Implementation of Algorithm for Vehicle Anti-Collision Alert System in FPGA

Vehicle safety has becoming one of the important issues nowadays, due to the fact the number of road accidents, which cause injuries, deaths and also damages, keeps on increasing. One of the main factors which contribute to these accidents are human's lack of awareness and also carelessness. This paper presents the development and implementation of an algorithm to be utilized for vehicle anti-collision alert system, which may be useful to reduce the occurrence of accidents. This algorithm, which is to be deployed with the front sensors of the vehicle, is capable of alerting any occurrence of sudden slowing or static vehicles ahead, by sensing the rate of distance change. Furthermore, it also triggers an alert if the driver is breaching the safe distance from the vehicle ahead. This algorithm has been successfully implemented in Altera DE0 FPGA and its functionality was validated via hardware experimental tests

Intelligent Fire Detection And Alert System Using LabVIEW

Fire detection systems are designed to discover fires and allow the safe evacuation of occupants as well as protecting the safety of emergency response personnel. This paper describes the design and development of a fire detection and alert system. Temperature and flame sensors are used to indicate the occurrence of fire. This work consists of two parts, which are transmitter and receiver, both using ZigBee wireless technology. Arduino Uno is used as the microcontroller at the transmitter part to control the sensor nodes and give alert when over temperature and flame are detected. At the transmitter, the collected data from the sensors are transmitted by an XBee module operated as router node. At the receiver side, an XBee coordinator module which is attached to a computer using USB to serial communication captured the data for further processing. In addition, an interactive and user-friendly Graphical User Interface (GUI) is developed. LabVIEW software is used to design the GUI which displays and analyze the possibility of fire happening. The system can display the fire location and provides early warning to allow occupants to escape the building safely

Automatic traffic light controller for emergency vehicle using peripheral interface controller

Traffic lights play such important role in traffic management to control the traffic on the road. Situation at traffic light area is getting worse especially in the event of emergency cases. During traffic congestion, it is difficult for emergency vehicle to cross the road which involves many junctions. This situation leads to unsafe conditions which may cause accident. An Automatic Traffic Light Controller for Emergency Vehicle is designed and developed to help emergency vehicle crossing the road at traffic light junction during emergency situation. This project used Peripheral Interface Controller (PIC) to program a priority-based traffic light controller for emergency vehicle. During emergency cases, emergency vehicle like ambulance can trigger the traffic light signal to change from red to green in order to make clearance for its path automatically. Using Radio Frequency (RF) the traffic light operation will turn back to normal when the ambulance finishes crossing the road. Result showed the design is capable to response within the range of 55 meters. This project was successfully designed, implemented and tested

Development Of Residential Energy Harvesting System With Arduino Application

The common electricity source is currently being generated through solar panel, wind turbine and hydroelectric. However, these methods require high cost and large space for the system operation. This paper proposed a Pico hydro free charging system from high pressure water of the home piping system stored to power bank for home lighting application. The usage of small-scale turbine in the system generated electricity to continuous charging the power bank. The power bank then acted as a free power source used to light up a 5V LED bulb as a free lighting source. The additional usage of Arduino helped to provide the information display on the generated current, voltage and the water flow rate. The piping size and length were evaluated and it was found that they significantly affected the voltage performance with the help of Arduino application for monitoring purpose. The uniqueness of this project is that it can be operated with a minimum water pressure of 15 Psi to generate renewable energy storage from incoming housing piping system

Low-Cost and Portable Interactive Sinusoidal Digital Signal Generator by Using FPGA

This paper presents the development of a low-cost and portable interactive Sinusoidal signal generator which has been implemented on FPGA device. The sine wave is generated by using a Lookup Table method, where the sine values are precalculated and stored in the onboard memory. The frequency of the generated signal is modified by changing the value of the memory address incremental step. In addition, the implemented signal generator is serially connected to a graphical user interface (GUI) on a PC, which can be used to select the type of the desired signal to be generated and to set the signal frequency. The proposed design was successfully implemented in ALTERA Cyclone II DE0 FPGA Development Board, where the sine wave can be generated within the range of 1 kHz to 1 MHz, with 1 kHz frequency resolution

Converged Classification Network For Matching Cost Computation

Stereoscopic vision lets us identify the world around us in 3D by incorporating data from depth signals into a clear visual model of the world. The stereo matching algorithm capable of producing the disparity or depth map in computer. This map is crucial for many applications such as 3D reconstruction, robotics and autonomous driving.The disparity map also prone to errors such as noises in the region which contains object occlusions, reflective regions, and repetitive patterns.So we propose this stereo matching algorithm to produce a disparity map and to reduce the errors by incorporating a deep learning approach. This paper focused on matching cost computation step as an initial step to produce the disparity or depth map. The proposed convolutional neural network designed with the output neurons in the classification part scaled-downin converging style. The raw cost generated aggregated by the normalized box filter. Then the disparity map computed using Winner Take All approach. The final disparity map refined using Weighted Median Filter. Overall quantitative results for the proposed work performed competitively compared to other established stereo matching algorithm based on the Middlebury standard benchmark online system

Stereo matching algorithm based on hybrid convolutional neural network and directional intensity difference

Fundamentally, a stereo matching algorithm produces a disparity map or depth map. This map contains valuable information for many applications, such as range estimation, autonomous vehicle navigation and 3D surface reconstruction. The stereo matching process faces various challenges to get an accurate result for example low texture area, repetitive pattern and discontinuity regions. The proposed algorithm must be robust and viable with all of these challenges and is capable to deliver good accuracy. Hence, this article proposes a new stereo matching algorithm based on a hybrid Convolutional Neural Network (CNN) combined with directional intensity differences at the matching cost stage. The proposed algorithm contains a deep learning-based method and a handcrafted method. Then, the bilateral filter is used to aggregate the matching cost volume while preserving the object edges. The Winner-Take-All (WTA) is utilized at the optimization stage which the WTA normalizes the disparity values. At the last stage, a series of refinement processes will be applied to enhance the final disparity map. A standard benchmarking evaluation system from the Middlebury Stereo dataset is used to measure the algorithm performance. This dataset provides images with the characteristics of low texture area, repetitive pattern and discontinuity regions. The average error produced for all pixel regions is 8.51%, while the nonoccluded region is 5.77%. Based on the experimental results, the proposed algorithm produces good accuracy and robustness against the stereo matching challenges. It is also competitive with other published methods and can be used as a complete algorithm

JPG, PNG and BMP image compression using discrete cosine transform

This paper proposes image compression using discrete cosine transform (DCT) for the format of joint photographic expert groups (JPEG) or JPG, portable network graphic (PNG) and bitmap (BMP). These three extensions are the most popular types used in current image processing storage. The purpose of image compression is to produce lower memory usage or to reduce memory file. This process removes redundant information of each pixel. The challenge for image compression process is to maintain the quality of images after the compression process. Hence, this article utilizes the DCT technique to sustain the image quality and at the same time reduces the image storage size. The effectiveness of the DCT technique has been reasonable over some real images and the implementation of the technique has been compared with different types of image extensions. Matlab software is an important platform for this project in order to write a program and perform the progress of project phase by phase to achieve the expected results. Based on the tested images, the DCT technique in image compression is capable to reduce the image storage memory in average about 50% of each image tested

Improved stereo matching algorithm based on census transform and dynamic histogram cost computation

Stereo matching is a significant subject in the stereo vision algorithm. Traditional taxonomy composition consists of several issues in the stereo correspondences process such as radiometric distortion, discontinuity, and low accuracy at the low texture regions. This new taxonomy improves the local method of stereo matching algorithm based on the dynamic cost computation for disparity map measurement. This method utilised modified dynamic cost computation in the matching cost stage. A modified Census Transform with dynamic histogram is used to provide the cost volume. An adaptive bilateral filtering is applied to retain the image depth and edge information in the cost aggregation stage. A Winner Takes All (WTA) optimisation is applied in the disparity selection and a left-right check with an adaptive bilateral median filtering are employed for final refinement. Based on the dataset of standard Middlebury, the taxonomy has better accuracy and outperformed several other state-of-the-art algorithms