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Notice boards are of primary importance in any organization and in places such as bus and railway stations, when a need of for circulating notices arises it becomes tedious job. Thus an electronic notice board is an extremely efficient method of providing messages. It is difficult to update the messages at once. Thus this project focuses on development of a wireless board. This apparatus has the capability of displaying the latest messages using an Android application from a smart phone. This help susintransmitting any message with in a fraction of a second eliminating any delay by simply sending a command which is much efficient compared to any other traditional method of transmitting the message. Thus the proposed technology can be of great utility in many public places such as malls or commercial buildings to enhance the security system and also increase the awareness regarding emergency situations and avoid any possible dangers.

Post-quantum cryptography (PQC) is currently a growing area of research and NIST PQC Round 2 schemes are being actively analyzed and optimized for both security and efficiency. In this work, we repurpose the cryptographic accelerators in an energy-efficient pre-quantum TLS crypto-processor to implement post-quantum key encapsulation schemes SIKE, Frodo and ThreeBears and signature scheme SPHINCS + . We utilize the modular arithmetic unit inside the elliptic curve cryptography accelerator to implement SIKE, while we use the AES-256 and SHA2-256 hardware primitives to substitute SHA3-256 and SHAKE-256 computations and accelerate the other three protocols. We accelerate the most computationally expensive components of these PQC protocols in hardware, thereby achieving up to an order of magnitude improvement in energy-efficiency over software implementations.

In modern society, we often spend 5 to 10 minutes trying to find a spot in a parking garage. Parking garages are commonplace, but they are extremely inefficient. According to a study done by Inrix, the authority on vehicles and transportation, over 70 billion dollars are wasted every year searching for parking. In that process, we waste 3.6 billion hours of time and 1.7 billion gallons of fuel. That is a lot of wasted resource. In this project, I propose a novel parking management system, which consists of hardware and software modules. On the hardware side, Raspberry Pi device is used along with few other parts like sensors and cameras. On the software side, python modules are implemented with efficient data-management. To test the system, I have modeled different possible actions of the drivers and ensured that the proposed parking system performs perfectly. I have outlined three core situations. First one is when drivers park correctly, the second one has error-correction, and the third one requires error correction involving multiple cars. This system can be used in any large parking structure in the modern smart cities. Some examples are hospitals, companies, downtown, airports, malls, events etc. The system is affordable, as the hardware-cost is about $200 for each parking spot.

Vegetable crop production is the rudimentary process for the sustainability of mankind on this planet. Technological advancements have been shaping the crop production in a commendable way nonetheless, weeds play a deleterious role for the growth of the crops. Weed management has hence become the alarming solution for increasing the yield. Even though manual weeding is being practiced by many nonetheless, labor costs, time and tedium have become the major constraints for the crop production. The introduction of the chemical methods of weed control has affected the growth of crops. However, the continuous use of herbicide-resistant weedicides has a serious impact on crops and the environment. So an increasing demand for chemical-free food has led to the investigation of alternative methods of weed control. Most of the methods of mechanical weeding doesn't produce the accurate result and moreover, existing intra-row weeders have limitations. This paper proposes the construction and working of an autonomous mechanical weeder. It consists of an actuator which is developed to mechanically remove intra-row weeds. The mechanically weeding actuator consists of an integrated servo motor which is combined with the computer vision assisted system for detecting the crop plant locations and guiding the weeding actuator to execute mechanical weeding operations without damaging the crops. The image extraction is based on a novel algorithm which effectively works with the encoding system of the robot movement with a precision of plus or minus 1cm. The accuracy of the system is found to be 96.3% using haar cascade classifier using Open-CV open source framework.

In a real world situation, employees of different companies or organizations often need to travel to multiple destinations without passing through the same place twice. However, the problem of identifying an efficient path to travel through multiple destinations can be extremely difficult and time-consuming. In this work, I propose an algorithm to find an efficient path in real world scenarios, within a short amount of time. This heuristic uses Dijkstra's algorithm to find different paths between pairs of destinations and then determines the efficiency of each path. It runs in a recursive manner until it covers all of the destinations, including returning back to the starting location. With that, the algorithm creates an efficient path through all of the destinations. As a result, this approach allows people and organizations to save time, and money which could be used in other places. In order to test how beneficial this technique is, I have run several sample test situations involving between 5 and 50 destinations. My data shows that the algorithm produces an efficient path within 20 to 150 seconds, depending on the number of destinations. Since it requires very little time and knowledge, this approach will be quite appealing and useful to the companies and organizations.

In modern economy, investment in the stock market is a key strategy for growing wealth. With the high volatility in the stock market, there is a strong need for investment techniques to exploit the high volatility in the market. In this work, we propose a volatility-driven, medium-frequency stock trading framework. Our primary goal is to capitalize on little fluctuations that occur in individual stock prices in one day or within a span of few days. In our suggested framework, we perform a price-analysis in the morning, after the stock market opens and decide whether to purchase a given stock. Then, towards the end of the trading day, we perform another type of analysis to decide whether any specific stock form the portfolio should be sold. We have evaluated the effectiveness and performance of our proposed approach on all the 30 stocks of Dow Jones Industrial Average and on the portfolio of Warren Buffet, as of mid-2016. We compared the return obtained from our investment approach against the return obtained from the traditional long term buy-and-hold investment strategy. Our experimental data-set indicates that our approach produces 38% to 118% more profit, on an average. This proposed methodology is practical and extremely easy to use. Since it requires very little time and knowledge, this approach will be quite appealing and useful to the common investors.