This Project is to develop traffic light system that can reduce traffic congestion with the aid of counters for each lane and acts wisely with the intersection in real time based with a fixed time constrain, include both hardware and software requirements using SOC FPGA technology with fundamental specification for the Register Transfer Level (RTL).

The development of a Low-Cost and Low-Power Data Acquisition System(DAQs). The DAQs will be made up of end-terminal and a gateway. The end-terminal will be micro-controller-driven device built on a SoC FPGA technology with built-in capability for machine learning. The end-terminal will be able to transmit and receive data using the Low Power Wide Area Networking (LPWAN) communication protocol that functions on LoRA.LoRa is a wireless radio frequency technology that operates in a license-free radio frequency spectrum.

The dynamic range processor is a DSP function which does as it says on the tin; it compresses the dynamic range of the incoming signal. This is used most commonly in the music industry for its effects on the perceived loudness of audio. It is also used extensively in hearing aids to compensate for the user’s reduced dynamic range of hearing. In this project a hardware accelerator is developed for the purpose of dynamic range compression of digital audio. This accelerator will be implemented in a mixed-signal infrastructure.

Systolic arrays are critical in parallel computing. They efficiently accomplish tasks like matrix multiplication and signal processing by coordinating a grid of processing components to perform synchronized operations. The structured data flow reduces memory access while increasing processing, resulting in substantial speedups. Systolic arrays are used in a variety of domains, from AI model training to scientific simulations, to improve speed and enable complicated computations that typical sequential approaches struggle with.

The k-Nearest-Neighbours (kNN) algorithm is a popular Machine Learning technique that can be used for a variety of supervised classification tasks. In contrast to other machine learning algorithms which "encode" the knowledge gained from training data to a set of parameters, such as weights and biases, the parameter set of a kNN classifier consists of just labelled training examples. Classification of an unlabelled example takes place by calculating its Euclidean distance (or any other type of distance metric) from all the stored training examples.