This paper explores the application of FPGA programmable structures in the field of digital image signal processing (ISP). FPGAs offer high flexibility, speed and parallelism, making them ideal for general digital signal processing (DSP), as well as specific ISP tasks. The paper utilizes standard ISP algorithms such as morphological operations, filtering and edge detection to compare practical implementations of FPGA and CPU-based compute engines. Through illustrative examples and empirical results, we demonstrate the distinct advantages of employing FPGA for these use-cases, and contrast them with traditional CPU approaches, clearly showing FPGA capacity to significantly accelerate execution. The challenges that arise from resource-limited IOT-class hardware configurations are highlighted in the paper, namely resource optimization, memory management and maximal frequency.

This paper focuses on the design and implementation of a discrete digital PID (Proportional - Integral - Derivative) controller utilizing an FPGA (Field Programmable Gate Arrays) platform, which inherently supports parallel implementation of algorithms. Typically, cost-effective FPGA boards lacks peripherals, such as analog inputs and outputs, so they need to be added externally. The main hypothesis is that a DC motor system can be controlled with a low-cost variant of FPGA-based PID controllers. Therefore, an I2C (Inter-Integrated Circuit) based AD (Analog-to-digital) converter is added as input, while PWM (Pulse width modulation) based output signal is used as an output. The effectiveness of the designed regulator is demonstrated on an example of a DC (direct current) motor control. Additionally, for control and monitoring purposes, the FPGA is connected to the PC using the UART (Universal Asynchronous Receiver Transmitter) protocol. Experimental results indicate that the FPGA-based PID implementation offers solid performance.

The paper presents an automated method for solving traditional single side 2D jigsaw puzzles, focusing solely on shape features. Termed as semi-apictorial puzzles, our approach utilizes pictorial content solely for image segmentation, not for puzzle matching. Through enhancements in background separation, corner extraction, and feature matching, our method simplifies and accelerates puzzle reconstruction. A key contribution is the introduction of an edge matching technique that employs approximate triangles to evaluate a possible match, which notably improves computational efficiency and reduces algorithm complexity. Experimental results demonstrate that the proposed method outperforms existing solutions, enabling the handling of a larger puzzles within a reasonable timeframe.

This paper presents the development and implementation of a flexible industrial machine model for automated visual inspection, called ETFCam, designed to improve the learning outcomes of electrical engineering students in the field of machine vision and robotics. Unlike prefabricated didactic models, which are typically “closed” systems with a predefined set of experiments, custom didactic systems for teaching and training built from scratch tend to be more flexible and provide a deeper insight in engineering, machine design and planning, while being more cost-effective. The proposed system is based on a 3DOF stepper motor-based manipulator, a DC motor driven conveyor, a pneumatic actuated gripper and a machine vision system. The paper discusses several applications of such a system in an educational environment, with a special focus on machine vision applications. Due to the fact that the system is versatile, open, modular, and easy to upgrade, it has unlimited potential and possibilities for further development. In addition, it provides students with a perfect testbed for learning new engineering skills in many areas such as schematic drawing and understanding, PLC based control, sensing, and machine vision.

This paper introduces twisting sliding mode control method (TWSMC) to track 3D trajectories of a quadrotor unmanned aerial vehicle (UAV) exposed to bounded disturbances and perturbations. The key idea behind TWSMC is to introduce a nonlinear twisting term into the sliding surface design, which enables the system to switch between different sliding modes (SMs) smoothly, thereby reducing the chattering phenomenon and improving control performance. Moreover, a high-gain adaptation (HGA) algorithm is adopted in the TWSMC scheme to additionally attenuate the chattering effect, where the switching control gain increases during the convergent phase and decreases in the sliding phase. Through the comprehensive simulation study, it is shown that the proposed approach exhibits improved robustness and performance in tracking a reference under disturbances and perturbations.

This paper presents a full degrees-of-freedom (DOFs) robust control design for a nonlinear quadrotor unmanned aerial vehicle (UAV) operating under bounded disturbances. Second-order sliding modes controllers (SOSMCs) are designed so that the quadrotor UAV can follow a 3D trajectory in the presence of model uncertainties, underactuation, as well as external disturbances that may be matched or mismatched, and vanishing or nonvanishing. The stability analysis of the closed-loop system is presented via the Lyapunov method, showing the finite-time convergence of the system trajectories to the sliding surfaces, as well as the finite-time convergence of the quadrotor position and attitude to their reference values. The high-gain adaptation (HGA) method is adopted in the SOSMC technique, called SOSMC-HGA, to alleviate the chattering phenomenon. Simulation studies in different scenarios demonstrate that the SOSMC technique exhibits superior tracking performance and robustness properties compared to concurrent control methods for tracking reference trajectories of quadrotor UAVs. The simulation results confirm that SOSMC-HGA significantly attenuates the chattering phenomenon in control signals and system states, which is an important improvement, as it increases the safety of UAVs and reduces power consumption.

Control design for multi-rotor aerial vehicles (MAVs) is quite challenging problem due to their nonlinearitles, unknown dynamics, parametric uncertainties, an underactuated property, a nonlinear coupling dynamics and external disturbances. This paper introduces a first order sliding mode control (FOSMC) for robust stabilization of an under-actuated quad-rotor unmanned aerial vehicle (UAV) operating in the presence of external disturbances. The proposed FOSMC guarantees a finite time convergence of the system trajectories to the sliding surface. Obtained simulations show that the FOSM based approach improves robustness properties compared with the concurrent techniques, and enhance tracking performance of the quad-rotor UAV exposed to external disturbances.

Control design for trajectory tracking of multi-rotor aerial vehicles (MAVs) represents a challenging task due to the under-actuated property, highly nonlinear and cross-coupled dynamics, modeling errors, parametric uncertainties and external disturbances. This paper presents the design of the first order sliding mode control (FOSMC) algorithm for trajectory tracking of the octo-rotor unmanned aerial vehicle (UAV) in the presence of various disturbances. The highly nonlinear octo-rotor UAV dynamics is considered via the generalized framework for MAVs modeling. The stability analysis of the closed-loop system is presented using the Lyapunov based approach. The developed FOSMC exhibits finite-time convergence of the octo-rotor trajec-tories to the sliding manifold and the asymptotic stability of the equilibrium in the presence of vanishing disturbances. Simulation studies show a superior tracking performance and robustness properties of the FOSMC in comparison with the concurrent techniques for trajectory tracking of the octo-rotor UAV in the presence of internal and external disturbances.

Abstract The magnitude-based Fourier descriptors (FD) are frequently used in shape-based image retrieval, due to their efficiency and effectiveness. Unlike the phase-preserving Fourier descriptors, the magnitude-based Fourier descriptors are inherently invariant under rotation and starting point change, but they discard all valuable information contained in the phase of the Fourier coefficients (FCs). In order to preserve the coefficients’ phase, the orientation and starting point of the shape must be determined. In this paper, we conducted a comprehensive evaluation of different state-of-the-art methods for determining nominal shape orientation, which can be used to extract phase-preserving Fourier descriptors: the point of maximal radius, the axis of least inertia (moments), the phase of the first harmonic, the cross-correlation, the Procrustes distance and the pseudomirror points. The methods were compared in terms of sensitivity to non-rigid transformations, retrieval performance, computational complexity and computational time. The experimental results give insight into the pros and cons of all analyzed methods.

This paper introduces the robust internal-loop compensator based sliding mode control (SMRIC) scheme for multiple-input multiple-output (MIMO) nonlinear systems subjected to mismatched uncertainties, which are time-varying and non-vanishing with non-constant steady-state values. The proposed approach extends an application area of the robust internal-loop compensator (RIC), as well as a class of mismatched uncertainties that could be imposed on the system. The developed SMRIC technique allows substantial alleviation of the chattering phenomenon in the presence of disturbances while retaining the nominal performance of the system in the absence of disturbances. The stability analysis of the closed-loop system is performed using the Lyapunov-based approach. The proposed SMRIC method guarantees the finite-time convergence of the system trajectories to the sliding surface and provides asymptotic stability of the equilibrium. The simulation results of the numerical example and both simulation and experimental results of the application example show that the proposed SMRIC technique exhibits, in comparison with the concurrent algorithms, excellent tracking performance and robustness properties in the presence of modeling uncertainties, parameter variations, external disturbances, and mismatched uncertainties.