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Found 29 papers, 3 papers with code
Bistatic Doppler Frequency Estimation with Asynchronous Moving Devices for Integrated Sensing and Communications
In this letter, we present for the first time a method to estimate the bistatic Doppler frequency of a target with clock asynchronous and mobile Integrated Sensing And Communication (ISAC) devices.
Sensing in Bi-Static ISAC Systems with Clock Asynchronism: A Signal Processing Perspective
Integrated Sensing and Communications (ISAC) has been identified as a pillar usage scenario for the impending 6G era.
Learned Spike Encoding of the Channel Response for Low-Power Environment Sensing
We underline that existing spike encoding algorithms to do so generally produce inaccurate signal representations and dense (i. e., inefficient) spike trains.
VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning
In this domain, federated learning is one of the most effective and promising techniques for training global machine learning models, while preserving data privacy at the vehicles and optimizing communications resource usage.
An Experimental Prototype for Multistatic Asynchronous ISAC
We prototype and validate a multistatic mmWave ISAC system based on IEEE802. 11ay.
Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction
Remarkably, STAR enables human activity recognition with satisfactory accuracy even with 90% of missing channel measurements, for which existing techniques fail.
Accurate Calibration of Power Measurements from Internal Power Sensors on NVIDIA Jetson Devices
Power efficiency is a crucial consideration for embedded systems design, particularly in the field of edge computing and IoT devices.
DISC: a Dataset for Integrated Sensing and Communication in mmWave Systems
In this paper we present DISC, a dataset of millimeter-wave channel impulse response measurements for integrated human activity sensing and communication.
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization
Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms.
A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels
In the last years, several machine learning-based techniques have been proposed to monitor human movements from Wi-Fi channel readings.
JUMP: Joint communication and sensing with Unsynchronized transceivers Made Practical
We present JUMP, the first system enabling practical bistatic and asynchronous joint communication and sensing, while achieving accurate target tracking and micro-Doppler extraction in realistic conditions.
Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model
At the intersection of these trends, we hence find the energetic characterization of machine learning at the edge, which is attracting increasing attention.
ORACLE: Occlusion-Resilient and Self-Calibrating mmWave Radar Network for People Tracking
In this work, we present ORACLE, an autonomous system that (i) integrates automatic relative position and orientation estimation from multiple radar devices by exploiting the trajectories of people moving freely in the radars' common fields of view, and (ii) fuses the tracking information from multiple radars to obtain a unified tracking among all sensors.
Human Tracking with mmWave Radars: a Deep Learning Approach with Uncertainty Estimation
mmWave radars have recently gathered significant attention as a means to track human movement within indoor environments.
SPARCS: A Sparse Recovery Approach for Integrated Communication and Human Sensing in mmWave Systems
Our results show that the micro-Doppler signatures obtained by SPARCS enable a typical downstream application such as human activity recognition with more than 7 times lower overhead with respect to existing methods, while achieving better recognition performance.
DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning
We present DeepCSI, a novel approach to Wi-Fi radio fingerprinting (RFP) which leverages standard-compliant beamforming feedback matrices to authenticate MU-MIMO Wi-Fi devices on the move.
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing
There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL).
A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications
The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks.
MilliTRACE-IR: Contact Tracing and Temperature Screening via mm-Wave and Infrared Sensing
Moreover, milliTRACE-IR performs contact tracing: a person with high body temperature is reliably detected by the thermal camera sensor and subsequently traced across a large indoor area in a non-invasive way by the radars.
RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing
In this work we present RAPID, the first joint communication and radar system based on next-generation IEEE 802. 11ay WiFi networks operating in the 60 GHz band.
Real-time People Tracking and Identification from Sparse mm-Wave Radar Point-clouds
Mm-wave radars have recently gathered significant attention as a means to track human movement and identify subjects from their gait characteristics.
SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points
SHARP is trained on data collected as a person performs seven different activities in a single environment.
Multi-Person Continuous Tracking and Identification from mm-Wave micro-Doppler Signatures
We build a system that effectively works with multiple persons concurrently sharing and freely moving within the same indoor space.
Seq2Seq RNN based Gait Anomaly Detection from Smartphone Acquired Multimodal Motion Data
Smartphones and wearable devices are fast growing technologies that, in conjunction with advances in wireless sensor hardware, are enabling ubiquitous sensing applications.
Mobile Traffic Classification through Physical Channel Fingerprinting: a Deep Learning Approach
The automatic classification of applications and services is an invaluable feature for new generation mobile networks.
Deep Learning Techniques for Improving Digital Gait Segmentation
Wearable technology for the automatic detection of gait events has recently gained growing interest, enabling advanced analyses that were previously limited to specialist centres and equipment (e. g., instrumented walkway).
Machine Learning Approaches to Energy Consumption Forecasting in Households
We consider the problem of power demand forecasting in residential micro-grids.
Rate-Distortion Classification for Self-Tuning IoT Networks
Many future wireless sensor networks and the Internet of Things are expected to follow a software defined paradigm, where protocol parameters and behaviors will be dynamically tuned as a function of the signal statistics.
IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks
Here, we present IDNet, a user authentication framework from smartphone-acquired motion signals.
