Owing to small size, sensing capabilities and autonomous nature, the Unmanned Air Vehicles (UAVs) have enormous applications in various areas, e.g., remote sensing, navigation, archaeology, journalism, environmental science, and agriculture. However, the unmonitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. Therefore, timely detection of the AmDr is essential for the protection and security of sensitive organizations, human life and other vital infrastructure. AmDrs can be detected using different techniques based on sound, video, thermal, and radio frequencies. However, the performance of these techniques is limited in sever atmospheric conditions. In this paper, we propose an efficient unsupervise machine learning approach of independent component analysis (ICA) to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario. After unmixing the signals, the features like Mel Frequency Cepstral Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD signals are extracted by first passing the signals from octave band filter banks. Based on the above features the signals are classified using Support Vector Machines (SVM) and K Nearest Neighbor (KNN) to detect the presence or absence of AmDr. Unique feature of the proposed technique is the detection of a single or multiple AmDrs at a time in the presence of multiple acoustic interfering signals. The proposed technique is verified through extensive simulations and it is observed that the RMS values of PSD with KNN performs better than the MFCC with KNN and SVM.

Maintenance workers in the bowels of the Tokyo Metro system are being assisted by emote-controlled 8.5-inch wide drones. The remote-controlled 2.5 pound (1.15kg) drone is encased in a plastic sphere to protect it from any unfortunate bumps and knocks while navigating the labyrinth. Cameras on the custom-built drone will allow operators to scan hard to reach parts of the tunnel network for signs of damage. Current methods involve humans using a torch and looking up to see signs of damage and then having to use vehicles and platforms to reach them. Maintenance workers in the bowels of the Japanese metro system are being assisted by 8.5-inch wide drones.

In the summer of 2014, Ahti Heinla, one of the software engineers who helped develop Skype, started taking photos of his house. There is nothing particularly unusual about this, of course. Only he kept on doing it. Month after month, as summer turned to fall and fall gave way to winter, Heinla went out to the same exact spot on the sidewalk and snapped new, seemingly identical pictures of his home. Was the man who had played a crucial role in building a multibillion dollar telecommunications app losing his mind?

Sci-fi movies have created an impact on our minds that using robots in our life is a very bad idea. From The Terminator to The Matrix, almost every Hollywood movie shows that robots took control over humanity. Even RUR, the 1920s Karel Capek play introduced the term "robot,". Despite the cinematic warnings robots have moved from fiction stories to an important piece of modern world arsenal. Now the developed world is also debating on the point to use develop killer robots and machine to save human life. In 1960, a company started building something that meets the guidelines of making a robot, that's when SRI International in Silicon Valley developed first truly perceptive and mobile robot known as SHAKY.

This paper studies the trajectory optimization problem for an aerial vehicle with the mission of flying between a pair of given initial and final locations. The objective is to minimize the travel time of the aerial vehicle ensuring that the communication connectivity constraint required for the safe operation of the aerial vehicle is satisfied. We consider two different criteria for the connectivity constraint of the aerial vehicle which leads to two different scenarios. In the first scenario, we assume that the maximum continuous time duration that the aerial vehicle is out of the coverage of the ground base stations (GBSs) is limited to a given threshold. In the second scenario, however, we assume that the total time periods that the aerial vehicle is not covered by the GBSs is restricted. Based on these two constraints, we formulate two trajectory optimization problems. To solve these non-convex problems, we use an approach based on the double Q-learning method which is a model-free reinforcement learning technique and unlike the existing algorithms does not need perfect knowledge of the environment. Moreover, in contrast to the well-known Q-learning technique, our double Q-learning algorithm does not suffer from the over-estimation issue. Simulation results show that although our algorithm does not require prior information of the environment, it works well and shows near optimal performance.

In a recent post, we described what it would take to build a sustainable machine learning practice. By "sustainable," we mean projects that aren't just proofs of concepts or experiments. A sustainable practice means projects that are integral to an organization's mission: projects by which an organization lives or dies. These projects are built and supported by a stable team of engineers, and supported by a management team that understands what machine learning is, why it's important, and what it's capable of accomplishing. Finally, sustainable machine learning means that as many aspects of product development as possible are automated: not just building models, but cleaning data, building and managing data pipelines, testing, and much more. Machine learning will penetrate our organizations so deeply that it won't be possible for humans to manage them unassisted. Organizations throughout the world are waking up to the fact that security is essential to their software projects. Nobody wants to be the next Sony, the next Anthem, or the next Equifax. But while we know how to make traditional software more secure (even though we frequently don't), machine learning presents a new set of problems. Any sustainable machine learning practice must address machine learning's unique security issues. We didn't do that for traditional software, and we're paying the price now.

DUBAI, UNITED ARAB EMIRATES – Al-Qaida in the Arabian Peninsula on Sunday confirmed the death of its leader, Qassim al-Rimi, and appointed a successor, weeks after the U.S. said it had "eliminated" the Islamist militant chief, SITE Intelligence group said. The announcement came in an audio speech delivered by AQAP religious official, Hamid bin Hamoud al-Tamimi, said the group, which monitors jihadi networks worldwide. "In his speech, Tamimi spoke at length about Rimi and his jihadi journey, and stated that Khalid bin Umar Batarfi is the new leader of AQAP," it said. SITE said Batarfi has appeared in many AQAP videos over the past several years and appeared to have been Rimi's deputy and group spokesman. President Donald Trump announced Rimi's death earlier this month, saying he had been killed in a U.S. "counterterrorism operation in Yemen."

Skydio is an autonomous drone technology company that designs and develops GPS enabled software to navigate drones. Since 2014, Skydio has established its company which specializes in artificial intelligence, robotics and computer vision. Not until 2018 they launched R1 which was considered as a breakthrough in autonomous drones for customers and as a platform for commercial development. Nowadays, they are building autonomous systems, using artificial intelligence to duck under canopies and dive around branches in forests with caution all by itself. Furthermore, not only it is able to avoid obstacles, some other benefits are they provide remarkable pictures no other drones can.

One of the most important but generally overlooked missions of the U.S. Navy is port security. While incidents in peacetime are generally rare, the 2000 terrorist attack on the destroyer USS Cole remains a real danger. Now the Navy is experimenting with using one of its newest unmanned boats as a way to protect warships sitting pierside from attack. In October 2000, the guided-missile destroyer USS Cole was refueling at the port of Aden in Yemen when it came under attack by Al Qaeda terrorists. A small boat loaded with explosives sidled up to the 10,000 ton destroyer and exploded, killing 17 U.S. Navy sailors and injuring 39.

According to an American military official, the 358 missile in flight is about nine feet long and can run on kerosene or diesel fuel contained in flexible containers that do not require a separate fuel pump. A dozen infrared lenses arranged in a ring around the missile are believed to be able to defeat heat-seeking countermeasures that coalition helicopters typically use. Another United States military official said that the 358 missiles from Iran had been fired against American drones flying in Yemeni airspace, but they had not yet succeeded in hitting any. Three of the 358 missiles were captured in November by the Forrest Sherman, a Navy destroyer, and five more were recovered this month in an operation by the Normandy, a Navy cruiser. Those shipments also included more than 170 antitank guided missiles made in Iran, as well as 13,000 blasting caps, which are critical to making modern roadside bombs.