Looking to start a new hobby this summer? This beginner-friendly drone has a 4K front camera with a 90º control angle and a bottom camera with a 120º wide angle, allowing you to capture complete vistas while you're flying up high. Optical flow positioning keeps the drone in a hovering state when you want to record the ground or take a picture of a particular object, giving you great coverage of the land. It's easy for beginners to fly with gesture control, three-way obstacle avoidance, fixed point flight, gravity control, and headless mode. When you're done flying, you can easily fold it up for storage.

Communication and position sensing are among the most important capabilities for swarm robots to interact with their peers and perform tasks collaboratively. However, the hardware required to facilitate communication and position sensing is often too complicated, expensive, and bulky to be carried on swarm robots. Here we present Maneuverable Piccolissimo 3 (MP3), a minimalist, single motor drone capable of executing inter-robot communication via infrared light and triangulation-based sensing of relative bearing, distance, and elevation using message arrival time. Thanks to its novel design, MP3 can communicate with peers and localize itself using simple components, keeping its size and mass small and making it inherently safe for human interaction. We present the hardware and software design of MP3 and demonstrate its capability to localize itself, fly stably, and maneuver in the environment using peer-to-peer communication and sensing.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Fire broke out at an oil refinery in northwestern Russia on Sunday, resulting in deaths and injuries, local officials said. The regional governor said the fire was not caused by a Ukrainian drone strike and investigators opened a criminal case on suspicion of negligence. The fire near the city of Ukhta in Russia's northwestern Komi Republic left at least three people injured, Komi's emergencies ministry said.

The advent of Intelligent Reflecting Surfaces (IRS) and Unmanned Aerial Vehicles (UAVs) is setting a new benchmark in the field of wireless communications. IRS, with their groundbreaking ability to manipulate electromagnetic waves, have opened avenues for substantial enhancements in signal quality, network efficiency, and spectral usage. These surfaces dynamically reconfigure the propagation environment, leading to optimized signal paths and reduced interference. Concurrently, UAVs have emerged as dynamic, versatile elements within communication networks, offering high mobility and the ability to access and enhance coverage in areas where traditional, fixed infrastructure falls short. This paper presents a comprehensive survey on the synergistic integration of IRS and UAVs in wireless networks, highlighting how this innovative combination substantially boosts network performance, particularly in terms of security, energy efficiency, and reliability. The versatility of UAVs, combined with the signal-manipulating prowess of IRS, creates a potent solution for overcoming the limitations of conventional communication setups, especially in challenging and underserved environments. Furthermore, the survey delves into the cutting-edge realm of Machine Learning (ML), exploring its role in the strategic deployment and operational optimization of UAVs equipped with IRS. The paper also underscores the latest research and practical advancements in this field, providing insights into real-world applications and experimental setups. It concludes by discussing the future prospects and potential directions for this emerging technology, positioning the IRS-UAV integration as a transformative force in the landscape of next-generation wireless

Russian forces have launched a barrage of missiles and drones across Ukraine that damaged energy infrastructure in five regions, according to Ukrainian officials. Ukraine's national grid operator Ukrenergo said on Saturday the attacks struck facilities in the eastern Donetsk, southeastern Zaporizhia and Dnipropetrovsk regions, as well as the Kirovohrad and Ivano-Frankivsk region in the centre and west of the country, respectively. "Today morning the Russians launched another strike on Ukrainian energy facilities. Since March it is already the sixth massive, complex, missile and drone attack against the civilian energy infrastructure," Ukrenergo said. Ukrainian air defence shot down 35 of 53 Russian missiles and 46 of 47 Russian drones, the air force commander said.

Check out what's clicking on Foxnews.com. Turkish drone strikes in northeastern Syria on Friday evening killed four U.S.-backed fighters and wounded 11 civilians, the Kurdish-led force said. The strikes on areas held by the U.S.-backed and Kurdish-led Syrian Democratic Forces came a day after Turkey's president said his government won't hesitate to act against Kurdish-led groups in northern Syria if they proceed with plans to hold local elections. It accuses the groups of having links to outlawed Kurdish militants in Turkey. The SDF said drone strikes hit its positions eight times as well as civilian homes and vehicles in and near the northern city of Qamishli.

The recent embrace of machine learning (ML) in the development of autonomous weapons systems (AWS) creates serious risks to geopolitical stability and the free exchange of ideas in AI research. This topic has received comparatively little attention of late compared to risks stemming from superintelligent artificial general intelligence (AGI), but requires fewer assumptions about the course of technological development and is thus a nearer-future issue. ML is already enabling the substitution of AWS for human soldiers in many battlefield roles, reducing the upfront human cost, and thus political cost, of waging offensive war. In the case of peer adversaries, this increases the likelihood of "low intensity" conflicts which risk escalation to broader warfare. In the case of non-peer adversaries, it reduces the domestic blowback to wars of aggression. This effect can occur regardless of other ethical issues around the use of military AI such as the risk of civilian casualties, and does not require any superhuman AI capabilities. Further, the military value of AWS raises the specter of an AI-powered arms race and the misguided imposition of national security restrictions on AI research. Our goal in this paper is to raise awareness among the public and ML researchers on the near-future risks posed by full or near-full autonomy in military technology, and we provide regulatory suggestions to mitigate these risks. We call upon AI policy experts and the defense AI community in particular to embrace transparency and caution in their development and deployment of AWS to avoid the negative effects on global stability and AI research that we highlight here.

The Federal Aviation Administration (FAA) has given Amazon permission to fly its delivery drones beyond visual line of sight (BVLOS). With that hurdle cleared, the company claims it can fly farther and expand drone service, providing customers faster delivery and a larger selection of items, Amazon announced in a blog post. Until now, the FAA has only allowed Amazon to fly drones as far as someone could see them from the ground. That way, spotters or pilots could ensure that drones weren't interfering with aircraft. However, the constraint seriously limited how far the drones could travel.

Accurate geo-localization of Unmanned Aerial Vehicles (UAVs) is crucial for a variety of outdoor applications including search and rescue operations, power line inspections, and environmental monitoring. The vulnerability of Global Navigation Satellite Systems (GNSS) signals to interference and spoofing necessitates the development of additional robust localization methods for autonomous navigation. Visual Geo-localization (VG), leveraging onboard cameras and reference satellite maps, offers a promising solution for absolute localization. Specifically, Thermal Geo-localization (TG), which relies on image-based matching between thermal imagery with satellite databases, stands out by utilizing infrared cameras for effective night-time localization. However, the efficiency and effectiveness of current TG approaches, are hindered by dense sampling on satellite maps and geometric noises in thermal query images. To overcome these challenges, in this paper, we introduce STHN, a novel UAV thermal geo-localization approach that employs a coarse-to-fine deep homography estimation method. This method attains reliable thermal geo-localization within a 512-meter radius of the UAV's last known location even with a challenging 11% overlap between satellite and thermal images, despite the presence of indistinct textures in thermal imagery and self-similar patterns in both spectra. Our research significantly enhances UAV thermal geo-localization performance and robustness against the impacts of geometric noises under low-visibility conditions in the wild. The code will be made publicly available.

Capsule networks (CapsNets) were introduced to address convolutional neural networks limitations, learning object-centric representations that are more robust, pose-aware, and interpretable. They organize neurons into groups called capsules, where each capsule encodes the instantiation parameters of an object or one of its parts. Moreover, a routing algorithm connects capsules in different layers, thereby capturing hierarchical part-whole relationships in the data. This thesis investigates the intriguing aspects of CapsNets and focuses on three key questions to unlock their full potential. First, we explore the effectiveness of the routing algorithm, particularly in small-sized networks. We propose a novel method that anneals the number of routing iterations during training, enhancing performance in architectures with fewer parameters. Secondly, we investigate methods to extract more effective first-layer capsules, also known as primary capsules. By exploiting pruned backbones, we aim to improve computational efficiency by reducing the number of capsules while achieving high generalization. This approach reduces CapsNets memory requirements and computational effort. Third, we explore part-relationship learning in CapsNets. Through extensive research, we demonstrate that capsules with low entropy can extract more concise and discriminative part-whole relationships compared to traditional capsule networks, even with reasonable network sizes. Lastly, we showcase how CapsNets can be utilized in real-world applications, including autonomous localization of unmanned aerial vehicles, quaternion-based rotations prediction in synthetic datasets, and lung nodule segmentation in biomedical imaging. The findings presented in this thesis contribute to a deeper understanding of CapsNets and highlight their potential to address complex computer vision challenges.