Real-time System Applications Research Articles

Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint.

Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.

Aggregation induced emission enhancement behavior of conformationally rigid pyreneamide-based probe for ultra-trace detection of picric acid (PA)

In this study, we designed and prepared pyrene-based dipodal probes with a range of molecular flexibility for picric acid (PA) detection. These probes commonly contain a central rigid core, but probe 1 contains relatively short linkers/spacers compared to probes 2 and 3. The dipodal probes produced dominant pyrene excimer fluorescence in an aqueous solution, and aggregation-induced emission enhancement (AIEE) was observed. The fluorescence emission of probe 1 was significantly quenched only by the presence of PA, while probes 2 and 3 were responsive to 4-nitrophenol or 2,4-dinitrophenol as well as PA. Of the three dipodal probes, the highest binding strength was observed for probe 1 with PA, which gave an association constant of Ka = 3.96 × 107 M−1.1H NMR measurements and DFT calculations indicate that, upon addition of PA, these dipodal probes formed individual probe-PA complexes via π–π interactions between the pyrene ring and PA in an aqueous solution. This complex formation enabled energy transfer from electron-rich pyrene to electron-poor PA upon excitation, leading to fluorescence quenching. Probe 1 provided ultra-trace detection of PA in water, with observed and calculated limit of detections (LOD) of 1 nM and 0.13 pM, respectively, one of the lowest values reported. Effective fluorescence quenching was also obtained using probe 1-coated filter paper or TLC plate with PA solution/vapor, suggesting the significant potential of this sensing system in real-time applications. These studies were supported by FE-SEM, TEM, and UV-visible, fluorescence, NMR and DFT calculations.

Optimal harmonic period assignment: complexity results and approximation algorithms

Harmonic periods have wide applicability in industrial real-time systems. Rate monotonic (RM) is able to schedule task sets with harmonic periods up to 100% utilization. Also, if there is no release jitter and execution time variation, RM and EDF generate the same schedule for each instance of a task. As a result, all instances of a task are interfered by the same amount of workload. This property decreases the jitters that happen during sampling and actuation of the tasks, and hence, it increases the quality of service in control systems. In this paper, we consider the problem of optimal period assignment where the periods are constrained to be harmonic and the task set is required to be feasible. We study two variants of this problem. In the first one, the objective is to maximize the system utilization, while in the second one, the goal is to minimize the total weighted sum of the periods. First, we assume that an interval is determined a priori for each task from which its period can be selected. We show that both variants of the problem are (at least) weakly NP-hard. This is shown by reducing the NP-complete number partitioning problem to the mentioned harmonic period assignment problems. Afterwards, we consider a variant of the second problem in which the periods are not restricted to a special interval. We present two approximation algorithms with polynomial-time complexity for this problem and show that the maximum relative error of these algorithms is bounded by a factor of 1.125. Our evaluations show that, on the average, results of the approximation algorithms are very close to an optimal solution.

Real-time application of a fuzzy adaptive control to one level in a three-tank system

In this experimental work, a fuzzy combined control method has been described and applied to test the ability of the artificial intelligence technique in controlling nonlinear systems in real-time applications. A direct feedback linearization ideal control law is approximated with a Takagi–Sugeno fuzzy inference system. The adaptation law of the Takagi–Sugeno controller parameters is computed based on a fuzzy approximation term of the control error. This approximation is computed using a Mamdani fuzzy system. The experiment is applied on one level in a three-tank system and a test of the controller ability against perturbations is considered. The obtained results were compared to those leaded by a classical proportional–integral controller. The basic idea of this work is the use of the control error (between the actual control signal and the perfect control signal) instead of the tracking error (between the output of the one level in a three-tank system and the reference signal). As our approach is model free, that is, we do not use the mathematical model of the three-tank system, in the application part of this work, the output of the constructed Takagi–Sugeno fuzzy controller is injected to the real three-tank system.

Precision for Smallholder Farmers: A Small-Scale-Tailored Variable Rate Fertilizer Application Kit

Precision agriculture technology at the hands of smallholder farmers in the developing world is often deemed far-fetched. Low-resource farmers, however, are the most susceptible to negative changes in the environment. Providing these farmers with the right tools to mitigate adversity and to gain greater control of the production process could unlock their potential and support rural communities to meet the increasing global food demand. In this study, a real-time variable rate fertilizer application system was developed and tested as an add-on kit to conventional farm machinery. In the context of low investment costs for smallholder farmers, high user-friendliness and easy installment were the main concerns for the system to be viable. The system used nitrogen (N)-sensors to assess the plant nutrient status on the spot and subsequently adjust the amount of fertilizer deposited according to the plant’s needs. Test bench trials showed that the add-on kit performed well with basic operations, but more precision is required. Variability between N-sensors and metering systems, combined with power fluctuations, created inaccuracies in the resulting application rate. Nevertheless, this work is a stepping stone towards catalyzing the elaboration of more cutting-edge precision solutions to support small-scale farmers to become successful, high producing agro-entrepreneurs.

Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting

Real-time energy management systems that are designed to support consumer supply and demand spectrums of electrical energy continue to face challenges with respect to designing accurate and reliable real-time forecasts due to the stochasticity of model construction data and the model’s inability to disseminate both the short- and the long-term electrical energy demand (G) predictions. Using real G data from Queensland, Australia’s second largest state, and employing the support vector regression (SVR) model integrated with an improved version of empirical mode decomposition with adaptive noise (ICEEMDAN) tool, this study aims to propose a novel hybrid model: ICEEMDAN-PSO-SVR. Optimization of the model’s weights and biases was performed using the particle swarm optimization (PSO) algorithm. ICEEMDAN was applied to improve the hybrid model’s forecasting accuracy, addressing non-linear and non-stationary issues in time series inputs by decomposing statistically significant historical G data into intrinsic mode functions (IMF) and a residual component. The ICEEMDAN-PSO-SVR model was then individually constructed to forecast IMFs and the residual datasets and the final G forecasts were obtained by aggregating the IMF and residual forecasted series. The performance of the ICEEMDAN-PSO-SVR technique was compared with alternative approaches: ICEEMDAN-multivariate adaptive regression spline (MARS) and ICEEMDAN-M5 model tree, as well as traditional modelling approaches: PSO-SVR, MARS and M5 model tree algorithms. To develop the models, data were partitioned into different subsets: training (70%), validation (15%), and testing (15%), and the tuned forecasting models with near global optimum solutions were applied and evaluated at multiple horizons: short-term (i.e., weekends, working days, whole weeks, and public holidays), and long-term (monthly). Statistical metrics including the root-mean square error (RMSE), mean absolute error (MAE) and their relative to observed means (RRMSE and MAPE), Willmott’s Index (WI), the Legates and McCabe Index (ELM) and Nash–Sutcliffe coefficients (ENS), were used to assess model accuracy in the independent (testing) period. Empirical results showed that the ICEEMDAN-PSO-SVR model performed well for all forecasting horizons, outperforming the alternative comparison approaches: ICEEMDAN-MARS and ICEEMDAN-M5 model tree and the PSO-SVR, PSO-MARS and PSO-M5 model tree algorithm. Due to its high predictive utility, the two-phase ICEEMDAN-PSO-SVR hybrid model was particularly appropriate for whole week forecasts (ENS=0.95, MAPE=0.89%, RRMSE=1.22%, and ELM=0.79), and monthly forecasts (ENS=0.70, MAPE=2.18%, RRMSE=3.18%, and ELM=0.56). The excellent performance of the ICEEMDAN-PSO-SVR hybrid model indicates that the two-phase hybrid model should be explored for potential applications in real-time energy management systems.

An efficient motion magnification system for real-time applications

The human eye cannot see subtle motion signals that fall outside human visual limits, due to either limited resolution of intensity variations or lack of sensitivity to lower spatial and temporal frequencies. Yet, these invisible signals can be highly informative when amplified to be observable by a human operator or an automatic machine vision system. Many video magnification techniques have recently been proposed to magnify and reveal these signals in videos and image sequences. Limitations, including noise level, video quality and long execution time, are associated with the existing video magnification techniques. Therefore, there is value in developing a new magnification method where these issues are the main consideration. This study presents a new magnification method that outperforms other magnification techniques in terms of noise removal, video quality at large magnification factor and execution time. The proposed method is compared with four methods, including Eulerian video magnification, phase-based video magnification, Riesz pyramid for fast phase-based video magnification and enhanced Eulerian video magnification. The experimental results demonstrate the superior performance of the proposed magnification method regarding all video quality metrics used. Our method is also 60–70% faster than Eulerian video magnification, whereas other competing methods take longer to execute than Eulerian video magnification.

Extended Model Predictive Control Based on Multi-Structure RBF Networks: Design and Application to Clutch Control

For many control systems in engineering practice, the nonlinear, slow time varying and multistage dynamics make it difficult to realize precise control. Therefore, the extended model predictive control (EMPC) based on multi-structure radial basis function neural networks (MSRBFNN) is proposed in this paper. To begin with, the standard model predictive model (MPC) is established from system analysis or identification to formulate the problem with known disturbances. Secondly, the original MPC is extended with radial basis function neural networks (RBFNN) to deal with the nonlinearity and slow time varying dynamics of the system. Further, considering that a single RBFNN is too complicated to cope with the multistage dynamics of the system for real time application, the MSRBFNN, composed of a series of simpler RBFNNs, is designed to replace the original RBFNN. The running system can then switch between different RBFNNs at different working conditions. Finally, a successive iteration method is introduced to derive the controller with nonlinear compensations and the stability issue is also discussed. Application to the dry clutch control system shows that the proposed method has a much better performance than the standard MPC. Moreover, it can also be extended to other similar nonlinear control systems.

Low Complexity Parity Check Code for Futuristic Wireless Networks Applications

The Internet of Things refers to the aptitude of remotely connecting and monitoring anything, anytime, and anywhere via futuristic wireless networks. Due to the unreliable wireless links, broadcast nature of wireless transmissions, interference and noisy transmission channels, frequent topology changes, and the various quality of wireless channel, there are challenges in providing high data rate service, high throughput, high packet delivery ratio, low end-to-end delay, and reliable services. In wireless network and real time application systems, low complexity and shorter codeword length in channel coding scheme are preferred. Consequently, in order to address these challenges, we propose a novel error detection and correction codes called the low-complexity parity-check (LCPC) codes with short codeword lengths for futuristic wireless networks applications. The proposed codes have less complexity and lower memory requirement in comparison to turbo and low-density parity-check (LDPC) codes. Simulation results demonstrated that the proposed LCPC codes outperform the Hamming and Reed–Solomon codes, in addition to the renowned LDPC codes. It offers up to 3-dB coding gain.

High-Gain Transimpedance Amplifier for Flexible Radiation Dosimetry Using InGaZnO TFTs

This paper presents a novel high-gain transimpedance amplifier for flexible radiation sensing systems that can be used as large-area dosimeters. The circuit is implemented with indium-gallium-zinc-oxide thin-film-transistors and uses two stages for the amplification of the sensor signal (current). The first stage consists of cascode current mirrors with a diode connected load that performs current amplification and voltage conversion. Then, the first stage is followed by a voltage amplifier based on a positive feedback topology for gain enhancement. The proposed circuit converts nano-ampere (10 nA) currents into hundreds of millivolts (280 mV), showing a gain around 149 dB and a power consumption of 0.45 mW. The sensed radiation dose level, in voltage terms, can drive the next stages in the radiation sensing system, such as analog to digital converters. These radiation sensing devices can find potential applications in real-time, large area, flexible health, and security systems.

Методика построения гибридной нейросистемы реального времени для решения обратной задачи кинематики избыточного манипулятора

Solution of the inverse kinematics (IK) problem with the aid of neural networks is considered. The control system for a multi-link redundant manipulator is synthesized. The configuration of a control system on the basis of servo drives is presented. The proposed control system is based on an algorithm involving a new hybrid method for solving the IK problem. This method combines the ANFIS adaptive neural fuzzy inference network and an iterative refinement algorithm (according to the Newton-Raphson method). Thus, the proposed algorithm combines the advantages of the neural network and iterative approaches, namely, high precision and high response speed. The required coordinates (link rotation angles) for the inverse problem are calculated in the neural network (ANFIS), after which they are refined by the iteration method. Hence, a much fewer number of iterations have to be carried out in the numerical method, and, accordingly, much shorter time is taken to execute the algorithm. The developed algorithm ensures a controlled accuracy of calculations with due regard of its application in real-time control systems. The developed control system is universal in nature and can be used for manipulators having different design parameters. For investigating the control system synthesized proceeding from the developed method for solving the IK problem, a three-link manipulator design was considered. The mathematical description used for constructing the manipulator’s operating space and for training the control system neural network is presented. The results from experimental investigations of applying the hybrid algorithm for calculating the link and actuator coordinates are given. The method was investigated in the Matlab environment. The results of the performed experiments allowed a conclusion to be drawn about the possibility of using the developed method in real-time control systems. To solve the problem of shaping the operating spaces of certain types of manipulators, a graphical interface was developed using the Matlab GUI. The application includes features for adjusting the design parameters (the mechanical structure and accuracy). As an example, the workspaces of both planar and spatial manipulator designs with the specified parameters are taken.

A Data Imputation Model in Phasor Measurement Units Based on Bagged Averaging of Multiple Linear Regression

Synchrophasor data from the phasor measurement unit (PMU) indicate the health of the electrical system. However, the PMU performance relies entirely on a supported communication network. The existing communication approaches do not guarantee the error-free channel for the PMU network. Meanwhile, the mean or median approaches are commonly used to impute the missing value. These methods, however, fail to recover some valuable frequency events. In this paper, we first proved the multiple linear regression features in many synchronized frequency data streams in the short-time window. Then, we proposed the Bagged Averaging of Multiple Linear Regression model, which handles and fulfills the missing values in synchronized frequency data measurement fast and efficiently. This technique was based on the ensemble learning by bootstrapping and averaging many multiple linear regressions to predict the missing values. Various experiments on the synchronized frequency measurement data from the Texas synchrophasor network have demonstrated the effectiveness of our proposed approach in recovering the missing frequency events. Our proposed approach guarantees the performance of real-time wide area monitoring system applications, such as frequency analysis or stability monitoring and trending.

New Hybrid Genetic Based Approach for Real-Time Scheduling of Reconfigurable Embedded Systems

This journal article deals with the problem of real-time scheduling of operating systems (OS) tasks by a hybrid genetic-based scheduling algorithm. Indeed, most of real-time systems are framed with aid of priority-based scheduling algorithms. Nevertheless, when such a scenario is applied to save the system at the occurrence of hardware-software faults, or to improve its performance, some real-time properties can be violated at run-time. In contrast, most of the applications of real-time systems are based on timing constraints, i.e. OS tasks should be scheduled properly to finish their execution within the time specified by the real-time systems. For this reason, the authors propose in their article, a hybrid genetic-based scheduling approach that automatically checks the systems feasibility after any reconfiguration scenario was applied to an embedded system. A benchmark example is given, and the experimental results demonstrate the effectiveness of the originally proposed genetic-based scheduling approach over other such classical genetic algorithmic approaches.

Real-time reference-based dynamic phase retrieval algorithm for optical measurement.

To study dynamic behaviors of a phenomenon, measuring the evolving field of a specimen/material/structure is required. Optical interferometry, as a full-field, non-contact, and highly sensitive optical measurement technique, has been applied, where the evolving field is represented as dynamic phase distribution. A dynamic phase retrieval algorithm, called least-squares with 3 unknowns (LS3U), which estimates the phase change between each two consecutive patterns by a least-squares fitting method and denoises the phase change by a windowed Fourier filtering (WFF) algorithm, has been shown to be a simple yet effective algorithm. However, LS3U is computationally expensive, restricting its potential application in real-time dynamic phase retrieval systems. In this paper, a real-time LS3U algorithm powered by GPU parallel computing is proposed, with which frame rates of up to 64.5 frames per second (fps) and 131.8fps are achieved on NVIDIA's GTX 680 and GTX 1080 graphics cards, respectively.

Performance Analysis of Tags Collection Time on A New Architecture of 2.4 GHz Embedded Active RFID System for Indoor and Outdoor Tracking and Monitoring Applications Based on IEEE 802.15.4 Standard

This paper focuses on the analysis of tags collection time of 2.4 GHz embedded active Radio Frequency Identification (RFID) system for indoor and outdoor real-time tracking and monitoring applications based on IEEE 802.15.4 standard. The main novelty of the system is the implementation of the communication method in order to provide Machine to Machine (M2M) communication and automated switching mechanism between indoor and outdoor location by utilizing active RFID, Wireless Sensor Network (WSN), Global Positioning System (GPS) and mobile communication on a single platform. In this work, GPS receiver covers outdoor location tracking, while active RFID provides identification and Receive Signal Strength Indicator (RSSI) reading for each tag holder to cover indoor location tracking especially near or inside building where location information is not detected by GPS. Several experiments were conducted on three different RFID tags which were active RFID tag embedded with GPS and GSM (ERFIDG2), active RFID tag embedded with GPS (ERFIDG) and standalone RFID tag communicating with the same active RFID reader. The experiment was done to evaluate the communication performance of the active RFID in terms of tags collection time using Transparent (AT) and Application Programming Interface (API) mode. The experiment was extended to measure tags collection time in single hop and multi hops communication for Tag Talk First (TTF) and Reader Talk First (RTF) protocols. The results show that the proposed active RFID system (ERFIDG2) is better than the standalone and ERFIDG systems. The in-depth research done in this work is to study the experience and identify the challenges that will be faced in the development and implementation of a wireless RFID-based system for tracking and monitoring applications.

Geometric Stiffness for Real-time Constrained Multibody Dynamics

This paper focuses on the stable and efficient simulation of articulated rigid body systems for real-time applications. Specifically, we focus on the use of geometric stiffness which can dramatically increase simulation stability. We examine several numerical problems with the inclusion of geometric stiffness in the equations of motion, as proposed by previous work, and address these issues by introducing a novel method for efficiently building the linear system. This offers improved tractability and numerical efficiency. Furthermore, geometric stiffness tends to significantly dissipate kinetic energy. We propose an adaptive damping scheme, inspired by the geometric stiffness, that uses a stability criterion based on the numerical integrator to determine the amount of non-constitutive damping required to stabilize the simulation. With this approach, not only is the dynamical behavior better preserved, but the simulation remains stable for mass ratios of 1,000,000-to-1 at time steps up to 0.1 s. We present a number of challenging scenarios to demonstrate that our method improves efficiency, and that it increases stability by orders of magnitude compared to previous work.

Embedded Tracking System for Ground Moving Vehicle

Abstract: This paper is concerned with design and implementation of embedded tracking system capable of dealing with slow motion ground vehicle, which is carried out to upgrade the second generation anti-tank guided missile system (ATGM), which based on manual target tracking, to third generation ATGM (Fire and Forget system), which based on automatic target tracking. The nonlinear flight simulation model of underlying ATGM system is described in MATLAB environment. The new technology of system on chip embedded systems are a good platform especially embedded system under linux operating system for real time application especially computer vision application. The tracking algorithm is described using C++ programing language and implemented based on Raspberry pi system. Hardware-in-Loop experimental test is carried out to evaluate and validate a methodology of the proposed work for achieving the overall system requirement of accepted flight trajectory and accepted minimum miss-distance.

Evaluation of a low overhead predication system for a deterministic VLIW architecture targeting real-time applications

Recently, there is a considerable research effort on providing time-predictable architectures for real-time systems. The correct functioning of them depends not only on the logically correct response, but also the time when it is given. To provide such determinism, the use of VLIW (Very Long Instruction Word) architecture with in-order pipelines and predication support became attractive. Predication is an architecture technique which helps the compiler to translate control dependencies into data dependencies reducing control flow overhead and enhancing the worst-case timing analysis. Since hardware predication support does not come for free, the goal of this work is to investigate a low overhead predication system for multi-issue VLIW machines targeting real-time applications. This study was conducted using a VLIW 4-issue prototype based on the VLIW HP ST231 ISA describing the hardware implementation and the benefits on the worst-case performance.

Implementation of Unscented Kalman Filter-Based Online State-of-Charge Estimation in LiFePO4 Battery-Powered Electric Vehicle Applications

This paper proposes an online state-of-charge (SoC) estimation method based on a 2nd-order RC model. The open circuit voltage (OCV) of the battery is calculated through two adaptive filtering stage, which is then used to determine the SoC via a dynamic OCV–SoC curve. In the first stage a variable step-size least mean square (VSS-LMS) algorithm is employed to adaptively estimate the model parameters in real-time; in the second stage, an unscented Kalman filter (UKF) is used to estimate the OCV from the parameters. The proposed SoC estimation method with its simplified model was implemented in DSP system for real-time application. Plenty of experiments under different conditions were carried out to confirm the effectiveness and superiority of the proposed system.

Optical sensors for variable rate nitrogen application in dairy pastures

Reducing the amount of nitrogen (N) fertiliser applied to dairy pastures down to agronomically optimised levels would have positive economic and environmental results. The ability of commercially available optical sensors to estimate biomass yield and foliar-N uptake in pastures was investigated. Vegetative indices (Simple Ratio, SR; Water Index, WI; and Normalised Difference Vegetation Index, NDVI) from two active optical reflectance sensors (N-Sensor, Yara; and Greenseeker, Trimble) were compared with manually measured biomass and N-uptake in above-ground foliage. There were three measurements over time, from pastures that had received different N fertiliser applications rates (0, 10, 20, 40 and 80 kg N/ha). It was found that the sensors were able to detect differences in biomass and foliar N-uptake following defoliation of grazed pastures. The tested optical sensors have the potential to inform a real-time variable rate fertiliser application system. Keywords: pasture, nitrogen, optical sensors