Accenture Global Solutions Limited

Eric Michael Gallo

Lavinia Andreea Danielescu

Aditi Maheshwari

Timothy M Shea

Kenneth Stewart

Noah Gideon Pacik-Nelson

Katherine Wei Song

Lingyi Bi

Systems, methods, and devices are directed to providing charge mitigation via a MXene-based conductive pathway on a spacesuit. In one aspect, a system for planetary exploration to provide charge mitigation on an outer surface of the spacesuit comprises a multilayered fabric including a woven layer, wherein the woven layer includes a first side to face a space environment and an opposing side to face a body of a wearer of the spacesuit and a MXene coating layer applied on the opposing side, wherein the MXene coating layer extends through the thickness the woven layer to conductively couple a first location on an exterior surface of the spacesuit with a second location on the exterior surface of the spacesuit.

Providing charge mitigation on a spacesuit via a MXene-based conductive pathway

Packaging devices such as bags, pouches, envelops, containers, and the like, can include an integrated heating system. The packaging devices described herein have multiple potential uses such as for the containment and heating of single-serving meals, drinks, massage oils, masks, body wax, anti-wrinkle eye creams, and the like, to be heated on the go. The packaging devices described herein can also be used for instant heating of food and/or beverages for backcountry use. In some embodiments, the packaging devices use fibrous natural materials (e.g., leaf skeletons and soft biomaterials like chitosan) along with silver nanowires to create completely biodegradable, and reusable self-heating packaging.

Biodegradable packaging with integrated heating system

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for recognizing speech using a spiking neural network acoustic model implemented on a neuromorphic processor are described. In one aspect, a method includes receiving, a trained acoustic model implemented as a spiking neural network (SNN) on a neuromorphic processor of a client device, a set of feature coefficients that represent acoustic energy of input audio received from a microphone communicably coupled to the client device. The acoustic model is trained to predict speech sounds based on input feature coefficients. The acoustic model generates output data indicating predicted speech sounds corresponding to the set of feature coefficients that represent the input audio received from the microphone. The neuromorphic processor updates one or more parameters of the acoustic model using one or more learning rules and the predicted speech sounds of the output data.

Self-learning neuromorphic acoustic model for speech recognition

Growing community of inventors

Moretown, VT, United States of America

Eric Michael Gallo