Secondly, we use the learned This paper presents a novel self-supervised path-planning method world model as an internal generative model enriched with active for UAV-aided networks. First, we employed an optimizer to solve states to simulate the environment and plan actions that minimize training examples offline and then used the resulting solutions as the agent's surprise during online decision-making. This approach demonstrations from which the UAV can learn the world model to enables the UAV to navigate its surroundings with a reference model understand the environment and implicitly discover the optimizer's representing the goal, choosing actions that minimize unexpected or policy. UAV equipped with the world model can make real-time unusual observations (surprise) measured by how much they deviate autonomous decisions and engage in online planning using active from the expected goal. The main contributions of this paper are as inference. During planning, UAV can score different policies based follows: It expands on previous research [11] by exploring online on the expected surprise, allowing it to choose among alternative planning, a prospective form of cognition.

Palms play an outsized role in tropical forests and are important resources for humans and wildlife. A central question in tropical ecosystems is understanding palm distribution and abundance. However, accurately identifying and localizing palms in geospatial imagery presents significant challenges due to dense vegetation, overlapping canopies, and variable lighting conditions in mixed-forest landscapes. Addressing this, we introduce PalmProbNet, a probabilistic approach utilizing transfer learning to analyze high-resolution UAV-derived orthomosaic imagery, enabling the detection of palm trees within the dense canopy of the Ecuadorian Rainforest. This approach represents a substantial advancement in automated palm detection, effectively pinpointing palm presence and locality in mixed tropical rainforests. Our process begins by generating an orthomosaic image from UAV images, from which we extract and label palm and non-palm image patches in two distinct sizes. These patches are then used to train models with an identical architecture, consisting of an unaltered pre-trained ResNet-18 and a Multilayer Perceptron (MLP) with specifically trained parameters. Subsequently, PalmProbNet employs a sliding window technique on the landscape orthomosaic, using both small and large window sizes to generate a probability heatmap. This heatmap effectively visualizes the distribution of palms, showcasing the scalability and adaptability of our approach in various forest densities. Despite the challenging terrain, our method demonstrated remarkable performance, achieving an accuracy of 97.32% and a Cohen's kappa of 94.59% in testing.

In this paper, we investigate the performance of a Hybrid Quantum Neural Network (HQNN) and a comparable classical Convolution Neural Network (CNN) for detection and classification problem using a radar. Specifically, we take a fairly complex radar time-series model derived from electromagnetic theory, namely the Martin-Mulgrew model, that is used to simulate radar returns of objects with rotating blades, such as drones. We find that when that signal-to-noise ratio (SNR) is high, CNN outperforms the HQNN for detection and classification. However, in the low SNR regime (which is of greatest interest in practice) the performance of HQNN is found to be superior to that of the CNN of a similar architecture.

The death toll from a Russian drone strike that destroyed an apartment block in Ukraine's southern port city of Odesa on Saturday has risen to 10. Ukraine's interior ministry reported that rescue workers on Sunday morning retrieved the remains of an infant and the baby's mother, raising the number of children killed in the attack to three. "The mother tried to cover the 8-month-old child with her own [body]. She tried to save them. They were found in a firm embrace," the ministry said in a Telegram post. On Saturday, Ukrainian authorities reported that a baby was among those killed after falling debris from an Iranian-made Shahed drone hit the apartment building – one of eight Russian-launched drones reported by officials.

In one of the biggest strikes on the Black Sea fleet, last September Ukraine attacked naval targets and port infrastructure, using as many as 10 missiles and three unmanned boats. It caused a large fire at a Sevastopol shipyard.

Fox News contributor Mike Pompeo weighs in on Hungary's parliament approving Sweden's bid to join NATO and a resurfaced clip of Russian President Vladimir Putin's warning about NATO expansion on'The Story.' A drone crashed into an apartment building in St. Petersburg Saturday morning, according to Russian state news agency RIA Novosti. The local state news agency said that Ukrainian forces had damaged the apartment building. Two buildings were damaged in St. Petersburg's Krasnogvardeisky district following the alleged attack. Photos from the dilapidated-looking apartment complex showed large craters on the building's exterior.

A Russian drone attack on an apartment block in the southern Ukrainian port city of Odesa has killed at least seven people, including a three-year-old and a woman with an infant child, regional authorities said. "Rescuers in Odesa have just uncovered the bodies of a mother with a three-month-old baby," Interior Minister Ihor Klymenko said in a post on the Telegram app on Saturday. At the scene, smoke poured from rubble strewn across the ground where the drone had ripped a chunk several storeys high out of the building. Clothes and furniture were scattered in the ruined mass of concrete and steel hanging off the side of the apartment block. Ukraine's State Emergencies Service posted photos, including of a dead toddler being placed in a body bag by rescuers.

Israeli forces have hit a tent in Rafah housing displaced Palestinians, killing at least 11 people, according to local authorities, hours after 17 people were killed in attacks elsewhere in the Gaza Strip. At least 50 people were injured in Saturday's drone attack, which took place next to the entrance of the Al-Helal Al-Emirati Maternity Hospital in Tal as-Sultan, Rafah City, Gaza's Ministry of Health said in a statement. The ministry said Abdel Fattah Abu Marhi, the head of the paramedic unit at the hospital, was killed, and that children were among the injured. "A tent filled with displaced evacuees in the area, including an entire family, has been directly hit by a drone strike," said Al Jazeera's Hani Mahmoud, reporting from Rafah. He said eight of the bodies had been taken to the Kuwait Hospital "where the scene is very chaotic" as the small facility is unprepared for the large number of injuries arriving there.

In this paper, we propose a cost-effective strategy for heterogeneous UAV swarm systems for cooperative aerial inspection. Unlike previous swarm inspection works, the proposed method does not rely on precise prior knowledge of the environment and can complete full 3D surface coverage of objects in any shape. In this work, agents are partitioned into teams, with each drone assign a different task, including mapping, exploration, and inspection. Task allocation is facilitated by assigning optimal inspection volumes to each team, following best-first rules. A voxel map-based representation of the environment is used for pathfinding, and a rule-based path-planning method is the core of this approach. We achieved the best performance in all challenging experiments with the proposed approach, surpassing all benchmark methods for similar tasks across multiple evaluation trials. The proposed method is open source at https://github.com/ntu-aris/caric_baseline and used as the baseline of the Cooperative Aerial Robots Inspection Challenge at the 62nd IEEE Conference on Decision and Control 2023.

A disc-shaped object longer than the height of the Empire State Building emerged from the horizon of Ukraine's embattled Donetsk province last Friday, hovering eerily still a mile off the ground, a soldier has told DailyMail.com That soldier, a drone operator, had cautiously guided his infrared quadcopter 500-feet for a reconnaissance mission, struggling against high winds, when he suddenly spotted the flat, 1,300-foot-long UFO, which stood motionless despite those winds. In an interview from the warzone, the soldier, who is with the Ukrainian army's 406th Battalion, said he and his fellow servicemen had'never seen things like this before.' 'Initially, I thought that it was something new invented by the Russians,' he added, 'but then I understood... 'No! It might be [a] UFO.''