arXiv subjects

Ali Khaleghi

Publications and source records attributed to Ali Khaleghi.

10 recordsLinked to original sources

arXiv2026

Advancing Brain-Machine Interfaces: High Data Rate Battery-Free Implants

Implantable wireless brain-machine interfaces (BMI) encounter significant challenges in miniaturization, power consumption, and high data volume. While systems utilizing high resolution microelectrode arrays offer precision brain readout and/or stimulation, achieving high-rate wireless connectivity (32-128 Mbps) consumes excessive power, unsuitable for long-term use with implant batteries. This paper addresses wireless connectivity and power challenges by employing radio frequency backscatter and near-field wireless charging. This approach eliminates transceiver electronics in the implantable, reducing implant power consumption by offloading complexity to off-body reader electronics. It enables wireless powering of implantable neural recording and stimulation chips through magnetic coupling, enabling a fully implantable brain-machine interface. We present preliminary test results for this design scenario, demonstrating the feasibility of our approach.

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arXiv2026

Safety Analysis of Metasurface-Based Near-field Wireless Power Transfer System for Deep Implant

Wireless power transfer is a method for energizing future implantable medical electronics. In this study, a metasurface-based near-field magnetic wireless power transfer system for deep implants is presented, and electromagnetic safety parameters, including field distributions, specific absorption rate (SAR), and temperature variations, are evaluated. The power transfer is modeled for a receiver implant at a distance of 8.5 cm from the designed metasurface. Based on the results, a maximum localized SAR of 0.072 mW/kg is achieved when the efficiency is 1.62%. Moreover, continuous power transfer shows that the local tissue temperature rises by less than 1.1 degrees Celsius.

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arXiv2026

Frequency demodulation with magnetoelectric coreshells: A novel approach to enhanced bio-stimulation

Magnetoelectric (ME) coreshell devices have interesting applications in biomedical technologies including biosensing and communication, due to their strong inter-coupling between the magnetostrictive core and the piezoelectric shell. This property could be utilized for specific applications in localized bio-stimulation of cells. This paper provides a conceptual proof of using the non-linear property of ME coreshell devices for frequency demodulation and stimulation. We use the Multiphysics simulation approach, wherein the ME coreshell was biased with a DC magnetic field and perturbed through dual coil alternating magnetic fields. The combined effect of the ME non-linear magnetostriction and the dual coil perturbation resulted in the demodulation of the interference frequency component, that induced equivalent electrical hotspots on the piezoelectric shell. We provide a cross model verification, where the generated electrical current density on the piezo shell was provided as an input to a Hodgkin-Huxley (HH) neural cell model to actively induce membrane potentials on the cell. Future applications as a standalone, battery- and electronics-free, controllable, multi-functional and localized coreshells for targeted drug delivery, backscatter communication and bio-stimulation, is envisioned.

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arXiv2026

Ultra-wideband statistical propagation channel model for implant sensors in the human chest

Implant medical wireless sensors for monitoring physiological parameters, automatic drug provision, and so on represent a new promising healthcare technology. Inherent characteristics of ultra-wideband (UWB) radio make this technology highly suitable for the wireless interface of implant sensors. A communication channel model is essential for developing these wireless systems. However, there are currently few models describing the radio propagation inside the human body. To address this problem, a statistical model is presented for UWB propagation channels inside the human chest in the 1-6 GHz frequency range. The proposed statistical model is developed from numerical simulations using a heterogeneous anatomical model that includes the frequency-dependent dielectric properties of different human tissues. Mathematical formulas for the computation of path loss, scattering and the statistical implementation of the channel impulse response at different depths inside the chest are described. Two typical depths for implanted sensors in the chest, namely 20 and 80 mm are analysed in detail. Average path loss of approximately 20 and 50 dB is observed in each case, respectively. Moreover, the channel exhibits little time dispersion with a root-mean-square delay spread below 1 ns in both cases. These results aim at facilitating the tasks associated with the design of in-body medical communication systems.

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arXiv2026

Propagation models for IEEE 802.15.6 standardization of implant communication in body area networks

A body area network is a radio communication protocol for short-range, low-power, and highly reliable wireless communication for use on the surface, inside, or in the peripheral proximity of the human body. Combined with various biomedical sensors, BANs enable realtime collection and monitoring of physiological signals. Therefore, it is regarded as an important technology for the treatment and prevention of chronic diseases, and health monitoring of the elderly. The IEEE 802 LAN/MAN Standards Committee approved Task Group TG15.6 in December 2007. As a result of more than four years of effort, in February 2012, TG15.6 published the first international standard for BANs, IEEE Std 802.15.6. Throughout the development of this standard, ample collaboration between the standardization group and the research community was required. In particular, understanding the radio propagation mechanisms for BANs demanded the most research effort. Technical challenges were magnified for the case of implant communication because of the impossibility of conducting in-body measurements with human subjects. Therefore, research in this field had to make use of intricate computer simulations. This article outlines some of the research that has been done to obtain accurate propagation models supporting the standardization of implant communication in BANs. Current research to enhance the channel models of IEEE Std 802.15.6 through the use of ultra wideband signals for implantable devices along with physical measurements in animals is also presented.

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arXiv2026

Antenna System for Simultaneous Wireless Power and Information Transfer to Brain Implants

Brain-Computer Interfaces (BCIs) have revolutionized neuroscience applications, from motor rehabilitation to neuroergonomics. Traditional implantable BCIs with invasive microelectrode arrays pose challenges, notably the need for wired connections and inherent implantation risks. This paper introduces a battery-free wireless BCI system, consolidating an implant and its external supporting system. Our design centers on a dual-function antenna system: firstly, an inductive coupling mechanism enables wireless power transfer, sufficiently powering the implant's Application-Specific Integrated Circuit (ASIC) for stimulation and readout without an implant battery. Secondly, a backscatter antenna in the implant facilitates battery-free, high-data-rate wireless connectivity (up to 32 Mbps). This system not only enhances the BCI experience by eliminating wires but also retains data fidelity and energy efficiency, promising a safer, more efficient interface for tasks like robotic arm control.

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arXiv2021

Accurate and Rapid Diagnosis of COVID-19 Pneumonia with Batch Effect Removal of Chest CT-Scans and Interpretable Artificial Intelligence

COVID-19 is a virus with high transmission rate that demands rapid identification of the infected patients to reduce the spread of the disease. The current gold-standard test, Reverse-Transcription Polymerase Chain Reaction (RT-PCR), has a high rate of false negatives. Diagnosing from CT-scan images as a more accurate alternative has the challenge of distinguishing COVID-19 from other pneumonia diseases. Artificial intelligence can help radiologists and physicians to accelerate the process of diagnosis, increase its accuracy, and measure the severity of the disease. We designed a new interpretable deep neural network to distinguish healthy people, patients with COVID-19, and patients with other pneumonia diseases from axial lung CT-scan images. Our model also detects the infected areas and calculates the percentage of the infected lung volume. We first preprocessed the images to eliminate the batch effects of different devices, and then adopted a weakly supervised method to train the model without having any tags for the infected parts. We trained and evaluated the model on a large dataset of 3359 samples from 6 different medical centers. The model reached sensitivities of 97.75% and 98.15%, and specificities of 87% and 81.03% in separating healthy people from the diseased and COVID-19 from other diseases, respectively. It also demonstrated similar performance for 1435 samples from 6 different medical centers which proves its generalizability. The performance of the model on a large diverse dataset, its generalizability, and interpretability makes it suitable to be used as a reliable diagnostic system.

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arXiv2016

Design and Simulation of a Power Efficient Waveguide Rotman Lens

Rotman lens is a true-time-delay beam forming network with parallel plate structure. Owing to its frequency- independency and wide-band operation, it has gained vast applications as feeding element of linear array antennas. However, the lens suffers from high intrinsic power loss and the problem worsens in microstrip structures by their inevitable dielectric loss. On the other hand, waveguides experience extremely lower loss and also have higher power capacity. Thus, waveguides can be advantageously utilized in Rotman lens structure. Here, a waveguide Rotman lens for the frequency range of 8 to 12 GHz is designed and simulated in CST Microwave Studio. Phase error and power efficiency of the proposed structure are satisfactory.

cs.IT↗

arXiv2008

Demonstration of Time-Reversal in Indoor Ultra-Wideband Communication: Time Domain Measurement

Using time domain measurements, we assess the feasibility of time-reversal technique in ultra-wideband (UWB) communication. A typical indoor propagation channel is selected for the exploration. The channel response between receive and transmit antenna pairs is measured using time domain equipments which include an arbitrary wave generator (AWG) and a digital storage oscilloscope (DSO). The time-reversed version of the channel response is constructed with AWG and re-transmitted in the channel. The equivalent time reversed channel response is recorded. The properties of the time reversal technique in the line of sight (LOS) co-polar and cross-polar scenarios are measured.

cs.NI↗

arXiv2008

Performance Enhancement of Multiuser Time Reversal UWB Communication System

UWB communication is a recent research area for indoor propagation channels. Time Reversal (TR) communication in UWB has shown promising results for improving the system performance. In multiuser environment, the system performance is significantly degraded due to the interference among different users. TR reduces the interference caused by multiusers due to its spatial focusing property. The performance of a multiuser TR communication system is further improved if the TR filter is modified. In this paper, multiuser TR in UWB communication is investigated using simple TR filter and a modified TR filter with circular shift operation. The concept of circular shift in TR is analytically studied. Thereafter, the channel impulse responses (CIR) of a typical indoor laboratory environment are measured. The measured CIRs are used to analyze the received signal peak power and signal to interference ratio (SIR) with and without performing the circular shift operation in a multiuser environment.

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