Defense Notices

Coherent Raman Scattering (CRS) spectroscopy and microscopy is a widely used technique in biology, chemistry, and physics to determine the chemical structure as well as provide a label-free image of the sample. The system uses two coherent laser beams one of which is constantly tuned in wavelength. Thus, a tunable laser source or optical parametric oscillator (OPO) is commonly used to achieve this requirement. However, the aforementioned devices are extremely expensive and work only for a specific wavelength range. In this study, we replace an OPO system with a photonic crystal fiber (PCF) in order to significantly reduce the cost and increase the flexibility of our microscopy system. Here, by exploiting the nonlinear phenomenon in the fiber called the soliton self-frequency shift (SSFS), we are able to shift the pulse central frequency while preserving its shape. Also, by switching to a near-infrared (NIR) source, the undesired fluorescence is reduced while the penetration depth increases. Moreover, the NIR laser source is more biologically friendly as each photon carries less energy than the visible laser counterpart. This reduces the probability of the photodamage effect. Based on this system, we designed and implemented CRS microscopy and spectroscopy, using Coherent anti-Stokes Raman Scattering (CARS) and Stimulated Raman Scattering (SRS) spectroscopy techniques. 

Depression is a common and serious medical condition that negatively affects how one thinks, feels, and acts. Emotional symptoms of depression include loss of interest and/or sad mood. Lack of hope, a sense of guilt or worthlessness, and recurring thoughts of death or suicide are also reported in some cases. After the recent pandemic, depression rates have increased dramatically. Although depression is a major burden for the healthcare system worldwide, it is treatable. Only 47.3% of mental health cases are detected accurately by professionals as Patient Health Questionnaire is used as a screening tool that is heavily dependent on what the patient can remember from the past few weeks. Considering the challenges Healthcare professionals are facing, we can supply helpful resources to those users who have been detected with any depressive symptoms from their social media posts. As social media platforms have altered our world, most people are now connected than ever and are showing a digital persona. We can use all the user-generated content to help them. Sentiment Analysis, also called opinion mining, is a process of detecting the emotional tone behind any piece of text. It is majorly used to analyze news articles, User-generated content, and the text of research papers. This project aims to create a dataset by scrapping tweets and detecting a probably depressed twitter user based on their tweets using Natural Language Processing techniques. Currently, Social media platforms like Twitter have A.I. systems that flag tweets about misinformation, misleading tweets, or those tweets that violate the site’s terms and conditions. Like that, we can also have a depression detection system that will supply users who are probably exhibiting depressive emotions with helpful articles, images, or videos.

Given the ever-increasing use of unmanned aerial vehicles (UAV), there are great potentials as well strict requirements for their safe operation in beyond-visual-line-of-sight (BVLOS) environments. Commercial package delivery, emergency services, tracking, inspection, arejust some of those applications. To support these applications under the BVLOS scenarios, a reliable command and control (C2) communication channel with an extended range is needed. To investigate performance of different communication technologies, we use an open-source simulator that integrates the flight simulator ArduPilot with the network simulator NS-3. We implement several flight missions and investigate the performance of 4G cellular network compared with Wi-Fi for establishing the connection between the UAV and groundcontrol station (GCS). Our simulation results demonstrate the benefits of using 4G to satisfy the C2 requirements. Our simulated flight mission consists of multiple UAVs on the same network and also using external interference to observe network performance in terms of average delay, communication range, and received signal strength. In the second part of this project, we explore wireless connectivity between unmanned (autonomous) vehicles on the ground. To this end, we use Amazon’s Deepracer autonomous car that is primarily used for developing and testing machine learning algorithms for multi-vehicle racing, track completion, and obstacle avoidance. We leverage Deepraccer cars to establish peer-to-peer wireless connection between multiple vehicles operating in the same environment. This will enable autonomous vehicles to share crucial information such as positions, velocity, obstacle,and accidents on the way to enhance roads safety.

Ice dynamics are a major factor in ice sheet mass balance and play a huge role in sea level rise (and future sea-level rise projections). Ice velocity measures the direction and rate at which ice is redistributed from the accumulation to the ablation regions of glaciers and ice sheets. We propose to apply multipass differential interferometric synthetic aperture radar (DInSAR) techniques to data from the Multichannel Coherent Radar Depth Sounder (MCoRDS) to measure the vertical displacement of englacial layers within an ice sheet. DInSAR’s accuracy is usually on the order of a small fraction of the wavelength (e.g. millimeter to centimeter precision is common) in monitoring ground displacement along the radar line of sight (LOS).  Unlike ground-based Autonomous phase-sensitive Radio-Echo Sounder (ApRES) units that can be precisely positioned and used to produce vertical velocity fields, airborne systems suffer from unknown baseline errors. In the case of ice sheet internal layers, vertical displacement is estimated by compensating for the spatial baseline using precise trajectory information and estimates of the cross-track layer slope from direction of arrival analysis. The current DInSAR algorithm is applied to radar depth sounder data to produce results for Summit camp in central Greenland and a high accumulation region near Camp Century in northwest Greenland using the CReSIS toolbox. This approach has a drawback arising from the baseline error due to the GPS being estimated after Direction of Arrival (DOA) estimation yet DOA estimation is dependent on the baseline being accurate. We propose to extend this work by implementing a maximum likelihood estimator that jointly estimates the vertical velocity, the cross-track internal layer slope, and the unknown baseline error due to GPS and INS (Inertial Navigation System) errors. The multipass algorithm will be applied to additional flights from the decade long NASA Operation IceBridge airborne mission that flew MCoRDS on many repeated flight tracks. We also propose to improve the accuracy of tomographic swaths produced from multipass measurements and investigate the possibility to use focusing matrices to improve wideband tomographic processing.

Reinforcement learning is a method to map situations to actions to maximize a numerical reward signal. In most forms of machine learning, the model must discover which actions to take unlike reinforcement learning in which the model must discover which actions yield the most reward by trying them. These actions may affect not only the immediate reward, but also the next situation and, through that, all subsequent rewards. This type of learning is different from supervised learning, where domain knowledge comes from an external supervisor. This is an important kind of learning, but alone it is not adequate for learning from interaction. In interactive problems it is often impractical to obtain examples of desired behavior that are both correct and representative of all the situations in which the agent must act and must be able to learn from its own experience. As a part of this project, we attempt to train a Smart-cab agent that will navigate through its environment towards a goal. With following elements as our Reinforcement model, Agent – We use a Car as the agent to interact with the environment. The goal for the agent is to reach the destination with the maximum value; Environment – Our environment is a grid like structure with pathways that represent the roads with cars (5 without the agent) moving along them stochastically; Policy – We have a set of actions and constraints within which states and actions would be mapped. The agent has to perform the appropriate action that results into maximum Q-value. We use the Pygame tool to build our environment to visualize the interaction of the agent with our environment and Q-Learning to find the optimal policy that determines the optimal action that can be taken keeping all the constraints under purview.

We know that AI and Machine learning are being used in every industry and in every experience. Chatbots are among most visible applications of AI technology and since the time chatbots have entered the digital world, every industry wants to use chatbots to help their customers with the common issues. Most of the chatbots are kind of a messaging interface where instead of humans answering, message bots will be responding.

Chatbots are an artificial intelligence software that can initiate a human like conversation with a real human based on the training.AI uses a natural language to communicate with Artificial Intelligence features embedded in them. The conversation humans have with bots is powered by Machine Learning algorithms which breaks down the messages received into human understandable languages using Natural Language Processing techniques and responds to your queries like what you can expect from any human on the other side. In this project I've trained a chatbot using a dataset which responds to our messages.

In the recent years, organization's attack surfaces continue to increase with the rise of data storage, application diversity, and ransomware attacks.

The response typically falls to the enterprise Security Operation Center (SOC). However, even with an expanding attack surface, organizations continue to decommission or completely remove their SOCs due to the uncertainty around their respective value. This work traces and analyzes (1) the SOC's effort to reimpose endpoint monitoring and content handling as a result of the Internet's new and different sociotechnical environment resulting from COVID-19's “work from home” and (2) propose a metrics framework that captures the gaps within the SOC analysts' core function: the alert handling process. By intersecting historical analysis (starting in the 1970s) and ethnography (analyzed 256 field notes and performed two rounds of semi-structured interviews across 770+ hours in a SOC over 26 months) whilst complementing with quantitative interviews (covering 7 other SOCs), we find additional causal forces that, for decades, have pushed network management toward endpoints and content. With a similar ethnographic approach (participation observation paired with semi-structured interviews), we further locate expert judgement in the alert handling process and utilize those limitations as key performance indicators to identify gaps and capture the needs within the SOC.

Artificial Intelligence has been gathering tremendous support lately by bridging the gap between humans and machines. Amazing discoveries in numerous fields are paving way for state-of-the-art technologies. Deep Learning has shown immense progress in the field of computer vision in recent years, with neural networks repeatedly pushing the frontier of visual recognition technology. Recent years have witnessed an exponential increase in the use of mobile and embedded devices. With great success of deep learning, there is an emerging trend to deploy deep learning models on mobile and embedded devices. However, it is not a simple task, the limited resources of mobile and embedded devices make it challenging to fulfill the intensive computation and storage demand of deep learning models and state-of-the-art Convolutional Neural Networks (CNN) require computation at billions of floating-point operations per second (FLOP) which inhibit them from being utilized in mobile and embedded devices. Mobile convolutional neural networks use depth wise and group convolutions rather than standard “fully-connected” convolutions.  In this project we will be applying mobile convolutional models to identify the diseases in plants. Plant diseases are responsible for serious economic losses every year. Due to various reasons, the crops are affected based on climate conditions, various kinds of diseases, heavy usage of pesticides and many other factors. Due to the rise in use of pesticides, the farmers are experiencing irreplaceable losses. Less use of pesticides can help in better crop production. Using these mobile CNNs we can identify the diseases in plants with leaf images and based on the type of disease pesticides can be used respectively. The main goal is to use an efficient model which can assist farmers in recognizing leaf symptoms and providing targeted information for rational use of pesticides. 

Motivated by the shortcomings of common transport protocols, e.g., TCP, UDP, and MPTCP, in modern networking and the belief that a general-purpose transport-layer protocol, which can operate efficiently over diverse network environments while being able to provide desired services for various application types, we design a new transport protocol, ResTP. The rapid advancement of networking technology and use paradigms is continually supporting new applications. The configurable and adaptable multipath-capable ResTP is not only distinct from the standard protocols by its flexibility in satisfying the requirements of different traffic classes considering the characteristics of the underlying networks, but by its emphasis on providing resilience. Resilience is an essential property that is unfortunately missing in the current Internet. In this dissertation, we present the design of ResTP, including the services that it supports and the set of algorithms that implement each service. We also discuss our modular implementation of ResTP in the open-source network simulator ns-3. Finally, the protocol is simulated under various network scenarios, and the results are analyzed in comparison with conventional protocols such as TCP, UDP, and MPTCP to demonstrate that ResTP is a promising new transport-layer protocol providing resilience in the Future Internet (FI).

Algorithmic trading (sometimes termed automated trading, black-box trading, or algo-trading) is a computerized trading system where a computer program follows a set of specified instructions to make a transaction. Theoretically, the transaction should allow traders to make profits at a rate and frequency that a human trader cannot attain. Algorithmic trading is an automated trading method that is carried out using a computer algorithm. Trade theory theoretically posits that humans cannot earn profits at a pace and frequency comparable to those generated by computers.  

Traders have a tough time keeping track of the many handles that originate data. NLP (Natural Language Processing) can be used to rapidly scan various news sources, identifying opportunities to gain an advantage before other traders do. 

Based on this background, this project aims to select and implement an NLP and Machine Learning process that produces an algorithm, which can detect OR predict the future value from scraped data using Natural language processing and Machine Learning. This algorithm builds the basic structure for an approach to evaluate these documents.