Average SNR Research Articles

An Analytical Model for Coherent Transmission Performance Estimation After Generic Jones Matrices

In this paper, we propose the extension of a previously presented analytical model for the estimation of the signal-to-noise ratio (SNR) at the output of an adaptive equalizer in polarization multiplexed (PM)-QAM coherent optical systems when transmission is modeled as a generic 2x2 frequency dependent transfer function matrix. We present the model and then we statistically test its accuracy in two possible application environments. Our findings show a remarkable agreement between time-domain simulations and analytical results, with average SNR discrepancies of the order of 0.1 dB. We believe our model can find important applications in next generation physical layer aware network planning tools that need to take into account polarization dependent loss/gain and strong filtering, for instance in ultra high baud rate coherent systems.

On the diversity-multiplexing trade-off of hybrid diamond relay channels

The diversity-multiplexing trade-off (DMT) of a diamond relay channel in a quasi-static slow Rayleigh fading environment is studied, where one of the relays operates in half-duplex mode while the other one is full-duplex; hence, the term hybrid is used to refer to this channel. DMT is a known powerful tool that captures the inherent tension between the reliability and the transmission rate in a wireless communication network at high signal-to-noise ratios (SNR). In this study, we have assumed that the average SNRs of different links vary at different rates, each represented by an exponent at high SNR. These average SNR exponents are categorized into two different classes: In Class I, both of the relays are placed either in close proximity to the source or in close proximity to the destination, whereas in Class II one of the relays is closer to the source while the other one is closer to the destination. Subsequently, for Class I and for a special symmetric case of Class II, an explicit expression of the DMT of the static quantize-map-and-forward (SQMF) strategy and the optimal static schedule in the half-duplex relay, are derived. The non-symmetric case of Class II can numerically be studied using the optimization problem provided in the paper for any given parameters. As a corollary, our analysis shows that for Class I, the SQMF strategy in the proposed hybrid diamond relay channel reaches the ideal full-duplex DMT upper bound if the degrees of freedom provided by the path through the full-duplex relay is greater than or equal to that provided by the path through the half-duplex relay, or if the degrees of freedom provided by the former is less than that provided by the latter, but still large enough to support the desired multiplexing gain.

A novel whole-head RF coil design tailored for concurrent multichannel brain stimulation and imaging at 3T

PurposeMultichannel Transcranial Magnetic Stimulation (mTMS) [1] is a novel non-invasive brain stimulation technique allowing multiple sites to be stimulated simultaneously or sequentially under electronic control without movement of the coils. To enable simultaneous mTMS and MR imaging, we have designed and constructed a whole-head 28-channel receive-only RF coil at 3T. MethodsA helmet-shaped structure was designed considering a specific layout for a mTMS system with holes for positioning the TMS units next to the scalp. Diameter of the TMS units defined the diameter of RF loops. The placement of the preamplifiers was designed to minimize possible interactions and to allow straightforward positioning of the mTMS units around the RF coil. Interactions between TMS-MRI were analyzed for the whole-head system extending the results presented in previous publications [2]. Both SNR- and g-factors maps were obtained to compare the imaging performance of the coil with commercial head coils. ResultsSensitivity losses for the RF elements containing TMS units show a well-defined spatial pattern. Simulations indicate that the losses are predominantly caused by eddy currents on the coil wire windings. The average SNR performance of the TMSMR 28-channel coil is about 66% and 86% of the SNR of the 32/20-channel head coil respectively. The g-factor values of the TMSMR 28-channel coil are similar to the 32-channel coil and significantly better than the 20-channel coil. ConclusionWe present the TMSMR 28-channel coil, a head RF coil array to be integrated with a multichannel 3-axisTMS coil system, a novel tool that will enable causal mapping of human brain function.

Rethinking the clinical utility of distortion-product otoacoustic emission (DPOAE) signal-to-noise ratio

Objective Distortion-product otoacoustic emission (DPOAE) levels are repeatable over time in normal-hearing individuals making DPOAE levels an ideal measurement for monitoring cochlear status in clinic and research applications. However, if DPOAE signal-to-noise ratio (SNR) values instead of levels are used for monitoring, the repeatability of this value needs to be established. This retrospective, cross-sectional study sought to determine DPOAE SNR repeatability in younger children, older children, young adults and a patient population with normal hearing. Design Each participant attended four sessions where DPOAE discrete frequency sweeps were collected at conventional (≤ 8 kHz) and/or extended-high frequencies (> 8 kHz). To examine the extent of variability to be expected for DPOAE SNR, average absolute SNR differences-between-trials were determined and compared to average absolute DPOAE level differences-between-trials. Study samples One hundred forty-five participants, incorporating four different groups from three different studies. Ages ranged from 3 to 55 years. Results Average SNR differences-between-trials across all frequencies are greater than differences for average DPOAE levels. Improved calibration methods result in SNR differences-between-trials that are similar across all frequencies Conclusions When monitoring cochlear health over an extended bandwidth, DPOAE levels are less variable across trials than SNR values, thus allowing earlier indicators of cochlear damage.

On the Physical-Layer Security of a Dual-Hop UAV-Based Network in the Presence of Per-Hop Eavesdropping and Imperfect CSI

In this paper, the physical layer security of a dual-hop unmanned aerial vehicle-based wireless network, subject to imperfect channel state information (CSI) and mobility effects, is analyzed. Specifically, a source node (S) communicates with a destination node (D) through a decode-and-forward relay (R), in the presence of two wiretappers (E1,E2) independently trying to compromise the two hops. Furthermore, the transmit nodes (S,R) have a single transmit antenna, while the receivers (R,D,E1,E2) are equipped with multiple receive antennas. Based on the per-hop signal-to-noise ratios (SNRs) and correlated secrecy capacities’ statistics, a closed-form expression for the secrecy intercept probability (IP) metric is derived, in terms of key system parameters. Additionally, asymptotic expressions are revealed for two scenarios, namely (i) mobile nodes with imperfect CSI and (ii) static nodes with perfect CSI. The results show that a zero secrecy diversity order is manifested for the first scenario, due to the presence of a ceiling value of the average SNR, while the IP drops linearly at high average SNR in the second one, where the achievable diversity order depends on the fading parameters and number of antennas of the legitimate links/nodes. Furthermore, for static nodes, the system can be castigated by a 15 dB secrecy loss at IP=3×10-3, when the CSI imperfection power raises from 0 to 10-3. Lastly, the higher the legitimate nodes’ speed, carrier frequency, delay, and/or relay’s decoding threshold SNR, the worse is the system’s secrecy. Monte Carlo simulations endorse the derived analytical results.

5T magnetic resonance imaging: radio frequency hardware and initial brain imaging.

We aimed to demonstrate the feasibility of generating high-resolution human brain magnetic resonance imaging (MRI) at 5 Tesla (T) using a quadrature birdcage transmit/48-channel receiver coil assembly. A quadrature birdcage transmit/48-channel receiver coil assembly was designed for human brain imaging at 5T. The radio frequency (RF) coil assembly was validated by electromagnetic (EM) simulations and phantom imaging experimental studies. The simulated B1+ field inside a human head phantom and inside a human head model generated by the birdcage coils driven in circularly polarized (CP) mode at 3T, 5T and 7T was compared. Signal-to-noise ratio (SNR) maps, the inverse g-factor maps for evaluation of parallel imaging performance, anatomic images, angiography images, vessel wall images and susceptibility weighted images (SWI) were acquired using the RF coil assembly at 5T and compared to those acquired using a 32-channel head coil on a 3T MRI scanner. For the EM simulations, 5T MRI provided less RF inhomogeneity compared to that of 7T. In the phantom imaging study, the distributions of the measured B1+ field were consistent with the distributions of the simulated B1+ field. In the human brain imaging study, the average SNR value of the brain in the transversal plane at 5T was 1.6 times of that at 3T. The 48-channel head coil at 5T had higher parallel acceleration capability than the 32-channel head coil at 3T. The anatomic images at 5T also showed higher SNR than those at 3T. Improved delineation of the hippocampus, lenticulostriate arteries, and basilar arteries was observed at 5T compared to 3T. SWI with a higher resolution of 0.3 mm ×0.3 mm ×1.2 mm could be acquired at 5T, which enabled better visualization of small blood vessels compared to that at 3T. 5T MRI can provide significant SNR improvement compared to that of 3T with less RF inhomogeneity than that of 7T. The ability to obtain high quality in vivo human brain images at 5T using the quadrature birdcage transmit/48-channel receiver coil assembly has significant in clinical and scientific research applications.

Tunable performance of indium tin oxide-zinc oxide as Q-switcher

This work successfully demonstrated the tunable performance of a Q-switched pulse erbium-doped fiber laser (QPEDFL) using Indium Tin Oxide-Zinc Oxide (ITO-ZnO)-based SA. The ITO-ZnO is a material growth between transparent conducting oxides and transition metal oxides on a glass slide. The mechanical exfoliation technique is applied to serve the ITO-ZnO as SA and inserted in a single-ring cavity design. The QPEDFL in a single ring cavity gives the repetition rate, pulse width, average output power, pulse energy, and SNR of 38.49 kHz, 5.50 µs, 0.47 mW, 12.72 nJ, and 64.20 dB, respectively. Furthermore, the ITO-ZnO as SA provides a tuning range of 30.40 nm. By tuning the TBF, the wavelength changes from 1531.60 nm to 1562.0 nm, causing the pulse repetition rate and pulse width of the QPEDFL to vary from 41.50 kHz to 17.20 kHz, and 4.22 µs to 10.05 µs, respectively. According to the results obtained, it can confidently say the suggested pulse laser has potential in the application of optical communication systems as a light source.

Performance analysis of linear detection for uplink massive MIMO system based on spectral and energy efficiency with Rayleigh fading channels in 3D plotting pattern

AbstractMassive multiple‐input multiple‐output (MIMO) is a critical component of 5G cellular networks, which utilizes large numbers of antennas at both the transmitter and receiver to enhance throughput and radiated energy efficiency. Various linear detection techniques are employed with massive MIMO to counteract path loss and interference, and maximize throughput. The first aim of this paper is to analyse the performance of uplink massive MIMO system for different linear detection techniques including: Maximum ratio combining (MRC), zero‐forcing (ZF), regularized ZF (RZF) and minimum mean squared error (MMSE) over Rayleigh channel model. The second aim is to jointly investigate the optimal values of signal‐to‐noise ratio (SNR), the number of antennas M and the number of users K for maximizing the spectral efficiency (SE) and energy efficiency (EE) through simulation using MATLAB and 3D plotting patterns. The obtained results show that the best SE and EE are achieved by uplink massive MIMO setup while using optimal values of SNR, M and K. It is observed that MMSE achieved the best performance. However, it requires estimation of average SNR at BS. Therefore, the best choice is ZF or RZF without any need for SNR estimation.

LoRa Communication in the Service Level Monitoring Satu Duit Bogor Bridge

Lora is the solution to the problem of the need for long-distance two-way communication between machines that are targeted by IoT (Internet of Thins. LoRa has long-distance transmission capabilities, has power, and a low bit rate. Based on the needs related to LoRa, further research is needed, to analyze the performance of LoRa communication. The LoRa communication protocol will be applied to the One Duit Bogor bridge monitoring system using the Website and LabVIEW. This study used LoRa SX1276 with a frequency of 915MHz with the LoRa point-to-point method and LoRa gateway. The parameters analyzed include RSSI (Received Signal Strength Indicator), SNR (Signal to Noise Ratio), Delay, Throughput, and Packet loss to determine the quality of LoRa performance with TIPHON standards. Based on the tests that have been carried out, it proves that LoRa communication has good performance. In urban areas or around the Satu Duit Bogor bridge, LoRa can transmit data from a distance of 0 to 500 m with an average delay of 217 ms, an average packet loss of 10.237%, an average throughput of 137.881 bps, an average SNR of 7.54 dB, and an average RSSI of -71,798 dBm. At a distance of 0-400 m there is an insignificant change in LoRa parameters, but at a distance of 500 m a high change occurs, this is due to the fact that the distance greatly affects the transmission of data. The longer the range, the more obstacles will be passed so that data transmission is disrupted.

Optimization of the fluid-attenuated inversion recovery (FLAIR) imaging for use in autopsy imaging of the brain region using synthetic MRI.

The failure of cerebrospinal fluid (CSF) signal suppression in postmortem fluid-attenuated inversion recovery (FLAIR) of the brain is a problem. The present study was to clarify the relationship between the temperature of deceased persons and CSF T1, and to optimize the postmortem brain FLAIR imaging method using synthetic MRI. Forehead temperature was measured in 15 deceased persons. Next, synthetic MRI of the brain was performed, the CSF T1 was measured, and the optimal TI was calculated. Two types of FLAIR images were obtained with the clinical and optimal TI. The relationship between forehead temperature and the CSF T1 and optimal TI was evaluated. The optimized FLAIR images were physically and visually evaluated. The CSF T1 and optimal TI were strongly correlated with forehead temperature. Comparing the average SNR and CNR ratios and visual evaluation scores of the two FLAIR images, those captured with the optimal TI showed statistically lower SNR, higher CNR, and higher visual evaluation scores (p< 0.01). Synthetic MRI enables the quantification of the CSF T1 resulting from postmortem temperature decreases and calculation of the optimal TI, which could aid in improving the failure of CSF signal suppression and in optimizing postmortem brain FLAIR imaging.

Small training dataset convolutional neural networks for application-specific super-resolution microscopy.

Machine learning (ML) models based on deep convolutional neural networks have been used to significantly increase microscopy resolution, speed [signal-to-noise ratio (SNR)], and data interpretation. The bottleneck in developing effective ML systems is often the need to acquire large datasets to train the neural network. We demonstrate how adding a "dense encoder-decoder" (DenseED) block can be used to effectively train a neural network that produces super-resolution (SR) images from conventional microscopy diffraction-limited (DL) images trained using a small dataset [15 fields of view (FOVs)]. The ML helps to retrieve SR information from a DL image when trained with a massive training dataset. The aim of this work is to demonstrate a neural network that estimates SR images from DL images using modifications that enable training with a small dataset. We employ "DenseED" blocks in existing SR ML network architectures. DenseED blocks use a dense layer that concatenates features from the previous convolutional layer to the next convolutional layer. DenseED blocks in fully convolutional networks (FCNs) estimate the SR images when trained with a small training dataset (15 FOVs) of human cells from the Widefield2SIM dataset and in fluorescent-labeled fixed bovine pulmonary artery endothelial cells samples. Conventional ML models without DenseED blocks trained on small datasets fail to accurately estimate SR images while models including the DenseED blocks can. The average peak SNR (PSNR) and resolution improvements achieved by networks containing DenseED blocks are and , respectively. We evaluated various configurations of target image generation methods (e.g., experimentally captured a target and computationally generated target) that are used to train FCNs with and without DenseED blocks and showed that including DenseED blocks in simple FCNs outperforms compared to simple FCNs without DenseED blocks. DenseED blocks in neural networks show accurate extraction of SR images even if the ML model is trained with a small training dataset of 15 FOVs. This approach shows that microscopy applications can use DenseED blocks to train on smaller datasets that are application-specific imaging platforms and there is promise for applying this to other imaging modalities, such as MRI/x-ray, etc.

190km Φ-OTDR with bidirectional Raman and relay erbium-doped fiber hybrid amplification

In this paper, a long-distance coherent detection phase-sensitive optical time-domain reflectometer (Φ-OTDR) scheme with bidirectional Raman and relay erbium-doped fiber hybrid amplification is proposed. The bidirectional Raman pump light acting on the erbium-doped fiber (EDF) can realize the passive amplification of pulsed signal light and Rayleigh backward scattering (RBS) light at the same time. The scheme utilizes bidirectional 1455 nm pumps as the pump source of relay amplification to achieve a longer sensing distance. Experimental results demonstrate that, an ultra-long Φ-OTDR system with 190 km sensing distance and 22 m spatial resolution is realized. Multi-point vibration signals are located and restored at 69.67 km, 120.13 km and 190.79 km, respectively. The average signal-to-noise ratio (SNR) of positioning curve is 10.32 dB, the average SNR of demodulated phase signal is 26.02 dB, and the frequency response range is from 5 Hz to 150 Hz. Furthermore, the sensing system has good linear characteristics of phase demodulation with a correlation coefficient of 0.997. The actual disturbance events are also detected at the far-end of sensing fiber. To the best of our knowledge, this system achieves the longest-distance Φ-OTDR without relay power supply. This passive hybrid amplification method can avoid the intermediate power supply along sensing fiber, which provides a reference for long-distance vibration monitoring of Φ-OTDR system.

Flexible boron-doped diamond spiral electrode for application in brain–computer interface based on steady-state visual evoked potential

We propose a flexible boron-doped diamond (BDD) spiral electrode for electroencephalography (EEG) recording and introduce the application of a flexible BDD-based EEG cap in brain–computer interfaces (BCI). The B-doping difference of diamond crystal planes in flexible polycrystalline BDD electrodes makes them act like a microelectrode. Hydrogen bonds between hydroxy fatty acid in the cuticle and BDD surface terminal and electrostatic adsorption between BDD-boron sites and the cuticle enhance the capacitive electric field. Combining a skin-conformal BDD electrode cap with the simplest feature extraction and classification coding can record highly stable EEG in steady-state visual evoked potential (SSVEP), with a high average SNR of 8.6 dB. The BCI system demonstrated competitive accuracy and information transfer rate in five channels in the occipital and parietal areas. Collectively, the BDD-based SSVEP-BCI system offers unique advantages for the real-time monitoring of brain signals, neurological rehabilitation, and disease diagnosis.

KSIM: simulating KIDSpec, a Microwave Kinetic Inductance Detector spectrograph for the optical/NIR

Abstract KIDSpec, the Kinetic Inductance Detector Spectrometer, is a proposed optical to near-IR Microwave Kinetic Inductance Detector (MKID) spectrograph. MKIDs are superconducting photon counting detectors which are able to resolve the energy of incoming photons and their time of arrival. KIDSpec will use these detectors to separate incoming spectral orders from a grating, thereby not requiring a cross-disperser. In this paper, we present a simulation tool for KIDSpec’s potential performance upon construction to optimize a given design. This simulation tool is the KIDSpec Simulator (KSIM), a Python package designed to simulate a variety of KIDSpec and observation parameters. A range of astrophysical objects are simulated: stellar objects, an SDSS observed galaxy, a Seyfert galaxy, and a mock galaxy spectrum from the JAGUAR catalogue. Multiple medium spectral resolution designs for KIDSpec are simulated. The possible impact of MKID energy resolution variance and dead pixels was simulated, with impacts on KIDSpec performance observed using the Reduced Chi-Squared (RCS) value. Using dead pixel percentages from current instruments, the RCS result was found to only increase to 1.21 at worst for one of the designs simulated. SNR comparisons of object simulations between KSIM and X-Shooter’s ETC were also simulated. KIDSpec offers a particular improvement over X-Shooter for short and faint observations. For a Seyfert galaxy (mR = 21) simulation with a 180 s exposure, KIDSpec had an average SNR of 4.8, in contrast to 1.5 for X-Shooter. Using KSIM the design of KIDSpec can be optimized to improve the instrument further.

Improved demodulation method for fibre optic sensors applied in transformer partial discharge detection

AbstractThe 2 × 2 couplers, as a demodulation core of the fibre optic sensing system, have been extensively employed in fibre optic sensors for partial discharge (PD) detection of power equipment. However, the unstable demodulation sensitivity and low signal‐to‐noise ratio (SNR) of such a system are still unsatisfactory in engineering. This work uses a distributed feedback fibre laser as the sensor and employs a common asymmetric 2 × 3 coupler instead of a 2 × 2 coupler as the demodulation core to design a PD detection system. Then, we propose a new demodulation method for PD acoustic emission signals based on the Naval Postgraduate School method. On the PD detection platform, the performance of the conventional detection system and the proposed system is compared. The experimental results show that the proposed system has better reliability and can identify the PD signals in all instances. Furthermore, the average SNR of the new demodulation method is 6.11 dB higher than the traditional 2 × 2 coupler system and can be extended to most PD detection systems using fibre optic sensors.

A novel tissue mechanics-based method for improved motion tracking in quasi-static ultrasound elastography.

Most cancers are associated with biological and structural changes that lead to tissue stiffening. Therefore, imaging tissue stiffness using quasi-static ultrasound elastography (USE) can potentially be effective in cancer diagnosis. USE techniques developed for stiffness image reconstruction use noisy displacement data to obtain the stiffness images. In this study, we propose a technique to substantially improve the accuracy of the displacement data computed through ultrasound tissue motion tracking techniques, especially in the lateral direction. The proposed technique uses mathematical constraints derived from fundamental tissue mechanics principles to regularize displacement and strain fields obtained using Global Ultrasound Elastography (GLUE) and Second-Order Ultrasound Elastography (SOUL) methods. The principles include a novel technique to enforce (1) tissue incompressibility using 3D Boussinesq model and (2) deformation compatibility using the compatibility differential equation. The technique was validated thoroughly using metrics pertaining to Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR) and Normalized Cross Correlation (NCC) for four tissue-mimicking phantom models and two clinical breast ultrasound elastography cases. The results show substantial improvement in the displacement and strain images generated using the proposed technique. The tissue-mimicking phantom study results indicate that the proposed method is superior in improving image quality compared to the GLUE and SOUL techniques as it shows an average axial strain SNR and CNR improvement of 44% and 63%, and lateral strain SNR and CNR improvement of 130% and 435%, respectively. The results of the phantom study also indicate higher accuracy of displacement images obtained using the proposed technique, including improvement ranges of 7-84% and 26-140% for axial and lateral displacement images, respectively. For the clinical cases, the results indicate average improvement of 48% and 64% in SNR and CNR, respectively, in the axial strain images, and average improvement of 40% and 41% in SNR and CNR, respectively, in the lateral strain images. The proposed method is very effective in producing improved estimate of tissue displacement and strain images, especially with the lateral displacement and strain where the improvement is highly remarkable. While the method shows promise for clinical applications, further investigation is necessary for rigorous assessment of the method's performance in the clinic.

Average signal-to-noise ratio maximization for an intelligent reflecting surface and angle diversity receiver jointly assisted indoor visible light communication system.

The intelligent reflecting surface (IRS) and angle diversity receiver (ADR) jointly assisted indoor visible light communication (VLC) system is proposed to improve average signal-to-noise ratio (ASNR) performance. Specifically, to maximize the ASNR at the receiving plane, the roll angle and yaw angle of IRS and the inclination angle of the side detector in the ADR structure are optimized simultaneously as one non-convex problem. With the bat algorithm, the optimal solution is numerically obtained. Results show that when the transmit power of the light emitting diode lamp array is 1W, the ASNRs of this VLC system optimized by IRS and ADR are approximately 7.89dB, 3.58dB, and 2.09dB higher than those of the original, IRS-assisted, and ADR-assisted VLC systems, respectively. Furthermore, the transmission rate and bit error rate performances of the original, IRS-assisted, ADR-assisted, and IRS and ADR jointly assisted indoor VLC systems are also simulated and compared; it is found that the performance improvement of the indoor VLC system jointly optimized by IRS and ADR is more evident than that of the other three VLC systems. This study will benefit the research and development of indoor VLC systems.

A comparison of simultaneous multislice and conventional diffusion tensor imaging techniques for ischemic stroke evaluation at 1.5T.

Diffusion tensor imaging (DTI) is a promising technique for ischemic stroke evaluation; however, acquisition time is longer than DWI. Simultaneous multislice (SMS) imaging acquires multiple slices together and reduces scan time. This study compared conventional and SMS DTI for ischemic stroke workup. Following IRB approval, the departmental stroke protocol was supplemented with SMS DTI on a clinical 1.5T MRI. Cases suspicious for ischemic stroke outside the treatment window were included. Standard DTI (STD-DTI, 20-direction, b = 2000 s/mm2), was followed by SMS-2-DTI (two slices simultaneously imaged). Two blinded neuroradiologists independently assessed image quality and DTI-trace status (positive/negative = stroke/other). Average image quality, interrater reliability (κ), receiver operating characteristic area under the curve (AUC), signal-to-noise ratio (SNR = DTI-source min/max/average), coefficient of variation (CV), mean diffusivity (MD), and fractional anisotropy (FA, of DTI-trace) were compared using two-tailed t-tests and a p < .05. 41 patients were evaluated. SMS-2-DTI decreased DTI time by 132.17 ± 15.33 s, a 45% reduction. SMS-2-DTI reduced image quality (STD-DTI 4.7 ± 0.5 vs SMS-2-DTI 3.8 ± 0.6, p < .001). Diagnostic accuracy persisted, AUC was high for observer 1 (STD-DTI 0.95, 95%CI = 0.88-1.00 vs SMS-2-DTI 0.94, 95%CI = 0.87-1.00, p = .86) and observer 2 (STD-DTI 0.89, 95%CI = 0.79-0.99 vs SMS-2-DTI 0.86, 95%CI = 0.76-0.97, p = .66). Interrater reliability was high for STD-DTI (κ = 0.80, 95%CI = 0.61-0.98) and SMS-2-DTI (κ = 0.84, 95%CI = 0.67-1.00). SMS-2-DTI significantly decreased average SNR (STD-DTI 42.85 ± 4.44 vs SMS-2-DTI 32.58 ± 4.30, p < .001), and CV MD (STD-DTI 0.23 ± 0.03 vs 0.20 ± 0.04, p < .001). CV FA and CV DTI-trace were not statistically different. This study supports using SMS to accelerate DTI for ischemic stroke workup at 1.5T in the non-hyper-acute setting. This study highlights the feasibility of accelerated multislice DTI for faster diagnostic DTI-trace images capable of ischemic stroke detection.

Research on Sparse Denoising of Strong Earthquakes Early Warning Based on MEMS Accelerometers.

In view of the fact that the noise in the same frequency band as the useful signal in the MEMS acceleration sensor observation data cannot be effectively removed by traditional filtering methods, a denoising method for strong earthquake signals based on the theory of sparse representation and compressive sensing is proposed in this paper. This skillfully realized the separation of strong earthquake signals from noise by adopting a fixed dictionary and utilizing sparse characteristics. Furthermore, considering the weakness of the sparse denoising method based on the fixed dictionary in the high signal-to-noise ratio, a spare denoising method based on learning an over-complete dictionary is proposed. Through the initial given seismic data, the ideal over-complete dictionary is trained to achieve seismic data denoising. In addition, for the interference waves of non-seismic events, this paper proposes an idea based on sparse representation classification to remove such non-seismic interference directly. Combining the ideas of noise reduction and non-seismic event elimination, we can obtain a standard sparse anti-interference denoising model for earthquake early warning. It's innovative that this model implements the sparse theory into the field of earthquake early warning. According to the experimental results, in the case of heavy noise, the denoising model based on sparse representation can reach average SNR of 8.73 and an average MSE of 29.53, and the denoising model based on compression perception can reach average SNR of 7.29 and an average MSE 41.34, and the denoising model based on learning dictionary can reach average SNR 11.07 and average MSE 17.32. The performance of these models is better than the traditional FIR filtering method (average SNR -0.73 and average MSE 260.37) or IIR filtering method (average SNR 4.73 and average MSE 73.95). On the other hand, the anti-interference method of the sparse classification proposed in this paper can accurately distinguish non-seismic interference events from natural earthquakes. The classification accuracy of the method based on the noise category of the selected test data set reaches 100% and achieves good results.

Simultaneous Wireless Information and Power Transfer Over KG Fading Channels

In this paper, we proposed a novel expression for the probability density function (PDF) and the cumulative density function (CDF) of the output SNR for the M-branch Selection Combining (M-SC) receiver over Generalized-K (KG) fading channels. Unlike conventional energy sources, the proposed scheme uses a Power splitter (PS) scheme at the receiver side. The received signal is split into information decoding and energy harvesting receiver with adjustable power levels. The expressions of our system consider arbitrary channel parameters and diversity branches. We analyze the proposed model and derive the closed-form expression for average SNR and average bit error rate (ABER) to evaluate the system performance. The obtained results are corroborated with the help of Monte-Carlo simulations. The effects of system parameters, such as power splitter ratio, modulation order, and shaping parameters of KGfading channels, are studied. This type of system will benefit reliable data transmission in an energy-limited scenario.