Abstract-- We present Phaser, a flexible system that directs narrow-beam laser light to moving robots for concurrent wireless power delivery and communication. We design a semiautomatic calibration procedure to enable fusion of stereo-vision-based 3D robot tracking with high-power beam steering, and a low-power optical communication scheme that reuses the laser light as a data channel. We fabricate a Phaser prototype using off-the-shelf hardware and evaluate its performance with battery-free autonomous robots. We demonstrate Phaser fully powering gram-scale battery-free robots to nearly 2x higher speeds than prior work while simultaneously controlling them to navigate around obstacles and along paths. Code, an open-source design guide, and a demonstration video of Phaser is available at https: //mobilex.cs.columbia.edu/phaser/. Mobile, autonomous robots play an increasingly important role in today's world, with the potential to perform tasks in warehouses, factories, and homes and conduct advanced environmental explorations [1]. However, the significant power needed for locomotion, on-board computation, and communication presents a key barrier to the broader deployment of such robots. Given the energy density of current batteries [2], most autonomous robots today either remain tethered by charging wires or must routinely return to charging stations, reducing deployment time. This problem is exacerbated in miniaturized robots, which cannot support the 100s of milligrams of battery payload [3]-[7] needed for extended operation, even on their milliwatt power budgets.

Monitoring and recognizing patterns in continuous sensing data is crucial for many practical applications. These real-world time-series data are often nonstationary, characterized by varying statistical and spectral properties over time. This poses a significant challenge in developing learning models that can effectively generalize across different distributions. In this work, based on our observation that nonstationary statistics are intrinsically linked to the phase information, we propose a time-series learning framework, PhASER. It consists of three novel elements: 1) phase augmentation that diversifies non-stationarity while preserving discriminatory semantics, 2) separate feature encoding by viewing time-varying magnitude and phase as independent modalities, and 3) feature broadcasting by incorporating phase with a novel residual connection for inherent regularization to enhance distribution invariant learning. Upon extensive evaluation on 5 datasets from human activity recognition, sleep-stage classification, and gesture recognition against 10 state-of-the-art baseline methods, we demonstrate that PhASER consistently outperforms the best baselines by an average of 5% and up to 13% in some cases. Moreover, PhASER's principles can be applied broadly to boost the generalization ability of existing time series classification models.

Machine learning approaches to modelling analog audio effects have seen intensive investigation in recent years, particularly in the context of non-linear time-invariant effects such as guitar amplifiers. For modulation effects such as phasers, however, new challenges emerge due to the presence of the low-frequency oscillator which controls the slowly time-varying nature of the effect. Existing approaches have either required foreknowledge of this control signal, or have been non-causal in implementation. This work presents a differentiable digital signal processing approach to modelling phaser effects in which the underlying control signal and time-varying spectral response of the effect are jointly learned. The proposed model processes audio in short frames to implement a time-varying filter in the frequency domain, with a transfer function based on typical analog phaser circuit topology. We show that the model can be trained to emulate an analog reference device, while retaining interpretable and adjustable parameters. The frame duration is an important hyper-parameter of the proposed model, so an investigation was carried out into its effect on model accuracy. The optimal frame length depends on both the rate and transient decay-time of the target effect, but the frame length can be altered at inference time without a significant change in accuracy.

Low frequency oscillator (LFO) driven audio effects such as phaser, flanger, and chorus, modify an input signal using time-varying filters and delays, resulting in characteristic sweeping or widening effects. It has been shown that these effects can be modeled using neural networks when conditioned with the ground truth LFO signal. However, in most cases, the LFO signal is not accessible and measurement from the audio signal is nontrivial, hindering the modeling process. To address this, we propose a framework capable of extracting arbitrary LFO signals from processed audio across multiple digital audio effects, parameter settings, and instrument configurations. Since our system imposes no restrictions on the LFO signal shape, we demonstrate its ability to extract quasiperiodic, combined, and distorted modulation signals that are relevant to effect modeling. Furthermore, we show how coupling the extraction model with a simple processing network enables training of end-to-end black-box models of unseen analog or digital LFO-driven audio effects using only dry and wet audio pairs, overcoming the need to access the audio effect or internal LFO signal. We make our code available and provide the trained audio effect models in a real-time VST plugin.

Read the complete tutorial about how to implement a machine learning algorithm for the Flappy Bird video game here: http://www.askforgametask.com/tutoria... This video shows a creation of an artificial intelligence controller for the Flappy Bird game using neural networks and a genetic algorithm. There is also used Synaptic Neural Network library (https://synaptic.juancazala.com/) It is a fine tuning process of learning that incrementally improves an initial random system. The form of machine learning implemented in this program uses a genetic algorithm to train artificial neural networks.

There are all kinds of creative ways of dealing with rogue drones: Radio jamming. None of these are really designed to handle military drones, however, and large, fast-moving UAVs are still a potential threat, especially if more than one is coming at you at once. It's no surprise that the U.S. Army has been developing solutions for this potential threat-- we're not sure what they're working on now, but as of late 2013, Raytheon was successfully testing a long range, high power directed microwave weapon capable of taking out swarms of drones in milliseconds. The Phaser is essentially a high powered microwave (HPM) cannon that runs on a diesel engine. Exactly how powerful this thing is (and what its range is) is still classified, but we do know that it can be tuned to either "disrupt" or "damage," where for most drones, "damage" seems to be synonymous with "destroy."

Watch the US Army's real-life PHASER GUN in action: Weapon can knock out a swarm of drones, cars and even smart missiles with a single blast of microwave energy Raytheon's Phaser weapon uses a technique known as high-power microwave (HPM) to knock drones out of the sky with a single pulse of energy. The'Stone Age Atlantis': Stunning video reveals the... Does teenage BLOOD hold the key to the fountain of youth?... Goodbye trolls! Twitter adds the ability to mute unwelcome... Snapchat confidentially files for IPO that could value it at... The'Stone Age Atlantis': Stunning video reveals the... Does teenage BLOOD hold the key to the fountain of youth?... Goodbye trolls! Twitter adds the ability to mute unwelcome... Snapchat confidentially files for IPO that could value it at... Riker (Jonathan Frakes, left) and Geordi (LeVar Burton, right) fire phasers to hinder Zefram Cochrane's escape in the science-fiction action thriller:'Star Trek: first contact'.

While the US military has enjoyed several decades of aerial dominance with few enemy fighter planes to shoot down, the emergence of ISIS drones presents a new threat to American ground troops. To combat swarms of these cheap, small dangers, the US Army is testing a new anti-air device that is designed to blow multiple UAVs out of the sky in a single shot. They call it the Phaser. The Raytheon-built "Phaser" is a microwave-emitting dish that sits atop a shipping container containing its diesel generator power source. It relies on external radar systems to track targets, then fires a burst of radiation powerful enough to fry control systems, enough to knock drones out of the air.