Real-time Operation Research Articles

Optimized Task Deployment in Dynamic Voltage and Frequency Scaling-Enabled Network-on-Chip Systems: Enhancing Energy Efficiency and Real-Time Responsiveness

In modern multi-design computing systems, which employ dynamic voltage and frequency scaling (DVFS) and network-on-chip (NoC) communications, the optimization of task deployment is precarious for enhancing overall system performance. It introduces a comprehensive methodology that integrates task allocation, scheduling, frequency management, redundancy handling, and diverse data routing approaches. The aim is to optimize energy intake, real-time responsiveness, and system heftiness. The system design features a primary processing element associated with three slave computing units (CUs) within a 2D mesh network, with the primary CU connected to a co-processor for dependent task scheduling. This research also proposes innovative algorithms for co-processor and real-time operating system (RTOS) scheduling to reduce latency and boost power efficiency. These methodologies aim to maximize job scheduling efficiency in RTOS environments, thus refining overall system performance.

Optimized Edge-Cloud System for Activity Monitoring Using Knowledge Distillation

Driven by the increasing care needs of residents in long-term care facilities, Ambient Assisted Living paradigms have become very popular, offering new solutions to alleviate this burden. This work proposes an efficient edge-cloud system for indoor activity monitoring in long-term care institutions. Action recognition from video streams is implemented via Deep Learning networks running at edge nodes. Edge Computing stands out for its power efficiency, reduction in data transmission bandwidth, and inherent protection of residents’ sensitive data. To implement Artificial Intelligence models on these resource-limited edge nodes, complex Deep Learning networks are first distilled. Knowledge distillation allows for more accurate and efficient neural networks, boosting recognition performance of the solution by up to 8% without impacting resource usage. Finally, the central server runs a Quality and Resource Management (QRM) tool that monitors hardware qualities and recognition performance. This QRM tool performs runtime resource load balancing among the local processing devices ensuring real-time operation and optimized energy consumption. Also, the QRM module conducts runtime reconfiguration switching the running neural network to optimize the use of resources at the node and to improve the overall recognition, especially for critical situations such as falls. As part of our contributions, we also release the manually curated Indoor Action Dataset.

Hardware simulation of real-time wavelength corrected phase projection

We demonstrate the real-time signal processing operation of a dispersion-free phase projection algorithm intended for atmospheric correction of multi-aperture optical phased arrays. It uses interferometric phase measurements at multiple sensing wavelengths, offset by 50 GHz, to compute a phase correction at a third, remote wavelength. This is useful where phase sensing cannot be implemented at the wavelength of interest, enabling interferometric level control from wavelength offset targeting beacons or guidestars. The digital signal processing implementation we demonstrate has a residual temporal phase error of 4×10−4rad/Hz while being capable of 100 MHz throughput with 0.53 µs latency, making it a viable approach for either feedback or feed-forward atmospheric correction in segmented piston-phase control systems.

Initial design concepts for solid boron injection in ITER

As part of ITER’s consideration to change its first wall material from beryllium to tungsten, the ITER Organization has proposed studying the feasibility of real-time solid boron injection (SBI) into the plasma to coat the walls and divertor to supplement glow discharge boronization (GDB) [1]. Boron deposits getter oxygen and reduce sputtering of tungsten from plasma facing components (PFCs). Particularly in areas with significant plasma wall interactions, boron coatings are expected to be short-lived under high performance plasma conditions. The proposed SBI system aims to maintain boron layers in these areas to avoid excessive radiation from tungsten in the plasma as a risk mitigation to ensure ITER will be able to reach and sustain Q = 10 conditions. The system will be used sparingly, as redeposition of boron can lead to significant tritium retention, which must be minimized in ITER to comply with nuclear safety concerns. SBI is proposed to limit and precisely control the amount of boron injected in real-time during plasma operation. Here, some of the design requirements and initial concepts for an SBI system in ITER are presented based on previous results carried out with SBI systems on a number of tokamaks and stellarators around the world [2–18].Previous results using SBI systems installed by PPPL have injected boron particles from 5 µm–2 mm diameter at calibrated rates of 2–200 mg/s in real-time during plasma operation on AUG [4], DIII-D [5], EAST [6], KSTAR [7], LHD [8], TFTR [9], WEST [10], and W7-X [11,12], leading to improved wall conditions with reduced plasma impurity concentrations and radiated power and improved plasma performance. The boron is ionized in the plasma edge and then deposited on plasma-wetted surfaces. On AUG [4], EAST [6] and WEST [10] reduced tungsten sputtering sources were observed following several discharges with SBI. Extrapolation of these SBI results are presented to estimate the amount of boron needed for wall conditioning in ITER. Real-time SBI control requirements and plasma operation scenarios for ITER are also described.

Real-time anomaly detection in electric motor operation noise

Anomaly detection plays a very important role in many fields to identify abnormalities occurring in the system earlier. This study proposes a new abnormality detection solution for 3-phase electric motors based on their working noise. Normal and abnormal operating noise data sets for an electric motor were acquired in the laboratory. These datasets are converted into the corresponding two-dimensional gray spectrogram image sets. The normal set is used to train the autoencoder (AE) model to find the abnormality evaluation threshold. This threshold is validated again with anomalous data sets. The trained AE is then quantized to be installed on a system consisting of two duo-core microcontroller units (MCUs) for real-time testing. Free real-time operating system (FreeRTOS), a real-time operating system, is used to schedule tasks on the system. Experimental results show that the designed anomaly detector can accurately detect over 99% of abnormal events. The system can communicate with a supervisory control and data acquisition (SCADA) application running on the S7-1200 programmable logic controller (PLC) platform using the Modbus transmission control protocol (TCP) protocol. The SCADA application can continuously record evaluated results from the system and adjust abnormal thresholds for the system directly on the human-machine interface (HMI) screen.

Fast measurement of group index variation with optimum precision using Hong–Ou–Mandel interferometry

Hong–Ou–Mandel (HOM) interferometry has emerged as a valuable means for quantum sensing applications, particularly in measuring physical parameters that influence the relative optical delay between photon pairs. Unlike classical techniques, HOM-based quantum sensors offer higher resolution due to the intrinsic dispersion cancellation property of correlated photon pairs. Due to the use of single photons, HOM-based quantum sensors typically involve a large integration time to acquire the signal and subsequent post-processing for high-resolution measurements, restricting their use for real-time operations. Based on our understanding of the relationship between measurement resolution and the gain medium length that produces photon pairs, we report here on the development of an HOM-based quantum sensor for high-precision group index measurements. Using a 1 mm long periodically poled KTP (PPKTP) crystal for photon-pair generation, we have measured the group index with a precision of ∼6.75×10−6 per centimeter of sample length at an integration time of 100 ms, surpassing the previous reports by 400%. Typically, the measurement range reduces with the increase in the resolution. However, using a novel scheme compensating photon delay due to group index changes stepwise with an optical delay stage, we have measured the group index variation of PPKTP crystal over a range of 3.5 × 10−3 for a temperature change from 25 to 200 °C, corresponding to an optical delay adjustment of ∼200 μm while maintaining the same precision (∼6.75×10−6 per centimeter of sample length). The current results establish the usefulness of HOM-interferometer-based quantum sensors for fast, precise, and long-range measurements in various applications, including quantum optical coherence tomography.

A novel linear programming-based predictive control method for building battery operations with reduced cost and enhanced computational efficiency

Battery energy storage systems can be readily integrated with buildings to enhance renewable energy self-consumptions while leveraging time-variant electricity tariffs for possible operation cost reductions. The extensive variability in building operating conditions presents significant challenges in developing universally applicable methods for optimal controls. To ensure reliable and robust controls, this study integrates predictive control with efficient linear programming to effectively fine-tune battery controls for real-time operations. An adaptive time aggregation scheme has been proposed to streamline the optimization process by accounting for unique changes in energy balances and tariffs. Comprehensive data experiments, based on measurements from 95 unique building operation scenarios, have been conducted to quantify the control performance given different optimization formulations, varying types and levels of prediction uncertainties in building energy demands and PV generations. The results validate the value of the method proposed, leading to 11.75% to 34.63% operation cost reductions on average, while reducing computation steps by 87.75% to 92.60% compared with conventional linear programming approaches. The insights obtained are useful for developing flexible building control strategies with improved computation efficiency and robustness, while providing extensible optimization frameworks for buildings with various energy patterns and storage systems.

Design and Implementation of a High-Performance VLSI Architecture for Canonical Huffman Encoding

A key component of contemporary computer systems, data compression makes it possible for information to be stored and transmitted efficiently. A popular technique for lossless data compression, Huffman coding can drastically cut down on data size without sacrificing intensity. But conventional Huffman encoding techniques can have scalability and speed issues, especially when used in hardware. A high-throughput Very Large-Scale Integration (VLSI) design for a Canonical Huffman Encoder is shown in this study. To accomplish quick and effective encoding, the suggested approach makes use of parallel processing and improved algorithms. The system's use of the canonical Huffman algorithm lowers computational complexity and streamlines hardware implementation without sacrificing compression performance. The architecture incorporates real-time operation-optimized modules for frequency analysis, code generation, and symbol encoding. Results from simulations show that the suggested design achieves significantly higher throughput compared to conventional approaches, making it suitable for applications in data storage, multimedia, and communication systems. This study contributes to advancing VLSI designs for high-performance hardware Key Words: Data Compression, Huffman Coding, Lossless Compression, Very Large-Scale Integration (VLSI), Canonical Huffman Encoder, High-Throughput Encoding, Parallel Processing, Efficient Encoding, Hardware Implementation, Computational Complexity, Code Generation, Symbol Encoding, Frequency Analysis, Real-Time Operation, Throughput Optimization, Multimedia Systems, Communication Systems, Data Storage, Hardware Architecture, VLSI Design, Performance Optimization, Simulations, Compression Performance, Hardware Design, Throughput Enhancement, System-Level Optimization, Power Efficiency, Resource Management, Data Transfer Efficiency, Low-Cost Implementation.

An Innovative Applied Control System of Helicopter Turboshaft Engines Based on Neuro-Fuzzy Networks

This study focuses on helicopter turboshaft engine innovative fault-tolerant fuzzy automatic control system development to enhance safety and efficiency in various flight modes. Unlike traditional systems, the proposed automatic control system incorporates a fuzzy regulator with an adaptive control mechanism, allowing for dynamic fuel flow and blade pitch angle adjustment based on changing conditions. The scientific novelty lies in the helicopter turboshaft engines distinguishing separate models and the fuel metering unit, significantly improving control accuracy and adaptability to current flight conditions. During experimental research on the TV3-117 engine installed on the Mi-8MTV helicopter, a parametric modeling system was developed to simulate engine operation in real time and interact with higher-level systems. Innovation is evident in the creation of the failure model that accounts for dynamic changes and probabilistic characteristics, enabling the prediction of failures and minimizing their impact on the system. The results demonstrate high effectiveness for the proposed model, achieving an accuracy of 99.455%, while minimizing the loss function, confirming its reliability for practical application in dynamic flight conditions.

Holistic Idle Periodic Evaluation method for mass balance monitoring at hydrogen refuelling stations

Hydrogen refuelling station (HRS) is an intricate system consisting typically of compression, storage, and dispensing subsystems. Thus, monitoring of HRSs has become a challenge in a real-time operation. Mass balance analysis of such a system consists of evaluating for a given timespan the amount of hydrogen received (produced or delivered on-site) and the amount of hydrogen dispensed to the vehicles. Previous work focused on off-line evaluation of hydrogen losses of HRSs with on-site hydrogen production. This article proposed a solution to holistically evaluate hydrogen mass balance and periodically evaluate hydrogen losses at HRSs in a continuous operation environment. We applied the Holistic Idle Periodic Evaluation method on real-time operational data of two HRSs with off-site hydrogen production and deployed the system for online monitoring of the hydrogen losses. The two stations were found to have hydrogen mass balance of 94.2% and 83.6% with an uncertainty of ±5.0%. Our method can track normal and anomalous system operations in real time. It is applicable for HRSs with both off-site and on-site hydrogen production and only requires one mass flow meter used for metering hydrogen being dispensed to vehicles.

Resource Efficiency as an environmental performance metric for industry: Exergetic analysis of a clinker manufacturing plant

Control-data exergy-based Resource Efficiency (RE) analysis is applied to investigate resource use and efficiency improvements in the cement industry to understand what insights can be gained into the efficiency of clinker manufacturing and possible decarbonisation options when having full access to material and energy flows from the manufacturing plant control system. Mapping of the resources in and out of the clinker manufacturing section showed that total resource input is dominated by the flow of fuels, with the clinker burning section constituting the most energy and emissions-intensive part of the process. The plant is found to have an annual mean RE of 47.8% in compound operation, and 38.3% in direct operation. Three improvement options are identified: recycling of dust, reduction of heat transfer losses across the kiln, and optimisation of the plant's continuous operation. Heat losses reduction was found to have the largest improvement potential in terms of RE and CO2 emissions reduction. Despite providing a detailed representation of the real-time operation capable of identifying potential improvement interventions, the use of control-data is found to be highly intensive and time-consuming. In the future, it would be interesting to investigate whether the same level of insights into the efficiency and potential for decarbonisation of clinker manufacturing could be gained using less data.

UAV Mission Computer Operation Mode Optimization Focusing on Computational Energy Efficiency and System Responsiveness

The rising popularity of UAVs and other autonomous control systems coupled with real-time operating systems has increased the complexity of developing systems with the proper robustness, performance, and reactivity. The growing demand for more sophisticated computational tasks, proportionally larger payloads, battery limitations, and smaller take-off mass requires higher energy efficiency for all avionics and mission computers. This paper aims to develop a technique for experimentally studying the indicators of reactivity and energy consumption in a computing platform for unmanned aerial vehicles (UAVs). The paper provides an experimental assessment of the ‘Boryviter 0.1’ computing platform, which is implemented on the ATSAMV71 microprocessor and operates under the open-source FreeRTOS operating system. The results are the basis for developing algorithms and energy-efficient design strategies for the mission computer to solve the optimization problem. This paper provides experimental results of measurements of the energy consumed by the microcontroller and estimates of the reduction in system energy consumption due to additional time costs for suspending and resuming the computer’s operation. The results show that the ‘Boryviter 0.1’ computing platform can be used as a UAV mission computer for typical flight control tasks requiring real-time computing under the influence of external factors. As a further work direction, we plan to investigate the proposed energy-saving algorithms within the planned NASA F’Prime software flight framework. Such an investigation, which should use the mission computer’s actual flight computation load, will help to qualify the obtained energy-saving methods and their implementation results.

Deep Reinforcement Learning for Multi-Objective Real-Time Pump Operation in Rainwater Pumping Stations

Rainwater pumping stations located near urban centers or agricultural areas help prevent flooding by activating an appropriate number of pumps with varying capacities based on real-time rainwater inflow. However, relying solely on rule-based pump operations that monitor only basin water levels is often insufficient for effective control. In addition to maintaining a low maximum water level to prevent flooding, pump operation at rainwater stations also requires minimizing the number of pump on/off switches. Reducing pump switch frequency lowers the likelihood of mechanical failure and thus decreases maintenance costs. This paper proposes a real-time pump operation method for rainwater pumping stations using Deep Reinforcement Learning (DRL) to meet these operational requirements simultaneously, based only on currently observable information such as rainfall, inflow, storage volume, basin water level, and outflow. Simulated rainfall data with various return periods and durations were generated using the Huff method to train the model. The Storm Water Management Model (SWMM), configured to simulate the Gasan rainwater pumping station located in Geumcheon-gu, Seoul, South Korea, was used to conduct experiments. The performance of the proposed DRL model was then compared with that of the rule-based pump operation currently used at the station.

Beyond Composting Basics: A Sustainable Development Goals—Oriented Strategic Guidance to IoT Integration for Composting in Modern Urban Ecosystems

IoT-based composting provides clear advantages over conventional urban composting in areas such as enhanced monitoring, efficiency, resource utilization, and management. Bibliometric analysis of 121 publications on IoT-based urban composting identified critical research gaps and emphasizes the necessity for a strategic framework for full implementation and execution of sustainable development goals-oriented IoT-based composting in modern cities across. Under the key theme of IoT-based urbanized composting automation, 16.5% of publications focus on urbanized composting automation but overlook the system’s scalability. The lowest mean citations of 72.7 (22.3% of publications) in intelligent composting process optimization show the lack of broader applications. A total of 28.9% of total publications focus on urban composting sustainability assessment but lack IoT integration in their scope. The composting process, pollution, environmental impact, cost, and life cycle analysis of modern city composting share 19% and 13.3%, respectively. However, both key themes lack real-time monitoring, operation, and economic feasibility for scalable models. The article highlights a fragmented landscape providing sustainable development goals-oriented strategic guidance for the full implementation and execution of IoT-based composting facilities in modern city ecosystems. The article comprehensively explains the budgetary constraints, scalability, data management, technological compatibility, privacy, security, and regulatory compliance essential for sustainable operation.

Implementation of ESP32-Based Web Host For Control and Monitoring of Robotic Arm

In the modern technological era, the Internet of Things (IoT) plays a significant role in developing robotic control systems, including robotic arms. This study explores the implementation of an ESP32-based webhost to control and monitor robotic arm operations in real time via a web interface connected to a Wi-Fi network. The system is designed to provide ease of access, reliable performance, and low latency, making it suitable for education, technical training, and other applications. Testing revealed that the system delivers fast response times, high stability under moderate loads, and an intuitive interface for users with varying skill levels. These findings present opportunities for further development, including integration with cloud-based technologies and data security enhancements

Enhanced YOLOv8 Ship Detection Empower Unmanned Surface Vehicles for Advanced Maritime Surveillance

The evolution of maritime surveillance is significantly marked by the incorporation of Artificial Intelligence and machine learning into Unmanned Surface Vehicles (USVs). This paper presents an AI approach for detecting and tracking unmanned surface vehicles, specifically leveraging an enhanced version of YOLOv8, fine-tuned for maritime surveillance needs. Deployed on the NVIDIA Jetson TX2 platform, the system features an innovative architecture and perception module optimized for real-time operations and energy efficiency. Demonstrating superior detection accuracy with a mean Average Precision (mAP) of 0.99 and achieving an operational speed of 17.99 FPS, all while maintaining energy consumption at just 5.61 joules. The remarkable balance between accuracy, processing speed, and energy efficiency underscores the potential of this system to significantly advance maritime safety, security, and environmental monitoring.

Toward real-time operations of modular-vehicle transit services: From rolling horizon control to learning-based approach

Recent technological advancements have opened doors for real-time adjustments and controls during public transport operations. In particular, the introduction of modular vehicles has the potential to significantly enhance public transit service quality. This innovative public transit service with modular vehicles, characterized by its flexible schedules and vehicle formations, allows for the dynamic management of transit capacity to meet the fluctuating passenger demands. This paper proposes to schedule the flexible modular-vehicle transit service in real time considering the varying demands. To jointly optimize the service schedule and vehicle formations, we propose the rolling horizon control approach to decompose the complex problem into subproblems that can be solved efficiently during the process. On top of this, we introduce a learning-based optimization proxy to streamline the optimization process within the rolling horizon framework, enabling near-optimal decisions to be made with minimal execution time without directly solving the optimization problem. Through numerical studies, we demonstrate the effectiveness and efficiency of the proposed methods in terms of solution quality and efficiency. Furthermore, our case studies show that modular vehicles can adapt to the changing demand and effectively reduce the total costs in the transit system.

Drone Swarm for Distributed Video Surveillance of Roads and Car Tracking

This study proposes a swarm-based Unmanned Aerial Vehicle (UAV) system designed for surveillance tasks, specifically for detecting and tracking ground vehicles. The proposal is to assess how a system consisting of multiple cooperating UAVs can enhance performance by utilizing fast detection algorithms. Within the study, the differences in one-stage and two-stage detection models have been considered, revealing that while two-stage models offer improved accuracy, their increased computation time renders them impractical for real-time applications. Consequently, faster one-stage models, such as the tested YOLOv8 architectures, appear to be a more viable option for real-time operations. Notably, the swarm-based approach enables these faster algorithms to achieve an accuracy level comparable to that of slower models. Overall, the experimentation analysis demonstrates how larger YOLO architectures exhibit longer processing times in exchange for superior tracking success rates. However, the inclusion of additional UAVs introduced in the system outweighed the choice of the tracking algorithm if the mission is correctly configured, thus demonstrating that the swarm-based approach facilitates the use of faster algorithms while maintaining performance levels comparable to slower alternatives. However, the perspectives provided by the included UAVs hold additional significance, as they are essential for achieving enhanced results.

Phase Retrieval of One-Dimensional Objects by the Multiple-Plane Gerchberg–Saxton Algorithm Implemented into a Digital Signal Processor

In the current study, we address the phase retrieval of one-dimensional phase objects from near-field diffraction patterns using the multiple-plane Gerchberg–Saxton algorithm, which is still widely used for phase retrieval. The algorithm was implemented in a low-cost digital signal processor capable of fast Fourier transform using Q15 arithmetic, which is used by the previously mentioned algorithm. We demonstrate similarity between one-dimensional phase objects, i.e., vectors cut out of a phase map of the tertiary spherical aberration retrieved by the multiple-plane Gerchberg–Saxton algorithm, and these vectors are measured with a non-contact profiler. The tertiary spherical aberration was induced by a phase plate fabricated using grayscale lithography. After subtracting the vectors retrieved by the algorithm from those measured with the profiler, the root mean square error decreased, while a corresponding increase in the Strehl ratio was observed. A single vector of size 64 pixels was retrieved in about 2 min. The results suggest that digital signal processors that are capable of one-dimensional FFT and fixed-point arithmetic in Q15 format can successfully retrieve the phase of one-dimensional objects, and they can be used for applications that do not require real-time operation, i.e., analyzing the quality of cylindrical micro-optics.

Practical magneto-optical imaging of the current density of coated conductors within liquid nitrogen.

Magneto-optical imaging (MOI) is widely used for magnetic studies of superconducting materials due to its advantages of full-field, real-time operation and high resolution. However, a traditional MOI system requires vacuum pumping, thermal shielding, and cooling by thermal conducting, thereby making the system very complex and expensive and increasing the time required to complete a set of experiments. In this study, a novel (to our knowledge) and practical approach for MOI within liquid nitrogen (LN) is proposed in which thermal conducting, thermal shielding, and vacuum pumping are no longer necessary. The key technique is realized through a semi-immersed polymethyl methacrylate (PMMA) bar in LN, and its size is optimized to ensure a stable temperature difference and polarized optical visualization within LN. With the improvised method, a defect in a superconducting layer of length approximately 250 µm in the coated conductor (CC) sample was detected. Additionally, the current density reduced by approximately 50% in magnitude compared to its neighbor region, thus demonstrating the effectiveness of the new approach. It is expected that this technique can further enhance the application of MOI as an efficient tool for industrial inspection of superconducting CCs.