Microscopic robots in the body could harvest energy from ultrasound to provide on-board control of autonomous behaviors such as measuring and communicating diagnostic information and precisely delivering drugs. This paper evaluates the acoustic power available to micron-size robots that collect energy using pistons. Acoustic attenuation and viscous drag on the pistons are the major limitations on the available power. Frequencies around 100kHz can deliver hundreds of picowatts to a robot in low-attenuation tissue within about 10cm of transducers on the skin, but much less in high-attenuation tissue such as a lung. However, applications of microscopic robots could involve such large numbers that the robots significantly increase attenuation, thereby reducing power for robots deep in the body. This paper describes how robots can collectively manage where and when they harvest energy to mitigate this attenuation so that a swarm of a few hundred billion robots can provide tens of picowatts to each robot, on average.

Challenges inherent to autonomous wintertime navigation in forests include lack of reliable a Global Navigation Satellite System (GNSS) signal, low feature contrast, high illumination variations and changing environment. This type of off-road environment is an extreme case of situations autonomous cars could encounter in northern regions. Thus, it is important to understand the impact of this harsh environment on autonomous navigation systems. To this end, we present a field report analyzing teach-and-repeat navigation in a subarctic forest while subject to fluctuating weather, including light and heavy snow, rain and drizzle. First, we describe the system, which relies on point cloud registration to localize a mobile robot through a boreal forest, while simultaneously building a map. We experimentally evaluate this system in over 18.8 km of autonomous navigation in the teach-and-repeat mode. Over 14 repeat runs, only four manual interventions were required, three of which were due to localization failure and another one caused by battery power outage. We show that dense vegetation perturbs the GNSS signal, rendering it unsuitable for navigation in forest trails. Furthermore, we highlight the increased uncertainty related to localizing using point cloud registration in forest trails. We demonstrate that it is not snow precipitation, but snow accumulation, that affects our system's ability to localize within the environment. Finally, we expose some challenges and lessons learned from our field campaign to support better experimental work in winter conditions. Our dataset is available online.

In this paper, we present an online planning-scheduling approach for battery-powered autonomous aerial robots. The approach consists of simultaneously planning a coverage path and scheduling onboard computational tasks. We further derive a novel variable coverage motion robust to airborne constraints and an empirically motivated energy model. The model includes the energy contribution of the schedule based on an automatic computational energy modeling tool. Our experiments show how an initial flight plan is adjusted online as a function of the available battery, accounting for uncertainty. Our approach remedies possible in-flight failure in case of unexpected battery drops, e.g., due to adverse atmospheric conditions, and increases the overall fault tolerance.

Amazon has discounted most of Ring's Video Doorbells for Prime Day this year. The cheapest of the bunch is the 2020 Ring Video Doorbell, which is 25 percent off and down to $75. The upgraded Ring Video Doorbell 3 is $40 off and down to $160, while the latest model, the Video Doorbell 4, is $50 off and down to $170. While they all have some differences between them, each of these IoT devices do the same thing: let you see who's outside your front door at all times. The standard Video Doorbell likely has everything most people would need in a gadget like this.

Artificial intelligence (AI) is often presented in binary terms in both popular culture and political analysis. Either it represents the key to a futuristic utopia defined by the integration of human intelligence and technological prowess, or it is the first step toward a dystopian rise of machines. This same binary thinking is practiced by academics, entrepreneurs, and even activists in relation to the application of AI in combating climate change. The technology industry's singular focus on AI's role in creating a new technological utopia obscures the ways that AI can exacerbate environmental degradation, often in ways that directly harm marginalized populations. In order to utilize AI in fighting climate change in a way that both embraces its technological promise and acknowledges its heavy energy use, the technology companies leading the AI charge need to explore solutions to the environmental impacts of AI.

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.