In this thesis a probabilistic framework is developed and proposed for Dynamic Object Recognition in 3D Environments. A software package is developed using C++ and Python in ROS that performs the detection and tracking task. Furthermore, a novel Gaussian Process Regression (GPR) based method is developed to detect ground points in different urban scenarios of regular, sloped and rough. The ground surface behavior is assumed to only demonstrate local input-dependent smoothness. kernel's length-scales are obtained. Bayesian inference is implemented sing \textit{Maximum a Posteriori} criterion. The log-marginal likelihood function is assumed to be a multi-task objective function, to represent a whole-frame unbiased view of the ground at each frame because adjacent segments may not have similar ground structure in an uneven scene while having shared hyper-parameter values. Simulation results shows the effectiveness of the proposed method in uneven and rough scenes which outperforms similar Gaussian process based ground segmentation methods.

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

As per report of, "Recent results from a large survey of machine learning researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024) all the way to working as a surgeon (by 2053). Researchers also believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years." Nearly every aspect of our lives is being affected by artificial intelligence machines in order to boost profitability and enhance our human capabilities. After playing a significant role in defining the area devoted to the creation of intelligent machines, John McCarthy, an American computer scientist pioneer and inventor, was called the "Father of Artificial Intelligence." In his 1955 proposal for the 1956 Dartmouth Conference, the first artificial intelligence conference, the cognitive scientist coined the term.

The world never changes quite the way you expect. But at The Verge, we've had a front-row seat while technology has permeated every aspect of our lives over the past decade. Some of the resulting moments -- and gadgets -- arguably defined the decade and the world we live in now. But others we ate up with popcorn in hand, marveling at just how incredibly hard they flopped. This is the decade we learned that crowdfunded gadgets can be utter disasters, even if they don't outright steal your hard-earned cash. It's the decade of wearables, tablets, drones and burning batteries, and of ridiculous valuations for companies that were really good at hiding how little they actually had to offer. Here are 84 things that died hard, often hilariously, to bring us where we are today. Everyone was confused by Google's Nexus Q when it debuted in 2012, including The Verge -- which is probably why the bowling ball of a media streamer crashed and burned before it even came to market.

Given that, I've written this series to help give guidance to those wanting to start learning more about data science, machine learning, and/or artificial intelligence, and need help choosing a language. This series is also intended for practitioners that wonder which language and packages work best in certain scenarios. Although we'll cover most considerations in this series, including fundamental computer science concepts, the short answer is that you should learn Python and R, and should definitely learn SQL too. If you're really feeling ambitious, give Java, C, and Scala a shot as well. While not specific to data science, the TIOBE Index is a great, up-to-date way to assess the popularity and relevance of different programming languages. When I say'learn', I mean learn fundamental programming concepts and control flow structures, which are applicable to any computer programming language.

Tesla's long-awaited $35,000 Model 3 electric car is finally available to buy, just under three years after hundreds of thousands of people started placing deposits for pre-order vehicles. The standard Model 3 has a range of 220 miles and its single electric motor is capable of 0-60mph in 5.6 seconds with a top speed of 130mph. The standard interior includes heated cloth seats, manual seat and steering adjustment, basic audio, standard maps and navigation, and a center console with storage and four USB ports. Tesla has also announced a Standard Range Plus model, which has a 240-mile range with a top speed of 140mph and can do 0-60mph in 5.3 seconds. Along with the Model 3 announcement, Tesla said it will also close most of its showrooms around the world and shift sales to online only.

Elon Musk's new tractor trailer can handle most US shipping routes on a single charge. Linux is everywhere including your car. While some companies, like Tesla, run their own homebrew Linux distros, most rely on Automotive Grade Linux (AGL). AGL is a collaborative cross-industry effort developing an open platform for connected cars with over 140 members. This Linux Foundation-based organization is a who's who of Linux-friendly car manufacturers.

Google Cloud Platform is down (cloud.google.com) Credit card thieves using free-to-play apps to launder their ill-gotten gains (kromtech.com) How SSH port became 22 (www.ssh.com) Federal Reserve chair says decline in workers' share of profits'very troubling' (www.latimes.com) Trump's sycophants sink to new lows after (back.ly) U.S. To Make More Drugs Easily Available, Cutting Role Docs Play (www.bloombergquint.com) Iron Ox is hiring a Project Manager to help build the robotic farm (jobs.lever.co) Facebook's algorithm change leads to plummeting traffic and layoffs (thelogic.co)

Elon Musk's new tractor trailer can handle most US shipping routes on a single charge. Way back in 2004, Jonathan Schwartz, then Sun's chief operating officer, suggested that cars could become software platforms the same way feature phones were. But, it's Linux, not Java, which is making the most of "smart cars". That's because Linux and open-source software are flexible enough to bring a complete software stack to any hardware, be it supercomputer, smartphone, or a car. There are other contenders, such as Blackberry's QNX and Microsoft IoT Connected Vehicles, but both have lost ground to Linux.

Ah yes, the debate about which programming language, Python or R, is better for data science. In this series, I am considering machine learning and artificial intelligence as included in the term data science. This is almost the data science equivalent of tabs vs spaces for software engineers, at least at the time of this writing. This series is intended to be a somewhat definitive guide on this topic, including recommendations for languages and packages (aka libraries) applicable to different use cases, including data science in production and big data scenarios. This series is not intended to give side-by-side code comparisons, as there are plenty of other articles covering that. From my experience, which language to use is one of, if not the first question that someone interested in learning data science wants answered.