First responders will be able to use drones equipped with Augmented Reality technology to better deal with emergency situations. Drones have been getting a really bad rep of late, specially in the United Kingdom, after rogue operators managed to shut down operations at both Gatwick and Heathrow airports, effectively ruining Christmas for thousands of travellers and prompting widespread clamour for greater regulation against them. Yet like all technology, it's not the tech itself, but what you do with it that counts, and which makes it a force for evil – or for the greater good. The other side of all the fear and annoyance that drones can cause in the wrong hands are the life-saving applications that companies like Edgybees are working on. Edgybees was initially founded as AR video game enhancement software, then pivoted to specialize in rescue drone technology that collects geospatial data and overlays information onto video feeds to bring emergency responders accurate and real-time information.

Artificial Intelligence (AI) will be a strategic capability and competitive advantage, no doubt. Statistics speak to that point. According to AI startup advisor Steve Ardire during his presentation (co-presented with Mico Yuk) at the DATAVERSITY Enterprise Data World Conference titled "Why Enterprise Organizations Use AI as a Strategic Capability and Competitive Advantage,"AI is expected to drive $15.7 trillion dollars of global GDP gains by 2030. It will grow to a $36.8 billon market by 2025, up from $643 million in 2016. Investment in AI over the last six years has grown from $300 million to more than $5 billion.

Mumbai: Chief minister Devendra Fadnavis on Wednesday met ASEA Brown Boveri (ABB) CEO Ulrich Spiesshofer and discussed ways to use advanced technologies to address the state's requirements. The discussion, which was held on the sidelines of the NASSCOM Technology and Leadership Forum 2019, focused on how a partnership with the Swedish multinational corporation could be forged to deploy Artificial Intelligence (AI) and digitalisation technologies to meet the state's needs, including using AI to take skills' development to the next level. "Maharashtra has been a forerunner in AI technology in India and ABB, as a pioneering technology leader in digital industries, is ready to support it in developing its digital economy. By rapidly adopting advanced technologies, Maharashtra can expand its role as an export hub and strengthen India's competitive position in global markets," said Spiesshofer. The discussion comes in the backdrop of the state exploring the potential of AI to address various issues surrounding crop yields, innovation and citizen programmes.

I have never seen an autonomous train/driverless train/self-driving train in my whole life and I happened to see it in Singapore. I couldn't believe my eyes, I had to double check and triple to see that I wasn't seeing things. So when I got onto the train, I made my way to the front carriage and lo and behold. It was an autonomous train/driverless train/self-driving train. There were no driver or any room for a driver anywhere.

Never mind airport security, artificial intelligence (AI) may also be rooting through your luggage in the near future at Incheon International Airport. Incheon International Airport Corporation said Wednesday it will incorporate AI into its security systems in a bid to improve accuracy in screening passenger luggage for prohibited items. The airport has already started working on the project to develop an AI-based X-ray screening system to be tested in the second half of next year. Instead of the existing system that relies on X-ray scanning, manual image checking by security officers and a final physical check, artificial intelligence will crosscheck the X-ray scan and the analysis will be available to officers along with the X-ray image. The first-stage AI scan is expected to complement and improve the accuracy of the security check as an officer will continue to be responsible for the final call to physically inspect luggage.

It's been a big week for 5G, the next generation of wireless networks. Samsung announced its first 5G capable phone, the S10, on Wednesday. Qualcomm announced a new 5G modem on Tuesday. But President Trump is aiming higher. "I want 5G, and even 6G, technology in the United States as soon as possible," Trump wrote in a tweet urging carriers to pick up their pace.

Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output LC filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Though it is an intuitive controller easy to understand and implement, it has the significant disadvantage of requiring a large number of online calculations for solving the optimization problem. On the other hand, the application of model-free approaches such as artificial neural network-based (ANN-based) approaches is currently growing rapidly in the area of power electronics and drives. This paper presents a new control scheme for a two-level converter based on combining MPC with feed-forward ANN, with the aim of getting lower THD and improving the steady and dynamic performance of the system for different types of loads. First, MPC is used, as an expert, in the training phase to generate data required for training the proposed neural network. Then, once the neural network is fine-tuned, it can be successfully used online for voltage tracking purpose, without the need of using MPC. The proposed ANN-based control strategy is validated through simulation, using MATLAB/Simulink tools, taking into account different loads conditions. Moreover, the performance of the ANN-based controller is evaluated, on several samples of linear and non-linear loads under various operating conditions, and compared to that of MPC, demonstrating the excellent steady-state and dynamic performance of the proposed ANN-based control strategy.

Inferring the structural properties of a protein from its amino acid sequence is a challenging yet important problem in biology. Structures are not known for the vast majority of protein sequences, but structure is critical for understanding function. Existing approaches for detecting structural similarity between proteins from sequence are unable to recognize and exploit structural patterns when sequences have diverged too far, limiting our ability to transfer knowledge between structurally related proteins. We newly approach this problem through the lens of representation learning. We introduce a framework that maps any protein sequence to a sequence of vector embeddings -- one per amino acid position -- that encode structural information. We train bidirectional long short-term memory (LSTM) models on protein sequences with a two-part feedback mechanism that incorporates information from (i) global structural similarity between proteins and (ii) pairwise residue contact maps for individual proteins. To enable learning from structural similarity information, we define a novel similarity measure between arbitrarylength sequences of vector embeddings based on a soft symmetric alignment (SSA) between them. Our method is able to learn useful position-specific embeddings despite lacking direct observations of position-level correspondence between sequences. We show empirically that our multi-task framework outperforms other sequence-based methods and even a top-performing structure-based alignment method when predicting structural similarity, our goal. Proteins are linear chains of amino acid residues that fold into specific 3D conformations as a result of the physical properties of the amino acid sequence. These structures, in turn, determine the wide array of protein functions, from binding specificity to catalytic activity to localization within the cell.

Hindi writing often Given a user input in the form of an ink, i.e. a list of contains a connecting'Shirorekha' line and characters touch or pen strokes, output the textual interpretation can form larger structures (grapheme clusters) which of this input. A stroke is a sequence of points (x, y, t) influence the written shape of the components. Arabic with position (x, y) and timestamp t. is written right-to-left (with embedded left-to-right sequences Figure 1 illustrates example inputs to our online used for numbers or English names) and characters handwriting recognition system in different languages change shape depending on their position within and scripts. The left column shows examples in English a word. Emoji are non-text Unicode symbols that we with different writing styles, with different types also recognize. of content, and that may be written on one or multiple lines. The center column shows examples from Online handwriting recognition has recently been five different alphabetic languages similar in structure gaining importance for multiple reasons: (a) An increasing to English: German, Russian, Vietnamese, Greek, and number of people in emerging markets are obtaining Georgian. The right column shows scripts that are significantly access to computing devices, many exclusively using different from English: Chinese has a much mobile devices with touchscreens. Many of these users larger set of more complex characters, and users often have native languages and scripts that are not as easily overlap characters with one another. Korean, while an typed as English, e.g.

In industrial systems, certain process variables that need to be monitored for detecting faults are often difficult or impossible to measure. Soft sensor techniques are widely used to estimate such difficult-to-measure process variables from easy-to-measure ones. Soft sensor modeling requires training datasets including the information of various states such as operation modes, but the fault dataset with the target variable is insufficient as the training dataset. This paper describes a semi-supervised approach to soft sensor modeling to incorporate an incomplete dataset without the target variable in the training dataset. To incorporate the incomplete dataset, we consider the properties of processes at transition points between operation modes in the system. The regression coefficients of the operation modes are estimated under constraint conditions obtained from the information on the mode transitions. In a case study, this constrained soft sensor modeling was used to predict refrigerant leaks in air-conditioning systems with heating and cooling operation modes. The results show that this modeling method is promising for soft sensors in a system with multiple operation modes.