Real-time Processing Algorithms Research Articles

Artificial intelligence in the Internet of Things: Integrating and optimizing AI algorithms for real-time data processing and decision-making

Abstract. The introduction of Artificial Intelligence (AI) will provide great opportunities and pose significant challenges in Internet of Things (IoT) systems. IoT devices can create huge amount of data in real-time. Meanwhile, it is crucial to handle immediate processing and decision-making in IoT applications. However, IoT devices are usually constrained by limited computation power, memory, and energy compared to traditional devices, making the deployment of efficient AI algorithms a challenging task. In this paper, we present different algorithmic strategies to overcome the above problems, including model compression, quantization, and hardware accelerators. On the other hand, we also focus on the role of decentralization and edge computing architectures to increase the scalability and performance of AI-IoT systems. Finally, this paper also reviews energy-efficient AI algorithms and latency reduction methods to make the decision process real-time for various IoT applications.

Design studies on electronics and data acquisition of a real time diamond spectrometer for the SPARC neutron camera.

The design of a compact 2 × 2 diamond matrix with independent and redundant pixels optimized for the spectrometric neutron camera of the SPARC tokamak is presented in this article. Such a matrix overcomes the constraints in dynamic range posed by the size of a single diamond sensor while keeping the ability to perform energy spectral analysis, marking a significant advancement in tokamak neutron diagnostics. A charge pre-amplifier based on radio frequency amplifiers based on InGaP technology transistors, offering up to 2GHz bandwidth with high robustness against radiation, has been developed. A first single-channel device has been tested and proven to provide a fast signal development time of 20-25ns, necessary to mitigate pileup effects while offering precise energy measurements. As the diamond sensors may suffer from polarization effects due to the trapping of charges at the diamond/metal interface, a periodical bias inversion can guarantee optimal performance. To facilitate that, a reversible high voltage power supply has been developed. The ongoing development of data acquisition equipment and real-time processing algorithms based on programmable gate arrays further enhances the neutron camera's capabilities.

AIoT-Based Visual Anomaly Detection in Photovoltaic Sequence Data via Sequence Learning

Anomaly detection is a common analytical task aimed at identifying rare cases that differ from the majority of typical cases in a dataset. In the management of photovoltaic (PV) power generation systems, it is essential for electric power companies to effectively detect anomalies in PV sequence data, as this helps operators and experts understand and interpret anomalies within PV arrays when making response decisions. However, traditional methods that rely on manual labor and regular data collection are difficult to monitor in real time, resulting in delays in fault detection and localization. Traditional machine learning algorithms are slow and cumbersome in processing data, which affects the operational safety of PV plants. In this paper, we propose a visual analytic approach for detecting and exploring anomalous sequences in a PV sequence dataset via sequence learning. We first compare the sequences with their reconstructions through an unsupervised anomaly detection algorithm (Long Short-Term Memory) based on AutoEncoders to identify anomalies. To further enhance the accuracy of anomaly detection, we integrate the artificial intelligence of things (AIoT) technology with a strict time synchronization data collection and real-time processing algorithm. This integration ensures that data from multiple sensors are synchronized and processed in real time. Then, we analyze the characteristics of the anomalies based on the visual comparison of different PV sequences and explore the potential correlation factors to analyze the possible causes of the anomalies. Case studies based on authentic enterprise datasets demonstrate the effectiveness of our method in the anomaly detection and exploration of PV sequence data.

An improved whale algorithm for real-time boundary processing and its convergence analysis

Abstract To tackle the deficiencies in the whale algorithm, including its weak stability, sluggish convergence rate, and tendency to get trapped in local optima, an adapted whale optimization algorithm has been created, which introduces quasi-opposition learning and a real-time boundary processing mechanism. In the enhanced algorithm, a quasi-opposition learning mechanism is implemented to broaden the search scope and augment the variety of the population during the initial phases of iteration. This approach also helps to guarantee the convergence of the algorithm in the later phases of iteration. The unified boundary processing method of delay concentration is improved to solve the problem that a large amount of convergence damages diversity if a large number of transgressive individuals are unified and centralized. The algorithm flow is given and the asymptotic fitness and the asymptotic global optimal solution are proved. The time complexity is analyzed theoretically. Ultimately, six recognized algorithms were utilized to simulate the CEC 2017 function set across different scales. Findings indicate that the enhanced algorithm significantly boosts convergence rate, optimization precision, and solution robustness, demonstrating strong convergence characteristics.

Application of ultra-weak fiber Bragg grating sensing array to road vehicle detection

Road vehicle detection is a significant task for tackling traffic issues in intelligent transportation systems (ITS). In this paper, we introduce ultra-weak fiber Bragg grating (UWFBG) sensing technology for road vehicle flow, vehicle axle and wheelbase detection. We carry out pilot installation of UWFBG sensing optic cables in the asphalt base of the road in a new-built expressway. As opposed to the conventional linear installation along the road running direction, the deployment of cables is mainly perpendicular to the road. Using the acquired vehicle data, we analyze signal characteristics and propose real-time data processing algorithms based on identification and classification of peaks/valleys of vehicle waveforms as well as cross-correlation calculation. We test the proposed method in different scenes. Test results show that the calculated wheelbases achieve good accuracy (relative errors are <1 %) and the algorithms can well distinguish individual vehicle in complex vehicle conditions.

Optimization Algorithms for Real-Time Image Processing in IoT Networks

This study explores the application of optimization algorithms, with a focus on Particle Swarm Optimization (PSO), to enhance real-time image processing in Internet of Things (IoT) networks. Given the constraints of computational power and energy resources in IoT devices, efficient processing of image data is a critical challenge. The proposed PSO-based framework aims to minimize processing time and energy consumption while maintaining high accuracy in image analysis tasks. Experimental results demonstrate that PSO can reduce processing time by 10% and energy consumption by 15% on average, with a slight improvement in accuracy. Comparisons with traditional and other evolutionary optimization techniques reveal that PSO provides a superior balance between efficiency and performance, making it particularly well-suited for real-time IoT applications. The findings suggest that the integration of advanced optimization algorithms like PSO into IoT systems can significantly enhance their operational efficiency, scalability, and effectiveness, particularly in resource-constrained environments.

A Method for Correction of Dynamic Errors When Measuring Flat Surfaces.

This paper presents a new method for correction of dynamic errors occurring when measuring flat surfaces in the presence of mechanical effects. Mechanical effects cause inertial forces and moments that affect the moving components of measuring instruments, thereby causing dynamic errors. The study proposes a mathematical model, on the basis of which algorithms for correction of dynamic errors can be developed. The basic concept of the model is based on determining the optimal estimate in the current coordinate point on the basis of the theoretical model of the measured surface and the information from the measurement that contains errors caused by internal and external factors. Based on this model, an algorithm for real-time data processing has been developed. The algorithm works in "predictor-corrector" mode at each step of which the best estimate is obtained. The estimate is based on minimizing the variance of a random component in which the main values are formed from the accumulated statistical data of the error of the model and the measurement error. This paper presents the results of experimental studies, carried out with simulations of mechanical effects in four modes. The results confirm the high efficiency of the algorithm for high-accuracy measurement of flat surfaces in the presence of mechanical effects.

Vehicle Accident Alerting System

This journal presents a detailed exploration of the design, development, and implementation of a vehicle accident alerting system aimed at significantly enhancing road safety and improving emergency response to accidents. The system employs a multi-faceted approach, integrating advanced sensor technology, real-time data processing algorithms, and efficient communication protocols to detect accidents promptly and notify emergency services and relevant stakeholders. The core components of the system include sensors strategically installed within vehicles to monitor motion, orientation, and environmental conditions. These sensors provide raw data, which is processed in real-time using sophisticated algorithms to detect patterns indicative of potential accidents. The system incorporates confirmation mechanisms to reduce false positives and accurately assess the severity of detected events. Key Words: Accident detection, alert system, GPS, GSM, Accelerometer, Android application.

Voice Recognition Based Vital Parameter Monitoring Patient BED

In healthcare protocols continuous monitoring of vital parameters plays a crucial role in patient care. Integrating advanced technologies for both monitoring and patient comfort enhances the efficiency of healthcare delivery. This paper presents the design and development of a novel patient bed system equipped with vital parameter monitoring capabilities and adjustable head position controlled through voice commands. The proposed system leverages state-of-the-art sensors to monitor vital parameters such as heart rate, blood pressure, oxygen saturation, and temperature. These sensors are seamlessly integrated into the bed, providing non-intrusive and continuous monitoring of the patient's health status. Real-time data acquisition and processing algorithms ensure accurate and timely assessment of the patient's condition. Furthermore, the patient bed is designed with an adjustable head position feature, allowing for optimal comfort and support. Unlike conventional manual or automated adjustment mechanisms, our system employs voice control technology for intuitive and hands-free operation. Patients can simply issue verbal commands to adjust the head position according to their preferences, enhancing overall comfort and facilitating better rest.

Interferometer-based chemical sensor on chip with enhanced responsivity and low-cost interrogation

We report experimental results of an interferometric chemical sensor integrated on a silicon chip. The sensor measures refractive index variations of the liquid that contacts exposed spiraled silicon waveguides on one branch of a Mach-Zehnder interferometer. The system requires neither laser tuning nor spectral analysis, but a laser at a fixed wavelength, and a demodulation architecture that includes an internal phase modulator and a real-time processing algorithm based on multitone mixing. Two devices are compared in terms of sensitivity and noise, one at 1550 nm wavelength and TE polarization, and an optimized device at 1310 nm and TM polarization, which shows 3 times higher sensitivity and a limit of detection of 2.24·10−7 RIU.

Creation of a High-Precision, Single-Board Computer-Based Intelligent Passenger Counting System

This paper introduces research aimed at developing and implementing an intelligent passenger counting system in public transportation. The goal is to enhance fare evasion control and reduce financial losses for transit operators by automatically monitoring the entering and exiting of passengers. Utilizing the advanced capabilities of YOLO and DeepSORT algorithms, the system demonstrates high accuracy in detecting and tracking passengers, even in congested scenarios. Practical experiments have highlighted several challenges, including real-time frame processing rates, single-board computer performance, and the accuracy of passenger counts. It was discovered that the selection and positioning of USB cameras significantly influence system efficiency. Notably, changing the IP cameras to USB cameras significantly improves the processing speeds. A dataset containing images was created to improve the YOLO model's accuracy in detecting passengers. This dataset was particularly relevant considering the winter season, where identifying passengers could be challenging due to their bulky outerwear. To upgrade the performance of the DeepSORT system, the YOLO model was also improved with the pre-designed dataset to focus solely on the heads. This trained model achieved a recognition accuracy of 91% during peak hours. During periods of lighter traffic, when there were fewer passengers, the accuracy increased further due to reduced interference and higher precision. To enhance real-time processing efficiency, this research incorporates the status of public transport doors as a key parameter. By optimizing the number of frames forwarded to the YOLO algorithm for processing, we ensure that the system maintains accuracy without compromising on speed. Future development will focus on incorporating depth-sensing cameras and developing advanced data analysis algorithms for real-time processing on single-board computers. This will further enhance the precision of passenger counts during high-traffic periods. Expected benefits of this project include more efficient transport route planning, better management of passenger flows, improved rider satisfaction, and more effective measures against fare evasion.

Towards adversarial robustness verification of no-reference image- and video-quality metrics

In this paper, we propose a new method of analysing the stability of modern deep image- and video-quality metrics to different adversarial attacks. The stability analysis of quality metrics is becoming important because nowadays the majority of metrics employ neural networks. Unlike traditional quality metrics based on nature scene statistics or other hand-crafter features, learning-based methods are more vulnerable to adversarial attacks. The usage of such unstable metrics in benchmarks may lead to being exploited by the developers of image and video processing algorithms to achieve higher positions in leaderboards. The majority of known adversarial attacks on images designed for computer vision tasks are not fast enough to be used within real-time video processing algorithms. We propose four fast attacks on metrics suitable for real-life scenarios. The proposed methods are based on creating perturbations that increase metrics scores and can be applied frame-by-frame to attack videos. We analyse the stability of seven widely used no-reference image- and video-quality metrics to proposed attacks. The results showed that only three metrics are stable against our real-life attacks. This research yields insights to further aid in designing stable neural-network-based no-reference quality metrics. Proposed attacks can serve as an additional verification of metrics’ reliability.

Evaluating the effects of audiovisual delays on speech understanding with hearables

A key consideration in the development of real-time speech processing algorithms is latency between when speech is first captured and when the processed speech is transmitted. Much of the previous work informing the acceptability of processing latency has focused on the thresholds at which delays are detectable or annoying. This study aimed to explore the effects of delays on responding to a task necessitating speech understanding. Sentence-length audiovisual speech stimuli were modified such that the audio stream was delayed relative to the visual stream as well as to an attenuated copy of the audio stream by 10 and 400 ms, respectively. These stimuli were presented in conjunction with a pictorial referent-choice task to 50 normal-hearing participants. Participants responded slower to the task when the audio stream was delayed by 400 ms relative to the video stream. With an additional attenuated copy of the audio stream synchronized with the video stream, reaction time decreased, but this effect was limited to when the target word appeared near the onset of the sentence, highlighting the complexity of delay perception in the context of continuous speech. Implications for determining permissible processing delays for digital electronic hearing protection devices are discussed.

Machine learning accurately predicts food exchange list and the exchangeable portion.

Food Exchange Lists (FELs) are a user-friendly tool developed to help individuals aid healthy eating habits and follow a specific diet plan. Given the rapidly increasing number of new products or access to new foods, one of the biggest challenges for FELs is being outdated. Supervised machine learning algorithms could be a tool that facilitates this process and allows for updated FELs-the present study aimed to generate an algorithm to predict food classification and calculate the equivalent portion. Data mining techniques were used to generate the algorithm, which consists of processing and analyzing the information to find patterns, trends, or repetitive rules that explain the behavior of the data in a food database after performing this task. It was decided to approach the problem from a vector formulation (through 9 nutrient dimensions) that led to proposals for classifiers such as Spherical K-Means (SKM), and by developing this idea, it was possible to smooth the limits of the classifier with the help of a Multilayer Perceptron (MLP) which were compared with two other algorithms of machine learning, these being Random Forest and XGBoost. The algorithm proposed in this study could classify and calculate the equivalent portion of a single or a list of foods. The algorithm allows the categorization of more than one thousand foods with a confidence level of 97% at the first three places. Also, the algorithm indicates which foods exceed the limits established in sodium, sugar, and/or fat content and show their equivalents. Accurate and robust FELs could improve implementation and adherence to the recommended diet. Compared with manual categorization and calculation, machine learning approaches have several advantages. Machine learning reduces the time needed for manual food categorization and equivalent portion calculation of many food products. Since it is possible to access food composition databases of various populations, our algorithm could be adapted and applied in other databases, offering an even greater diversity of regional products and foods. In conclusion, machine learning is a promising method for automation in generating FELs. This study provides evidence of a large-scale, accurate real-time processing algorithm that can be useful for designing meal plans tailored to the foods consumed by the population. Our model allowed us not only to distinguish and classify foods within a group or subgroup but also to perform the calculation of an equivalent food. As a neural network, this model could be trained with other food bases and thus improve its predictive capacity. Although the performance of the SKM model was lower compared to other types of classifiers, our model allows selecting an equivalent food not from a group previously classified by machine learning but with a fully interpretable algorithm such as cosine similarity for comparing food.

Submerged single-photon LiDAR imaging sensor used for real-time 3D scene reconstruction in scattering underwater environments.

We demonstrate a fully submerged underwater LiDAR transceiver system based on single-photon detection technologies. The LiDAR imaging system used a silicon single-photon avalanche diode (SPAD) detector array fabricated in complementary metal-oxide semiconductor (CMOS) technology to measure photon time-of-flight using picosecond resolution time-correlated single-photon counting. The SPAD detector array was directly interfaced to a Graphics Processing Unit (GPU) for real-time image reconstruction capability. Experiments were performed with the transceiver system and target objects immersed in a water tank at a depth of 1.8 meters, with the targets placed at a stand-off distance of approximately 3 meters. The transceiver used a picosecond pulsed laser source with a central wavelength of 532 nm, operating at a repetition rate of 20 MHz and average optical power of up to 52 mW, dependent on scattering conditions. Three-dimensional imaging was demonstrated by implementing a joint surface detection and distance estimation algorithm for real-time processing and visualization, which achieved images of stationary targets with up to 7.5 attenuation lengths between the transceiver and the target. The average processing time per frame was approximately 33 ms, allowing real-time three-dimensional video demonstrations of moving targets at ten frames per second at up to 5.5 attenuation lengths between transceiver and target.

Research on Real-time and High-precision Positioning Method of Ground Target through UAV Stereo Vision and Spatial Information Fusion

Background: Positioning accuracy is the most important index of the reconnaissance positioning system. Positioning accuracy involves many factors, such as the position, attitude and motion state of the flight platform, the pointing accuracy of the stable platform, and various coordinate transformations. A reasonable fusion strategy can guarantee stable positioning accuracy, so it is very important to study the precise fusion of machine vision, 3D geographic data and UAV airborne positioning information to realize the optimal combination of positioning data and complete the accurate and rapid positioning of the ground target. Methods: In this paper, a location model based on stereo vision and spatial information fusion method is proposed. It fully integrates visual information, satellite positioning information and spatial geographic information, greatly improves positioning accuracy, and through a real-time processing algorithm, significantly improves real-time positioning. Results: Through the related experiments, positioning accuracy and real-time ability of positioning could reach about three meters. Conclusion: The proposed real-time and high-precision positioning method of the ground target through UAV stereo vision and spatial information fusion is proposed showing significant improvement compared to other traditional methods.

Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities.

Scientific user facilities present a unique set of challenges for image processing due to the large volume of data generated from experiments and simulations. Furthermore, developing and implementing algorithms for real-time processing and analysis while correcting for any artifacts or distortions in images remains a complex task, given the computational requirements of the processing algorithms. In a collaborative effort across multiple Department of Energy national laboratories, the "MLExchange" project is focused on addressing these challenges. MLExchange is a Machine Learning framework deploying interactive web interfaces to enhance and accelerate data analysis. The platform allows users to easily upload, visualize, label, and train networks. The resulting models can be deployed on real data while both results and models could be shared with the scientists. The MLExchange web-based application for image segmentation allows for training, testing, and evaluating multiple machine learning models on hand-labeled tomography data. This environment provides users with an intuitive interface for segmenting images using a variety of machine learning algorithms and deep-learning neural networks. Additionally, these tools have the potential to overcome limitations in traditional image segmentation techniques, particularly for complex and low-contrast images.

Real-time Data Processing with Machine Learning Algorithms

In the era of information abundance, organizations are faced with the challenge of harnessing real-time data streams to extract valuable insights swiftly. This research paper explores the intersection of real-time data processing and machine learning algorithms, aiming to develop a comprehensive understanding of their integration for efficient decision-making in dynamic environments. The paper begins by delineating the landscape of real-time data processing, emphasizing the significance of timely and accurate information in contemporary business scenarios. It delves into the challenges posed by the velocity and volume of data generated continuously, necessitating advanced processing mechanisms capable of handling data streams in real-time. As the focus shifts to machine learning algorithms, the research outlines the diverse range of algorithms suitable for real-time applications. From online learning methods to streaming algorithms, the exploration encompasses techniques tailored to adapt and evolve with incoming data. This section also addresses the trade-offs between accuracy and computational efficiency, crucial considerations in real-time processing environments. The core of the paper lies in the synthesis of real-time data processing and machine learning algorithms. It investigates how machine learning models can be seamlessly integrated into data processing pipelines to analyze and respond to streaming data instantaneously. Case studies and practical implementations exemplify instances where predictive analytics and anomaly detection algorithms contribute to real-time decision support. Ethical considerations and challenges related to the deployment of machine learning in real-time settings are also examined. The paper advocates for responsible and transparent use of algorithms, emphasizing the importance of mitigating biases and ensuring accountability in decision-making processes driven by machine learning insights. this research paper provides a roadmap for organizations seeking to harness the synergy between real-time data processing and machine learning. The insights gained from this exploration pave the way for advancements in adaptive decision-making systems, offering a competitive edge in industries where rapid response to evolving data is paramount.

Hybrid Brain–Computer Interface System Using Auditory Stimulation and Speech Imagination Paradigms for Assistive Technology

In the case of paralysis with visual and tactile impairments, brain–computer interfaces (BCIs) based on auditory and mental imagery paradigms are alternative methods for controlling external devices. This study demonstrates the use of a hybrid BCI via auditory stimulation and speech imagination for assistive technology. The proposed auditory BCI using Thai vowel and numeral stimulus patterns as well as multi-loudspeaker position settings for multi-command BCI are investigated. To avoid auditory stimulation during resting periods, a speech imagery method is used to enable an audio stimulator. We observe the classification efficiency of speech imagery and auditory BCIs from selected electroencephalogram channels using the proposed algorithms. We examine the efficiency of using the proposed BCI in the presence of background noise (speech). One command is created using the proposed speech-imagination paradigm. Four commands are created using the proposed auditory stimulation paradigm. We design an experiment to verify the proposed BCI paradigm and classification algorithms for real-time processing. The results show that the average classification accuracy of the proposed auditory BCI using numeral stimuli and scatter patterns without speech noise is 72.2% to 83.3%, respectively. The efficiency under background noise is approximately two times lower than that without background noise.

Continuous online flaws detection with photodiode signal and melt pool temperature based on deep learning in laser powder bed fusion

The continuous online flaws detection of the melt pool plays a significant role in assessing the quality of the printed sample in metal additive manufacturing (AM). However, the continuous detection of melt pool flaws faces great challenges because the suitable continuous acquisition system and real-time processing algorithm for melt pool feature extraction are difficult to establish. In this work, a continuous online flaws detection method combining the photodiode signal and melt pool temperature based on deep learning algorithms in the Laser Powder Bed Fusion (LPBF) is proposed. A multi-signal fusion system is designed to synchronously obtain the photodiode signal and temperature signal of the melt pool. The characteristics of the melt pool photodiode signal under the action of pulsed laser and the characteristics of the melt pool temperature are explored. The aliasing of photodiode signals is found due to the different settings of pulse laser frequency and photodiode sensor acquisition rate in AM. According to the two principles that the melt pool temperature is related to the flaw formation, and accurate flaws identification can be carried out based on the melt pool temperature field, the Back Propagation Neural Network (BPNN), Stacked Sparse AutoEncoder (SSAE), and Long Short-Term Memory (LSTM) are used to build the correlation model between the photodiode signal and the average melt pool temperature, and the flaw detection is carried out through the average melt pool temperature error. Therefore, once the correlation model is established, continuous online flaws detection can be realized only by using photodiode signals. The results demonstrated that a robust correlation can be established between the photodiode signal and the average melt pool temperature through the neural network, and the correlation error can be as low as 2.2 %. The detection of flaws is simplified into a binary classification problem through a reasonable threshold setting, and the LSTM with 74.39 % detection accuracy is more suitable for flaws detection based on the photodiode signal of the melt pool.