Enhance Signal Quality Research Articles

Touchscreens Can Reveal User Identity: Capacitive Plethysmogram-Based Biometrics

Biometrics are widely used for user identification/authentication, but the fact has rarely been noticed that general capacitive touchscreens can reveal user identities by touch signals. This paper proposes a new biometric method with inherent liveness detection for reliable user recognition based on the cardiac signal captured by the capacitive touchscreen, namely Capacitive Plethysmogram (CPG). And a systematic framework is designed for CPG collection, processing, and exploitation to identify users. Specifically, since the finger usually forms capacitors with multiple sensing electrodes during touching, we can extract several CPG signals simultaneously from the screen output. Then we propose a series of preprocessing algorithms to filter CPG for signal quality enhancement. Finally, to further leverage filtered CPG signals and extract efficient features for identifying users, we build an encoder based on 3D attention CNN and metric learning. Experimental results demonstrate that the proposed method can achieve an average accuracy of 96.73%, FAR of 3.03%, and FRR of 7.35% in the laboratory environment, which reveals the potential of CPG for user privacy protection and data security on various devices laced with capacitive touchscreens.

Parallel High Capacitance Capacitors Connected With Flexible Electrodes for Enhancement of cECG Signal Quality

Parallel High Capacitance Capacitors Connected With Flexible Electrodes for Enhancement of cECG Signal Quality

Ten-Port MIMO Inverted-F Antenna for LTE Bands 43/48/49 Bands Smartphone Applications

This paper presents a design and performance analysis of a 10-element 5G massive Multiple Input Multiple Output (m-MIMO) antenna array for sub-6 GHz mobile handsets, specifically for Long Term Evolution (LTE) bands 43 (3600–3800 MHz) and 48/49 (3550–3700 MHz) applications. The proposed antenna array consists of ten closely spaced inverted-F antennas with a compact size of 20 × 9 mm2 of a single element. The proposed antenna array provides high efficiency and low correlation between the antenna elements, which result in increased data rate and enhanced signal quality. The performance of the antenna array is evaluated in terms of the radiation pattern, diversity gain, efficiency, and correlation coefficient. The simulation and measured results show that the proposed antenna array achieves an approximate peak gain of 2.8 dBi and a total efficiency of 65% at the resonance frequency of 37 GHz and a low correlation coefficient of 0.07 between the adjacent antenna elements. Moreover, the single and two-hand modes are also given in order to highlight the potential of such a structure as a smart mobile terminal. The simulated results are discovered to be in excellent agreement with the measured values. We think this structure has a bright future in the next generation of smart mobile phones based on the performance and the measured findings.

Meditation attenuates default-mode activity: A pilot study using ultra-high field 7 Tesla MRI

ObjectivesMapping the neurobiology of meditation has been bolstered by functional MRI (fMRI) research, with advancements in ultra-high field 7 Tesla fMRI further enhancing signal quality and neuroanatomical resolution. Here, we utilize 7 Tesla fMRI to examine the neural substrates of meditation and replicate existing widespread findings, after accounting for relevant physiological confounds. MethodsIn this feasibility study, we scanned 10 beginner meditators (N = 10) while they either attended to breathing (focused attention meditation) or engaged in restful thinking (non-focused rest). We also measured and adjusted the fMRI signal for key physiological differences between meditation and rest. Finally, we explored changes in state mindfulness, state anxiety and focused attention attributes for up to 2 weeks following the single fMRI meditation session. ResultsGroup-level task fMRI analyses revealed significant reductions in activity during meditation relative to rest in default-mode network hubs, i.e., antero-medial prefrontal and posterior cingulate cortices, precuneus, as well as visual and thalamic regions. These findings survived stringent statistical corrections for fluctuations in physiological responses which demonstrated significant differences (p < 0.05/n, Bonferroni controlled) between meditation and rest. Compared to baseline, State Mindfulness Scale (SMS) scores were significantly elevated (F(3,9) = 8.16, p < 0.05/n, Bonferroni controlled) following the fMRI meditation session, and were closely maintained at 2-week follow up. ConclusionsThis pilot study establishes the feasibility and utility of investigating focused attention meditation using ultra-high field (7 Tesla) fMRI, by supporting widespread evidence that focused attention meditation attenuates default-mode activity responsible for self-referential processing. Future functional neuroimaging studies of meditation should control for physiological confounds and include behavioural assessments.

Enhancing signal quality in dense wavelength division multiplexed systems by eliminating crosstalk with cascaded optical bandpass filters

Non-orthogonal wavelength division multiplexing (WDM) channels are crucial in achieving significantly higher capacity and spectral efficiency than conventional schemes. Additionally, conventional schemes face challenges in electrical processing of wide-bandwidth signals. To suppress crosstalk in non-orthogonal WDM channels, the use of cascaded bandpass filters with suitable transmissivity and bandwidth is investigated for effective channel separation of large-capacity and multiplexed optical signals. This study employs the bandwidth ratio as a parameter, which is defined as the ratio of the channel spacing to the signal bandwidth, to quantify the extent of signal overlapping, and uses optical bandpass filters to filter non-orthogonal signals with a bandwidth ratio ranging from 1.0 to 0.75. The non-overlapping portion is set as the transmission bandwidth of the filter, and the crosstalk is maximally reduced with two-stage filters using Nyquist, super Gaussian, and Butterworth shaped filters. According to the analysis, the combination of Gaussian N = 1 and Gaussian N = 5 exhibits the lowest error vector magnitude (EVM) performance within a bandwidth ratio of 0.8 to 1.0. Simulations showed that a two-stage OBPF with a combination of Gaussian N = 1 and Gaussian N = 5 provides the best EVM performance at a bandwidth ratio between 0.85 to 1.0 within the combination of filter shapes and their orders we used. Additionally, the study includes an analytical investigation of the impact of amplified-spontaneous-emission noise associated with the Erbium-doped fiber amplifier (EDFA) and frequency resolution tolerance of the LCOS filter. The results demonstrate that cascaded EDFAs can be achieved down to OSNR of 10 dB at a minimum resolution of 4 GHz. Finally, the optimal filter shape is analyzed for different bandwidth ratios. The investigation shows that the optimal filter shape is independent of the function used and remains consistent across all bandwidth ratios. The tolerance of the filter is primarily affected by the −0.5 dB transmittance bandwidth between subchannels, which varies depending on the bandwidth ratio. Furthermore, a high tolerance is observed in the −20 dB transmittance bandwidth.

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants.

Adaptive equalization is crucial in mitigating distortions and compensating for frequency response variations in communication systems. It aims to enhance signal quality by adjusting the characteristics of the received signal. Particle swarm optimization (PSO) algorithms have shown promise in optimizing the tap weights of the equalizer. However, there is a need to enhance the optimization capabilities of PSO further to improve the equalization performance. This paper provides a comprehensive study of the issues and challenges of adaptive filtering by comparing different variants of PSO and analyzing the performance by combining PSO with other optimization algorithms to achieve better convergence, accuracy, and adaptability. Traditional PSO algorithms often suffer from high computational complexity and slow convergence rates, limiting their effectiveness in solving complex optimization problems. To address these limitations, this paper proposes a set of techniques aimed at reducing the complexity and accelerating the convergence of PSO.

Accurate 2-D DoA Estimation Based on Active Metasurface With Nonuniformly Periodic Time Modulation

A novel direction of arrival (DoA) estimation method based on amplifier-integrated active metasurface is proposed to find multisource directions by a single-channel system. Nonuniformly periodic time modulation (NPTM) on the metasurface reduces the hardware complexity by taking full advantage of the spectral resources. As a result, only partial metasurface elements are required to achieve 2-D DoA estimation, while the remaining elements can be redesigned and encoded to control electromagnetic waves. Thus, adaptive beamsteering can be expected to realize in a full-duplex way. Moreover, the amplifiers in the metasurface can function as modulation units to generate the required harmonics and enhance signal quality. The nonideality of the modulated square wave due to the active devices is allowed without affecting the estimation accuracy. An active metasurface prototype composed of 5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> 5 elements is fabricated and measured in the microwave anechoic chamber for multisource direction finding. The measured maximum error is 0.31 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\circ}$</tex-math> </inline-formula> , which is high accuracy compared with the state-of-the-art works. Thus, the proposed method has application values in the Internet of Things (IoT), wireless communications, and radar.

Spreading CDMA via RIS: Multipath Separation, Estimation, and Combination

As a revolutionary technology for future wireless communications, reconfigurable intelligent surface (RIS), characterized by an efficient way of manipulating wireless signals, has been widely investigated in recent years toward enhancing signal quality, energy efficiency, throughput, and so on. However, in RIS-assisted Internet of Things (IoT), a new issue as multipath separation emerges especially when deploying multiple RISs to assist communication, since the devices may have limited signal processing capabilities. For alleviating this problem, we conceive a novel RIS-enabled code division multiple access (CDMA) structure, where each RIS holds a specified time-varying coefficient to tag the channel. Moreover, multipath extraction is further considered, including a practical channel estimation approach along with theoretical derivations in terms of Cramér-Rao lower bound, mean square error, as well as ergodic channel capacity. Simulation results corroborate the feasibility of the conceived RIS-CDMA structure and the effectiveness of the proposed multipath extraction approach.

Robust Arm Impedocardiography Signal Quality Enhancement Using Recursive Signal Averaging and Multi-Stage Wavelet Denoising Methods for Long-Term Cardiac Contractility Monitoring Armbands.

Impedance cardiography (ICG) is a low-cost, non-invasive technique that enables the clinical assessment of haemodynamic parameters, such as cardiac output and stroke volume (SV). Conventional ICG recordings are taken from the patient's thorax. However, access to ICG vital signs from the upper-arm brachial artery (as an associated surrogate) can enable user-convenient wearable armband sensor devices to provide an attractive option for gathering ICG trend-based indicators of general health, which offers particular advantages in ambulatory long-term monitoring settings. This study considered the upper arm ICG and control Thorax-ICG recordings data from 15 healthy subject cases. A prefiltering stage included a third-order Savitzky-Golay finite impulse response (FIR) filter, which was applied to the raw ICG signals. Then, a multi-stage wavelet-based denoising strategy on a beat-by-beat (BbyB) basis, which was supported by a recursive signal-averaging optimal thresholding adaptation algorithm for Arm-ICG signals, was investigated for robust signal quality enhancement. The performance of the BbyB ICG denoising was evaluated for each case using a 700 ms frame centred on the heartbeat ICG pulse. This frame was extracted from a 600-beat ensemble signal-averaged ICG and was used as the noiseless signal reference vector (gold standard frame). Furthermore, in each subject case, enhanced Arm-ICG and Thorax-ICG above a threshold of correlation of 0.95 with the noiseless vector enabled the analysis of beat inclusion rate (BIR%), yielding an average of 80.9% for Arm-ICG and 100% for Thorax-ICG, and BbyB values of the ICG waveform feature metrics A, B, C and VET accuracy and precision, yielding respective error rates (ER%) of 0.83%, 11.1%, 3.99% and 5.2% for Arm-IG, and 0.41%, 3.82%, 1.66% and 1.25% for Thorax-ICG, respectively. Hence, the functional relationship between ICG metrics within and between the arm and thorax recording modes could be characterised and the linear regression (Arm-ICG vs. Thorax-ICG) trends could be analysed. Overall, it was found in this study that recursive averaging, set with a 36 ICG beats buffer size, was the best Arm-ICG BbyB denoising process, with an average of less than 3.3% in the Arm-ICG time metrics error rate. It was also found that the arm SV versus thorax SV had a linear regression coefficient of determination (R2) of 0.84.

Multiresolution Dual-Polynomial Decomposition Approach for Optimized Characterization of Motor Intent in Myoelectric Control Systems.

Surface electromyogram (sEMG) is arguably the most sought-after physiological signal with a broad spectrum of biomedical applications, especially in miniaturized rehabilitation robots such as multifunctional prostheses. The widespread use of sEMG to drive pattern recognition (PR)-based control schemes is primarily due to its rich motor information content and non-invasiveness. Moreover, sEMG recordings exhibit non-linear and non-uniformity properties with inevitable interferences that distort intrinsic characteristics of the signal, precluding existing signal processing methods from yielding requisite motor control information. Therefore, we propose a multiresolution decomposition driven by dual-polynomial interpolation (MRDPI) technique for adequate denoising and reconstruction of multi-class EMG signals to guarantee the dual-advantage of enhanced signal quality and motor information preservation. Parameters for optimal MRDPI configuration were constructed across combinations of thresholding estimation schemes and signal resolution levels using EMG datasets of amputees who performed up to 22 predefined upper-limb motions acquired in-house and from the public NinaPro database. Experimental results showed that the proposed method yielded signals that led to consistent and significantly better decoding performance for all metrics compared to existing methods across features, classifiers, and datasets, offering a potential solution for practical deployment of intuitive EMG-PR-based control schemes for multifunctional prostheses and other miniaturized rehabilitation robotic systems that utilize myoelectric signals as control inputs.

Current Status and Development Tendency of Wearable Cardiac Health Monitoring

Wearable cardiac monitoring devices can provide uninterrupted monitoring of cardiac activities over a long period of time. They have developed rapidly in recent years in terms of convenience, comfort, and intelligence. Aided by the development of sensor and materials technology, big data and artificial intelligence, wearable cardiac monitoring can become a crucial basis for novel medical models in the future. Herein, the basic concepts and representative devices of wearable cardiac monitoring are first introduced. Subsequently, its core technologies and the latest representative research progress in physiology signal sensing, signal quality enhancement, and signal reliability are systematically reviewed. Finally, an insight and outlook on the future development trends and challenges of wearable cardiac monitoring are discussed.

A hybrid steady-state visual evoked response-based brain-computer interface with MEG and EEG

While recent developments in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have enabled a bridge between the brain and external devices with relatively high communication speed, there is still room for improvement. Notably, the phenomenon of “BCI illiteracy,” which refers to the 15%–30% of people who struggle to type or control devices using BCI, remains unsolved, limiting the practical application of BCI systems. The EEG-based BCIs performance is constrained by the low-quality scalp EEG signals due to the attenuation and distortion of the skull. To address these limitations, this study proposes a hybrid BCI system combining EEG with magnetoencephalogram (MEG), a neuroimaging technology not influenced by the volume conduction effect, to boost BCI performance by enhancing signal quality. Comparative experiments involving 22 subjects showed that the steady-state visual evoked response (SSVER) from MEG has a wider range of effective bandwidth and higher signal-to-noise ratio than EEG. Moreover, differences in the spectral and spatiotemporal characteristics of MEG and EEG explain better performance. Simultaneous MEG-EEG recording experiments suggested that the hybrid MEG-EEG BCI achieved a significantly higher information transfer rate than either modality alone (hybrid: 312 ± 17 bits/min, MEG: 272 ± 17 bits/min, EEG: 240 ± 27 bits/min). Moreover, the 40-target classification accuracy of “BCI illiterate” increased from 50% to 95% with the help of MEG. These results highlight the methodological advantages of a hybrid MEG-EEG BCI, suggesting a promising paradigm for implementing high-speed BCIs.

Limited effects of inbreeding on breeding coloration.

Animal color signals may function as indicators of fighting ability when males compete for access to females. This allows opponents to settle aggressive interactions before they escalate into physical combat and injury. Thus, there may be strong directional selection on these traits, toward enhanced signal quality. This renders sexually selected traits particularly susceptible to inbreeding depression, due to relatively low ratios of additive genetic variance to dominance variance. We measured the effects of inbreeding on an intrasexually selected color signal (the badge) in a population of Swedish sand lizards (Lacerta agilis) using the Rhh software based on 17 to 21 microsatellites. Males of this sexually dichromatic species use the badge during aggressive interactions to display, and assess, fighting ability. We found negative effects of homozygosity on badge size, saturation, and brightness. However, no such effects were observed on color hue. Pairwise correlations between badge size, hue, and saturation were all statistically significant. Thus, the sand lizard "badge" is a multicomponent signal with variation explained by covariation in badge size, saturation, and color hue. Body mass corrected for skeletal size (body condition) positively predicted badge size and saturation, encouraging future research on the extent that sexual signals may convey information on multigene targets (i.e. "genic capture").

Signal quality enhancement in multiplexed communication systems based on the simulation model of the optimum technical specifications of Raman fiber optical amplifiers

Abstract Raman optical amplifiers have more reliability than the repeater in the optical communication networks. In the optical amplifier, the transmission equals the gain bandwidth of the amplifiers. The optical amplifier has functionalities such as WDM amplification (gain equalization), gain control (rapidly variation of the gain), inter-stage access (compensation in the dispersion rate). This paper presents the optimum technical specifications of fiber Raman optical amplifiers (FROAs) with average power system for signal quality improvement in multiplexed systems. The signal has upgraded to a max. Q-factor of 11.47 applied along 1000 m as a range through the free-space optical (FSO) communication channel that has an attenuation rate of 10 dB/km. Our suggested system has clarified the best Q-factor that is greatly increased to reach 49.36 in the presence of the pump laser. The multiplexed communication systems signal quality is enhanced by the percentage ratio of 39.65%.

SpO2 Measurement: Non-Idealities and Ways to Improve Estimation Accuracy in Wearable Pulse Oximeters

The blood oxygen saturation level (SpO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) has become one of the vital body parameters for the early detection, monitoring, and tracking of the symptoms of coronavirus diseases 2019 (COVID-19) and is clinically accepted for patient care and diagnostics. Pulse oximetry provides non-invasive SpO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> monitoring at home and ICUs without the need of a physician/doctor. However, the accuracy of SpO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> estimation in wearable pulse oximeters remains a challenge due to various non-idealities. We propose a method to improve the estimation accuracy by denoising the red and IR signals, detecting the signal quality, and providing feedback to hardware to adjust the signal chain parameters like LED current or transimpedance amplifier gain and enhance the signal quality. SpO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> is calculated using the red and infrared photoplethysmogram (PPG) signals acquired from the wrist using Texas Instruments AFE4950EVM. We introduce the green PPG signal as a reference to obtain the window size of the moving average filter for baseline wander removal and as a timing reference for peak and valley detection in the red and infrared PPG signals. We propose the improved peak and valley detection algorithm based on the incremental merge segmentation algorithm. Kurtosis, entropy, and Signal-to-noise ratio (SNR) are used as signal quality parameters, and SNR is further related to the variance in the SpO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> measurement. A closed-loop implementation is performed to enhance signal quality based on the signal quality parameters of the recorded PPG signals. The proposed algorithm aims to estimate SpO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> with a variance of 1% for the pulse oximetry devices.

Efficiency enhancement techniques of microwave and millimeter‐wave antennas for 5G communication: A survey

AbstractEfficiency is a critical antenna parameter that describes how well an antenna emits and accepts electromagnetic signals. When an antenna has poor efficiency, the input power is only partially radiated and is primarily wasted due to internal losses such as conduction, dielectric, mismatching, and several other losses within the antenna. In order to enhance signal quality, a communication system requires an antenna with high efficiency and gain. The fifth‐generation wireless system will use millimeter wave spectrum along with sub 1 GHz and sub 6 GHz bands to fulfill wide bandwidth, ultra‐high‐speed, and low latency requirements of 5G communication. Compact low‐profile antenna with high radiation efficiency, high gain, and wideband characteristics are required to support bandwidth requirements of 5G and mitigate the signal degradation due to high link losses caused by environmental absorptions at the millimeter‐wave (mm‐wave) spectrum. Compact planar antennas have become an important part of modern wireless communication equipment, but these antennas are associated with many issues like low efficiency, poor gain, small radiation resistance, and high cross‐polarization, which is due to the increased conductor and dielectric losses, surface wave losses and so forth. Various techniques need to be employed for the improvement of the efficiency of the antenna, which results in high gain low loss antenna characteristics. This article presents the survey of various efficiency enhancement techniques for 5G antennas and a review of structures that provides good radiation efficiency for 5G communications.

Characterizing shallow subsurface using 3D seismic while drilling with a downhole pilot

Seismic while drilling (SWD) can provide high-resolution subsurface information and characterize near-surface geology. We present a case study of SWD analysis using a data set from a desert environment acquired over a complex overburden. The data were acquired with a system composed of wireless surface geophones as well as top-drive and downhole sensors. The drill-bit noise data were reprocessed using a specialized workflow with two essential elements. First, a downhole pilot from a near-bit sensor was used for deconvolution, which led to improved data quality, particularly in the shallow subsurface. Second, nonlinear beamforming leveraged the 3D carpet of geophones to enhance signal quality and enable picking 3D traveltimes. A simple workflow for building a 1D velocity model used 3D traveltimes from an offset of 180 to 500 m. We vertically projected and averaged all 3D traveltimes to obtain a robust checkshot profile for the section from 190 to 1855 m. For the shallow subsurface (0–800 m), we further applied an advanced workflow with 3D traveltime inversion that more accurately handled mid- and far-offset data using a ray-tracing engine. More than 100,000 offset traveltime picks were inverted for a 1D velocity model. The model closely ties with the geology of the near surface, namely the formation tops associated with major impedance and lithologic contrasts. Amplitude signatures of 3D SWD gathers also correlate with the velocity model and lithologic changes, showing weak energy associated with soft formations and higher-energy first-arrival waveforms associated with compacted formations. Finally, we used the data extracted from a 2D line to reconstruct a migrated image of the back-propagated drill-bit sources. Joint use of kinematic and dynamic signatures helps characterize markers associated with loss circulation zones and target layers.

Study on received signal strength of femtocell with circular and rectangular microstrip patch antenna designed at 2.55 GHz

In large cities, the coverage and quality of telecommunication services inside buildings is low and needs more effort for its improvement. Signal attenuators like number of walls, number of floors and others affect the quality of received signal. Therefore, femtocells are used for improving the poor performance of network inside buildings and other dead zone areas. The aim of this work is to compare the received signal strength of femtocell with rectangular and circular microstrip patch antenna designed at 2.55 GHz. This work considers the received signal strength of femtocell with the designed rectangular and circular microstrip patch antennas. The performance analysis uses Multi Wall Multi Floor indoor propagation model. The result showed that the total gain of rectangular and circular microstrip patch antenna are 3.6528 dB and 2.924 dB, respectively. The received signal strength of femtocell with rectangular microstrip patch antenna is larger than that of circular microstrip patch antenna designed at the same frequency. The effect of number of walls and floors on the received signal strength of femtocell is also clearly indicated. Generally, it is found that much higher received signal strength is observed in rectangular microstrip patch antenna than in circular one. The outcome of this work shows that femtocells are capable of enhancing signal quality for indoor users.

Measurement‐based delay, angular dispersion and propagation loss characteristics of outdoor propagation in beam domain and multi‐beam operation at 38 GHz for 5‐G communication systems

Owing to the global bandwidth shortage in frequency bands below 6 GHz, higher frequency bands such as millimetre-wave bands will play integral roles in fifth-generation (5-G) communication systems. Beamforming is necessary for high-frequency bands to maintain system coverage and increase system capacity through spatial division multiple access. Moreover, more than one beam might be used simultaneously to enhance signal quality and prevent blockages. Recent studies have investigated, focussed on power loss characteristics in multi-beam operation, and compared the channel characteristics for single-beam operation. To provide robust channel characteristics, knowledge of the multipath characteristics in multi-beam operation is required in the design of 5-G communication systems. To fill this gap such as the multipath dispersion characteristics of outdoor-to-outdoor propagation in delay and angular domains, the present study analysed outdoor data measured at 38 GHz and presented the propagation losses (path losses and shadowing), root-mean-square delay spread, and angular spread in multi-beam operation. We believe that the dispersion statistics (delay and angular spreads) and propagation loss characteristics in multi-beam operation would be useful in the development of 5-G communication systems in high-frequency bands.

Deep Learning for Instrumented Ultrasonic Tracking: From Synthetic Training Data to In Vivo Application.

Instrumented ultrasonic tracking is used to improve needle localization during ultrasound guidance of minimally invasive percutaneous procedures. Here, it is implemented with transmitted ultrasound pulses from a clinical ultrasound imaging probe, which is detected by a fiber-optic hydrophone integrated into a needle. The detected transmissions are then reconstructed to form the tracking image. Two challenges are considered with the current implementation of ultrasonic tracking. First, tracking transmissions are interleaved with the acquisition of B-mode images, and thus, the effective B-mode frame rate is reduced. Second, it is challenging to achieve an accurate localization of the needle tip when the signal-to-noise ratio is low. To address these challenges, we present a framework based on a convolutional neural network (CNN) to maintain spatial resolution with fewer tracking transmissions and enhance signal quality. A major component of the framework included the generation of realistic synthetic training data. The trained network was applied to unseen synthetic data and experimental in vivo tracking data. The performance of needle localization was investigated when reconstruction was performed with fewer (up to eightfold) tracking transmissions. CNN-based processing of conventional reconstructions showed that the axial and lateral spatial resolutions could be improved even with an eightfold reduction in tracking transmissions. The framework presented in this study will significantly improve the performance of ultrasonic tracking, leading to faster image acquisition rates and increased localization accuracy.