Technique For Real-time Applications Research Articles

Fast Tube-Based Robust Compensation Control for Fixed-Wing UAVs

When considering the robust control of fixed-wing Unmanned Aerial Vehicles (UAVs), a conflict often arises between addressing nonlinearity and meeting fast-solving requirements. In existing studies, the less nonlinear robust control methods have shown significant improvements that parallel computing and dimensionality reduction techniques in real-time applications. In this paper, a nonlinear fast Tube-based Robust Compensation Control (TRCC) for fixed-wing UAVs is proposed to satisfy robustness and fast-solving requirements. Firstly, a solving method for discrete trajectory tubes was proposed to facilitate fast parallel computation. Subsequently, a TRCC algorithm was developed that minimized the trajectory tube to enhance robustness. Additionally, considering the characteristics of fixed-wing UAVs, dimensionality reduction techniques such as decoupling and stepwise approaches are proposed, and a fast TRCC algorithm that incorporates the control reuse method is presented. Finally, simulations verify that the proposed fast TRCC effectively enhances the robustness of UAVs during tracking tasks while satisfying the requirements for fast solving.

A survey on deep learning techniques in real-time applications

In recent years, machine learning and Deep Learning have increased and gathered epic success in traditional application domains and new areas of Artificial Intelligence. The performance using Deep Learning has dominated experimental results compared to conventional machine learning algorithms. This paper presents an overview of the progress that has occurred in Deep Learning (DL) concerning some application domains like Autonomous Driving, Healthcare, Voice Recognition, Image Recognition, Advertising, Predicting Natural Calamities, National Stock Exchange and many more. Additionally, deeper insights into several Deep Learning techniques, their working principles, and experimental results are scrutinized. The survey covers Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).

ANFIS Supervised PID Controlled SAPF for Harmonic Current Compensation at Nonlinear Loads

ABSTRACT The smart devices comprise extensive embedding of small-size AC power-driven power electronics components. This improves the smart automation in the majority of applications in the industry and home system. But, the nonlinear characteristics of such component introduces several higher-order harmonics and generates possible deviation in performance measures. The long-term persistence of harmonic distortions in the current distribution may result in system failure and depreciation in system efficiency. This paper presents a soft computing technique for reactive power compensation of harmonic current using adaptive neural fuzzy interface system (ANFIS). The smart ANFIS supervises the PID controller for adequate gate signaling to shunt active power filter (SAPF) by hysteresis current control. The neural network is trained by adaptive data set of error and deviation in error with respect to hysteresis current control. The nonlinear load model is subjected to transformer-less SAPF to avoid the presence of insertion losses and improves the system compatibility. The proposed system reduces the harmonic distortion from 92.23 to 0.49% in the nonlinear power converter model with improved 0.99 power factor. Additionally, the proposed system is absolutely feasible and novel for real-time application in smart devices with adequate transient and regression response. The proposed system is realized for individual effects of PID, ANN and ANFIS to justify the candidature of the soft computing technique in real-time applications as per IEEE 519 standard.

FPGA Realization of Hyperbolic Function

Machine Learning (ML) is applied in many real world applications. Artificial Neuron (AN) is the main building block of Artificial Neural Networks (ANN) which is the backbone of ML. ML algorithms for different applications like image and speech recognition etc., speech with large data are implemented on workstations with GPUs. However, the power of ML techniques for real time applications like monitoring, control and diagnostics, typical in power and process plant is still in nascent stage. Most of these applications require FPGA realization of ANNs. Realization of AN with both sigmoid and hyperbolic tangent activation functions along with results is reported in this paper. Design of building blocks consisting of Floating Point Unit (FPU) and Function approximation (FA) are presented. Implementation using Verilog HDL along with results of accuracy with simulated inputs and gate count and timing diagrams are presented.

Improvement of Microphone Array Characteristics for Speech Capturing

This paper presents a new adaptive technique for speech capturing in adverse conditions using microphone arrays. The proposed technique is based on frequency-domain alignment of microphone signals with the output of the fixed beamformer directed to the target speaker. This alignment procedure improves pattern directivity and reduces sidelobes. The low complexity of the technique is achieved by means of a frequency-domain implementation of the algorithm. This makes it possible to implement this technique in real-time applications with a large number of microphones. The technique was evaluated on speech data corrupted by varying levels and directions of noise and interference. The proposed technique improves the directivity pattern of a traditional Dealy & Sum beamformer as well as provides additional suppression of spatially incoherent noise, diffuse noise and interference, with minimal loss of the target signal quality.

Sub-Pixel Anti-Aliasing Via Triangle-Based Geometry Reconstruction

Anti-aliasing has recently been employed as a post-processing step to adapt to the deferred shading technique in real-time applications. Some of these existing algorithms store supersampling geometric information as geometric buffer G-buffer to detect and alleviate sub-pixel-level aliasing artifacts. However, the anti-aliasing filter based on sampled sub-pixel geometries only may introduce unfaithful shading information to the sub-pixel color in uniform-geometry regions, and large G-buffer will increase memory storage and fetch overheads. In this paper, we present a new Triangle-based Geometry Anti-Aliasing TGAA algorithm, to address these problems. The coverage triangle of each screen pixel is accessed, and then, the coverage information between the triangle and neighboring sub-pixels is stored in a screen-resolution bitmask, which allows the geometric information to be stored and accessed in an inexpensive manner. Using triangle-based geometry, TGAA can exclude irrelevant neighboring shading samples and achieve faithful anti-aliasing filtering. In addition, a morphological method of estimating the geometric edges in high-frequency geometry is incorporated into the TGAA's anti-aliasing filter to complement the algorithm. The implementation results demonstrate that the algorithm is efficient and scalable for generating high-quality anti-aliased images.

Real-time processing for shape-from-focus techniques

An increase in the number of frames and computational complexity of the focus measure causes the shape-from-focus (SFF) method to become time consuming and occupy a lot of memory. As such, these factors become a limitation for using SFF techniques in real-time applications. However, the computational time can be significantly reduced using a parallel implementation of these methods on multiple cores. In this article, various SFF methods are compared in a parallel computing environment. The intent of this research is to analyze the speedup of various focus-measuring methods using a different number of cores to determine the optimal number of cores required for SFF applications.

A study on Real-time Artificial Intelligence

This paper highlights some of the important considerations when applying artificial intelligence techniques in real-time applications. There is a specific focus on real-time expert systems. The issues of reliability and safety in real-time control systems are also addressed. Important considerations like scheduling, real-time operating systems are also highfighted in designing real-time systems.

Performance comparisons of AC load-flow techniques for real time applications

The paper presents detailed results on the performance comparisons of the investigated AC load flow techniques, i.e. fast decoupled, second-order Newton Raphson and the super-decoupled load flows. The results are compared on the basis of the convergence rates, iteration counts, memory requirements and the CPU times. Comparative performance evaluations of these three methods with respect to ill-conditioning networks and line-outage simulations are also investigated. Extensive performance tests have been carried out on the standard IEEE 5, 14, 24 and 57 bus test systems.