The United States has said it will hold Russia accountable for "war crimes" and take action against companies and nations working with Iran's drone programme following a series of attacks on Ukrainian cities. At least four people – including a couple expecting a baby – were killed on Monday morning after a drone struck an apartment building in the Ukrainian capital Kyiv. The attacks also knocked out power to hundreds of towns and villages. Speaking in his regular evening address, Ukrainian President Volodymyr Zelenskyy said the air raids were continuing. "Right now, there is a new Russian drone attack," he said.

"Anyone doing business with Iran, that can have any link to UAVs or ballistic missile developments or the flow of arms from Iran to Russia, should be very careful and do their due diligence. The US will not hesitate to use sanctions," Mr Patel warned.

Unmanned aerial vehicles (UAVs) equipped with multiple complementary sensors have tremendous potential for fast autonomous or remote-controlled semantic scene analysis, e.g., for disaster examination. Here, we propose a UAV system for real-time semantic inference and fusion of multiple sensor modalities. Semantic segmentation of LiDAR scans and RGB images, as well as object detection on RGB and thermal images, run online onboard the UAV computer using lightweight CNN architectures and embedded inference accelerators. We follow a late fusion approach where semantic information from multiple sensor modalities augments 3D point clouds and image segmentation masks while also generating an allocentric semantic map. Label propagation on the semantic map allows for sensor-specific adaptation with cross-modality and cross-domain supervision. Our system provides augmented semantic images and point clouds with $\approx$ 9 Hz. We evaluate the integrated system in real-world experiments in an urban environment and at a disaster test site.

We investigate training machine learning (ML) models across a set of geo-distributed, resource-constrained clusters of devices through unmanned aerial vehicles (UAV) swarms. The presence of time-varying data heterogeneity and computational resource inadequacy among device clusters motivate four key parts of our methodology: (i) stratified UAV swarms of leader, worker, and coordinator UAVs, (ii) hierarchical nested personalized federated learning (HN-PFL), a distributed ML framework for personalized model training across the worker-leader-core network hierarchy, (iii) cooperative UAV resource pooling to address computational inadequacy of devices by conducting model training among the UAV swarms, and (iv) model/concept drift to model time-varying data distributions. In doing so, we consider both micro (i.e., UAV-level) and macro (i.e., swarm-level) system design. At the micro-level, we propose network-aware HN-PFL, where we distributively orchestrate UAVs inside swarms to optimize energy consumption and ML model performance with performance guarantees. At the macro-level, we focus on swarm trajectory and learning duration design, which we formulate as a sequential decision making problem tackled via deep reinforcement learning. Our simulations demonstrate the improvements achieved by our methodology in terms of ML performance, network resource savings, and swarm trajectory efficiency.

Considering the accelerated development of Unmanned Aerial Vehicles (UAVs) applications in both industrial and research scenarios, there is an increasing need for localizing these aerial systems in non-urban environments, using GNSS-Free, vision-based methods. Our paper proposes a vision-based localization algorithm that utilizes deep features to compute geographical coordinates of a UAV flying in the wild. The method is based on matching salient features of RGB photographs captured by the drone camera and sections of a pre-built map consisting of georeferenced open-source satellite images. Experimental results prove that vision-based localization has comparable accuracy with traditional GNSS-based methods, which serve as ground truth. Compared to state-of-the-art Visual Odometry (VO) approaches, our solution is designed for long-distance, high-altitude UAV flights. Code and datasets are available at https://github.com/TIERS/wildnav.

KYIV, Ukraine – A loud'boom' serves as an alarm clock in Kyiv. It's the same routine whenever you're awakened by a blast. I look out the window and don't see anything, so I look on Twitter. A Russian drone is seen during a Russian drone strike, which local authorities consider to be Iranian made unmanned aerial vehicles (UAVs) Shahed-136, amid Russia's attack on Ukraine, in Kyiv, Ukraine Oct. 17, 2022. Multiple explosions are reported in the Ukrainian capital.

Petrochemical workers strike as demonstrations continue across Iran in defiance of the regime. The European Union sanctioned Iran on Monday for the death of a 22-year-old woman while in custody of the regime's so-called morality police and the subsequent violent crackdown on protests. Numerous Iranian law enforcement officials were added to the sanctions list, including two leaders of the morality police, Mohammad Rostami and Hajahmad Mirzaei. Iran's Minister of Information and Communications Technology, Issa Zarepour, was also sanctioned for his role in censoring the internet and social media during widespread protests over the death of Mahsa Amini. Mahsa Amini, a 22-year-old Iranian woman, was reportedly murdered by Iran's morality police.

A Ukraine official has accused Iran of responsibility for the "murders of Ukrainians" after Russia attacked cities with what Kyiv called "kamikaze drones" made in the Islamic Republic and allegedly sold to Moscow. Ukraine has reported a barrage of Russian air attacks using Iranian-made Shahed-136 drones in recent weeks. Iran denies supplying the drones to Russia, while the Kremlin has not commented. Last month President Volodymyr Zelenskyy announced the accreditation of the Iranian ambassador was revoked as a result of Russian forces using Iranian drones to attack Ukraine. "Iran is responsible for the murders of Ukrainians," Ukrainian presidential adviser Mykhailo Podolyak wrote on Twitter on Monday.

A new wave of deadly Russian air strikes on Ukraine has killed more than 25 people and wounded over 100, according to authorities in Kyiv, in the most extensive attacks since the early days of the war. The current strikes, which began on October 10, have targeted at least 10 regions across the country and have been carried out using Russian missiles as well as Iranian-made drones, Ukrainian authorities have said. Swarms of explosive-laden, unmanned aircraft called "kamikaze" drones targeted Kyiv on Monday, killing at least four people and targeting energy facilities. Among the victims was a young couple expecting a baby in three months, according to the mayor of Kyiv. The Ukrainian Air Force said it destroyed at least 37 drones in one day.

The use by Russia of Iranian drones in its war against Ukraine makes clear the weaknesses of its domestic industry and Tehran's growing claim on the market for unmanned aircraft, experts say. Washington believes Iran has delivered hundreds of drones, which Ukrainian officials say are now being used in strikes like those launched against cities and energy infrastructure on Monday. So far two models of Iranian drone have been identified in Ukraine's skies, built for two different purposes. One of them, the Shahed 136, is a relatively low-cost "kamikaze drone" that can be programmed to fly automatically to a set of GPS coordinates with a payload of explosives. "It flies quite low, striking a target that must be stationary at a range of a few hundred kilometres," said Pierre Grasser, a researcher tied to Paris' Sorbonne University.