Search Results for author:
Found 8 papers, 1 papers with code
Coherent Feed Forward Quantum Neural Network
Moreover, the circuit depth and qubit needs of these models scale poorly with the number of data features, resulting in an efficiency challenge for real-world machine-learning tasks.
memorAIs: an Optical Character Recognition and Rule-Based Medication Intake Reminder-Generating Solution
Memory-based medication non-adherence is an unsolved problem that is responsible for considerable disease burden in the United States.
The role of hosting capacity study in power system advancements: a review
The fast depletion of conventional energy sources due to increased energy demands and environmental concern has motivated power utilities to integrate more renewable energy sources into their power systems.
Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach
From this model, we derive a concentration inequality that highlights the accuracy of our crowd count estimator.
A Research Review on Detection and Classification of Power Quality Disturbances caused by Integration of Renewable Energy Sources
With the increased interest in integrating renewable energy sources (RES) such as wind power and solar into the power systems owing to their zero greenhouse gas emissions and the involvement of power converters for integration in grid, the detection, classification and mitigation of power quality events has become indispensable.
MAC Address Anonymization for Crowd Counting
Research has shown that counting WiFi packets called probe requests (PRs) implicitly provides a proxy for the number of people in an area.
Cryptography and Security Networking and Internet Architecture
Comparing domain wall synapse with other Non Volatile Memory devices for on-chip learning in Analog Hardware Neural Network
We next incorporate the DW synapse as a Verilog-A model in the crossbar array based NN circuit we design on SPICE circuit simulator.
On-chip learning in a conventional silicon MOSFET based Analog Hardware Neural Network
On-chip learning in a crossbar array based analog hardware Neural Network (NN) has been shown to have major advantages in terms of speed and energy compared to training NN on a traditional computer.
