Real-time Operating System Research Articles

Bi-level energy management system for optimal real time operation of grid tied multi-nanogrids

• Proposing an efficient on-line EMS considering uncertainties of weather conditions, load demand and electricity price during real time. • Introducing an efficient day-ahead scheduling of interconnected nano-grids using an improved version of Aquila Optimization (IAO) algorithm. • Presenting demand side management to enhance the efficiency of the proposed EMS. • Exploiting the cooperation between the nano-grids for economic operation. Integrating distributed energy resources (DERs) utilizing renewable energy sources enhances the performance and efficiency of the power system. Nano-grid (NG) comprises a small scale DERs at a small building. This paper presents an efficient bi-level energy management system (EMS), day-ahead and real time scheduling, for economic operation of grid tied multi-interconnected NGs. Balancing between generation and consumption is secured through exchanging power with the utility grid so the daily energy consumption cost could be optimized. The proposed day-ahead scheduling is applied through two stages. The first is demand side management (DSM) while the second is determining the optimal set-points of different sources in NGs. The optimal set-points obtained from day-ahead scheduling are rescheduled and updated during real time to keep the economic operation under uncertainties of weather conditions, grid tariff and load demand. Advanced meta-heuristic techniques can handle with energy scheduling optimization problem, complex non-linear optimization problem, efficiently considering the different operation constraints. Comparative performance study of the original Aquila optimizer (AO), improved aquila optimizer (IAO), and particle swarm optimization technique (PSO) is investigated in this paper. Results obtained demonstrate that the interconnection between NGs with an IAO based bi-level EMS provides the optimal economic operation.

A New Virtual Tracking Sub-Algorithm Based Hybrid Active Control System for Narrowband Noise With Impulsive Interference

Mechanical noise is usually a mixture of narrowband and impulsive noise which needs complex active noise control (ANC) algorithms to improve the de-noising performance. But the ANC algorithm with a high computation load will reduce the real-time performance of an ANC system, thus decreasing the attenuation performance and even leading to divergence. To alleviate this contradiction in narrowband ANC systems, a new virtual filtered-x L0 norm discrete Fourier cancellation (FxL0DFC) based hybrid FxNLMS(filtered-x normalized least mean square)-FxDFC framework is proposed to decrease the total computing load and keep good attenuation performance. For a fast-changing noise, the FxNLMS algorithm is employed. The new virtual FxL0DFC algorithm serves to prepare parameters for steady-state, and when this happens, the FxDFC algorithm with the parameters provided by FxL0DFC is applied. Compared to using the FxNLMS algorithm to attenuate narrowband periodical noise, the FxDFC algorithm has nearly the same tracking performance while having a low computational load. As a result, the FxL0DFC-based FxNLMS-FxDFC algorithm leads to a reduction of the total computational load. Moreover, the proposed method performs excellently in terms of tracking in simulations and experiments on actual data, particularly in environments with rapid power changes and impulsive noise.

Blockchain Protocol-Based Predictive Secure Control for Networked Systems

Networked control systems (NCSs) are widely used in practical applications because of their flexibility in deployment. However, due to the dependence on the communication network, NCSs could be vulnerable to malicious cyberattacks. To address this problem, a novel blockchain technology-assisted networked predictive secure control approach is presented for the first time in this article. First, the introduction of blockchain technology brings a significant boost to the inherent resilience of the NCS in an active manner without relying on any prior knowledge of the system or potential attacks. However, blockchain technology would induce time delays unfavorable to the NCS, which could result in the low real-time performance of the control system. Subsequently, a networked Kalman filter-based predictive control is specially designed to compensate for the low real-time property of blockchain technology. A detailed analysis of the security and stability of the closed-loop NCS with the developed networked predictive secure controller is also presented, while sufficient conditions for the closed-loop system to be simultaneously stable and safe in a probabilistic sense are given. Finally, to verify the performance of the proposed approach in terms of practicality, an experimental prototype of a photovoltaic (PV)-based power generation system subjected to random cyber-attacks is built for voltage regulation.

A real-time phase processing system for phase sensitive optical time domain reflectometer.

In order to improve the real-time performance of a phase-sensitive optical-time-domain reflectometer (Φ-OTDR), a fully digital phase processing system is demonstrated. Using digital down-conversion and field programmable gate array (FPGA) hardware calculation, the phase of the Rayleigh scattering light can be demodulated in real-time. Benefiting from the pipeline calculation in the FPGA, the real-time performance of this system is not affected by the interrogation rate and fiber sensing distance. Experiments show that this real-time detection system has the same vibration detection performance as an offline data process and has good stability. The dynamic strain sensing with a sensing distance of 50 km, an interrogation rate of 1kHz, and a spatial sampling interval of 1m is experimentally demonstrated. The obtained results indicate that the fully digital signal processing system can ensure the real-time detection of Φ-OTDR under the condition of long distance and high resolution.

Integrating Reconfigurable Foot Design, Multi-modal Contact Sensing, and Terrain Classification for Bipedal Locomotion

The ability of bipedal robots to adapt to diverse and unstructured terrain conditions is crucial for their deployment in real-world environments. To this end, we present a novel, bio-inspired robot foot design with stabilizing tarsal segments and a multifarious sensor suite involving acoustic, capacitive, tactile, temperature, and acceleration sensors. A real-time signal processing and terrain classification system is developed and evaluated. The sensed terrain information is used to control actuated segments of the foot, leading to improved ground contact and stability. The proposed framework highlights the potential of the sensor-integrated adaptive foot for intelligent and adaptive locomotion.

STUDY OF REAL-TIME OPERATING SYSTEM INCLUDING PROGRAMMABLE LOGIC CONTROLLER FUNCTIONALITY

The article describes the developed real-time operating system “VladOS”, which is a set of modules and utilities from which the required system for controlling the eco-house should be assembled. Users can load their written applications into the file system in the form of binary files. System generation involves selecting an active configuration from a list of available user applications, setting values of system configurations, compiling system module parameters using this configuration, and linking system modules into an executable program. Additionally, the performance of the developed operating system is assessed through a comparison with the widely recognized Arduino core, with the “SmartHatka” treehouse serving as a representative example. Essentially, “SmartHatka” is a Ukrainian smart eco-house.

Spotted hyena optimizer with deep learning enabled vehicle counting and classification model for intelligent transportation systems

<abstract> <p>Traffic surveillance systems are utilized to collect and monitor the traffic condition data of the road networks. This data plays a crucial role in a variety of applications of the Intelligent Transportation Systems (ITSs). In traffic surveillance, it is challenging to achieve accurate vehicle detection and count the vehicles from traffic videos. The most notable difficulties include real-time system operations for precise classification, identification of the vehicles' location in traffic flows and functioning around total occlusions that hamper the vehicle tracking process. Conventional video-related vehicle detection techniques such as optical flow, background subtraction and frame difference have certain limitations in terms of efficiency or accuracy. Therefore, the current study proposes to design the spotted hyena optimizer with deep learning-enabled vehicle counting and classification (SHODL-VCC) model for the ITSs. The aim of the proposed SHODL-VCC technique lies in accurate counting and classification of the vehicles in traffic surveillance. To achieve this, the proposed SHODL-VCC technique follows a two-stage process that includes vehicle detection and vehicle classification. Primarily, the presented SHODL-VCC technique employs the RetinaNet object detector to identify the vehicles. Next, the detected vehicles are classified into different class labels using the deep wavelet auto-encoder model. To enhance the vehicle detection performance, the spotted hyena optimizer algorithm is exploited as a hyperparameter optimizer, which considerably enhances the vehicle detection rate. The proposed SHODL-VCC technique was experimentally validated using different databases. The comparative outcomes demonstrate the promising vehicle classification performance of the SHODL-VCC technique in comparison with recent deep learning approaches.</p> </abstract>

Towards Hard Real-Time and Energy-Efficient Virtualization for Many-Core Embedded Systems

In safety-critical computing systems, the I/O virtualization must simultaneously satisfy different requirements, including time-predictability, performance, and energy-efficiency. However, these requirements are challenging to achieve due to complex I/O access path and resource management at the system level, lack of support from preemptive scheduling at I/O hardware level, and missing an effective energy management method. In this paper, we propose a new framework, I/O-GUARD, which reconstructs the system architecture of I/O virtualization, bringing a dedicated hardware hypervisor to handle resource management throughout the system. The hypervisor improves system real-time performance by enabling preemptive scheduling in I/O virtualization with both analytical and experimental real-time guarantees. Furthermore, we also present a dedicated energy management unit to adjust <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I/O-GUARD</i> 's dynamic energy using frequency scaling. Associated with that, a frequency identification algorithm is proposed to find the appropriate executing frequency at run-time. As shown in experiments, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I/O-GUARD</i> simultaneously improves the predictability, performance and energy-efficiency compared to the state-of-the-art I/O virtualization.

Features and characters for distributed system

The introduction of microprocessors had created a number of product options that were just not there before. These clever processors have infiltrated and dispersed themselves across every aspect of our life, whether it be in the office (fax machines, pagers, laser printers, credit card readers), living rooms (televisions, air conditioners), or kitchens (food processors, microwave ovens). The usage of an operating system has many benefits as distributed applications get more complicated. The real-time demands of the majority of distributed systems need the usage of Real Time Operating Systems (RTOS) that can fulfill the needs of distributed systems. Real-time applications can be readily built and expanded thanks to RTOS. Through the division of the application code into different files, the use of RTOS streamlines the design process.

Exploring the Impact of Soft Errors on the Reliability of Real-Time Embedded Operating Systems

The continuous scaling of electronic components has led to the development of high-performance microprocessors that are suitable even for safety-critical applications where radiation-induced errors such as Single Event Effects (SEEs) can have a significant impact on the performance and reliability of the system. This work is dedicated to investigating the reliability of systems based on programmable hardware and Real-time operating Systems (RTOS) in the presence of architectural faults induced by soft errors in the configuration memory of the programmable hardware. We performed a proton radiation test campaigned at PSI radiation facility to identify the fault model affecting the configuration memory of Xilinx Zynq-7020 reconfigurable AP-Soc Device. The identified fault model in terms of SEU and MBU clusters has been used to evaluate the impact of proton-induced faults on applications running within FreeRTOS on a Microblaze soft processor. A Single Event Multiple Upset fault model resulting from a proton test is presented, focusing on characteristics such as shape, size, and frequency of observed cluster of errors. We conduct two fault injection campaigns and analyze the results to assess the effect of cluster size on system reliability. Moreover, we discuss software exceptions caused by faults that can affect the hardware structure of the soft processor.

Real-time performance improvement approach based on FPGA in OFDR system

This paper presents a novel approach to accelerate the data processing rate in optical frequency-domain reflectometry (OFDR) system. In conventional solution, data is sampled by data acquisition card and then sent to personal computer (PC) for data processing through high-speed interface such as peripheral component interconnect express (PCIE) and so on. But frequently-used general laptop can’t provide this interface. What’s more, long time is necessary to complete the calculation in PC due to the limitations of serial processing in central processing unit (CPU). To improve the real-time performance of OFDR system, in our work, field-programmable gate array (FPGA) is used as hardware circuit to complete the calculation task and then send the results to PC only to display through universal asynchronous receiver/transmitter (UART) interface in the OFDR system simultaneously. In this way, the refresh rate of output can be accelerated about 50 times, which means the real-time performance of the system is improved a lot.

Real-time and high-transmission middle-infrared optical imaging system based on a pixel-wise metasurface micro-polarization array

Real-time polarization medium-wave infrared (MIR) optical imaging systems enable the acquisition of infrared and polarization information for a target. At present, real-time polarization MIR devices face the following problems: poor real-time performance, low transmission and high requirements for fabrication and integration. Herein, we aim to improve the performance of real-time polarization imaging systems in the MIR waveband and solve the above-mentioned defects. Therefore, we propose a MIR polarization imaging system to achieve real-time polarization-modulated imaging with high transmission as well as improved performance based on a pixel-wise metasurface micro-polarization array (PMMPA). The PMMPA element comprises several linear polarization (LP) filters with different polarization angles. The optimization results demonstrate that the transmittance of the center field of view for the LP filters is up to 77% at a wavelength of 4.0 μm and an extinction ratio of 88 dB. In addition, a near-diffraction-limited real-time MIR imaging optical system is designed with a field of view of 5° and an F-number of 2. The simulation results show that an MIR polarization imaging system with excellent real-time performance and high transmission is achieved by using the optimized PMMPA element. Therefore, the method is compatible with the available optical system design technologies and provides a way to realize real-time polarization imaging in MIR wavebands.

Power sector decarbonization planning considering renewable resource variability and system operational constraints

Bottom-up long-term planning models generally consider low levels of spatial, temporal, and technical details to reduce associated computation. However, these details are required to capture spatial and temporal variability of Renewable Energy Sources (RESs) and perform harmonized dispatch of conventional generating units. In their absence, models may overestimate RESs uptake and underestimate the quantum of required flexible resources. This may create a false impression of sector decarbonization and the obtained capacity mix may result in frequent RES or load curtailment during real-time system operations. In this context, this study aims to improve upon the shortcomings of conventional planning approaches and develop a TIMES based planning model for the Indian power sector. The model adopts high spatial and temporal definitions of 5 regions and 288 sub-annual timeslices. RESs are categorized into sub-classes based on annual capacity factors to capture their intra-regional variations without further increasing the spatial resolution. Each month is represented by a day of chronological hourly resolution that facilitates the capture of RES temporal variations and incorporation of operational aspects of generating units such as minimum stable generation, ramp limits and partial load operation. The impact of these operational parameters on system planning is analysed to identify a trade-off between planning accuracy and computational complexity. The study is relevant for countries that are rigorously following their decarbonization plans with high penetration of variable RESs and have a similar trajectory as that of India. The results indicate that decarbonization levels can be better assessed if capacity mix and flexible resources are adequately valued by considering operational parameters at the planning stage for a power system dominated by low flexible generation.

Physics-Based LED Modeling and Nonlinear Distortion Mitigating With Real-Time Implementation

In this paper, a nonlinear model for Light Emitting Diodes (LEDs) inspired by semiconductor physics, and a corresponding post-compensator are implemented in a Field Programmable Gate Array (FPGA) for real-time Visible Light Communications (VLC). Our experiments demonstrate that the LED model effectively characterizes the dynamic LED nonlinearity, including the memory effects. The output signal of this nonlinear LED model shows a good resemblance with the measured LED output. In addition, a dedicated nonlinear equalizer, say, a post-compensator, inspired by this LED physical model can mitigate the nonlinear distortion substantially. Thereby it facilitates high data rate over the bandwidth-limited LED. It shows that the nonlinear compensator is attractive for practical real-time digital signal processing systems due to its high performance and low complexity.

Application of Fuzzy PID in Wheeled Robot

At this stage, wheeled robots are widely used in various fields. Usually, the gimbal is mounted above the wheeled robot as a platform for carrying other functional modules, and for the normal working environment of the module. We will use the traditional PID control algorithm to keep the gimbal relatively stable. However, in the face of complex terrain, the gimbal will be subject to huge disturbances from the chassis. The traditional PID algorithm has low robustness and cannot cope with this situation. In this paper, a fuzzy controller for the self-stabilization of the gimbal is added to the wheeled robot designed in the RoboMaster competition to maintain the stability of the gimbal under the condition of an uneven road to ensure that the gimbal has strong robustness. This robot uses STM32F427IH, the main control chip, and JetsonNanoA02, the host computer, with the FreeRTOS real-time operating system to complete the motion control of the robot.

Healthcare Monitoring System for Diabetic Patients Using Machine Learning

Abstract: Present-day technology provides quite an efficient way of monitoring an individual’s health. Bluetooth Low Energy (BLE)-based sensors can be considered as one of the solutions for checking personal vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG). In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device dataset, data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. The proposed real-time data processing system utilizes machine Learning algorithms to train the model. Machine learning–based classification methods were tested on a diabetes dataset in order to show that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. Furthermore, the proposed diabetes classification and prediction might be integrated with individualized diet and physical activity recommendations to improve patients' health quality and avert severe circumstances in the future.

Improving liver lesions classification on CT/MRI images based on Hounsfield Units attenuation and deep learning

The early sign detection of liver lesions plays an extremely important role in preventing, diagnosing, and treating liver diseases. In fact, radiologists mainly consider Hounsfield Units to locate liver lesions. However, most studies focus on the analysis of unenhanced computed tomography images without considering an attenuation difference between Hounsfield Units before and after contrast injection. Therefore, the purpose of this work is to develop an improved method for the automatic detection and classification of common liver lesions based on deep learning techniques and the variations of the Hounsfield Units density on computed tomography scans. We design and implement a multi-phase classification model developed on the Faster Region-based Convolutional Neural Networks (Faster R–CNN), Region-based Fully Convolutional Networks (R–FCN), and Single Shot Detector Networks (SSD) with the transfer learning approach. The model considers the variations of the Hounsfield Unit density on computed tomography scans in four phases before and after contrast injection (plain, arterial, venous, and delay). The experiments are conducted on three common types of liver lesions including liver cysts, hemangiomas, and hepatocellular carcinoma. Experimental results show that the proposed method accurately locates and classifies common liver lesions. The liver lesions detection with Hounsfield Units gives high accuracy of 100%. Meanwhile, the lesion classification achieves an accuracy of 95.1%. The promising results show the applicability of the proposed method for automatic liver lesions detection and classification. The proposed method improves the accuracy of liver lesions detection and classification compared with some preceding methods. It is useful for practical systems to assist doctors in the diagnosis of liver lesions. In our further research, an improvement can be made with big data analysis to build real-time processing systems and we expand this study to detect lesions from all parts of the human body, not just the liver.

NCOG-14. INTRAOPERATIVE COGNITIVE-LINGUISTIC MAPPING GUIDED BY VISUALIZATION OF GAMMA BAND MODULATION ELECTROCORTICOGRAMS: PROOF OF CONCEPT IN A PATIENT WITH LEFT TEMPORAL AND OCCIPITAL LOW-GRADE ASTROCYTOMA

Abstract OBJECTIVE Determine the feasibility and preliminary utility of a novel approach to intraoperative brain mapping guided by visualization of electrocorticography (ECoG) heat maps. METHODS A 39-year-old male with a biopsy-proven left posterior temporal and occipital WHO grade II IDH-mutant astrocytoma underwent awake craniotomy with intraoperative language mapping. Language mapping utilized a dual iPad stimulus presentation system (NeuroMapper) coupled to a portable real-time neural signal processing system capable of both recording cortical activity and delivering direct cortical stimulation in a closed-loop fashion. An ECoG grid (4x8 with 1cm pitch) which covered the majority of the left temporal lobe was used to assess oscillatory cortical activity during administration of language paradigms including object, action, auditory descriptive, and written descriptive naming. ECoG recording and cortical stimulation were synchronized with stimulus presentation via a photosensor attached to the patient-facing tablet. Gamma band modulations in response to language paradigms at each electrode were processed in real-time and visualized as heat maps in MATLAB/Simulink. Following recording and visualization, bipolar direct cortical stimulation from the grid was conducted for each neighboring electrode pair (up to an intensity of 6 mA) during administration of language tasks. RESULTS Despite mild fluent aphasia, a large set of reliable baseline stimuli were obtained for the language mapping paradigms. All naming paradigms resulted in strongest heat map activation at electrode 12 located in the anterior to mid superior temporal gyrus. During stimulation, consistent speech arrest was observed across all paradigms when stimulating electrode pair 11-12, indicating good correspondence with ECoG heat map recordings. Additionally, this region corresponded well with posterior language network representation via resting-state fMRI. CONCLUSION Intraoperative real-time visualization of task-based ECoG gamma band modulation is feasible and may help identify targets for direct cortical stimulation. If validated, this may improve the efficiency and accuracy of intraoperative language mapping.

A Low-Latency Optimization of a Rust-Based Secure Operating System for Embedded Devices.

Critical systems such as drone control or power grid control applications rely on embedded devices capable of a real-time response. While much research and advancements have been made to implement low-latency and real-time characteristics, the security aspect has been left aside. All current real-time operating systems available for industrial embedded devices are implemented in the C programming language, which makes them prone to memory safety issues. As a response to this, Tock, an innovative secure operating system for embedded devices written completely in Rust, has recently appeared. The only downside of Tock is that it lacks the low-latency real-time component. Therefore, the purpose of this research is to leverage the extended Berkeley Packet Filter technology used for efficient network traffic processing and to add the low-latency capability to Tock. The result is a secure low-latency operating system for embedded devices and microcontrollers capable of handling interrupts at latencies as low as 60 µs.

Formal Verification of Resource Synchronization Protocol Implementations: A Case Study in RTEMS

To avoid race conditions and ensure data integrity, resource synchronization protocols have been widely studied in real-time systems for decades, providing systematical policies to guarantee a bound on priority inversion-induced blocking time and the avoidance of deadlocks. However, the corresponding realization is often based on assumed abstractions and necessary adaptions in a real-time operating system, by which the theoretically proven properties of such a protocol may not be delivered, leading to potential mismatches. To prevent such mismatches, in this work, we propose to contract the obligations of involved primitives and operations, and apply the deductive verification on a corresponding implementation. To this end, we present a modularized verification framework and demonstrate its applicability by verifying the official implementation of the immediate ceiling priority protocol (ICPP) and the multiprocessor resource sharing protocol (MrsP) in RTEMS, resulting in the discovery of long-stayed mismatches for both synchronization protocols. To resolve them, we provide a possible remedy for the ICPP and an additional precondition regarding nested locking for the MrsP.