Manila – The head of the Philippines' military said Tuesday that the country is considering partnering with Japan to beef up its cyberdefense and drone capability as part of its force modernization program. Building cyberdefense and security infrastructure "is one aspect we are focusing on now and I think we can partner with Japan in this area," Chief of Staff Gen. Gilbert Gapay said during a media forum in Manila, noting a similar thrust for force upgrades within his country. The general said the military is also considering acquiring drones and other unmanned aerial vehicles from Japan to raise its maritime surveillance and monitoring capabilities. Japan has always been among the countries shortlisted for sourcing military hardware, based on studies conducted by different technical working groups, according to Gapay. In August, the Philippines signed a $103.5 million contract with Mitsubishi Electric Corp. for an air radar system, marking the first export of a newly made complete defense product since Japan eased its post-World War II arms export ban in 2014.

Manila – The head of the Philippines' military said Tuesday that the country is considering partnering with Japan to beef up its cyber defense and drone capability as part of its force modernization program. Building cyber defense and security infrastructure "is one aspect we are focusing on now and I think we can partner with Japan in this area," Chief of Staff Gen. Gilbert Gapay said during a media forum in Manila, noting a similar thrust for force upgrades within his country. The general said the military is also considering acquiring drones and other unmanned aerial vehicles from Japan to raise its maritime surveillance and monitoring capabilities. Japan has always been among the countries shortlisted for sourcing military hardware, based on studies conducted by different technical working groups, according to Gapay. In August, the Philippines signed a $103.5 million contract with Mitsubishi Electric Corp. for an air radar system, marking the first export of a newly made complete defense product since Japan eased its post-World War II arms export ban in 2014.

The introduction of unmanned fighter jets has been considered to succeed the Air Self-Defense Force's aging F-2s, which are expected to start being retired within two decades, as part of efforts to reduce development costs, according to government officials. The proposal was made earlier this year by Taro Kono, who was defense chief until last month before he became administrative reform minister in new Prime Minister Yoshihide Suga's Cabinet. The officials, who spoke on condition of anonymity, said discussions in the Defense Ministry were, however, suspended in the wake of the government's decision in June to scrap its plan to deploy the U.S.-developed Aegis Ashore land-based defense system, designed to counter missile threats from North Korea. Japan plans to start work on a new fighter jet in fiscal 2024 together with U.S. or British companies, and aims to introduce it in fiscal 2035 when the current F-2s are scheduled to start being retired. The ministry estimates that at least ¥1.2 trillion is needed to develop a manned fighter jet, while a drone -- which has no space for a pilot and requires no safety equipment -- costs much less to build.

The control of a tail-sitter aircraft is a challenging task, especially during transition maneuver where the lift and drag forces are highly nonlinear. In this work, we implement a Neural Network (NN) capable of estimate such nonlinearities. Once they are estimated, one can propose a control scheme where these forces can correctly feed-forwarded. Our implementation of the NN has been programmed in C++ on the PX4 Autopilot an open-source autopilot for drones. To ensure that this implementation does not considerably affect the autopilot's performance, the coded NN must be of a light computational load. With the aim to test our approach, we have carried out a series of realistic simulations in the Software in The Loop (SITL) using the PX4 Autopilot. These experiments demonstrate that the implemented NN can be used to estimate the tail-sitter aerodynamic forces, and can be used to improve the control algorithms during all the flight phases of the tail-sitter aircraft: hover, cruise flight, and transition.

DARPA, the Department of Defense's research arm, is paying scientists to invent ways to instantly read soldiers' minds using tools like genetic engineering of the human brain, nanotechnology and infrared beams. Thought-controlled weapons, like swarms of drones that someone sends to the skies with a single thought or the ability to beam images from one brain to another. This week, DARPA (Defense Advanced Research Projects Agency) announced that six teams will receive funding under the Next-Generation Nonsurgical Neurotechnology (N3) program. Participants are tasked with developing technology that will provide a two-way channel for rapid and seamless communication between the human brain and machines without requiring surgery. "Imagine someone who's operating a drone or someone who might be analyzing a lot of data," said Jacob Robinson, an assistant professor of bioengineering at Rice University, who is leading one of the teams.

At the end of September, amidst its usual flurry of fall hardware announcements, Amazon debuted two especially futuristic products within five days of each other. The first is a small autonomous surveillance drone, Ring Always Home Cam, that waits patiently inside a charging dock to eventually rise up and fly around your house, checking whether you left the stove on or investigating potential burglaries. The second is a palm recognition scanner, Amazon One, that the company is piloting at two of its grocery stores in Seattle as a mechanism for faster entry and checkout. Both products aim to make security and authentication more convenient--but for privacy-conscious consumers, they also raise red flags. Amazon's latest data-hungry innovations are not launching in a vacuum.

This robot is not exactly in the cloud: Wall-E from the movie by Pixar. Bringing a new robot to market is exciting: new capability, new hardware, new services. The problem is when you get to software, where everything feels harder and takes longer than you think it should. Like Tesla's full self-driving, which has all the hardware and intelligence it needs -- with the possible exception of LIDAR -- but is perpetually just ... about ... to ... arrive ... and even so, was recently savaged by Consumer Reports as buggy and ineffective. Hardware is necessary, but software provides the animating intelligence that allows it to do useful, efficient, and safe work.

Accurate flood detection in near real time via high resolution, high latency satellite imagery is essential to prevent loss of lives by providing quick and actionable information. Instruments and sensors useful for flood detection are only available in low resolution, low latency satellites with region re-visit periods of up to 16 days, making flood alerting systems that use such satellites unreliable. This work presents H2O-Network, a self supervised deep learning method to segment floods from satellites and aerial imagery by bridging domain gap between low and high latency satellite and coarse-to-fine label refinement. H2O-Net learns to synthesize signals highly correlative with water presence as a domain adaptation step for semantic segmentation in high resolution satellite imagery. Our work also proposes a self-supervision mechanism, which does not require any hand annotation, used during training to generate high quality ground truth data. We demonstrate that H2O-Net outperforms the state-of-the-art semantic segmentation methods on satellite imagery by 10% and 12% pixel accuracy and mIoU respectively for the task of flood segmentation. We emphasize the generalizability of our model by transferring model weights trained on satellite imagery to drone imagery, a highly different sensor and domain.

Researchers at the University of Washington have developed a tiny new sensor that can be carried around on a small drone or even the back of an insect – and then dropped on demand to track the environment for years at a time. Obviously there are a few main features that this kind of system needs. The sensor needs to be very lightweight, it needs to be securely attached to its transport until a "drop" command is issued, then it needs to be able to survive a fall from a high place, and finally it has to be able to run for a decent amount of time. The team addressed all of those points with the design. The whole sensor system weighs just 98 milligrams, which they describe as about one 10th the weight of a jellybean.

Wilkinson recently had a close call when a shark trailed him, only inches away. Wilkinson recently had a close call when a shark trailed him, only inches away. The surfer had no idea a shark was trailing him. Near Sharpes Beach in Australia, professional surfer Matt Wilkinson was paddling on his board on Wednesday. Unbeknownst to him, a shark quickly surfaced and began stalking the surfing world champion, at one point only inches away.