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Abstract: Presents the front cover for this issue of the publication. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Abstract: Reports on the scope and mission of this publication. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
Amir Javan-Khoshkholgh;Aydin Farajidavar;
Pages: 3 - 15 Abstract: Implantable medical systems for long-term monitoring of bioelectrical activity of the gastrointestinal (GI) tract, known as slow waves, and for treating GI dysmotility and functional disorders through electrical stimulation pulses have emerged over the recent years. These implants construct a bidirectional interface between the clinician and the patient's GI tract, to record and monitor slow waves and to provide electrical therapies. Because of the limited battery life of the implants, wireless power transfer (WPT) is a fundamental requirement to conduct studies and provide treatments for an extended period. Furthermore, the WPT link provides the opportunity to establish near-field data communication over the same link. Currently, there are three main wireless power and data transfer (WPDT) approaches for GI tract implants consisting of resonant inductive, ultrasonic, and capacitive couplings. Each approach provides a tradeoff based on the size of the implant, depth of implantation in vivo, the amount of power received by the implant, and the WPT efficiency. In this review, we present the theory and an overview of the major research works accomplished for each of the aforementioned WPT approaches. In particular, this paper focuses on simultaneous WPDT for systems implanted in the GI tract, while fluctuations of the wireless link efficiency and reliability due to body movements and stomach motility are investigated, and techniques are introduced to improve the WPDT fidelity. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
James C. Lin;
Pages: 16 - 28 Abstract: The microwave auditory effect has been widely recognized as one of the most interesting and significant biological phenomena from microwave exposure. The hearing of pulsed microwaves is a unique exception to sound waves encountered in human auditory perception. The hearing of microwave pulses involves electromagnetic waves. This paper reviews the research in humans and animals leading to scientific documentations that absorption of a single microwave pulse impinging on the head may be perceived as an acoustic zip, click, or knocking sound. A train of microwave pulses may be sensed as buzz, chirp, or tune by humans. It describes neurophysiological, psychophysical, and behavioral observations from laboratory studies involving humans and animals. Mechanistic studies show that the microwave pulse, upon absorption by tissues in the head, launches a pressure wave that travels by bone conduction to the inner ear, where it activates the cochlear receptors via the same process involved for normal sound hearing. Depending on the impinging microwave pulse powers, the level of induced sound pressure could be considerably above the threshold of auditory perception to cause tissue injury. The microwave auditory effects and associated pressures could potentially render damage to brain tissue to cause lethal or nonlethal injuries. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
Simone Nappi;Luca Gargale;Federica Naccarata;Pier Paolo Valentini;Gaetano Marrocco;
Pages: 29 - 40 Abstract: Manufacturing faults and aging are the main causes for micro-crack generation in implanted prostheses. An early detection of surface defects by means of local sensors can prevent dangerous complications and prompt for a preventive replacement of the medical device. For this purpose, a tattoo-like sensing mechanism based on pre-fractal Space Fulling Curves is wrapped onto the medical device and coupled with a zero-power RFID transponder. The resulting smart prosthesis is capable to identify the early formation of cracks and to communicate with the exterior of the body by backscattering communication. The crack detection method exploits the anti-tamper port of common Radiofrequency Identification (RFID) ICs and a small antenna, acting as harvester, closely integrated with the metal prosthesis. Simulations and tests with a mockup of metallic hip prosthesis and a leg phantom demonstrate that the device can identify surface cracks as small as 0.6 mm and can be wireless interrogated outside the body from up to 70 cm distance. The required geometrical change to the prosthesis is modest and does not hinder its mechanical robustness. Experiments also confirmed that the health status of the prosthesis could be even monitored on-the-fly when the patient crosses a door equipped with a UHF reader. The sensorized prosthesis could hence become an enabler for the emerging Precision Medicine and for the Internet of Bodies paradigm. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
Konstantinos Kossenas;Symon K. Podilchak;Davide Comite;Pascual D. Hilario Re;George Goussetis;Sumanth K. Pavuluri;Samantha J. Griffiths;Robert J. Chadwick;Chao Guo;Nico Bruns;Christine Tait-Burkard;Jürgen G. Haas;Marc P.Y. Desmulliez;
Pages: 41 - 51 Abstract: This paper describes an innovative remote surface sterilization approach applicable to the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The process is based on the application of a liquid film on the surface or object under sterilization (OUS). A beacon signal is used to self-steer the transmitted power from the designed retrodirective antenna array (RDA) towards the OUS using circularly polarized fields; then, the sterilization is completed by raising and maintaining the required temperature for a certain time. Results suggest that the process takes 5 minutes or less for an angular coverage range over 60 degrees whilst abiding by the relevant safety protocols. This paper also models the power incident onto the OUS, providing consistent results with full-wave simulations. A practical RDA system is developed using a 2 × 1 microstrip patch array operating at 2.5 GHz and tested through the positioning of a representative target surface. Measurements, developed by sampling the power transmitted by the heterodyne RDA, are reported for various distances and angles, operating in the near-field of the system. To further validate the methodology, an additional experiment investigating virus deactivation through microwave heating was also developed. Measurements have been performed with an open cavity microwave oven on the Coronavirus (strain 229E) and egg white protein in a cuvette. This demonstrates that the temperature increases of aqueous films up to 70 $^{circ }$C by remote microwave-induced heat can denature proteins and deactivate viruses. Possible applications of the method include sterilization of ambulances, medical equipment, and internet of things (IoT) devices. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
D. Ye;G. Cutter;T. P. Caldwell;S. W. Harcum;P. Wang;
Pages: 52 - 60 Abstract: Radio frequency (RF) non-thermal (NT) bio-effects have been a subject of debate and attracted significant interests due to potential health risks or beneficial applications. In this work, we report a systematic method for broadband investigation of RF NT effects on Saccharomyces cerevisiae yeast growth. The method includes a transverse electro-magnetic (TEM) device and a dielectric spectroscopy technique for RF frequency selection. A stripline-based TEM device has two 240-μL chambers 3D printed for cell cultures. The fabricated device operates up to a few GHz and produces uniform RF fields for cell exposure testing. A vector network analyzer (VNA) was used to provide −20 dBm continuous-wave (CW) RF power. The heating effects on cell growth were estimated to be negligible. Frequency regions, where large permittivity differences between the medium and yeast cultures were obtained and used to select RF testing frequencies, e.g., 1.0 MHz, 3.162 MHz, 10 MHz. These differences may indicate RF field gradients near cell membrane, and the gradients may affect local nutrient transport. Additionally, RF at 905 MHz is tested for comparison purpose. Yeast cells in the exponential growth phase were examined at four RF frequencies and compared with two controls. One control device held at the same temperature as the test device, while the other control was held at a temperature 1 °C higher. The results showed that the RF fields at 3.162 MHz reduced yeast growth rates by 15.1%; however, the RF fields at 1.0 MHz enhanced cell growth by 13.7%, while the observed 4.3% growth rate increase at 10 MHz is insignificant and the RF fields at 905 MHz had no effects on the cell growth. These results showed a clear RF NT effects on S. cerevisiae growth that was frequency dependent. The hypothesized mec-anisms of these effects, i.e., non-uniform RF fields near cell membranes and fluidic diodes in cell membrane ion channels may play important roles in nutrient transport, need to be further investigated. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
K. Smith;J. Bourqui;D. Garrett;S. Zarnke;M. Owjimehr;D. Deutscher;T. Fung;E. Fear;
Pages: 61 - 67 Abstract: Microwave imaging is of increasing interest for monitoring breast health and treatment. In these applications, the consistency of results over time is important to characterize so that relevant changes can be distinguished from normal measurement and tissue variation. In this paper, we analyze the consistency of microwave imaging when scanning frequently over several weeks, similar to the treatment monitoring timeframe. A custom microwave transmission system was used to acquire 15 scans from a breast phantom and 14-15 scans from each of 5 volunteers. As expected, the breast phantom showed high similarity when comparing signals, property estimates, and images at different time points. Scans of each volunteer also generally demonstrated consistency over time, as well as between right and left breasts. The characterization of consistency from scanning healthy women provides a baseline from which significant changes due to disease or treatment can be identified. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
Daniela M. Godinho;João M. Felício;Carlos A. Fernandes;Raquel C. Conceição;
Pages: 68 - 76 Abstract: The number of metastasised Axillary Lymph Nodes (ALNs) is a key indicator for breast cancer staging. Its correct assessment affects subsequent therapeutic decisions. Common ALN screening modalities lack high enough sensitivity and specificity. Level I ALNs produce detectable backscattering of microwaves, opening the way for Microwave Imaging (MWI) as a complementary screening modality. Radar-based MWI is a low-cost, non-invasive technique, widely studied for breast cancer and brain stroke detection. However, new specific challenges arise for ALN detection, which deter a simple extension of existing MWI methods. The geometry of the axillary region is more complex, limiting the antenna travel range required for maximum resolution. Additionally, unlike breast MWI setups, it is impractical to use liquid immersion to enhance energy coupling to the body; therefore, higher skin reflection masks ALNs response. We present a complete study that proposes dedicated imaging algorithms to detect ALNs dealing with the above constraints, and evaluate their effectiveness experimentally. We describe the developed setup based on a 3D-printed anthropomorphic phantom, and the antenna-positioning configuration. To the authors’ knowledge, this is the first ALN-MWI study involving a fully functional anatomically compliant setup. A Vivaldi antenna, operating in a monostatic radar mode at 2-5 GHz, scans the axillary region. Pre-clinical assessment in different representative scenarios shows Signal-to-Clutter Ratio higher than 2.8 dB and Location Error lower than 15 mm, which is smaller than considered ALN dimensions. Our study shows promising level I ALN detection results despite the new challenges, confirming MWI potential to aid breast cancer staging. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
Imran M. Saied;Tughrul Arslan;Siddharthan Chandran;
Pages: 77 - 85 Abstract: Objectives: Alzheimer's disease is one of the most fastest growing and costly diseases in the world today. It affects the livelihood of not just patients, but those who take care of them, including care givers, nurses, and close family members. Current progression monitoring techniques are based on MRI and PET scans which are inconvenient for patients to use. In addition, more intelligent and efficient methods are needed to predict what the current stage of the disease is and strategies on how to slow down its progress over time. Technology or Method: In this paper, machine learning was used with S-parameter data obtained from 6 antennas that were placed around the head to noninvasively capture changes in the brain in the presence of Alzheimer's disease pathology. Measurements were conducted for 9 different human models that varied in head sizes. The data was processed in several machine learning algorithms. Each algorithm's prediction and accuracy score were generated, and the results were compared to determine which machine learning algorithm could be used to efficiently classify different stages of Alzheimer's disease. Results: Results from the study showed that overall, the logistic regression model had the best accuracy of 98.97% and efficiency in differentiating between 4 different stages of Alzheimer's disease. Clinical or Biological Impact: The results obtained here provide a transformative approach to clinics and monitoring systems where machine learning can be integrated with noninvasive microwave medical sensors and systems to intelligently predict the stage of Alzheimer's disease in the brain. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
Masahito Nakamura;Takuro Tajima;Michiko Seyama;
Pages: 86 - 93 Abstract: We show the measurement of the physiological range of glucose in a multicomponent aqueous solution through multivariate analysis of broadband dielectric spectra from 500 MHz to 50 GHz. To obtain higher glucose selectivity, we apply spectral preprocessing to the dielectric loss spectra to extract feature values of glucose. Using regression models derived from different concentrations of glucose in a solution with bovine serum albumin (BSA), we analyze solutions considering the physiological range of both components. Prediction errors for the glucose and BSA concentrations are estimated to be 54 and 83 mg/dL, respectively, even for varied concentrations of each component. We also investigate the dependence of the glucose prediction error on the solution temperature. The prediction error for the glucose concentrations is estimated to be 99 mg/dL at a difference of ±1 K. This technique will be ease to implement with a broadband microwave sensor or with biomedical sensors that require the capability to analyze multiple components in solutions. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
Bin Zhou;Yuandong Zhuang;Yueming Gao;Željka Lučev Vasić;Ivana Čuljak;Mario Cifrek;Min Du;
Pages: 94 - 102 Abstract: Objectives: During Neuromuscular Electrical Stimulation (NMES), a real-time monitoring method is urgently needed to reduce the negative effect induced by muscle fatigue. Technology or Method: A novel method named electrical impedance myography (EIM) is proposed to evaluate muscle fatigue degree during NMES. The experiments were performed on the anterior tibialis muscle on several voluunteers. Two EIM parameters: impedance amplitude (|Z|) and phase (θ), were measured in real time, while applying six NMES parameter combinations. The mean power frequency (MPF) of the surface electromyography (sEMG) was selected for verification of the proposed method. Results: |Z| and θ of EIM signals decreased significantly after NMES (p < 0.05). They also both showed a linear downward trend during NMES, which was also observed in MPF of sEMG signals. At the measurement frequency of 20 kHz, steady-state fatigue was achieved when |Z| decreased to approximately 14% of the initial value. Clinical or Biological Impact: This study provides a method for monitoring muscle fatigue induced by NMES, which is beneficial to the application of NMES. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
Xuesong Luo;Shaoping Wang;Benjamin Sanchez Terrones;
Pages: 103 - 110 Abstract: Objective: Needle electrical impedance myography (EIM) is a recently developed technique for neuromuscular evaluation. Despite its preliminary successful clinical application, further understanding is needed to aid interpreting EIM outcomes in nonhomogeneous skeletal muscle measurements. Methods: The framework presented models needle EIM measurements in a bidomain isotropic model. Finite element method (FEM) simulations verify the validity of our model predictions studying two cases: a spherical volume surrounded by tissue and a two-layered tissue. Results: Our models show that EIM is influenced by the vicinity of tissue with different electrical properties. The apparent resistance, reactance and phase relative errors between our theoretical predictions and FEM simulations in the spherical volume case study are $leq$0.2%, $leq$1.2% and $leq$1.0%, respectively. For the two-layered tissue model case study, the relative errors are $leq$2%. Conclusions: We propose a bio-physics driven analytical framework describing needle EIM measurements in a nonhomogeneous bidomain tissue model. Clinical impact: Our theoretical predictions may lead to new ways for interpreting needle EIM data in neuromuscular diseases that cause compositional changes in muscle content, e.g. connective tissue deposition within the muscle. These changes will manifest themselves by changing the electric properties of the conductor media and will impact impedance values. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
Toan K. Vo Dai;Kellen Oleksak;Tsotne Kvelashvili;Farnaz Foroughian;Chandler Bauder;Paul Theilmann;Aly E. Fathy;Ozlem Kilic;
Pages: 111 - 122 Abstract: Remote non-contact monitoring of human vital signs has recently received lots of attention due to the advancement and availability of millimeter wave (mmWave) radars. These sensors are significantly reduced in size, but still face serious electromagnetic (EM) propagation loss and signal obstructions resulting in lower signal-to-noise ratios (SNR). As mmWave received signals also have higher sensitivity to body motions, these effects typically degrade the accuracy of heart rate (HR) detection. To overcome this challenge, MIMO configuration can be used to improve the SNR level by taking advantage of its channel diversity. We use here a Frequency Modulated Continuous Wave (FMCW) radar from Texas Instruments (TI) at 77 GHz to collect data from 192 channels. Additionally, vital sign information is extracted using Arctangent Demodulation (AD) and Maximal Ratio Combining (MRC) combined with an adapted-wavelet Continuous Wavelet Transform (CWT) are utilized to demonstrate improvement of HR estimation accuracy. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
Baosheng Wang;Naping Xiong;Yifei Sun;Lejia Zhang;Chenzhe Li;Jianian Li;Zhicheng Wang;Ziling Chen;Yifeng Zhang;Xiong Wang;
Pages: 123 - 130 Abstract: The traditional microwave-induced thermoacoustic imaging (MITAI) technique usually suffers from the problem of inhomogeneous microwave power distribution in the sample under test, which in turn renders the loss of some features of the sample in the image. To address this problem, circularly polarized antennas (CPA) have been used in MITAI to improve the image quality. However, the advantage of CPA in homogenizing the field distribution has been only validated using small samples with very simple structures. For complicated biological samples like small animals or human breasts featured with highly inhomogeneous tissue distributions, the performance of CPA-based MITAI is significantly worse than expected. Moreover, the applied CPAs generally have low efficiency, limited power capacity and narrow bandwidth. Thus, a more feasible and efficient method for homogenizing the power distribution is demanded. This work proposes a novel MITAI modality to homogenize the microwave power deposition in the sample and improve the image quality for small animal imaging via scanning orthogonal polarization (SOP) excitation, which uses a linearly polarized antenna. Two small animal samples, a small mouse and a frog, are experimentally investigated by the MITAI-SOP method. The results indicate that the SOP excitation mechanism can completely reveal the structure of the small animals due to improved power homogeneity and the proposed technique is superior to circularly polarized antenna (CPA) based modality since the former avoids the deficiency of CPA in bandwidth, power capacity and efficiency. This work presents a practical and easy-to-implement paradigm for high-quality imaging of small animals and big biological samples using MITAI. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
Priyansha Kaurav;Shiban Kishen Koul;Ananjan Basu;
Pages: 131 - 138 Abstract: The potential of higher band millimeter waves to characterize different types of breast tissues based on the dielectric contrast between normal and malignant tissues has been reported. Low–cost phantom samples of normal and malignant tissues have been prepared using naturally occurring components, i.e., agar, water, olive oil, and pectin. A waveguide probe-based calibration technique was used to extract the complex permittivities of the two-component (water–agar) and three-component (water-oil–agar) phantoms. The two-component phantoms were used to study the change in the dielectric properties of phantom with change in agar concentration. Water, agar, and varying proportions of oil were used to develop three-component phantom mixtures to mimic the dielectric properties of fat, fibrous and malignant breast tissues. Finally, the reflection and transmission properties of tissue-mimicking phantoms were tested using the waveguide probe calibration-based measurement setup. The difference in the S parameters between the three types of phantoms demonstrates the potential utility of D band-based data acquisition setups for tumor margin assessment applications. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
Tyson Reimer;Stephen Pistorius;
Pages: 139 - 145 Abstract: Objective: This paper assesses the diagnostic performance of deep learning methods for tumour detection in breast microwave sensing (BMS). Methods: A convolutional neural network (CNN) was used to predict the presence of a cancerous lesion in data from experimental scans of MRI-derived phantoms. An experimental dataset containing data from 1257 scans was used. The CNN was compared to a similarly sized dense neural network (DNN) and logistic regression classifier. Results: The CNN was able to exploit the sinogram data structure to achieve diagnostic performance significantly better than random classification, while neither the DNN nor logistic regression classifiers could generalize to unseen test data. The area under the curve of the receiver operating characteristic curve of the CNN classifier was estimated to be between (78 $pm$ 3)% and (90 $pm$ 3)%, where the upper estimate was obtained when the testing set was constrained to consist of phantoms with breast volumes that are within the volume bounds of the training set and when the tumour was located at the same vertical position as the system antennas. Conclusion: The results obtained in this investigation demonstrate the potential of combining deep learning and BMS systems for breast cancer detection. Impact: This paper provides an estimate of the diagnostic performance of an air-based BMS system using deep learning methods for automatic tumour detection on a sizable experimental dataset. The performance was found to be comparable to that of AI-assisted mammography and marks a first step toward larger-scale investigations in BMS. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
Matthias Malzacher;Lothar R. Schad;Jorge Chacon-Caldera;
Pages: 146 - 152 Abstract: Rapid prototyping and safety assessments are essential in the modern development of radio frequency coils for magnetic resonance imaging. The use of 3D electromagnetic simulations can avoid expensive physical coil prototyping while providing quantities to determine local patient safety which is not measurable in vivo. Currently, 3D electromagnetic simulations are mostly solved on variations of two solver types, namely the time domain and the frequency domain solver. In this work, we compared these solvers and tested key simulation parameters using a single computational platform with state-of-the-art computational methods and computational human models. Our analysis included computational cost, B$_1^+$-field and power loss density for a range of common radio frequency setups used at different Larmor frequencies (field strengths, respectively) in magnetic resonance imaging systems. We found that a coarse mesh in a time solver has large unpredictable focal errors ($>$50%) due to partial volume artifacts while the frequency solver showed more linearity in the errors when reducing computational cost which could allow for more efficient simulations with reduced safety concerns. We expect our work to provide further insights into parameter and solver selection and contribute towards standardization in coil simulation in magnetic resonance imaging. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Authors:
Dawood Alsaedi;Alexander Melnikov;Khalid Muzaffar;Andreas Mandelis;Omar M. Ramahi;
Pages: 153 - 163 Abstract: This paper proposes a hybrid breast cancer detection modality consisting of microwaves as a radiation source and an infrared thermography method as a heat-imaging recorder, supported by a Convolution Neural Network (CNN). This hybrid method is based on the difference in the transmitted electromagnetic power between healthy and tumorous breasts. This variation in transmitted power results from the electrical property variance between healthy and cancerous tissues. Under microwave radiation, the power of the transmitted waves leads to a heat distribution pattern on a sensitive screen placed under the breast. This work utilizes the change in the heat pattern to indicate the presence of abnormality inside the breast. Involving a CNN elevates the proposed technique’s detection capability and extracts quantitative data that characterize the tumor’s location and size. The proposed modality shows a capability to detect and determine the size and location of an artificial tumor with a 5 mm radius and a 2:1 permittivity contrast with normal tissue. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
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Pages: 164 - 165 Abstract: Presents a listing of reviewers who contributed to this publication in 2021. PubDate:
March 2022
Issue No:Vol. 6, No. 1 (2022)
Hybrid journal (It can contain Open Access articles)
