This podcast considers the history of online courses in higher education and research into them, focusing on how well they serve a diverse student population. It considers how online learning developed, and how studies into the practices and effectiveness of online courses find inequality in academic outcomes and access. The podcast explores how research approaches bring to light these inequalities or fail to consider them. The future of online learning is also considered.

Radiation hardening of electronic devices is generally necessary when designing for the space environment. Non-volatile memory technologies are of particular concern when designing for the mitigation of radiation effects. Among other radiation effects, single-event upsets can create bit flips in non-volatile memories, leading to data corruption. In this paper, a Verilog implementation of a Reed-Solomon error-correcting code is considered for its ability to mitigate the effects of single-event upsets on non-volatile memories. This implementation is compared with the simpler procedure of using triple modular redundancy.

Collective human attitudes influenced by macro-forces that impact environmental issues are partially correlated to our behaviors for the good and the harm of the planet. In this thesis, I will explore how collective human attitudes contribute to pro-environmental behaviors, common and pre-existing frames of mind on major conservation dilemmas, and finally suggest future directions on how humans could be inclined to take on more environmental responsibility through an increase in human-environmental connectivity. It is found that humans are largely driven by institution structures, education, and social influence. In conclusion, more efforts should be placed to further analyze these structural incentives for pro-environmental behaviors and use them to make environmental stewardship more accessible for all people and diverse circumstances. This can be done by evaluating the human dimensions of what influences human attitudes and behaviors, how to use these forces to systematically influence pro-environmental choices, applying these structural forces to main conservation issues, and further incorporating moral discourse into the environmental research in order to appeal correctly to all aspects and perspectives. Only when human connectivity is understood in relation to the natural sciences will we be able to make positive change in the direction of a healthier Earth.

Every communication system has a receiver and a transmitter. Irrespective if it is wired or wireless.The future of wireless communication consists of a massive number of transmitters and receivers. The question arises, can we use computer vision to help wireless communication? To satisfy the high data requirement, a large number of antennas are required. The devices that employ large-antenna arrays have other sensors such as RGB camera, depth camera, or LiDAR sensors.These vision sensors help us overcome the non-trivial wireless communication challenges, such as beam blockage prediction and hand-over prediction.This is further motivated by the recent advances in deep learning and computer vision that can extract high-level semantics from complex visual scenes, and the increasing interest of leveraging machine/deep learning tools in wireless communication problems.[1] <br/><br/>The research was focused solely based on technology like 3D cameras,object detection and object tracking using Computer vision and compression techniques. The main objective of using computer vision was to make Milli-meter Wave communication more robust, and to collect more data for the machine learning algorithms. Pre-build lossless and lossy compression algorithms, such as FFMPEG, were used in the research. An algorithm was developed that could use 3D cameras and machine learning models such as YOLOV3, to track moving objects using servo motors and low powered computers like the raspberry pi or the Jetson Nano. In other words, the receiver could track the highly mobile transmitter in 1 dimension using a 3D camera. Not only that, during the research, the transmitter was loaded on a DJI M600 pro drone, and then machine learning and object tracking was used to track the highly mobile drone. In order to build this machine learning model and object tracker, collecting data like depth, RGB images and position coordinates were the first yet the most important step. GPS coordinates from the DJI M600 were also pulled and were successfully plotted on google earth. This proved to be very useful during data collection using a drone and for the future applications of position estimation for a drone using machine learning. <br/><br/>Initially, images were taken from transmitter camera every second,and those frames were then converted to a text file containing hex-decimal values. Each text file was then transmitted from the transmitter to receiver, and on the receiver side, a python code converted the hex-decimal to JPG. This would give an efect of real time video transmission. However, towards the end of the research, an industry standard, real time video was streamed using pre-built FFMPEG modules, GNU radio and Universal Software Radio Peripheral (USRP). The transmitter camera was a PI-camera. More details will be discussed as we further dive deep into this research report.

"Generating an astounding $110.7 billion annually in domestic revenue alone [1], the world of accounting is one deceptively lacking automation of its most business-critical processes. While accounting tools do exist for the common person, especially when it is time to pay their taxes, such innovations scarcely exist for many larger industrial tasks. Exceedingly common business events, such as Business Combinations, are surprisingly manual tasks despite their $1.1 trillion valuation in 2020 [2]. This work presents the twin accounting solutions TurboGAAP and TurboIFRS: an unprecedented leap into these murky waters in an attempt to automate and streamline these gigantic accounting tasks once entrusted only to teams of experienced accountants.
A first-to-market approach to a trillion-dollar problem, TurboGAAP and TurboIFRS are the answers for years of demands from the accounting sector that established corporations have never solved."

Lossy compression is a form of compression that slightly degrades a signal in ways that are ideally not detectable to the human ear. This is opposite to lossless compression, in which the sample is not degraded at all. While lossless compression may seem like the best option, lossy compression, which is used in most audio and video, reduces transmission time and results in much smaller file sizes. However, this compression can affect quality if it goes too far. The more compression there is on a waveform, the more degradation there is, and once a file is lossy compressed, this process is not reversible. This project will observe the degradation of an audio signal after the application of Singular Value Decomposition compression, a lossy compression that eliminates singular values from a signal’s matrix.

When you are sitting at the terminal waiting for your flight or taking the bus to get to work, have you ever thought about who used your seat last? More importantly, have you ever thought about the last time that seat was cleaned? Sadly, it is uncertain to see if it was properly sanitized in the last hour, yesterday, in the last week, or even last month. Especially during these tough times, everyone wants to be assured that they are always in a safe and healthy environment. Through the Founders Lab, our team is collaborating with an engineering capstone team to bring automated seat cleaning technology into the market. This product is a custom-designed seat cover that is tear-resistant and provides a sanitary surface for anyone to sit on. When someone leaves the seat, a pressure sensor is triggered, and the cover is replaced with a secondary cover that was stored in a UV radiated container. The waterproof fabric and internal filters prevent spills and food crumbs from remaining when the user changes. The reason for bringing this product into the market is due to the unsanitary conditions in many high traffic areas. This technology can be implemented in public transportation, restaurants, sports stadiums, and much more. It will instantly improve the efficiency of sanitation for many businesses and keep a promise to its users that they will never bring something they sat on back home. #Safeseating

As much as SARS-CoV-2 has altered the way humans live since the beginning of 2020,<br/>this virus's deadly nature has required clinical testing to meet 2020's demands of higher<br/>throughput, higher accuracy and higher efficiency. Information technology has allowed<br/>institutions, like Arizona State University (ASU), to make strategic and operational changes to<br/>combat the SARS-CoV-2 pandemic. At ASU, information technology was one of the six facets<br/>identified in the ongoing review of the ASU Biodesign Clinical Testing Laboratory (ABCTL)<br/>among business, communications, management/training, law, and clinical analysis. The first<br/>chapter of this manuscript covers the background of clinical laboratory automation and details<br/>the automated laboratory workflow to perform ABCTL’s COVID-19 diagnostic testing. The<br/>second chapter discusses the usability and efficiency of key information technology systems of<br/>the ABCTL. The third chapter explains the role of quality control and data management within<br/>ABCTL’s use of information technology. The fourth chapter highlights the importance of data<br/>modeling and 10 best practices when responding to future public health emergencies.

Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.

Generating an astounding $110.7 billion annually in domestic revenue alone [1], the world of accounting is one deceptively lacking automation of its most business-critical processes. While accounting tools do exist for the common person, especially when it is time to pay their taxes, such innovations scarcely exist for many larger industrial tasks. Exceedingly common business events, such as Business Combinations, are surprisingly manual tasks despite their $1.1 trillion valuation in 2020 [2]. This work presents the twin accounting solutions TurboGAAP and TurboIFRS: an unprecedented leap into these murky waters in an attempt to automate and streamline these gigantic accounting tasks once entrusted only to teams of experienced accountants.
A first-to-market approach to a trillion-dollar problem, TurboGAAP and TurboIFRS are the answers for years of demands from the accounting sector that established corporations have never solved.