Tokyo and Washington are exploring the possibility of deploying U.S. military drones to a Maritime Self-Defense Force base in Kyushu – the first time American drones would be sent to an SDF base. Defense Minister Nobuo Kishi said Friday that the government was considering the temporary deployment of U.S. Air Force MQ-9 unmanned surveillance aircraft to the MSDF's Kanoya Air Base in Kagoshima Prefecture. Around seven MQ-9 drones would be deployed to the base, with about 100 U.S. personnel expected to operate and maintain the aircraft, according to media reports. The move to deploy the drones would be "part of efforts to improve the alliance's surveillance capabilities," Kishi told a news conference. Prime Minister Fumio Kishida pledged to bolster Japan's alliance with the U.S. during a virtual summit earlier this month with U.S. President Joe Biden, and the deployment of U.S. drones to an SDF base could be part of that.

Drones are Unmanned Aerial Vehicles (UAVs), and people use them for completing various tasks. Drones that employ artificial intelligence to automate part or all of their duties are becoming increasingly popular. Drone makers may now use data from sensors on the drone to collect and use visual and atmospheric data. Drones are becoming a component of the smart transportation services that are offered commercially to firms and customers. Drones powered by AI rely heavily on computer vision.

US Defense Secretary Lloyd Austin has issued a directive ordering the United States military to do more to protect civilians from harm in drone attacks and other combat operations. In a two-page memo to top Pentagon civilian and military officials, Austin on Thursday ordered a comprehensive overhaul of the US Defense Department's posture towards protecting civilians in conflict zones. "The protection of innocent civilians in the conduct of our operations remains vital to the ultimate success of our operations and as a significant strategic and moral imperative," the memo reads. The defence secretary asked for an action plan from the Joint Chiefs of Staff to prevent harm to civilians and improve US responses when such incidents occur. That plan is due within 90 days.

The US State Department has added the "threat of missile or drone attacks" to a travel advisory for the United Arab Emirates, which was already on a United States list of "do not travel" destinations due to the COVID-19 pandemic. The department added the new potential threat to its travel warning for the UAE – already at the highest, "do not travel" level – on Thursday. "The possibility of attacks affecting US citizens and interests in the Gulf and Arabian Peninsula remains an ongoing, serious concern," the Department of State said. "Rebel groups operating in Yemen have stated an intent to attack neighboring countries, including the UAE, using missiles and drones. Recent missile and drone attacks targeted populated areas and civilian infrastructure."

Warehouse inventory management has become critical in light of pandemic-related supply chain issues. Unfortunately, it's a practice that can sometimes fall by the wayside. According to one estimate, 43% of businesses in the U.S. don't track inventory or do so using a manual system. Inventory accuracy often suffers as a result. A 2017 Peoplevox survey found that 34% of businesses have shipped an order late because they inadvertently sold a product that was not in stock.

Artificial Intelligence has already moved into many facets of our daily lives from Siri to Cortana, Alexa to Google Duplex, in banks, in CCTV cameras on the street, Conversational AI, Emotional AI, flying drone swarms, Chatbots, language translators, facial recognition and Social media. We are all surrounded by variety of new Artificial Intelligence devices. We have become accustomed to sharing our reality with intelligence simulations. By means of smart algorithms, machines today are capable of doing incredible things with facial and speech recognition. With error rates of under five percent, many systems can perform better than humans.

Not only are these books enjoyable on their own, fiction can serve as teachable moments in robots and STEM and inspire a robot-obsessed teen to read more and improve their reading comprehension. Let's start with the scifi book I most frequently recommended to friends to read in 2021: Termination Shock by Neal Stephenson. It is not a robot book per se but robots and automation are realistically interspersed through it- and the book is one of Stephenson's best, pulling together LOTS of technology, subplots, and themes similar to what he did in Diamond Age. One of the technology threads is how drones are ubiquitous throughout the book, with small drones being used singly or in swarms for surveillance and social media and bigger drones used for delivery, human transport, and, well, mayhem. Nominally the book is about climate change and how a group of individuals led by a rich Texan plan to cut through the COP26 meetings blather and get on with geoengineering the environment.

Unmanned Aerial Vehicle (UAV) localization capability is critical in a Global Navigation Satellite System (GNSS) denial environment. The aim of this paper is to investigate the problem of locating the UAV itself through a purely visual approach. This task mainly refers to: matching the corresponding geo-tagged satellite images through the images acquired by the camera when the UAV does not acquire GNSS signals, where the satellite images are the bridge between the UAV images and the location information. However, the sampling points of previous cross-view datasets based on UAVs are discrete in spatial distribution and the inter-class relationships are not established. In the actual process of UAV-localization, the inter-class feature similarity of the proximity position distribution should be small due to the continuity of UAV movement in space. In view of this, this paper has reformulated an intensive dataset for UAV positioning tasks, which is named DenseUAV, aiming to solve the problems caused by spatial distance and scale transformation in practical application scenarios, so as to achieve high-precision UAV-localization in GNSS denial environment. In addition, a new continuum-type evaluation metric named SDM is designed to evaluate the accuracy of model matching by exploiting the continuum of UAVs in space. Specifically, with the ideas of siamese networks and metric learning, a transformer-based baseline was constructed to enhance the capture of spatially subtle features. Ultimately, a neighbor-search post-processing strategy was proposed to solve the problem of large distance localisation bias.

Cross-view geo-localization is a task of matching the same geographic image from different views, e.g., unmanned aerial vehicle (UAV) and satellite. The most difficult challenges are the position shift and the uncertainty of distance and scale. Existing methods are mainly aimed at digging for more comprehensive fine-grained information. However, it underestimates the importance of extracting robust feature representation and the impact of feature alignment. The CNN-based methods have achieved great success in cross-view geo-localization. However it still has some limitations, e.g., it can only extract part of the information in the neighborhood and some scale reduction operations will make some fine-grained information lost. In particular, we introduce a simple and efficient transformer-based structure called Feature Segmentation and Region Alignment (FSRA) to enhance the model's ability to understand contextual information as well as to understand the distribution of instances. Without using additional supervisory information, FSRA divides regions based on the heat distribution of the transformer's feature map, and then aligns multiple specific regions in different views one on one. Finally, FSRA integrates each region into a set of feature representations. The difference is that FSRA does not divide regions manually, but automatically based on the heat distribution of the feature map. So that specific instances can still be divided and aligned when there are significant shifts and scale changes in the image. In addition, a multiple sampling strategy is proposed to overcome the disparity in the number of satellite images and that of images from other sources. Experiments show that the proposed method has superior performance and achieves the state-of-the-art in both tasks of drone view target localization and drone navigation. Code will be released at https://github.com/Dmmm1997/FSRA

Ruben Neves' second-half strike earns Wolves three points at Brentford, in a game that was halted for over 25 minutes in the first half.