Ukraine said Friday it repelled a nighttime drone attack from Russia, a day after Moscow launched a new wave of missile strikes in the run-up to New Year celebrations. The attacks came 10 months into Moscow's invasion of Ukraine. In recent months Russian strikes have targeted the energy grid, leaving millions in the cold in the middle of winter. Ukraine's air force said on Friday morning that Russia attacked Ukraine overnight using "Iranian-made kamikaze drones". A total of 16 drones were launched from the southeastern and northern directions and they were "all" destroyed by Ukraine's air defence, it said.

Residents of the Ukrainian capital Kyiv were urged to head to air raid shelters as sirens wailed across the city early on Friday morning, a day after Russia carried out the biggest aerial assault since it started the war in February. Shortly after 2:00am (00:00 GMT), Kyiv's city government issued an alert on its Telegram messaging app calling on residents to proceed to shelters. Oleksiy Kuleba, governor of the Kyiv region, said on Telegram that an "attack by drones" was under way. A Reuters witness 20km (12 miles) south of Kyiv heard several explosions and the sound of anti-aircraft fire. Local media outlet The Kyiv Independent reported that air raid alerts were blaring in the Kyiv, Cherkasy and Kirovohrad regions due to possible Russian drone attacks.

We demonstrate how efficient autonomous drone swarms can be in detecting and tracking occluded targets in densely forested areas, such as lost people during search and rescue missions. Exploration and optimization of local viewing conditions, such as occlusion density and target view obliqueness, provide much faster and much more reliable results than previous, blind sampling strategies that are based on pre-defined waypoints. An adapted real-time particle swarm optimization and a new objective function are presented that are able to deal with dynamic and highly random through-foliage conditions. Synthetic aperture sensing is our fundamental sampling principle, and drone swarms are employed to approximate the optical signals of extremely wide and adaptable airborne lenses.

The 911 caller reported seeing a man wrestle a woman to the ground in an attempt to rob her before taking off on a bicycle. Beverly Hills police responded, but before a patrol car could get to the scene, officers already had their eyes on the suspect. The police recently added a new drone known as "Hawkeye" to its drone patrol that will give officers a view of crime scenes before they arrive, locate suspects before they're lost, and help patrol the streets of the upscale community. The drone's high-resolution camera is capable of reading a license plate a half-mile away. The city's police department introduced Hawkeye to the city on Tuesday in a set of social media posts that included video of the drone in action.

Unmanned aerial vehicles (UAVs) mobility enables flexible and customized federated learning (FL) at the network edge. However, the underlying uncertainties in the aerial-terrestrial wireless channel may lead to a biased FL model. In particular, the distribution of the global model and the aggregation of the local updates within the FL learning rounds at the UAVs are governed by the reliability of the wireless channel. This creates an undesirable bias towards the training data of ground devices with better channel conditions, and vice versa. This paper characterizes the global bias problem of aerial FL in large-scale UAV networks. To this end, the paper proposes a channel-aware distribution and aggregation scheme to enforce equal contribution from all devices in the FL training as a means to resolve the global bias problem. We demonstrate the convergence of the proposed method by experimenting with the MNIST dataset and show its superiority compared to existing methods. The obtained results enable system parameter tuning to relieve the impact of the aerial channel deficiency on the FL convergence rate.

The LiDAR and inertial sensors based localization and mapping are of great significance for Unmanned Ground Vehicle related applications. In this work, we have developed an improved LiDAR-inertial localization and mapping system for unmanned ground vehicles, which is appropriate for versatile search and rescue applications. Compared with existing LiDAR-based localization and mapping systems such as LOAM, we have two major contributions: the first is the improvement of the robustness of particle swarm filter-based LiDAR SLAM, while the second is the loop closure methods developed for global optimization to improve the localization accuracy of the whole system. We demonstrate by experiments that the accuracy and robustness of the LiDAR SLAM system are both improved. Finally, we have done systematic experimental tests at the Hong Kong science park as well as other indoor or outdoor real complicated testing circumstances, which demonstrates the effectiveness and efficiency of our approach. It is demonstrated that our system has high accuracy, robustness, as well as efficiency. Our system is of great importance to the localization and mapping of the unmanned ground vehicle in an unknown environment.

Ukraine has bought some 1,400 drones, mostly for reconnaissance, and plans to develop combat models that can attack the exploding drones Russia has used during its invasion of the country, according to the Ukrainian government minister in charge of technology. In a recent interview with The Associated Press, Minister of Digital Transformation Mykhailo Fedorov described Russia's war in Ukraine as the first major war of the internet age. He credited drones and satellite internet systems like Elon Musk's Starlink with having transformed the conflict. Ukraine has purchased drones like the Fly Eye, a small unmanned aerial vehicle used for intelligence, battlefield surveillance and reconnaissance. "And the next stage, now that we are more or less equipped with reconnaissance drones, is strike drones," Fedorov said.

Lt. Gen. Keith Kellogg weighs in on North Korea's long-range ballistic missile launch and China's belligerence toward Taiwan on'Your World.' North Korean leader Kim Jong Un set new goals for the country's military at the Sixth Enlarged Plenary Meeting of the party's 8th Central Committee as tensions continue to escalate on the Korean Peninsula. Kim told party leaders that North Korea faces a "newly created challenging situation" on the Korean Peninsula and set the direction for the "anti-enemy struggle," the country's state media reported Wednesday, according to Reuters. "He specified the principles of foreign affairs and the direction of the struggle against the enemy that our party and government must thoroughly abide by in order to protect sovereign rights and defend national interests," the KCNA news agency said in the report. North Korean leader Kim Jong Un attends a politburo meeting of the ruling Workers' Party of Korea in Pyongyang, North Korea. To accomplish those goals, KCNA said Kim called for a "strengthening self-defensive capabilities to be strongly pursued in 2023," though the report did not offer specific details on what the increased military build up would look like. The dictator's remarks come amid rising tensions between the isolated country and its neighbors South Korea and Japan, which have both pushed for a stronger military in response to an unprecedented amount of missile tests conducted by North Korea.

With the help of a variety of cutting-edge technologies, such as telemedicine, electronic medical records, home-based care transitioning from hospital-based care, drone technology, genome sequencing, digital tools, and artificial intelligence (AI), the healthcare sector has been transforming drastically. Undoubtedly, the pandemic accelerated the acceptance and advancement of technology in healthcare. Patients can now obtain medical care more quickly and easily outside of the typical hospital setting, improving convenience and accessibility for everyone. Furthermore, the exponential growth of the diagnostic sector has contributed to the growth in overall healthcare industry in India. The current situation has been significantly changed by modern and high-end diagnostics, which have replaced the conventional ways of diagnosis with new age, digital-led infrastructures backed by AI and ML.

Precise geolocalization is crucial for unmanned aerial vehicles (UAVs). However, most current deployed UAVs rely on the global navigation satellite systems (GNSS) or high precision inertial navigation systems (INS) for geolocalization. In this paper, we propose to use a lightweight visual-inertial system with a 2D georeference map to obtain accurate and consecutive geodetic positions for UAVs. The proposed system firstly integrates a micro inertial measurement unit (MIMU) and a monocular camera as odometry to consecutively estimate the navigation states and reconstruct the 3D position of the observed visual features in the local world frame. To obtain the geolocation, the visual features tracked by the odometry are further registered to the 2D georeferenced map. While most conventional methods perform image-level aerial image registration, we propose to align the reconstructed points to the map points in the geodetic frame; this helps to filter out the large portion of outliers and decouples the negative effects from the horizontal angles. The registered points are then used to relocalize the vehicle in the geodetic frame. Finally, a pose graph is deployed to fuse the geolocation from the aerial image registration and the local navigation result from the visual-inertial odometry (VIO) to achieve consecutive and drift-free geolocalization performance. We have validated the proposed method by installing the sensors to a UAV body rigidly and have conducted two flights in different environments with unknown initials. The results show that the proposed method can achieve less than 4m position error in flight at 100m high and less than 9m position error in flight about 300m high.