A Russian ballistic missile strike killed at least four people and wounded 17 in the city of Kryvyi Rih, Ukrainian President Volodymyr Zelenskyy's hometown. The attack also sparked a fire in the city, said Oleksandr Vilkul, the head of Kryvyi Rih's military administration. Russian drone attacks overnight targeted the Ukrainian regions of Zaporizhia and Kharkiv, killing one person and injuring several others, officials said. Two people were killed and at least 32, including two children, were injured by a Russian drone attack which hit several multistorey apartment blocks in Kharkiv, the region's governor said. One person was also injured in a separate drone attack on Ruski Tyshky, a village outside Kharkiv.

One person was killed and two others injured in a Russian overnight attack on southeast Ukraine's Zaporizhia region, Regional Governor Ivan Fedorov said. A Russian ballistic missile strike on Ukraine's Kryvyi Rih killed at least four people and injured 14 others, including two children, Ukrainian authorities said. An infant, a seven-year-old boy and six others were also injured in a drone attack on Ukraine's Kharkiv region, said Oleh Syniehubov, the region's governor. Kharkiv's Mayor Ihor Terekhov said 15 drone strikes were carried out in the region. At least 60 people were forced to evacuate from their homes in the Russian city of Kursk after falling debris from intercepted Ukrainian drones hit their apartment buildings, acting governor, Alexander Khinshtein, said.

With the increasing complexity of mobile device applications, these devices are evolving toward high agility. This shift imposes new demands on mobile sensing, particularly in terms of achieving high accuracy and low latency. Event-based vision has emerged as a disruptive paradigm, offering high temporal resolution, low latency, and energy efficiency, making it well-suited for high-accuracy and low-latency sensing tasks on high-agility platforms. However, the presence of substantial noisy events, the lack of inherent semantic information, and the large data volume pose significant challenges for event-based data processing on resource-constrained mobile devices. This paper surveys the literature over the period 2014-2024, provides a comprehensive overview of event-based mobile sensing systems, covering fundamental principles, event abstraction methods, algorithmic advancements, hardware and software acceleration strategies. We also discuss key applications of event cameras in mobile sensing, including visual odometry, object tracking, optical flow estimation, and 3D reconstruction, while highlighting the challenges associated with event data processing, sensor fusion, and real-time deployment. Furthermore, we outline future research directions, such as improving event camera hardware with advanced optics, leveraging neuromorphic computing for efficient processing, and integrating bio-inspired algorithms to enhance perception. To support ongoing research, we provide an open-source \textit{Online Sheet} with curated resources and recent developments. We hope this survey serves as a valuable reference, facilitating the adoption of event-based vision across diverse applications.

Ch Supt Dan Card from the Met, local policing commander for north-east London, said the force was committed to finding Kaliyah, and were using drone technology and boats as part of their "thorough search over a wide area". "Specialist officers are supporting Kaliyah's family through this deeply upsetting time and our thoughts go out to all those impacted by what has happened." He added: "I'd like to thank the members of public, our first-responding officers, and colleagues from other emergency services, as they responded rapidly to carry out a large-scale search during a highly pressurised and distressing time." The force is appealing for witnesses. The search on Monday involved boats and helicopters from HM Coastguard, the Royal National Lifeboat Institution and London Fire Brigade.

Heavy rainfall caused flashing flooding on Greece's Paros island on Monday, 31 March. Drone footage captures the scale of destruction in the capital, Naousa, with damage to vehicles and authorities working to clear mud from the streets. Schools were closed Monday and authorities urged residents to avoid travel, according to local media. Further heavy rainfall is expected to hit this week.

Autonomous navigation is usually trained offline in diverse scenarios and fine-tuned online subject to real-world experiences. However, the real world is dynamic and changeable, and many environmental encounters/effects are not accounted for in real-time due to difficulties in describing them within offline training data or hard to describe even in online scenarios. However, we know that the human operator can describe these dynamic environmental encounters through natural language, adding semantic context. The research is to deploy Large Language Models (LLMs) to perform real-time contextual code adjustment to autonomous navigation. The challenge not evaluated in literature is what LLMs are appropriate and where should these computationally heavy algorithms sit in the computation-communication edge-cloud computing architectures. In this paper, we evaluate how different LLMs can adjust both the navigation map parameters dynamically (e.g., contour map shaping) and also derive navigation task instruction sets. We then evaluate which LLMs are most suitable and where they should sit in future edge-cloud of 6G telecommunication architectures.

We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR) and demonstrate that AIM can support indoor spaces with arbitrary ranges and layouts. We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than that of commercial UWB-based systems in a complex 10m\times10m indoor scenario, where state-of-the-art infrared systems would not even work because of NLoS situations. When distributed microphone arrays are deployed, the mean error can be reduced to less than 0.5m in a 20m range, and even support spaces with arbitrary ranges and layouts.

All the images in this paper are best viewed in color on a computer screen at 200% zoom. Abstract -- Autonomous aerial robots are becoming commonplace in our lives. Hands-on aerial robotics courses are pivotal in training the next-generation workforce to meet the growing market demands. Such an efficient and compelling course depends on a reliable testbed. We utilize pose from an external localization system to hallucinate real-time and photorealistic visual sensors using 3D Gaussian Splatting. This enables stress-free testing of autonomy algorithms on aerial robots without the risk of crashing into obstacles. We achieve over 100Hz of system update rate. Lastly, we build upon our past experiences of offering hands-on aerial robotics courses and propose a new open-source and open-hardware curriculum based on VizFlyt for the future. We test our framework on various course projects in real-world HITL experiments and present the results showing the efficacy of such a system and its large potential use cases. Code, datasets, hardware guides and demo videos are available at https://pear .wpi.edu/research/vizflyt.html

Russian drones have struck Ukraine's eastern city of Kharkiv, killing two people and wounding dozens, the city's mayor Ihor Terekhov has said. "For the second time in a week, the enemy launched a combined attack, launching seven'Shaheed' at residential areas, hospitals, and the city's infrastructure," Terekhov said in a Telegram message on Sunday, referring to Iranian-made Shahed drones. The swarm of drones also targeted a military hospital, a shopping centre and apartment blocks, he said. Five of the 35 people wounded in the attack overnight were children. At least 13 have been hospitalised, including a teenage girl who is in serious condition.

Fox News contributor Dan Hoffman joins'Fox & Friends' to discuss Ukraine's claims that Russia has violated the partial ceasefire deal and Putin's threat to Trump over his effort to acquire Greenland. Two people were killed and 35 others were injured after a Russian drone attack struck a military hospital and shopping center in Ukraine late Saturday night, Ukrainian officials say. Regional Gov. Oleh Syniehubov condemned the attack on Kharkiv in a statement on Sunday, saying a 67-year-old man and a 70-year-old woman were killed. The attack comes as Russia's aggression in Ukraine shows no signs of stopping despite efforts by President Donald Trump's administration to speed along peace talks. Ukraine says that many of the casualties were servicemen undergoing treatment at the military hospital.