{"title":"Programmable Pulse Generator by Envelope Detection for Implantable Medical Devices","authors":"Hao Zhang;Xiaozhou Zhou;Wenlong Zhou","doi":"10.1109/JERM.2024.3435075","DOIUrl":"https://doi.org/10.1109/JERM.2024.3435075","url":null,"abstract":"Pulse generators in implantable medical devices need to be programmable and miniaturized. However, the existing designs of pulse generator cannot satisfy both of the requirements at the same time. This paper presents a novel design of pulse generators applying magnetic resonance, which is composed of a class-C inverter and an envelope detector, for implantable medical devices. Through simulation and tests, we verify the superiority of our design in programmability of the output pulse signal and miniaturization of the implants, compared with the conventional designs. The amplitude, frequency and duty cycle of the output pulse signal of the implanted receiver can be modulated by controlling the input signal of the transmitter outside the human body. And the footprint of the implanted receiver can be miniaturized to 12 mm × 14 mm × 5 mm, which is smaller than half the size of most of the existing products.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"9 1","pages":"80-89"},"PeriodicalIF":3.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pouya Mehrjouseresht;Oluwatosin J. Babarinde;Vladimir Volski;Alexander Ye. Svezhentsev;Dominique M. M.-P. Schreurs
{"title":"Safeguarding Humans From Indoor Wireless Powering via Radar Detection","authors":"Pouya Mehrjouseresht;Oluwatosin J. Babarinde;Vladimir Volski;Alexander Ye. Svezhentsev;Dominique M. M.-P. Schreurs","doi":"10.1109/JERM.2024.3447469","DOIUrl":"https://doi.org/10.1109/JERM.2024.3447469","url":null,"abstract":"Ensuring the safety of electromagnetic exposure stands as an important concern in wireless power transfer (WPT) systems. This work proposes a distributed Fusion Radar WPT (FRWPT) system designed to maintain safe Electric Field Amplitude (EFA) levels at specific locations detected by the radar, primarily where an individual is present. This approach allows for higher EFA in areas without the person, thus optimizing overall power utilization within the system. Also, the radar's ability to detect a person's velocity allows for projecting the person's upcoming location to ensure safety in advance. We introduce an algorithm including power weighting factors for controlling power to not only mitigate dangerous radiation but also maximize power utilization. One significant challenge is the estimation of EFA considering multipath propagation, a common issue in indoor environments. To overcome this, we explore the indoor EFA distribution and suggest a simulation-based method for EFA estimation, taking into account the amplifying effect of the human body on EFA. Experimental results demonstrate that the system successfully maintains EFA below a predefined threshold across various human locations. Moreover, these experiments highlight the system's capability to maximize power utilization ratio (PUR), achieving a value exceeding 50%.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"9 1","pages":"62-69"},"PeriodicalIF":3.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Propagation of Radio-Frequency Electromagnetic Fields Emitted by Surface-Mounted Parallel-Plate Couplers Along Human Limbs","authors":"Arno Thielens","doi":"10.1109/JERM.2024.3442693","DOIUrl":"https://doi.org/10.1109/JERM.2024.3442693","url":null,"abstract":"Wearables on human limbs commonly require wireless connections with other body-worn devices. These links can be established using radio-frequency electromagnetic fields emitted by parallel-plate capacitors (PPCs) as transducing elements. The propagation of the electric (E-) fields emitted by such PPCs on the surface of human limbs is studied by simulations with a stratified, lossy, dielectric cylinder as a limb model. In contrast to currently existing models, this analysis demonstrates that this propagation depends strongly on propagating modes within the lossy dielectric waveguide and that this is associated with an optimal frequency band of operation for such wireless links, which is tied to cut-off frequencies for propagation along the cylindrical waveguide and the radiation efficiency of the PPC, which is also dependent on the limb size. A channel-loss model in the 0.1–1 GHz frequency range is determined based on the simulations. This model is validated using channel loss measurements using a PPC placed on the limbs of three human subjects.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"9 1","pages":"70-79"},"PeriodicalIF":3.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chandler Bauder;Abdel-Kareem Moadi;Vijaysrinivas Rajagopal;Tianhao Wu;Jian Liu;Aly E. Fathy
{"title":"mm-MuRe: mmWave-Based Multi-Subject Respiration Monitoring via End-to-End Deep Learning","authors":"Chandler Bauder;Abdel-Kareem Moadi;Vijaysrinivas Rajagopal;Tianhao Wu;Jian Liu;Aly E. Fathy","doi":"10.1109/JERM.2024.3443782","DOIUrl":"https://doi.org/10.1109/JERM.2024.3443782","url":null,"abstract":"This study presents <sc>mm-MuRe</small>, a novel method to perform multi-subject contactless respiration waveform monitoring by processing raw multiple-input-multiple-output mmWave radar data with an end-to-end deep neural network. The traditional vital signs monitoring signal processing scheme for mmWave radar involves analog or digital beamforming, human subject localization, phase variation extraction, filtering, and rate or biomarker analysis. This traditional method has many downsides, including sensitivity to selected beamforming weights and over-reliance on phase variation. To avoid these drawbacks, <sc>mm-MuRe</small> (for MM-wave based MUlti-subject REspiration monitoring) is developed to improve reconstruction accuracy and reliability by taking in unprocessed 60 GHz MIMO FMCW radar data and outputting respiratory waveforms of interest, effectively mimicking an adaptive beamformer and bypassing the need for traditional localization and vital signs extraction techniques. Extensive testing across scenarios differing in range, angle, environment, and subject count demonstrates the network's robust performance, with an average cosine similarity exceeding 0.95. Results are compared to two baseline methods and show more than a 10% average improvement in waveform reconstruction accuracy across single and multi-subject scenarios. Coupled with a rapid inference time of 8.57 ms on a 10 s window of data, <sc>mm-MuRe</small> shows promise for potential deployment to efficient and accurate near-real-time contactless respiration monitoring systems.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"9 1","pages":"49-61"},"PeriodicalIF":3.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"IEEE Journal of Electromagnetics, RF, and Microwaves in Medicine and Biology About this Journal","authors":"","doi":"10.1109/JERM.2024.3442073","DOIUrl":"https://doi.org/10.1109/JERM.2024.3442073","url":null,"abstract":"","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"8 3","pages":"C3-C3"},"PeriodicalIF":3.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643730","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology Publication Information","authors":"","doi":"10.1109/JERM.2024.3442071","DOIUrl":"https://doi.org/10.1109/JERM.2024.3442071","url":null,"abstract":"","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"8 3","pages":"C2-C2"},"PeriodicalIF":3.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643733","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Computation of Effective Dielectric Properties Using Dielectric Mixing Model Approach for Breast Cancer Detection","authors":"Rakesh Singh;Dharmendra Singh;Manoj Gupta","doi":"10.1109/JERM.2024.3433008","DOIUrl":"https://doi.org/10.1109/JERM.2024.3433008","url":null,"abstract":"Breast cancer imaging technology requires the artificial breast phantom for early-stage breast cancer testing. The creation of a breast phantom that can replicate the dielectric properties found in real breast tissue holds significant importance in the optimization of the imaging system where computation of the effective dielectric properties of the breast, with and without the tumor needs more attention. Therefore, in this paper, an attempt has been made to develop the dielectric mixing model approach which may represent the real scenario of breast cancer like breast with different size of the tumor. This paper is also proposed to fabricate the phantom using gelatin and water and different size of tumor such as 2 mm, 4 mm, 6 mm, 8 mm and 10 mm which has been inserted in the phantom, and obtained result were compared with dielectric mixing model approach. The dielectric properties of a fabricated phantom, and phantom embedded with different sizes of tumor, were obtained using an open-ended coaxial probe method and computed the effective dielectric properties using dielectric mixing model approach spanning the frequency range from 1 GHz to 10 GHz. It is observed that the measurement results are in quite good agreement with the result of the dielectric mixing model. The main aim of the paper is to observe the change in dielectric properties when the tumor sizes are changing and it is found that there are considerable changes in dielectric with different dimension of the tumor in the frequency range 1 GHz to 10 GHz.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"9 1","pages":"42-48"},"PeriodicalIF":3.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Robert Streeter;Jooeun Lee;Gabriel Santamaria Botello;Zorana Popović
{"title":"Classification of Multi-Layer Tissue-Mimicking Dielectric Stacks From 2 to 20 GHz","authors":"Robert Streeter;Jooeun Lee;Gabriel Santamaria Botello;Zorana Popović","doi":"10.1109/JERM.2024.3434519","DOIUrl":"https://doi.org/10.1109/JERM.2024.3434519","url":null,"abstract":"Determination of the thickness, permittivity, and conductivity of tissue layers in the microwave region of the electromagnetic spectrum is relevant to a number of applications, such as breast-cancer imaging and non-invasive subcutaneous tissue thermometry. Many current characterization approaches are limited to one or two layers, often required to be aqueous. This paper presents simplified modeling of a stack of tissue layers as a series of complex impedance transmission lines in the 2–20 GHz decade. A near-field, broadband interrogation antenna designed for this frequency range and placed on the skin is validated with complex reflection coefficient measurements on seventeen different stacks of materials. Initial measurements are used to build a lookup table of features that are then used to classify three independent sets of follow-up measurements on the same stacks. After processing and consideration of very thin and very low loss materials, the error rates for classification are found to be between 5.9% and 14.7%. This confirms that features extracted from a simple, calibrated one-port broadband reflection coefficient measurement provide sufficient information to identify the composition of a layered stack, modeling tissue layers.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"9 1","pages":"36-41"},"PeriodicalIF":3.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Models of Melanoma Growth for Assessment of Microwave-Based Diagnostic Tools","authors":"Jasmine Boparai;Rachel Tchinov;Oliver Miller;Yanis Jallouli;Milica Popović","doi":"10.1109/JERM.2024.3430315","DOIUrl":"https://doi.org/10.1109/JERM.2024.3430315","url":null,"abstract":"Malignant melanoma, the aggressive form of skin cancer, progresses via radial and vertical growth. The aim of this study is to assess the feasibility of microwave-based diagnosis of melanoma at different stages of tumor progression. To this end, we used the physiological data for melanoma progression to develop a theoretical model of melanoma growth, followed by the oil-in-gelatin based tissue phantoms, which aim to mimic the dielectric behavior of the tissues under consideration. The phantoms are then dielectrically characterized using a slim-form open-ended coaxial probe by systematically sampling dielectric values across the mimicked skin surfaces at a range of points over the 0.5 – 26.5 GHz frequency range. The resulting observations revealed that the microwave spectroscopy exhibits the capability not only to distinguish between healthy and malignant skin, but also differentiate between tumors at different stages of vertical growth, which may not be visually discernible from the skin surface. The measured results are compared with the estimated dielectric values of malignant melanoma using Lichteneker's mixing equation obtained from the literature and it was observed that the measured results closely agree with the literature values.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"8 3","pages":"305-315"},"PeriodicalIF":3.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Estimating Heart Rate Variability in Challenging Low SNR Regimes Using Wearable Magnetocardiography Sensors","authors":"Ali Kaiss;Md. Asiful Islam;Asimina Kiourti","doi":"10.1109/JERM.2024.3426270","DOIUrl":"https://doi.org/10.1109/JERM.2024.3426270","url":null,"abstract":"We report <sc>Beat Estimation</small>, a novel method used to calculate Heart Rate Variability (HRV) from low Signal to Noise Ratio (SNR) data (−7 dB to −4 dB in this work) acquired via wearable magnetocardiography (MCG). MCG activity is first collected using an in-house wearable sensor and filtered to remove noise outside the band of interest. <sc>Beat Estimation</small> extracts a single heart beat from the filtered recording and correlates it with a small number of beats individually to average out the remaining noise. The de-noised beat is then correlated with the full recording to identify the location of each of the heart beats. Using these locations, HRV parameters are, finally, calculated. Results show <inline-formula><tex-math>$sim$</tex-math></inline-formula>99.9% accuracy in estimating HRV metrics using beat-to-beat intervals as opposed to traditional R-to-R-peak intervals. The average accuracy of detecting the true location of beats is shown to increase to 96.43% using <sc>Beat Estimation</small> as opposed to 59.98% using our previous method that relied on R-peak detection. In summary, <sc>Beat Estimation</small> renders wearable MCG sensors capable of accurately estimating HRV, despite the low SNR levels associated with sensor operation. The approach can be game-changing in assessing heart health, cardiovascular fitness, stress levels, cognitive workload, and more.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"9 1","pages":"27-35"},"PeriodicalIF":3.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}