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The purpose of the study is to evaluate the effect of energy level on the modulation transfer functions (MTF) and noise power spectra (NPS) of virtual monochromatic images (VMIs) obtained using four types of computed-tomographic (CT) scanners: Revolution, SOMATOM, IQon, and Aquilion. VMIs were obtained at 70, 60, and 50 kiloelectron volts (keV), and also at the lowest keV available in each scanner. We evaluated the MTF and NPS in the VMIs obtained at each keV. No significant effect of the energy level on the MTF was observed in IQon, whereas the spatial resolution decreased as the energy level decreased in the other types of scanners. The NPS curves tended to increase as the energy levels decreased with three types of scanners other than Aquilion. The spatial resolution and noise frequency characteristics of VMIs may be affected by the energy level, and the effects of energy level on these characteristics differ depending on the type of CT scanners.

In this letter, we investigate the problem of frame synchronization in a free-space optical (FSO) communications link, where a known synch pattern is periodically embedded in the transmitted bitstream. The modulation format is on-off keying (OOK) and the electrical signal provided by the photo-detector is plagued by a mixture of thermal and shot noise with signal-dependent power. Due to atmospheric turbulence, channel attenuation can exhibit large random fluctuations, so that no prior knowledge of the signal level and noise variances is assumed. These parameters, together with the start-of-frame, are jointly estimated using a simplified maximum likelihood (ML) approach. Numerical simulations indicate that the proposed scheme is able to effectively exploit the presence of shot noise to improve its detection capability, and outperforms the standard frame synchronization method tailored for an AWGN channel with signal-independent noise power.

Detecting vibrations with high probability and low false alarm probability is crucial for prompting distributed acoustic sensors (DASs) to real applications. It is known that detection performance mainly depends on signal-to-noise ratio (SNR) and many efforts have been made to improve it. However, the relationship between SNR and detection performance has not been quantitatively analyzed so far. Threshold-based vibration detection is a simple and commonly used technique, but how to set the decision threshold in DAS is still an open question. In this work, for the first time, we propose a model to quantify the relationship between SNR and detection performance and provide a method for setting the decision threshold. Firstly, we build a model to differentiate vibrations from the background noise based on their short-time average energy. This model reveals that setting decision threshold requires perfect knowledge of noise power, which is a difficult task in DAS since noise power varies frequently with time and position. To solve this problem, secondly, we propose a noise-irrelevant threshold setting method based on autocorrelation-energy. Finally, experimental validation is performed on a DAS system along 47.4km sensing fiber with 5m spatial resolution. Results of autocorrelation-energy-based method show 100% and 98.1% detection probability for two vibrations with $1.12 \times {10^{ - 7}}$ false alarm probability in a one-hour measurement period.

OBJECTIVE. Previous advances over filtered back projection (FBP) have incorporated model-based iterative reconstruction. The purpose of this study was to characterize the latest advance in image reconstruction, that is, deep learning. The focus was on applying characterization results of a deep learning approach to decisions about clinical CT protocols. MATERIALS AND METHODS. A proprietary deep learning image reconstruction (DLIR) method was characterized against an existing advanced adaptive statistical iterative reconstruction method (ASIR-V) and FBP from the same vendor. The metrics used were contrast-to-noise ratio, spatial resolution as a function of contrast level, noise texture (i.e., noise power spectra [NPS]), noise scaling as a function of slice thickness, and CT number consistency. The American College of Radiology accreditation phantom and a uniform water phantom were used at a range of doses and slice thicknesses for both axial and helical acquisition modes. RESULTS. ASIR-V and DLIR were associated with improved contrast-to-noise ratio over FBP for all doses and slice thicknesses. No dose or contrast dependencies of spatial resolution were observed for ASIR-V or DLIR. NPS results showed DLIR maintained an FBP-like noise texture whereas ASIR-V shifted the NPS to lower frequencies. Noise changed with dose and slice thickness in the same manner for ASIR-V and FBP. DLIR slice thickness noise scaling differed from FBP, exhibiting less noise penalty with decreasing slice thickness. No clinically significant changes were observed in CT numbers for any measurement condition. CONCLUSION. In a phantom model, DLIR does not suffer from the concerns over reduction in spatial resolution and introduction of poor noise texture associated with previous methods.

The HY-2B scatterometer (HSCAT-B) has now completed a one-year orbit. The instrument was declared operational after six months of in-orbit testing. The ocean wind field products are routinely being made available to the global science community. The HY-2B will work with the HY-2A to provide the Ku-band sea surface observations on a long-term scale, and this collaboration is expected to be important for disaster prevention and mitigation, as well as globally focused climate change research. However, to obtain reasonable evaluations of the system’s functional, performance, and instrument stability results, the temperature, internal calibration parameters, attitude angles, and normalized radar cross section (NRCS) of the instrument were rigorously analyzed during the in-orbit tests. This study provided an overview of the six-month in-orbit testing process results. The temperature levels of some important parts of the scatterometer were found to meet the design requirements. The internal calibration and noise power had shown similar trends and were both within the normal range. The attitude angles, which mainly included the roll and pitch, had displayed variations within a very small scope. The evaluation results of the Amazon rainforest observations indicated that the scatterometer instrument had met all the design requirements and had established a good database for geophysical processing.

Based on cyclostationarity properties of Orthogonal Frequency Division Multiplexing with Offset Quadrature Amplitude Modulation (OFDM/OQAM) signal, a novel blind signal-to-interference-plus-noise ratio (SINR) estimation method for OFDM/OQAM system in the presence of Radio Frequency Interference (RFI) is proposed. By analyzing the power distribution of cyclic correlation of transmitted signal, RFI and AWGN, by appropriately selecting the time delay variable and cyclic frequency of the OFDM/OQAM signal, the signal and interference plus noise power could be efficiently isolated in the power domain, which formed the SINR estimation method. Computer simulation results verify that the performance of the proposed method is superior to that of second-order and fourth-order moments (M2M4) method and the complexity of the new method is also lower than that of M2M4 method. The proposed method can effectively estimate SINR under the fast-fading Rayleigh channel and is robust to the density of RFI signal.

This letter investigates the problem of determining the static target in multistatic systems using bistatic range (BR) measurements, which can be obtained by some local sensor signal processing methods. The localization problem is first formulated as a semidefinite programming problem by using the semidefinite relaxation technique. Then, an initial position estimate is given by augmenting the unknown parameter space, which is required for solving the SDP problem. Specifically, this initialization is also a closed-form solution. Compared with existing algorithms, the proposed estimator has the advantage of not relying on the prior knowledge of BR measurement noise power. Simulation results validate the performance of the proposed estimator.

The delay-and-sum (DAS) beamformer is the most commonly used method in medical ultrasound imaging. Compared with the DAS beamformer, the minimum variance (MV) beamformer has an excellent ability to improve lateral resolution by minimizing the output of interference and noise power. However, it is hard to overcome the tradeoff between satisfactory lateral resolution and speckle preservation performance due to the fixed subarray length of covariance matrix estimation. In this study, a new approach for MV beamforming with adaptive spatial smoothing is developed to address this problem. In the new approach, the generalized coherence factor (GCF) is used as a local coherence detection tool to adaptively determine the subarray length for spatial smoothing, which is called adaptive spatial-smoothed MV (AMV). Furthermore, another adaptive regional weighting strategy based on the local signal-to-noise ratio (SNR) and GCF is devised for AMV to enhance the image contrast, which is called GCF regional weighted AMV (GAMV). To evaluate the performance of the proposed methods, we compare them with the standard MV by conducting the simulation, in vitro experiment, and the in vivo rat mammary tumor study. The results show that the proposed methods outperform MV in speckle preservation without an appreciable loss in lateral resolution. Moreover, GAMV offers excellent performance in image contrast. In particular, AMV can achieve maximal improvements of speckle signal-to-noise ratio (SNR) by 96.19% (simulation) and 62.82% ( in vitro ) compared with MV. GAMV achieves improvements of contrast-to-noise ratio by 27.16% (simulation) and 47.47% ( in vitro ) compared with GCF. Meanwhile, the losses in lateral resolution of AMV are 0.01 mm (simulation) and 0.17 mm ( in vitro ) compared with MV. Overall, this indicates that the proposed methods can effectively address the inherent limitation of the standard MV in order to improve the image quality.

Deep learning-based speech enhancement algorithms have shown their powerful ability in removing both stationary and non-stationary noise components from noisy speech observations. But they often introduce artificial residual noise, especially when the training target does not contain the phase information, e.g., ideal ratio mask, or the clean speech magnitude and its variations. It is well-known that once the power of the residual noise components exceeds the noise masking threshold of the human auditory system, the perceptual speech quality may degrade. One intuitive way is to further suppress the residual noise components by a postprocessing scheme. However, the highly non-stationary nature of this kind of residual noise makes the noise power spectral density (PSD) estimation a challenging problem. To solve this problem, the paper proposes three strategies to estimate the noise PSD frame by frame, and then the residual noise can be removed effectively by applying a gain function based on thedecision-directedapproach. The objective measurement results show that the proposed postfiltering strategies outperform the conventional postfilter in terms of segmental signal-to-noise ratio (SNR) as well as speech quality improvement. Moreover, the AB subjective listening test shows that the preference percentages of the proposed strategies are over 60%.

Electroencephalogram (EEG) is a biomedical technique for capturing the human brain’s electrical information to process its activities and actions. EEG is one of the main methods commonly used in neuroscience, from clinical analysis to the design of brain–computer interfaces. The first one of the many challenges in an EEG system design regards the ultra-low amplitude of signals (i.e., of the order of $$20~\upmu \hbox {V}$$ ) and their susceptibility to several kinds of interferences. Electromagnetic interference (EMI) omnidirectionally irradiated by the power line supplies disturbs the EEG instrumentation system with a noise power mostly concentrated from 50 to $$60\,\hbox {Hz}$$ . Our paper investigates the energy efficiency of adaptive filtering (AF) techniques based on the least mean square (LMS), normalized LMS, and set-membership (SM) families for meeting the protection against EMI in EEG systems. The results demonstrate that the LMS algorithm presents an unstable behavior with unsatisfactory results against the normalized LMS filters when subject to noisy scenarios. Therefore, we herein explore dedicated VLSI hardware architectures for the following filters: (a) normalized LMS (NLMS), (b) SM-NLMS, (c) partial update NLMS (PU-NLMS), and (d) SM bi-normalized LMS (SM-BNLMS). PU-NLMS architecture offers the best trade-off between the capability of eliminating the EMI versus its power dissipation, circuit area, and maximum clock frequency. PU-NLMS provides an artifact reduction level of up to $$8.8\,\hbox {dB}$$ , with low energy consumption of 0.62 nJ/operation and just $$1.41\%$$ larger circuit area than the NLMS architecture.

In this letter, we present a lower performance bound for discrete Fourier transform (DFT)-based beamspace channel estimation (CE) in 64 antennas Massive Multiple-Input Multiple-Output (MIMO) receiver. The beamspace CE is implemented after antennas digital signal transformation to a priori estimated DFT sub-space resulting in less complexity. To estimate the performance, we calculate noise power after the beamspace CE unit by decomposing an ideal channel response into separate taps and transforming them into the beam domain. Then the minimum mean squared error (MMSE) method is employed with taps to estimate the CE error power. An artificial CE is calculated as a sum of the ideal beamspace channel and additive Gaussian noise with the same error power. The artificial CE is utilized in the MIMO detector and decoder to achieve performance bound. Simulation results are presented for the non-line-of-sight model of the 5G QuaDRiGa 2.0 channel.

Anthropogenic noise is relatively new to natural soundscapes and may have adverse effects on acoustically active species. In birds, adverse effects include changes in vocalization patterns. Helicopters and songbirds are ubiquitous in protected natural areas but the effect of helicopter noise on songbirds has never been assessed. Our objective was to determine if helicopter noise affects temporal characteristics of songbird vocalizations within protected natural areas. We collected soundscape recordings in three protected areas with varying levels of helicopter tour activity, including one with the second highest air tour activity in the United States. We examined songbird response to helicopter noise at the species level by measuring changes in vocalization time, and at the community level by employing the Bioacoustic Index, an acoustic measure of biological sound. We found a strong association between increasing helicopter noise and songbird vocalizations, indicating that some bird species use temporal shifts in vocalizations to mitigate masking effects from helicopter noise. The strength and direction of the response was species-specific, suggesting differences in resilience to helicopter noise between species. Furthermore, our results suggest that bird response to helicopter noise is strongest in areas with very loud and frequent helicopter traffic. Our study is the first to examine and demonstrate impacts of helicopter noise on songbird vocalizations. Our results may serve as the foundation of an air tour management plan that considers reducing the number of helicopter overflights over protected natural areas and enforcing higher flight altitudes to decrease noise power levels.

Approximate circuit design has gained significance in recent years targeting error-tolerant applications. In the literature, there have been several attempts at optimizing the number of approximate bits of each approximate adder in a system for a given accuracy constraint. For computational efficiency, the error models used in these routines are simple expressions obtained using regression or by assuming inputs or the error is uniformly distributed. In this article, we first demonstrate that for many approximate adders, these assumptions lead to an inaccurate prediction of error statistics for multi-level circuits. We show that mean error and mean square error can be computed accurately if static probabilities of adders at all stages are taken into account. Therefore, in a system with a certain type of approximate adder, any optimization framework needs to take into account not just the functionality of the adder but also its position in the circuit, functionality of its parents, and the number of approximate bits in the parent blocks. We propose a method to derive parameterized error models for various types of approximate adders. We incorporate these models within an optimization framework and demonstrate that the noise power is computed accurately.

We show that the uniform distribution minimizes entropy among all one-dimensional symmetric log-concave distributions with fixed variance, as well as various generalizations of this fact to Renyi entropies of orders less than 1 and with moment constraints involving $p$ -th absolute moments with $p\leq 2$ . As consequences, we give new capacity bounds for additive noise channels with symmetric log-concave noises, as well as for timing channels involving positive signal and noise where the noise has a decreasing log-concave density. In particular, we show that the capacity of an additive noise channel with symmetric, log-concave noise under an average power constraint is at most 0.254 bits per channel use greater than the capacity of an additive Gaussian noise channel with the same noise power. Consequences for reverse entropy power inequalities and connections to the slicing problem in convex geometry are also discussed.

In AC motor powered by frequency converter, the random pulse width modulation (RPWM) can suppress vibration and noise power near the switching frequency and its integer multiple of the inverter. However, the traditional RPWM strategy cannot selectively eliminate the special frequency harmonics, such as the frequency that causes strong electromagnetic vibration and noise of the motor. A method of suppressing electromagnetic vibration and noise of induction motor based on RPWM selective spectrum shaping is proposed. Firstly, according to three different pulse positions, the general formula of selective harmonic elimination of RPWM is derived, so as to selectively eliminate the harmonic noise power at a specific frequency and its integer multiple. On this basis, further spectrum shaping can be realized by reasonably selecting the effective random number to avoid the inverter switching frequency distributing in a specific frequency band. The simulation and experimental results show that the method can not only suppress the vibration and noise near the integer times of the switching frequency, but also selectively suppress the electromagnetic vibration and noise at other frequencies.

Magnetic nanoparticles have proven to be extremely useful in a broad range of biomedical applications. To ensure optimal efficiency, a precise characterization of these particles is required. Thermal Noise Magnetrometry (TNM) is a recently developed characterization technique that has already been validated against other techniques. TNM offers a unique advantage in that no external excitation of the system is required to drive the measurement. However, the extremely small stochastic signal in the femtotesla range currently limits the accessibility of the method, and a better understanding of the influences of the sample characteristics on the TNM signal is necessary. In this study, we present a theoretical framework to model the magnetic noise power properties of particle ensembles and their signal as measured via TNM. Both intrinsic sample properties (such as the number of particles or their volume) and the geometrical properties of the sample in the setup have been investigated numerically and validated with experiments. It is shown that the noise power depends linearly on the particle concentration, quadratically on the individual particle size, and linearly on the particle size for a constant total amount of magnetic material in the sample. Furthermore, an optimized sample shape is calculated for the given experimental geometry and subsequently 3d printed. This geometry produces a 3.5 fold increase in TNM signal (0.007 to 0.026 pT2) using less than half of the magnetic material considered in the intial measurements.

A recent global crisis associated with COVID-19 has encouraged millions of people to work from home, thus causing a drastic increase in overall network traffic, data-rate requirements and end network capabilities This has also produced more noise, cross-talk and undesirable optical-fibre nonlinearities, especially a four-wave mixing (FWM) effect that deteriorates performance of dense wavelength-division multiplexing (DWDM) systems A presence of FWM in the DWDM systems imposes increasing complexity and latency of networks, and decreases their spectral efficiency In its turn, this degrades efficient utilization of optical bandwidth To mitigate the above problems, we suggest a supervised regression modelling (SRM) A relevant SRM-DWDM approach performs self-parametric optimization of the DWDM systems with machine-learning techniques and finds real trade-offs among various factors that affect the FWM Our model reduces complexity of modelling and computational time, resulting in accurate and reliable prediction of parameter values We also evaluate the performance of our SRM-DWDM technique by comparing its data with the iterative results obtained for different parameters (e g , output signal-to-noise ratio, Q-factor, signal power and noise power) Finally, we specify the procedures necessary for global optimization of DWDM systems © 2021, Institute of Physical Optics All rights reserved

Optical beam center position on an array of detectors is an important parameter that is essential for estimating the angle-of-arrival of the incoming signal beam. In this paper, we have examined the beam position estimation problem for photon-counting detector arrays, and to this end, we have derived and analyzed the Cramer-Rao lower bounds on the mean-square error of unbiased estimators of beam position. Furthermore, we have also derived the Cramer-Rao lower bounds of other system parameters such as signal peak intensity, and dark current noise power, on the array. In this sense, we have considered robust estimation of beam position in which none of the parameters are assumed to be known beforehand. Additionally, we have derived the Cramer-Rao lower bounds of beam and noise parameters for observations based on both pilot and data symbols of a pulse position modulation (PPM) scheme. Finally, we have considered a two-step estimation problem in which the signal peak and dark current noise intensities are estimated using a method of moments estimator, and the beam center position is estimated with the help of a maximum likelihood estimator.

For multi-microphone speech enhancement, different microphones might have different contributions, assome are even marginal. This is more likely to happen in wireless acoustic sensor networks (WASNs), where somesensors might be distant. In this work, we therefore consider sensor selection for linearly-constrained beamformers. Theproposed sensor selection approach is formulated by minimizing the total output noise power and constraining thenumber of selected sensors. As the considered sensor selection problem requires the relative acoustic transfer function(RTF), the covariance whitening based RTF estimation or a direct-path RTF approximation is exploited. For a singletarget source, we can thus substitute the estimated RTF or the assumed RTF to the original problem formulation in orderto design a minimum variance distortionless response (MVDR) beamformer. Alternatively, we can integrate the two RTFsto design a linearly constrained minimum variance (LCMV) beamformer in order to alleviate the effects of RTFestimation/approximation errors. By leveraging the superiority of LCMV beamformers, the proposed approach can beapplied to the multi-source case. An evaluation using a simulated large-scale WASN demonstrates that the integration ofRTFs for the sensor selection based LCMV beamformer can be beneficial as opposed to relying on either of theindividual RTF steered sensor selection based MVDR beamformers. We conclude that the sensors that are close to thetarget source(s) and also some around the coherent interferers are more informative.

The results of the man-made noise measurement campaign, organized by the Dutch society of radio amateurs, VERON, are compared with geographical data like the number of residential livings inside a circular area around the measurement location limited by a chosen distance. For each frequency band, correlation factors are derived as a function of the distance. It is shown that a strong correlation exists between the measured man-made noise floor and the density of habitation. This result proves that in this campaign, measured noise floor levels are mainly the result of accumulation of noise power from a large number of small sources spread in the houses, instead of being caused by a single, or a few, strong sources. The correlation is maximal at distances up to 200–300 m from the measurement location. Beyond that range, the correlation decreases, indicating that mainly sources in a range up to 300 m are relevant in determining the noise floor. This conclusion is supported by the observation that the slope of regression lines increasingly deviates from a theoretically derived slope at ranges over 300 m.

The ambient sound field in the ocean can be decomposed into a linear combination of two independent fields attributable to wind-generated wave action at the surface and noise radiated by ships. The vertical coherence (the cross-spectrum normalized by the power spectra) and normalized directionality of wind-generated noise in the ocean are stationary in time, do not vary with source strength and spectral characteristics, and depend primarily on the local sound speed and the geoacoustic properties which define the propagation environment. The contribution to the noise coherence due to passing vessels depends on the range between the source and receiver, the propagation environment, and the effective bandwidth of the characteristic source spectrum. Using noise coherence models for both types of the sources, an inversion scheme is developed for the relative and absolute contribution of frequency dependent ship noise to the total sound field. A month-long continuous ambient sound recording collected on a pair of vertically aligned hydrophones near Alvin Canyon at the New England shelf break is decomposed into time-dependent ship noise and wind-driven noise power spectra. The processing technique can be used to quantify the impact of human activity on the sound field above the natural dynamic background noise, or to eliminate ship noise from a passive acoustic monitoring data set.

The frequency-domain adaptive filter (FDAF) is a competitive candidate for stereo acoustic echo cancellation (SAEC) because of its good convergence and computational efficiency. However, there is a conflict between the convergence rate and the steady-state misalignment when using a constant step size. In this paper, a variable step-size approach is proposed for the stereo echo canceller in the frequency domain. The proposed method uses a first-order Markov model to describe the time-varying echo paths between the loudspeakers and the microphone. Following a statistical convergence analysis of the two-channel FDAF algorithm, the state recursion of the system distance in each frequency bin is given. The optimal step size is then derived by minimizing the system distance. Several practical problems, including noise power spectral density estimation and echo paths change detection, are discussed in details. Moreover, a close link between the proposed optimum step-size control method and the multichannel state–space frequency-domain adaptive filter is established. We show that the proposed variable step-size approach can also be incorporated into the block extended least-mean-square algorithm to fully exploit their benefits. Finally, computer simulations demonstrate that the proposed algorithm exhibits an excellent convergence performance and is very robust to the double talk.

Orthogonal frequency division multiplexing (OFDM) signals are widely employed in most wireless comm- unication systems, and the problem of spectrum sensing for OFDM signals has been extensively investigated. However, most of the available research reports on spectrum sensing for OFDM signals ignore the existence of pilots, which are usually inserted in the OFDM signals for synchronization, channel estimation and control information delivery. To sense weak OFDM signals with periodic pilots reliably in cognitive radio networks, we propose two likelihood ratio test (LRT) detectors for the scenarios with and without time synchronization information. In the ideal scenario that an unlicensed secondary user is perfectly synchronized with a licensed primary user, we propose a near-optimal LRT (NOLRT) detector, which constructs the test statistic by correlating the locally generated pilots with the received OFDM signal samples. Since the NOLRT detector relies on perfect time synchronization that is difficult to realize in practice, we further propose a more practical buffer-aided LRT (BALRT) detector that does not require time synchronization. To formulate the test statistic, the BALRT detector stores the averages of historical OFDM signal samples in a buffer and then correlates them with the currently received OFDM signal samples. Theoretical closed-form expressions for the probability of detection and probability of false alarm are derived for the proposed detectors. To alleviate the negative effects of noise uncertainty in low SNR scenarios, we also propose an effective method based on noise power estimation. Monte Carlo simulation results match with theoretical results very well and show that the NOLRT detector outperforms the counterparts significantly in additive white Gaussian noise (AWGN) channel with perfect time synchronization, while the BALRT detector outperforms the counterparts noticeably in multipath propagation environments without time synchronization.

Efficiency-enhanced and high-precision input common-mode feedback control (ICMFB) is proposed for a fully differential readout circuit of the MEMS capacitive sensor. This ICMFB improves the efficiency and performance of input common-mode voltage control using a smaller feedback capacitor. The noise performance of the readout circuit is improved as a decrease of a feedback capacitor. The correlated level shift (CLS) technique is used to improve the output swing of the amplifier therefore reduce the value of a feedback capacitor. Also, the oversampling successive approximation (OSA) technique is used to improve the accuracy of input common-mode voltage control using a low-gain op-amp. Simulation results show that the input common-mode voltage error decreases to 0.01% when the value of the feedback capacitor is the same as the MEMS sensor. Also, the noise power spectral density of the readout circuit increases by 10 dB.

Since the analysis of cell coverage faces complex environments in unmanned aerial vehicle base station (UAV-BS) systems, general coverage probability in a typical cell is derived to analyse a UAV-BS multi-tier network, and this coverage probability is closely related with power difference among UAV-BSs, two-dimension (2D) and three-dimension (3D) UAV-BS deployment, and general interference fading models. Especially, closed-form coverage probability expressions are also derived in the form of 2D scenario and 3D scenario, when the interference fading coefficient is assumed to follow a Gamma distribution and an exponential distribution, respectively. It shows that there exists maximum spectral efficiency density (SED) as the BS density increases, and such a BS density achieving the max-SED is independent of noise power, but is affected by various propagation parameters.

In wireless relay systems, interference often leads to system rate loss. Recently, the research on radio-frequency signal energy harvesting makes it possible to use interference energy. In this paper, based on the time switching (TS) relay operation strategy and amplify-and-forward protocol, the simultaneous wireless information and power transfer relay system under strong interference is studied. Firstly, we propose an interference energy harvesting (IEH) scheme to reduce the rate loss. Three theorems are proved, and the approximate optimal interference energy allocation factor is obtained. Secondly, we propose two relay operation schemes under strong interference, namely static optimal TS ratio scheme and adaptive optimal TS ratio scheme. Then, the expressions of the two schemes are derived, and the corresponding optimization problems are established. The algorithms for solving these two problems are given. The numerical simulation results show that the IEH schemes with static and adaptive optimal TS ratio can effectively improve the system rate, while the latter has better performance than the former. In addition, the effects of interference power and antenna noise power on the system rate are also studied. The results show that the performance of the two schemes is better than that of the scheme without IEH under different interference power and noise power.

This paper presents the analysis of noise in Single Gate Extended Source TFET (SG-ESTFET) considering the absence and presence of interface trap charges, when the device is subjected to scaling and variation of parameters like device gate length (Lg), extended source length and height, SiGe mole fraction (x), oxide thickness (tox), gate dielectric material, and frequency (f). Furthermore, the influence of variation of dimensionality and material parameters in presence of noise on Drain Current Noise Power Spectral Density (Sid) and Gate Voltage Electron Noise Power Spectral Density (Svgee) are studied for different trap charge conditions. Assuming Gaussian distribution of trap charges at the interface, it is perceived that the effect of noise is more as compared to the case of absence of trap charges. In reference to other FET devices, present paper reports that, the proposed SG-ESTFET device under absence of trap charges, illustrates an improved Sid and Svgee value of 1.4 × 10−29 A2/Hz and 5.21 × 10−16 V2/Hz, respectively whereas under the presence of trap charges, Sid and Svgee value are 6.6 × 10−26 A2/Hz and 8.74 × 10−12 V2/Hz, respectively. Moreover, this study also reports that the generation recombination (G-R) noise is mainly prevailing at low and mid-frequencies in presence of trap charges while diffusion noise is prevailing at high-frequencies. Likewise, the flicker noise is observed to be noteworthy at low and medium-frequencies in absence of trap charges.

The authors derive a closed-form exact expression and a fast convergent series for the probability of phased-array detection of non-fluctuating targets embedded in complex white Gaussian noise. To be realistic, they assume that the amplitude of the target echoes and the noise power are unknown parameters. Their series representation achieved impressive savings in computational load and computation time when compared to the numerical evaluation of the existing integral-form solution.

Edge traps in the gate oxide of silicon nanowire MOSFETs have been extracted from tunnel current noise measurement in a gated diode like arrangement. We have found that, low frequency noise in tunnel current results from collective response of the edge traps available within the gate oxide surrounding band-to-band generation (BTBG) region of silicon nanowire, and when the BTBG region is accessed by favorable terminal biases. From detail modeling of the phenomenon, we derived a closed form expression of the tunneling current noise power spectral density (PSD) employing which oxide edge trap density in nanowire MOSFET had been extracted through its fit with the experimental noise PSD data. We furthermore validated our model for different BTBG biases, and contrasted our result with the bulk oxide trap density in identical MOSFET, while the latter was separately estimated from the gate current noise PSD measurement. Mismatch between the oxide bulk trap and edge trap concentrations has been found, whereby actual edge trap density remains otherwise hidden from the widely employed gate current noise measurement technique. It is because, the present scheme improves the resolution of the extracted oxide edge trap concentration in surrounded gate MOSFET owing to constricted tunnel current flow near the corner of the gate which imposes selectivity on the oxide edge traps

This paper presents the design and the experimental characterization of a complete acoustic analog front-end (A-AFE) read-out channel for detecting thermo-acoustic pulses induced by heavy-charged particles interacting with matter. The most relevant applications are in particle physics experiments (bubble chambers for Dark Matter Weakly Interacting Massive Particles (WIMPs) detection) and biomedical (beam range verification in oncological hadron-therapy). Typical thermo-acoustic pulses are characterized by 50 kHz–500 kHz bandwidth and weak amplitudes (around 5–50 Pa) and thus require low-noise piezoelectric-based ultrasound sensors (with input-referred noise power spectral density, IRN-PSD, as low as 1–2 nV/√Hz) whose output signal is 1–10 μV0-PEAK amplitude. Thus, even lower noise electronics are needed to preserve the weakest signal and maximize the sensitivity of the whole detector. For this reason, the A-AFE has been designed to achieve a global 0.3 dB Noise Figure with an input-referred detector noise floor as low as 2.3 μVRMS (corresponding to 93 mPaRMS) thanks to a dedicated JFET-based low-noise amplifier. The A-AFE achieves 31.5 dB Signal-to-Noise-Ratio at 4.8 Pa and 160 kHz pressure source. The final A-AFE prototype has 45 dB in-band gain at 0.3 nV/√Hz Input Referred Noise and it is the first specifically designed and optimized for sensing the acoustic signal coming from interactions between radiations and matter.

In this study, the authors develop a new algorithm for passive source localisation using time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements. In this algorithm, the position and velocity of the target source are successively estimated by formulating two alone semidefinite programming problems, where the variances of the TDOA and FDOA measurement noises can be separated and neglected. Unlike the traditional methods, the authors’ approach does not require the prior knowledge of the TDOA/FDOA measurement noise power. Simulation results indicate that the proposed algorithm outperforms the existing methods under unknown noise power knowledge.

This article presents the validation of a digital signal processing technique that can be used to estimate radiometric sky noise, and hence atmospheric absorption, within existing digital receivers at little/no additional cost. To demonstrate this, a receiver was constructed that simultaneously records the beacon signal power from the ALPHASAT Aldo Paraboni technology demonstration payload, as well as the integrated noise power in the adjacent band. Calibration from the digital radiometer is performed using tip-curve calibration procedures. Atmospheric fading is then obtained by observing the beacon as well as the radiometric signals. This enables the comparison of fading obtained by the two techniques and provides a means to calibrate the received beacon power level to obtain total atmospheric attenuation. It is shown that for low levels of fading, up to a few dB, the two techniques provide good agreement. This approach can, therefore, provide a low-cost option for geostationary mm-wave satellite channel measurements in the low fading regime, which can be useful in the design and operation of the feeder links in emerging satcom systems.

The automatic identification of the modulation format of a detected signal is a major task of an intelligent receiver in both military and civilian applications. It is well known that the maximum likelihood (ML) classifier requires a priori knowledge of the incoming signal and channel (including amplitude, timing information, noise power, and the roll-off factor of the pulse-shaping filter). To relax this requirement, we introduce a novel estimator to estimate the parameters required by the ML classifier which is blind to the modulation scheme of the received signal, and this gives rise to a new blind modulation classifier for digital amplitude-phase modulated signals. While the proposed classifier is completely blind, the simulation results show that the performance of this classifier is very close to the optimal non-blind classifier.

Purpose We aimed to thoroughly characterize image quality of a novel deep learning image reconstruction (DLIR), and investigate its potential for dose reduction in abdominal CT in comparison with filtered back-projection (FBP) and a partial model-based iterative reconstruction (ASiR-V). Methods We scanned a phantom at three dose levels: regular (7 mGy), low (3 mGy) and ultra-low (1 mGy). Images were reconstructed using DLIR (low, medium and high levels) and ASiR-V (0% = FBP, 50% and 100%). Noise and contrast-dependent spatial resolution were characterized by computing noise power spectra and target transfer functions, respectively. Detectability indexes of simulated acute appendicitis or colonic diverticulitis (low contrast), and calcium-containing urinary stones (high contrast) (|ΔHU| = 50 and 500, respectively) were calculated using the nonprewhitening with eye filter model observer. Results At all dose levels, increasing DLIR and ASiR-V levels both markedly decreased noise magnitude compared with FBP, with DLIR low and medium maintaining noise texture overall. For both low- and high-contrast spatial resolution, DLIR not only maintained, but even slightly enhanced spatial resolution in comparison with FBP across all dose levels. Conversely, increasing ASiR-V impaired low-contrast spatial resolution compared with FBP. Overall, DLIR outperformed ASiR-V in all simulated clinical scenarios. For both low- and high-contrast diagnostic tasks, increasing DLIR substantially enhanced detectability at any dose and contrast levels for any simulated lesion size. Conclusions Unlike ASiR-V, DLIR substantially reduces noise while maintaining noise texture and slightly enhancing spatial resolution overall. DLIR outperforms ASiR-V by enabling higher detectability of both low- and high-contrast simulated abdominal lesions across all investigated dose levels.

The mechanism of a universal serial bus 3.0 connector caused radio frequency interference (RFI) to a nearby antenna is studied. The connector radiation is modeled by an equivalent stripline-fed slot antenna, based on the analysis of the common mode current path on the connector structure. With the proposed connector model, the original connector radiation source can be replaced by an equivalent magnetic dipole source, which is directly correlated with the physical quantity on the connector. Applying reciprocity theorem, the equivalent dipole source can be applied to predict the coupled noise power to the antenna. The proposed connector radiation mechanism and model are validated through full-wave simulation and measurement. The application of the magnetic dipole source for RFI estimation is demonstrated in a real laptop system.

This article proposes a low spurious noise and fast-transient-response buck converter with second-order continuous-time delta-sigma modulator (CT-DSM). The DSM technique has the superiority of oversampling and the characteristics of noise shaping to reduce spurious noise and speed up transient response. Moreover, the transient response eruption technique uses a dynamic current compensator to accelerate the transient response. Therefore, the proposed CT-DSM buck converter reduces spurious noise, reduces the output harmonic tones, and accelerates transient response, but it keeps high efficiency, low noise, and low ripple of the traditional switching converter. The proposed DSM buck converter is fabricated in TSMC $\text{0.35}\ \mu\text{m}$ 2P4M CMOS process with a chip area of 1.32 ×1.38 mm. The measurement results show that the peak noise power is −70 dBm across output spectrum, which is −10 dBm lower than a traditional CT-DSM buck converter. When the load current changes from 50 to 500 mA and from 500 to 50 mA, the transient responses are both 4 μs, which are 7 μs faster than the traditional CT-DSM buck converter. The maximum output overshoot voltage is 20 mV, the output voltage ripple is 10 mV, and the conversion peak efficiency is 90.4% at the output voltage of 2.4 V. These characteristics keep the same performances as or even better than the traditional CT-DSM buck converter.

This paper presents the analysis of radio frequency (RF) spectrum usage using cognitive radio. The aim was to determine the unused spectrum frequency bands for efficiently utilization. A program was written to reuse a range of vacant frequency with different model element working together to produce a spectrum sensing in MATLAB/Simulink environment. The developed Simulink model was interfaced with a register transfer level - software defined radio, which measures the estimated noise power of the received signal over a given time and bandwidth. The threshold estimation performed generates a 1\0 output for decision and prediction. It was observed that some spectrum, identified as vacant frequency, were underutilized in FM station in Benin City. The result showed that when cognitive radio displays “1” output, which is decision H1, the channel is occupied and cannot be used by the cognitive radio for communication. Conversely, when “0” output (decision H0) is displayed, the channel is unoccupied. There is a gradual decrease in the probability of detection (Pd), when the probability of false alarm (Pfa) is increased from 1% to 5%. In the presence of higher Pfa, the Pd of the receiver maintains a high stability. Hence, the analysis finds the spectrum hole and identifies how it can be reused