibility of our proposed approaches.

A Room Compensation Method by Modification of Reverberant Audio Objects

Conventional channel-based room equalisation can reduce overall colouration caused by the room response, however it cannot separately correct the colouration caused by the late and early parts of the response, or consider the reverberance in the source signal. A room compensation method is developed here for a source signal in which the dry source sound and the associated target reverberant response are encoded separately, which is possible in an object-based audio framework. The target response is modified using the reproduction room response. Subject to some conditions the combined response approximates the target, with accurate early and late equalisations, reverberant balance, and decay timing. Stochastic assumptions are used to simplify the processing, enabling efficient real-time processing of the encoded audio.

An Efficient Cryptosystem for Video Surveillance in the Internet of Things Environment

Surveillance systems paradigm envisions the pervasive interconnection and cooperation of interactive devices over the Internet infrastructure. Nevertheless, dissemination and processing of surveillance video amid the Internet of Things (IoT) applications become a susceptible issue due to the large volume and the significant information of these data. Moreover, surveillance devices on IoT have very limited resources such as memory and storage. The actual security methods are not quite appropriate for surveillance IoT systems. Thus, a particular cryptosystem technique is required for surveillance data security. In this paper, we propose an efficient cryptosystem to secure IoT‐based surveillance systems. The proposed cryptosystem framework contains three parts. First, a lightweight automatic summarization technique based on a fast histogram‐clustering approach is used to extract the keyframes from the surveillance video. Then, we employ a discrete cosine transform (DCT) technique to compress the extracted data size. Finally, the proposed framework performs an efficient image encryption algorithm by employing a discrete fractional random transform (DFRT). The testing results and analysis confirm the features of the proposed cryptosystem on surveillance systems. The proposed framework is fast and ensures secure and efficient real‐time processing by minimizing the transmission cost and storage.

An Analog–Digital Hardware for L-Estimate Space-Varying Image Filtering

An analog---digital hardware solution for implementation of the L-estimate space-varying filtering has been proposed. The considered filter form is based on the robust space/spatial-frequency representation and provides efficient denoising of two-dimensional signals/images corrupted by heavy-tailed noise. Moreover, for images with fast-varying details and textures, the L-estimate filtering outperforms the commonly used filters. However, it requires significant processing time, since the space/spatial-frequency representation is calculated for each pixel, on a window by window basis. Therefore, in order to make it feasible for practical applications, a fast implementation of L-estimate space-varying filtering is proposed using a combined analog---digital approach. It provides efficient real-time processing of images corrupted by strong mixed Gaussian and impulse noise.

Stream Processing of Healthcare Sensor Data: Studying User Traces to Identify Challenges from a Big Data Perspective

The Internet of Things (IoT) generates massive streams of data which call for ever more efficient real time processing. Designing and implementing a big data service for the real time processing of such data requires an extensive knowledge of both input load and data distribution in order to provide a service which can cope with the workload. In this context, we study in this paper the challenges inherent to the real time processing of massive data flows from the IoT. We provide a detailed analysis of traces gathered from a well-known healthcare sport-oriented application in order to illustrate our conclusions from a big data perspective.

Development of Embedded Intelligent Line-Following Robot Based on Image Processing

Aiming at dealing with the problems of traditional inspection robot, such as low accuracy of path identification, an embedded intelligent inspection robot based on image processing is developed. With uCOSs efficient real-time control ability and image processing technology, the robot can follow the line automatically. The experiment showed that the intelligent robot can identify the path accurately and adjust the direction and speed quickly