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We can overcome the pessimism in worst-case routing latency analysis of timing-predictable Network-on-Chip (NoC) workloads by single-digit factors through the use of a hybrid field-programmable gate array (FPGA)–optimized NoC and workload-adapted regulation. Timing-predictable FPGA-optimized NoCs such as HopliteBuf integrate stall-free FIFOs that are sized using offline static analysis of a user-supplied flow pattern and rates. For certain bursty traffic and flow configurations, static analysis delivers very large, sometimes infeasible, FIFO size bounds and large worst-case latency bounds. Alternatively, backpressure-based NoCs such as HopliteBP can operate with lower latencies for certain bursty flows. However, they suffer from severe pessimism in the analysis due to the effect of pipelining of packets and interleaving of flows at switch ports. As we show in this article, a hybrid FPGA NoC that seamlessly composes both design styles on a per-switch basis delivers the best of both worlds, with improved feasibility (bounded operation) and tighter latency bounds. We select the NoC switch configuration through a novel evolutionary algorithm based on Maximum Likelihood Estimation (MLE). For synthetic ( RANDOM , LOCAL ) and real-world ( SpMV , Graph ) workloads, we demonstrate ≈2–3× improvements in feasibility and ≈1–6.8× in worst-case latency while requiring an LUT cost only ≈1–1.5× larger than the cheapest HopliteBuf solution. We also deploy and verify our NoC (PL) and MLE framework (PS) on a Pynq-Z1 to adapt and reconfigure NoC switches dynamically. We can further improve a workload’s routability by learning to surgically tune regulation rates for each traffic trace to maximize available routing bandwidth. We capture critical dependency between traces by modelling the regulation space as a multivariate Gaussian distribution and learn the distribution’s parameters using Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We also propose nested learning, which learns switch configurations and regulation rates in tandem. Compared with stand-alone switch learning, this symbiotic nested learning helps achieve ≈ 1.5× lower cost constrained latency, ≈ 3.1× faster individual rates, and ≈ 1.4× faster mean rates. We also evaluate improvements to vanilla NoCs’ routing using only stand-alone rate learning (no switch learning), with ≈ 1.6× lower latency across synthetic and real-world benchmarks.

Evolutionary algorithms can outperform conventional placement algorithms such as simulated annealing, analytical placement as well as manual placement on metrics such as runtime, wirelength, pipelining cost, and clock frequency when mapping FPGA hard block intensive designs such as systolic arrays on Xilinx UltraScale+ FPGAs. For certain hard-block intensive, systolic array accelerator designs, the commercial-grade Xilinx Vivado CAD tool is unable to provide a legal routing solution without tedious manual placement constraints. Instead, we formulate an automatic FPGA placement algorithm for these hard blocks as a multi-objective optimization problem that targets wirelength squared and maximum bounding box size metrics. We build an end-to-end placement and routing flow called RapidLayout using the Xilinx RapidWright framework. RapidLayout runs 5-6$\times$ faster than Vivado with manual constraints and eliminates the weeks-long effort to generate placement constraints manually for the hard blocks. We also perform automated post-placement pipelining of the long wires inside each convolution block to target 650MHz URAM-limited operation. RapidLayout outperforms (1) the simulated annealer in VPR by 33% in runtime, 1.9-2.4$\times$ in wirelength, and 3-4$\times$ in bounding box size, while also (2) beating the analytical placer UTPlaceF by 9.3$\times$ in runtime, 1.8-2.2$\times$ in wirelength, and 2-2.7$\times$ in bounding box size. We employ transfer learning from a base FPGA device to speed-up placement optimization for similar FPGA devices in the UltraScale+ family by 11-14$\times$ than learning the placements from scratch., Comment: 8 pages, 10 figures. Conference: FPL 2020

Due to advancements in the internet and multimedia technologies, unauthorised users can easily modify video content. As a result, video authentication has been established as a viable solution for ensuring multimedia security. We propose a key-frame based video watermarking scheme based on discrete cosine transform (DCT). First, the pearson correlation coefficient (PCC) is used to detect the shot boundaries of the input video. To reduce the difficulties created by traditional video watermarking systems; an entropy measure is employed to detect key-frames from input video. Traditional schemes entail embedding the entire watermark into all frames of the video, which is inefficient and timeconsuming. To improve the security, robustness, and imperceptibility of the proposed video watermarking scheme, speeded up robust feature points are extracted from each key-frame of the shot and used as reference points for embedding and detection of watermark. The embedded watermark is extracted blindly without using the original data during the extraction process. The results of the experiments reveal that the proposed technique effectively detects shot boundaries under a variety of camera operations and outperforms in terms of imperceptibility and resilience.

This project presents a smart bike with monitoring system for cyclist via internet of things (IoT). The system is designed for real time monitoring of cyclist in terms of health condition and cyclist's performance. The whole system consists of various sensors such as heart rate sensor, pulse oximetry sensor, magnet reed sensor and GPS module. The sensors are connected to the microcontroller and Wi-Fi module that can be accessed through an IoT platform which is blink application. The data of the cyclist in terms of health condition and cyclist's performance can be monitored by the cyclist as well as their coach. First, heart rate sensor was used to monitor the heart rate of the cyclist within time whereas pulse oximetry sensor was used to measure the oxygen saturation inside the cyclist's body. Meanwhile, magnet reed sensor was mounted to the frame of the bike to measure the speed and distance travelled by the cyclist. Plus, GPS module was used to track and trace the position of the cyclist. The data that have been transmitted can be synchronized or displayed on smartphone via blink application. Therefore, GUI interface of blink application displayed the parameter such as heart rate, pulse oximetry, speed, distance travelled and position for the cyclist and coach to monitor their health condition and cyclist's while in training or tournament. IoT is the internetworking of physical devices, vehicles, and other devices embedded with the electronics, software, sensors and network connectivity that enable to get data of those objects. Confiscating details from their own vehicle without making any physical touch is a hardly possible one. Augmentation of Smartphone makes a large impact in society by diminishing their toil. A surge movement of applications in the Smartphone may cutback the human’s accent. In India transportation is a booming field where the count of vehicles increasing day by day. Security and maintenance of those vehicles is a risky one. Monitoring vehicle parameters like fuel, engine oil, tyre pressure is an ideal to know without making any physical touch of the vehicle. Digital locking system is a much needed one in this digital domain. Tracking of our vehicle is also a vital one when your vehicle is not with you. IoT based vehicle parameter monitoring system is a capable one which leads to monitor our vehicle’s parameters such as fuel level, tyre pressure, engine oil level through an Android App. It’s Smart RFID digital key secure your vehicle more than by accessing by keys. In this app we can save our vehicle documents, user driving license, insurance copy and other documents. Notification for engine oil, battery rejuvenation is also done. Message and share’s location to trusted persons whenever ensue accidents..

The objective of this project is to design and fabricate a river waste cleaning machine, "River cleaning machine", which removes waste from water surfaces and disposes them safely from the water bodies. As a result of the increase in pollution in the form of waste debris, it is hampering the life of aquatic animals and making their life at risk. This work has examined our national rivers that are dumping crores of litres of sewage and are loaded with toxic material, pollutants, and debris. This project will employ a machine to lift debris from the surface of water bodies, which will reduce water pollution and, ultimately, aquatic animal mortality from these problems will be decreased. The main aim of the project is to reduce manpower and time consumed in cleaning the river. Using a motor and chain drive arrangement, this project uses a battery to store energy for river cleaning. We are designing and developing a river cleaning machine as part of our project. After the 3D model is drawn, all the parts are manufactured, assembled, and then tested.

A rise in COVID-19 transmission risk led people, industries, and the government to adopt different approaches for controlling the spread rate of the virus. This project uses Arduino-Nano, Servo motor, and a servo motor. While each approach has its advantages, one approach in particular -- Arduino-based sanitizing systems -- has played a key role in preventing the spread of Coronavirus due to its cost-effectiveness and flexibility. According to our information, an automatic sanitizer for door handles and knobs that uses IR sensors and servo motors has not been reported so far. Therefore, we have demonstrated an automatic door handle sanitizer which is commonly used in hospitals, houses, and other places to sanitize the handles and knobs. Efforts have been made to prevent Coronavirus infection. A person who gets infected by touching a contaminated doorknob of any organization, house, hospital, etc., will suffer serious repercussions as well as the country in which he lives. By sanitizing the door handle, the said system removes the virus it contains from the door handle as the person touches it. An IR sensor is used to demonstrate the mechanism. To prevent the spread of COVID-19, it can be implemented in places such as hospitals and businesses where the doors are used frequently.

The purpose of this project is to build a Voice Controlled Car Assistant System and Automatic Breaking System. A Voice Controlled Car is an advanced robotic vehicle that can be operated by the power of voice commands. It is based on an Arduino microcontroller, motor drivers, and a Bluetooth module. The Arduino hardware is an open-source micro-controller kit used to build digital devices. In our project, we will design the hardware of the Voice Controlled Robotic Car first, then use our previous knowledge of programming to code the entire work. The code will then be simulated on IDE software, and then interfaced with the hardware. An android device with a Bluetooth application is used to control the control unit in coordination with the Bluetooth device, and a Bluetooth module is used to capture and read the voice commands. We choose this project because automation has become a significant part of our lives and also has a broad range of applications in the engineering field. Automation plays a vital role in the development of new technology.