If you're still looking for the perfect Father's Day gift, you have a bunch of options that you can get for less right now. A rare sale on the Sonos Roam and Move speakers discounts them both by 20 percent, while a number of Apple devices are on sale, too. The Google Pixel 6 Pro smartphone is still $100 off, plus Solo Stove's fire pits are up to 43 percent off. Here are the best tech deals from this week that you can still get today. Sonos' portable Roam speaker remains 20 percent off and down to just over $143.

Current advancements in waterborne autonomous systems, together with the development of cloud-based service-oriented architectures and the recent availability of low-cost underwater acoustic modems and long-range above water wireless devices, enabled the development of new applications to support ships and port activities. Unmanned Surface Vehicle (USV) can, for instance, be used to perform bathymetry and environmental data collection tasks to ensure under-keel clearance and to monitor the quality of the water. Similarly, Remotely Operated Vehicles (ROVs) can be deployed to inspect ship hulls and typical port infrastructure elements, such as quay and sheet pilling walls. In this paper we present the complete system deployed for the small-scale demonstrations of the Robotic Vessels as-a-Service (RoboVaaS) project, which introduces an on-demand service-based cloud system that dispatches Unmanned Vehicles (UVs) capable of performing the required service either autonomously or piloted. These vessels are able to interact with sensors deployed in the port and with the shore station through an integrated underwater and above water network. The developed system has been validated through sea trials and showcased through an underwater sensor data collection service. The results of the test presented in this paper provide a proof-of-concept of the system design and indicate its technical feasibility. It also shows the need for further developments for a mature technology allowing on-demand robotic maritime assistance services in real operational scenarios.

Frost & Sullivan has released its annual Top 50 emerging technologies that are poised to generate multi-billion-dollar markets and set new growth opportunities worldwide. The emerging technologies are distributed across nine key clusters and represent the bulk of the R&D and innovation activity happening today, Frost & Sullivan said. Some of the emerging technologies noted by the market research company include: Flash lidar, graphene sensors, 5G materials, smart object security, carbon upcycling, battery recycling, grid-scale energy storage, autonomous mobile robots, robotic exoskeletons, cognitive manufacturing and behavioral biometrics. Other emerging tech listed include digital biomarkers, hyperspectral imaging, solid-state batteries, multi-cloud automation, sub-millimeter wave sensing, adaptive computing and accelerated storage. Frost & Sullivan will be hosting a webinar called "The 2021 Top 50 Technologies Transforming the Future," on April 27 at 11 a.m. EDT, discussing these converging technologies and how companies will be able to take advantage of the opportunities for growth.

The Li-ion battery is a complex physicochemical system that generally takes applied current as input and terminal voltage as output. The mappings from current to voltage can be described by several kinds of models, such as accurate but inefficient physics-based models, and efficient but sometimes inaccurate equivalent circuit and black-box models. To realize accuracy and efficiency simultaneously in battery modeling, we propose to build a data-driven surrogate for a battery system while incorporating the underlying physics as constraints. In this work, we innovatively treat the functional mapping from current curve to terminal voltage as a composite of operators, which is approximated by the powerful deep operator network (DeepONet). Its learning capability is firstly verified through a predictive test for Li-ion concentration at two electrodes. In this experiment, the physics-informed DeepONet is found to be more robust than the purely data-driven DeepONet, especially in temporal extrapolation scenarios. A composite surrogate is then constructed for mapping current curve and solid diffusivity to terminal voltage with three operator networks, in which two parallel physics-informed DeepONets are firstly used to predict Li-ion concentration at two electrodes, and then based on their surface values, a DeepONet is built to give terminal voltage predictions. Since the surrogate is differentiable anywhere, it is endowed with the ability to learn from data directly, which was validated by using terminal voltage measurements to estimate input parameters. The proposed surrogate built upon operator networks possesses great potential to be applied in on-board scenarios, such as battery management system, since it integrates efficiency and accuracy by incorporating underlying physics, and also leaves an interface for model refinement through a totally differentiable model structure.

RIYADH: The UAE's share of Saudi non-oil exports dropped to 14.8 percent in February, down from 17 percent the previous month, according to initial data by the General Authority for Statistics. Despite the fall, it is still the leading destination for the Kingdom's non-oil exports. The drop is partly due to a decline in transport equipment exports. The equipment, which made up 30.7 percent of UAE's share of exports in February, fell to SR1.11 billion ($0.3 billion), from 1.42 billion in January. Machinery and electrical equipment fell to SR687 million, from SR752 million respectively.

Unmanned aerial vehicles (UAVs), commonly known as drones, are being increasingly deployed throughout the globe as a means to streamline monitoring, inspection, mapping, and logistic routines. When dispatched on autonomous missions, drones require an intelligent decision-making system for trajectory planning and tour optimization. Given the limited capacity of their onboard batteries, a key design challenge is to ensure the underlying algorithms can efficiently optimize the mission objectives along with recharging operations during long-haul flights. With this in view, the present work undertakes a comprehensive study on automated tour management systems for an energy-constrained drone: (1) We construct a machine learning model that estimates the energy expenditure of typical multi-rotor drones while accounting for real-world aspects and extrinsic meteorological factors. (2) Leveraging this model, the joint program of flight mission planning and recharging optimization is formulated as a multi-criteria Asymmetric Traveling Salesman Problem (ATSP), wherein a drone seeks for the time-optimal energy-feasible tour that visits all the target sites and refuels whenever necessary. (3) We devise an efficient approximation algorithm with provable worst-case performance guarantees and implement it in a drone management system, which supports real-time flight path tracking and re-computation in dynamic environments. (4) The effectiveness and practicality of the proposed approach are validated through extensive numerical simulations as well as real-world experiments.

Nissan Motor Co. is betting that its experience pioneering lithium-ion batteries for electric vehicles over a decade ago will give it an upper hand in producing a new battery type that, despite being new and still relatively unproven, is considered by some as key to unlocking the future potential of EVs. Nissan is producing prototype solid-state battery cells -- which replace the electrical current-conducting liquid found in conventional batteries with a solid substance -- at a facility resembling a pop-up lab inside its research grounds near its Yokohama headquarters. The Japanese automaker plans to bring the new type of batteries to market by fiscal year 2028, readying a pilot plant for them ahead of that around 2024. If they can be manufactured, solid-state batteries would unlock cheaper, safer and faster-charging EVs, according to automotive executives and battery experts. Using different material combinations, Nissan predicts it will eventually be able to produce a solid-state battery pack that costs $65 (¥8,063) per kilowatt-hour -- a level at which analysts say EVs could reach price parity with gasoline-engine cars.

Computer vision has great potential to improve our everyday lives – and there are many applications and uses for it. All of these applications use intelligent video analytics, driven by AI and Machine Learning (ML), to watch video, use intelligence to make decisions, and then take action. However, like many AI-driven applications, computer vision needs bursts of computing power, memory, and energy to do its complex analysis and make decisions. While this is fine in a data center with a lot of computer power, it can prevent the move of AI to the edge. Specifically, small devices that are located far from corporate data centers and operate on small batteries need a new breed of AI that is smaller, faster and "lighter" than traditional approaches.

Today's rechargeable batteries are a wonder, but far from perfect. Eventually, they all wear out, begetting expensive replacements and recycling. "But what if batteries were indestructible?" asks William Chueh, an associate professor of materials science and engineering at Stanford University and senior author of a new paper detailing a first-of-its-kind analytical approach to building better batteries that could help speed that day. The study appears in the journal Nature Materials. Chueh, lead author Haitao "Dean" Deng, PhD '21, and collaborators at Lawrence Berkeley National Laboratory, MIT and other research institutions used artificial intelligence to analyze new kinds of atomic-scale microscopic images to understand exactly why batteries wear out.

Using artificial intelligence to analyse vast amounts of data in atomic-scale images, researchers answered long-standing questions about an emerging type of rechargeable battery posing competition to lithium-ion chemistry.