FPGA Implementation of Low-Light Enhancement Algorithm_ To Enhance Low-Light Images/Videos Matlab

This example performs LLE by inverting an input image and then applying a de-haze algorithm on the inverted image.

After inverting the low-light image, the pixels representing non-sky region have low intensities in at least one color channel.

The algorithm consists of six stages. Step 1: Scaling & Inversion The input image   is converted to range [0,1] by dividing by 255 and then inverting pixel-wise. Step 2: Dark Channel Estimation The dark channel is estimated by finding the pixel-wise minimum across all three channels of the inverted image. Step 3: Refinement The airlight image from the previous stage is refined by iterative smoothing. This stage consists of five filter iterations with a 3-by-3 kernel for each stage. Step 4: Non-Linear Correction To reduce over-enhancement, the refined image is corrected using a non-linear correction. Step 5: Restoration Restoration is performed pixel-wise across the three channels of the inverted and corrected image. Step 6: Inversion To obtain the final enhanced image, this stage inverts the output of the restoration stage, and scales to the range [0,255].

The figure shows the input image and the enhanced output images obtained from the LLESimplified subsystem and the LLEHDL subsystem. Click here to get the simulink file: https://drive.google.com/file/d/1nBBRRzjqXsKmImOCqdXWTZ8NXM6hmi3m/view?usp=sharing

Implementation of Haze Removal Algorithm to Enhance Low-Light Images Using Matlab

This example shows how to enhance low-light images using an algorithm suitable for FPGAs.

Haze removal or image dehazing is required in real-world weather conditions to obtain a fast and high-quality hazy free image which is used in various fields like satellite systems and aircraft systems.

Some of the wide important areas in which the haze removal methods used are air- crafts, remote sensing, intelligent transportation systems, underwater image processing, Object detection, outdoor surveillance, railway systems, aerial imagery, computational photography/vision applications, video analysis and recognition, image classification, military and defense surveillance system, etc. Example Code: % Import an RGB image captured in low light. A = imread('lowlight_21.jpg'); % Invert the image. AInv = imcomplement(A); % Apply the dehazing algorithm. BInv = imreducehaze(AInv,'ContrastEnhancement','none'); % Invert the results. B = imcomplement(BInv); %Display the original image and the enhanced images, side-by-side. montage({A,B}); click here to download the Matlab Livescript file: https://drive.google.com/file/d/1S8iBE42W0akwTQp2BksTsSd-NRb5MvV5/view?usp=sharing

Design & Implementation of Delta Robot for Pick-and-Place Operations Using Simulink

This example shows how to model a delta robot performing a pick and place task.

Contents

Introduction – Delta Robot Model Matlab KinematicsSolver object Simulink Model Delta Robot Subsystem Planning and Control Subsystem: Forward and Inverse Kinematics Planning and Control Subsystem: Path Planner Planning and Control Subsystem: Controller Simulation Results from Scopes The robot picks up a part using a vacuum gripper, moves the part to each of the four markers on the table, drops the part at the first marker, and then returns to the home position.

These forward and inverse kinematics computations are done using KinematicsSolver objects.

The Planning and Control/Controller subsystem contains a simple PID controller that drives the actual positions and velocities of the actuators to their desired values. The objects are defined as persistent variables in the functions sm_pick_and_place_robot_fk and sm_pick_and_place_robot_ik. These functions are called by the MATLAB function blocks Planning and Control/Forward Kinematics and Planning and Control/Inverse Kinematics 

Modeling and Simulation of a Piezoelectric Vibration Energy Harvester Using Matlab Simulink

This example shows how to model a device that harvests energy from a vibrating object by using a piezo bender.

This example shows how to model a device that harvests energy from a vibrating object by using a piezo bender.

The device uses this energy to charge a battery and power a load.

These devices are common in low-power applications that require energy autonomy, such as wearable devices or sensors in vehicles.

This energy harvester consists of a piezo bender, a rectifier, and a DC-DC converter. The left end of the piezo bender is clamped to a vibrating object, forcing the motion.

The right end of the piezo bender is connected to an extra mass.

Due to the elasticity, mass, and inertia of the piezo bender, the motion of the right end is not synchronous to the left end.

The deformations produce then a charge and voltage across the electrical terminals of the piezo bender, that are harvested into power.

The full-wave rectifier transforms the AC power generated by the piezo bender into DC power. It comprises four diodes and a capacitor that acts as a filter to smooth the DC voltage.

The buck converter regulates the voltage to transfer the maximum possible power to the load and ensures that the transfer of power is unidirectional.

In this example a pulse generator controls the converter in open-loop with a fixed switching frequency and duty cycle.

If the vibration source does not have a constant frequency or it contains harmonics, you can design a more sophisticated closed-loop controller to optimize the transfer of power and improve the efficiency of the energy harvester in different conditions. Initially, the energy harvester charges a battery.

Then both the energy harvester and the battery power up a constant power load.

Simulation & Analysis of Grid Connected Photo Voltaic Residential System with MPPT Controller

This example shows the operation of a photovoltaic (PV) residential system connected to the electrical grid.

Run the simulation and observe the resulting signals on the various scopes. 1) At 0.25s, with a solar irradiance of 1000 W/m2 on all PV modules, steady state is reached. The solar system generates 2400 Watts and the DC link is maintained at 400 volts with a small 120-Hz ripple due to the single-phase power extracted from the PV string. The Utility meter indicates that the system takes almost no power from the grid to supply the home total load. 2) At 0.3s, a partial shading condition is created by reducing the irradiance on some PV modules. When steady-state is reached at 0.35s, the MPPT controller has set the boost duty cycle at 0.44, generating a PV string voltage of 225 V. With this voltage, 920 W is extracted from the PV string. As you can see on the PV curve characteristic, the system is operating at a local maximum power point but not at the global maximum power point. 3) At 0.4s, a duty cycle scan of 0.25 seconds is performed by the MPPT controller to find the GMPP point. 4) At 0.7s, the MPPT controller has set the boost duty cycle at 0.58 generating a PV string voltage of 168 V. With this voltage, 1364 W is extracted from the PV string which is the GMPP value. The Utility meter indicates that it takes now around 1100 W (2500 W residential load - 1364 W supplied by PV) from the grid to supply the home total load.