Unmanned Aerial Vehicles (UAVs) are increasingly used in surveillance, logistics, agriculture, disaster management, and military operations. Accurate detection and classification of UAV flight states, such as hovering, cruising, ascending, or transitioning, which are essential for safe and effective operations. However, conventional time series classification (TSC) methods often lack robustness and generalization for dynamic UAV environments, while state of the art(SOTA) models like Transformers and LSTM based architectures typically require large datasets and entail high computational costs, especially with high-dimensional data streams. This paper proposes a novel framework that integrates a Transformer-based Generative Adversarial Network (GAN) with Multiple Instance Locally Explainable Learning (MILET) to address these challenges in UAV flight state classification. The Transformer encoder captures long-range temporal dependencies and complex telemetry dynamics, while the GAN module augments limited datasets with realistic synthetic samples. MIL is incorporated to focus attention on the most discriminative input segments, reducing noise and computational overhead. Experimental results show that the proposed method achieves superior accuracy 96.5% on the DroneDetect dataset and 98.6% on the DroneRF dataset that outperforming other SOTA approaches. The framework also demonstrates strong computational efficiency and robust generalization across diverse UAV platforms and flight states, highlighting its potential for real-time deployment in resource constrained environments.

Unmanned aerial vehicles (UAVs) equipped with integrated sensing and communication (ISAC) capabilities are envisioned to play a pivotal role in future wireless networks due to their enhanced flexibility and efficiency. However, jointly optimizing UAV trajectory planning, multi-user communication, and target sensing under stringent resource constraints and time-critical conditions remains a significant challenge. To address this, we propose an Age of Information (AoI)-centric UAV-ISAC system that simultaneously performs target sensing and serves multiple ground users, emphasizing information freshness as the core performance metric. We formulate a long-term average AoI minimization problem that jointly optimizes the UAV's flight trajectory and beamforming. To tackle the high-dimensional, non-convexity of this problem, we develop a deep reinforcement learning (DRL)-based algorithm capable of providing real-time decisions on UAV movement and beamforming for both radar sensing and multi-user communication. Specifically, a Kalman filter is employed for accurate target state prediction, regularized zero-forcing is utilized to mitigate inter-user interference, and the Soft Actor-Critic algorithm is applied for training the DRL agent on continuous actions. The proposed framework adaptively balances the trade-offs between sensing accuracy and communication quality. Extensive simulation results demonstrate that our proposed method consistently achieves lower average AoI compared to baseline approaches.

Drones have rapidly evolved from backyard novelties into critical components of today's infrastructure and now, into one of the fastest-growing threats to our national security. As the CEO of one of the nation's largest drone technology companies and a former naval officer, I've seen firsthand how powerful these tools can be. I've also seen how dangerous they are when left unregulated; they become liabilities, capable of disruption, destruction and danger. Just days ago, amid deadly flash floods in Texas, a private drone collided with a rescue helicopter during an active life-saving mission. The crash forced the crew to land, grounding a critical asset in the middle of an unfolding emergency.

While no one knows when or where the next major war will break out, what is becoming clear is that next time the United States engages directly in a conflict, U.S. combat units will be sharing their battle space with a different type of force -- drones, lots of them. In a push for the world's most powerful military to "meet the demands of 21st-century warfighting," Defense Secretary Pete Hegseth has ordered the Pentagon to fast-track the adoption and boost the number of various small drones deployed across the force, treating them as "consumable or expendable" capabilities similar to bullets, hand grenades and other munitions. The new initiative aims to ramp up the production, experimentation and fielding of small unmanned systems weighing less than 55 pounds (25 kilograms). This includes one-way, "kamikaze" attack drones and loitering munitions to maintain "battlefield superiority" as Washington's geopolitical and technological rivalry with Beijing intensifies.

Real-time trajectory planning for unmanned aerial vehicles (UAVs) in dynamic environments remains a key challenge due to high computational demands and the need for fast, adaptive responses. Traditional Particle Swarm Optimization (PSO) methods, while effective for offline planning, often struggle with premature convergence and latency in real-time scenarios. To overcome these limitations, we propose PE-PSO, an enhanced PSO-based online trajectory planner. The method introduces a persistent exploration mechanism to preserve swarm diversity and an entropy-based parameter adjustment strategy to dynamically adapt optimization behavior. UAV trajectories are modeled using B-spline curves, which ensure path smoothness while reducing optimization complexity. To extend this capability to UAV swarms, we develop a multi-agent framework that combines genetic algorithm (GA)-based task allocation with distributed PE-PSO, supporting scalable and coordinated trajectory generation. The distributed architecture allows for parallel computation and decentralized control, enabling effective cooperation among agents while maintaining real-time performance. Comprehensive simulations demonstrate that the proposed framework outperforms conventional PSO and other swarm-based planners across several metrics, including trajectory quality, energy efficiency, obstacle avoidance, and computation time. These results confirm the effectiveness and applicability of PE-PSO in real-time multi-UAV operations under complex environmental conditions.

The latest attacks come as the Kremlin's spokesman, Dmitry Peskov, said that Russian President Vladimir Putin was ready to move towards a peace settlement with Ukraine but that Moscow's priority was to "achieve our goals". "President Putin has repeatedly spoken of his desire to bring the Ukrainian settlement to a peaceful conclusion as soon as possible. This is a long process, it requires effort, and it is not easy," he said in a televised interview. It has been nearly three-and-a-half years since Moscow launched its full-scale invasion of Ukraine. On Saturday, Ukrainian President Volodymyr Zelensky proposed a new round of talks with Moscow, aimed at restarting negotiations that halted last month.

Mallal says he wants to see a "broadly accessible and easy-to-use programme where artists are compensated for their work". Beeban Kidron, a cross-bench peer and leading campaigner against the government proposals, says AI film-making tools are "fantastic" but "at what point are they going to realise that these tools are literally built on the work of creators?" She adds: "Creators need equity in the new system or we lose something precious." YouTube says its terms and conditions allow Google to use creators' work for making AI models – and denies that all of YouTube's inventory has been used to train its models. Mallal calls his use of AI to make films "prompt craft", a phrase that uses the term for giving instructions to AI systems. When making the Ukraine film, he says he was amazed at how quickly a camera angle or lighting tone could be adjusted with a few taps on a keyboard.

At least 30 Palestinians have been killed since dawn across Gaza in Israeli attacks, medical sources have told Al Jazeera, as the besieged and bombarded enclave's decimated health system, overwhelmed by a daily flow of wounded, is forcing doctors to make decisions on who to treat first. In the latest killings on Friday, three people died in an Israeli attack on the Tuffah neighbourhood of eastern Gaza City. Five people were also killed in an Israeli air attack in Jabalia an-Nazla, in northern Gaza. Earlier, an Israeli attack hit tents sheltering displaced Palestinians in al-Mawasi, southern Gaza – previously designated a so-called "safe zone" – igniting a major fire and killing at least five people, including infants. Al-Mawasi has come under repeated, deadly Israeli fire.

The images come in every day. Men and equipment being hunted down along Ukraine's long, contested front lines. Everything filmed, logged and counted. And now put to use too, as the Ukrainian military tries to extract every advantage it can against its much more powerful opponent. Under a scheme first trialled last year and dubbed "Army of Drones: Bonus" (also known as "e-points"), units can earn points for each Russian soldier killed or piece of equipment destroyed.

Ukrainian President Volodymyr Zelenskyy told the US publication The New York Post that he and United States President Donald Trump are considering a deal that involves Washington buying battlefield-tested Ukrainian drones in exchange for Kyiv purchasing weapons from the US. The US has informed Switzerland of delays to the delivery of Patriot air defence systems, the Swiss Defence Ministry said, adding that Washington wants to prioritise delivery of the systems to Ukraine.