Cotton is a major cash crop in the United States, with the country being a leading global producer and exporter. Nearly all U.S. cotton is grown in the Cotton Belt, spanning 17 states in the southern region. Harvesting remains a critical yet challenging stage, impacted by the use of costly, environmentally harmful defoliants and heavy, expensive cotton pickers. These factors contribute to yield loss, reduced fiber quality, and soil compaction, which collectively threaten long-term sustainability. To address these issues, this study proposes a lightweight, small-scale, vision-guided autonomous robotic cotton picker as an alternative. An autonomous system, built on Clearpath's Husky platform and integrated with the CottonEye perception system, was developed and tested in the Gazebo simulation environment. A virtual cotton field was designed to facilitate autonomous navigation testing. The navigation system used Global Positioning System (GPS) and map-based guidance, assisted by an RGBdepth camera and a YOLOv8nseg instance segmentation model. The model achieved a mean Average Precision (mAP) of 85.2%, a recall of 88.9%, and a precision of 93.0%. The GPS-based approach reached a 100% completion rate (CR) within a $(5e-6)^{\circ}$ threshold, while the map-based method achieved a 96.7% CR within a 0.25 m threshold. The developed Robot Operating System (ROS) packages enable robust simulation of autonomous cotton picking, offering a scalable baseline for future agricultural robotics. CottonSim code and datasets are publicly available on GitHub: https://github.com/imtheva/CottonSim

In recent times, voice assistants have become a part of our day-to-day lives, allowing information retrieval by voice synthesis, voice recognition, and natural language processing. These voice assistants can be found in many modern-day devices such as Apple, Amazon, Google, and Samsung. This project is primarily focused on Virtual Assistance in Natural Language Processing. Natural Language Processing is a form of AI that helps machines understand people and create feedback loops. This project will use deep learning to create a Voice Recognizer and use Commonvoice and data collected from the local community for model training using Google Colaboratory. After recognizing a command, the AI assistant will be able to perform the most suitable actions and then give a response. The motivation for this project comes from the race and gender bias that exists in many virtual assistants. The computer industry is primarily dominated by the male gender, and because of this, many of the products produced do not regard women. This bias has an impact on natural language processing. This project will be utilizing various open-source projects to implement machine learning algorithms and train the assistant algorithm to recognize different types of voices, accents, and dialects. Through this project, the goal to use voice data from underrepresented groups to build a voice assistant that can recognize voices regardless of gender, race, or accent. Increasing the representation of women in the computer industry is important for the future of the industry. By representing women in the initial study of voice assistants, it can be shown that females play a vital role in the development of this technology. In line with related work, this project will use first-hand data from the college population and middle-aged adults to train voice assistant to combat gender bias.

Can John Deere become one of the leading AI and robotics companies in the world alongside Tesla and Silicon Valley technology giants over the next decade? That notion may seem incongruous with the general perception of the 185-year-old company as a heavy-metal manufacturer of tractors, bulldozers and lawnmowers painted in the signature green and yellow colors. But that is what the company sees in its future, according to Jorge Heraud, vice president of automation and autonomy for Moline, Illinois-based Deere, a glimpse of which was showcased at last January's Consumer Electronics Show in Las Vegas, where Deere unveiled its fully autonomous 8R farm tractor, driven by artificial intelligence rather than a farmer behind the wheel. The autonomous 8R is the culmination of Deere's nearly two decades of strategic planning and investment in automation, data analytics, GPS guidance, internet-of-things connectivity and software engineering. While a good deal of that R&D has been homegrown, the company also has been on a spree of acquisitions and partnerships with agtech startups, harvesting know-how as well as talent.

The autonomous tractor is a version of Deere's existing 8R series machine, the largest of which has 410 horsepower. Current 8R users can upgrade their tractors with the autonomous driving system. The Deere tractor, unveiled at the CES 2022 tech conference in Las Vegas, isn't the world's first autonomous tractor. Smaller autonomous tractors are being used in specialty-crop farms. The application of the technology to larger vehicles is just getting started and promises to be highly consequential, according to Aron Cory, research manager for world-wide agriculture at International Data Corp. "The move from conventional tractors to autonomous tractors is going to be comparable from the move from horses to the combustion engine," he said.

To further explore the intersection of 5G and manufacturing, register for the 5G Manufacturing Forum. Global manufactuers are starting to adopt 5G to improve manufacturing processes. Low latency and high reliability are needed to support critical applications in the manufacturing field. Several top manufacturers are already taking advantage of 5G implementation to improve operations in different industrial environments. Here we briefly describe some implementations by large manufacturers globally.

This paper explores the hypothesis that the diversity of human languages, right now a barrier to interoperability in communication and trade, will become significantly less of a barrier as machine translation technologies are deployed over the next several years.But this new boundary-breaking technology does not reduce all boundaries equally, and it creates new challenges for the distribution of ideas and thus for innovation and economic growth.

Farming and advanced AI may seem antithetical, but they're not. The venerable farm equipment company has not only long embraced advanced technologies, the company for years has evangelized adoption of high performance clusters and simulation software for product design. And Deere freely states it's an extremely complex undertaking. In a recent article in IEEE, Deere's Julian Sanchez, who heads the Moline, IL, company's intelligent vehicles strategy, said that while the company is working on autonomous driving, "it's not just about driving tractors around." The more difficult problem, he said, is crop classification.

John Deere (Moline, Ill.), a manufacturer of agricultural and industrial equipment, has adopted a genetic algorithm-based solution to solve its factory scheduling problems. John Deere is using genetic algorithms to streamline scheduling at its factories, Sarasota County Detention Center knowledge in a system model and balancing an increasing number of (Sarasota, Fla.) has incorporated The center will use the The Royal Sonesta Hotel Boston Martin Marietta Magnesia Specialties new system to identify and confirm (Cambridge, Mass.) has deployed a (Woodville, Ohio), a producer of identities of inmates prior to being speech-driven automated attendant magnesia chemicals for industrial released from the facility. LucasArts Entertainment (San technology to automatically answer Primary objectives for the system Rafael, Calif.) has deployed a casebased and direct telephone calls, enabling are to increase production yet maintain reasoning self-service customer each caller direct access to a registered quality and decrease energy costs. This Report (Cuyahoga Falls, Ohio; Shanghai PuDong International Airport resource-allocation application evaluates www.lionhrtpub.com), The react to unforeseen events in real time.