A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study uses deep learning to quickly estimate tissue properties for personalized radio-frequency dosimetry.
New deep learning model generates accurate personalized human head models for electromagnetic dosimetry.
Deep learning models, especially CNNs, can predict radio frequency power faster than traditional methods.
A new ML framework for RF fingerprinting across various applications.
Radio frequency (RF) sensors are used alongside other sensing modalities to provide rich representations of the world. Given the high variability of complex-valued target responses, RF systems are susceptible to attacks masking true target characteristics from accurate identification. In this work, we evaluate differen…
Study uses machine learning to analyze solar emissions.
Machine learning speeds up RIS design for efficient RF components.
The distribution of absorbed dose in radionuclide therapy with Lu can be approximated by convolving an image of the time-integrated activity distribution with a dose voxel kernel representing different tissue types. This fast but inaccurate approximation is unsuitable for personalised dosimetry because it negle…
Radio on Free Space Optics (RoFSO), as a universal platform for heterogeneous wireless services, is able to transmit multiple radio frequency signals at high rates in free space optical networks. This paper investigates the optimal design of power allocation for Wavelength Division Multiplexing (WDM) transmission in Ro…
In portable, three dimensional, and ultra-fast ultrasound imaging systems, there is an increasing demand for the reconstruction of high quality images from a limited number of radio-frequency (RF) measurements due to receiver (Rx) or transmit (Xmit) event sub-sampling. However, due to the presence of side lobe artifact…
Adversarial examples in machine learning for images are widely publicized and explored. Illustrations of misclassifications caused by slightly perturbed inputs are abundant and commonly known (e.g., a picture of panda imperceptibly perturbed to fool the classifier into incorrectly labeling it as a gibbon). Similar atta…
Deep reinforcement learning boosts throughput in RF-powered cognitive radio networks.
Maximin UCB algorithm optimizes energy harvesting for sensor networks.
Robust state-space radio interferometric imaging using Stochastic Approximation Expectation Maximization
Radio tomographic imaging (RTI) is an emerging technology for localization of physical objects in a geographical area covered by wireless networks. With attenuation measurements collected at spatially distributed sensors, RTI capitalizes on spatial loss fields (SLFs) measuring the absorption of radio frequency waves at…
Recent advancements in radio frequency machine learning (RFML) have demonstrated the use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet, deep learning techniques have been shown, in other applications, to be vulnerable to adversarial machine learning (ML) techniques, which seek to …
CNN improves frame selection for ultrasound elastography.
While research on adversarial examples in machine learning for images has been prolific, similar attacks on deep learning (DL) for radio frequency (RF) signals and their mitigation strategies are scarcely addressed in the published work, with only one recent publication in the RF domain [1]. RF adversarial examples (Ad…
A fast method for approximate TDE in ultrasound elastography.
Deep learning optimizes wireless band switching without measurement gaps.
Overcoming the visual barrier and developing "see-through vision" has been one of mankind's long-standing dreams. Unlike visible light, Radio Frequency (RF) signals penetrate opaque obstructions and reflect highly off humans. This paper establishes a deep-learning model that can be trained to reconstruct continuous vid…
Paper uses UKS to improve BLE RSSI for proximity inference in mobile phone apps.
Indoor localization is a supporting technology for a broadening range of pervasive wireless applications. One promis- ing approach is to locate users with radio frequency fingerprints. However, its wide adoption in real-world systems is challenged by the time- and manpower-consuming site survey process, which builds a …
Deep learning uses WiFi CSI for reliable human presence detection.
The paper detects amateur drones using acoustic signals, overcoming interference.
Graph neural network optimizes energy-efficient precoding for massive MIMO systems.