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For almost a half-decade, the unique autocorrelation properties of Golay complementary pairs (GCP) have added a significant value to the key performance of conventional time-domain multiplexed fiber Bragg grating sensors (TDM-FBGs). However, the employment of the unipolar form of Golay coded TDM-FBG has suffered from several performance flaws, such as limited improvement of the signal-to-noise ratio (SNIR), noisy backgrounds, and distorted signals. Therefore, we propose and experimentally implement several digital filtering techniques to mitigate such limitations. Moving averages (MA), Savitzky-Golay (SG), and moving median (MM) filters were deployed to process the signals from two low reflectance FBG sensors located after around 16 km of fiber. The first part of the experiment discussed the sole deployment of Golay codes from 4 bits to 256 bits in the TDM-FBG sensor. As a result, the total SNIR of around 8.8 dB was experimentally confirmed for the longest 256-bit code. Furthermore, the individual deployment of MA, MM, and SG filters within the mentioned decoded sequences secured a further significant increase in SNIR of around 4, 3.5, and 3 dB, respectively. Thus, the deployment of the filtering technique alone resulted in at least four times faster measurement time (equivalent to 3 dB SNIR). Overall, the experimental analysis confirmed that MM outperformed the other two techniques in better signal shape, fastest signal transition time, comparable SNIR, and capability to maintain high spatial resolution.

Background/objective: There are two routine pulse-sequences for single voxel spectroscopy (SVS), point resolved spectroscopy (PRESS) and stimulated echo acquisition mode (STEAM). Although STEAM has several advantages in comparison to PRESS, signal/noise ratio (SNR) superiority of PRESS makes it the first choice for SVS. Application of fast Padé transform (FPT) instead of Fast Furrier transform (FFT) might increase the SNR of the signal produced by STEAM pulse-sequence and therefore allows the benefits of its advantages. We aimed to evaluate and compare the noise root mean square (RMS) and SNR provided by STEAM pulse-sequence using both FPT and FFT., Materials and Method: A gelatin-based phantom was constructed in a 19-cm acrylic cylinder. The phantom had two normal/tumoral parts. The SVS was performed using a 3T MRI scanner. STEAM pulse-sequence were used with the following parameters: TR = 2000 ms, TM = 10 ms, and three TEs of 20, 135 and 270 ms with two data-points of 1024 and 512 and voxel-size of 1 cm3. The raw data were extracted and processed using both FFT and FPT estimators to produce the spectrum. The noise RMS and SNR of Cho and Cr metabolites were assessed., Results: According to the results, noise RMS of spectra provided by FPT were decreased between 3619.01-14252.94% in comparison to FFT (p< 0.00001). The SNR of Cr1 and Cho peaks of the spectra provided by FPT were increased more than 96.80 and 97.18, respectively (0.00006

Badarneh and da Costa (IEEE Wirel Commun Lett, 2024. https://doi.org/10.1109/LWC.2024.3353620) introduced the fluctuating fading distribution by taking the signal envelope to be the ratio of two independent random variables, one having the Nakagami m distribution and the other a uniform random variable. The Nakagami m distribution corresponds to signals following the normal distribution which may not always hold in practice. In this paper, we derive twenty other fluctuating fading distributions. For each distribution, we give explicit expressions for the average channel capacity and the average bit error rate. Their correctness is checked numerically. [ABSTRACT FROM AUTHOR]

In many areas, such as speech processing, communication, and measurement systems, where the actual signal is not directly available, Wiener deconvolution is a widely used signal restoration technique. The accuracy of the estimated signal in Wiener deconvolution is determined by the unknown filter parameter. To compute this unknown parameter, complex and time-consuming numerical as well as iterative computation approaches were proposed in the literature, which also often compute an unoptimized unknown filter parameter. To address the computational problem and estimate a more accurate unknown filter parameter, the authors reported an analytical solution in a prior work for a given signal to noise ratio. However, it was revealed that the actual optimum parameter value also varies with noise also. In this paper, the authors propose a new analytical solution for optimum parameter estimation that includes noise as well. The authors compared the modified analytical solution to existing numerical computation methods for different values of Signal to Noise Ratio and demonstrated that the proposed methods' optimum parameter estimation is always close to the actual optimum value, which resulted in more accurate signal deconvolution. Proposed mathematical solutions are also verified with experimental signals, acquired in a laboratory. [ABSTRACT FROM AUTHOR]

This study aims to investigate, using patient studies, the impact of Injected Dose (ID), Body Mass Index (BMI), and image noise assessments in PET imaging. This study included 59 liver cancer patients weighing between 45 and 107 kg. After intravenously injecting 0.1 millicurie (mCi) of 18F-FDG per kilogram of body weight, PET scans were obtained for 1, 1.5, and 3 min/bed position based on the patient's weight. Weight, height, and body mass index were calculated using a spreadsheet. Five regions of interest (ROIs) were placed at the same site in the liver, which was considered to have a homogeneous metabolism, in five successive slices of PET/CT scans to determine the mean uptake (signal) values and their standard deviation (noise). The ratio of both gives the liver's Signal-to-Noise Ratio (SNR). Graphs were created to determine the relationship between these characteristics. The plots demonstrated that the dose injected increased when body weight and/or BMI increased, and that the SNR fell even as the dose administered increased. This is owing to the fact that heavier patients having a higher administered dose and, have a lower SNR even when greater 18F-FDG doses are delivered. These data indicate that the image quality, as measured by the SNR, is inferior in heavier persons compared to those who are thinner. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to investigate whether ABO and Rhesus (Rh) blood type systems are associated with distortion product otoacoustic emission (DPOAE) and transient otoacoustic emission (TEOAE) amplitudes, with the hypothesis that blood types affect hearing thresholds. Methods: Seventy participants with normal hearing, aged 18-26 years, with normal tympanometry and otoscopic examination findings, were included in the study. TEOAE and DPOAE tests were conducted on all participants. Results: The Rh factor did not significantly affect the OAE results. It was found that the TEOAE amplitudes of blood type B at 1.4 kHz in the left ear were higher than those of blood types A and AB. The amplitude of AB blood was lower than that of O, A, and B blood types at 2 kHz. Conclusion: Our study results did not indicate a consistent pattern for a specific blood type, in contrast to previous findings. Additional research is required to investigate the potential correlation between hearing function and ABO and Rh blood types. [ABSTRACT FROM AUTHOR]

Background: The digit in the noise test is a simple hearing screening tool that can identify hearing loss at the early stage. This screening tool takes less time than traditional pure-tone audiometry. There is a scarcity of hearing health professionals with few resources for carrying out hearing screening on a large scale in India. Hence, a hearing screening tool for the Hindi-speaking population is needed. Aim and Objective: The study aimed to develop and validate the Digit in Noise test in Hindi (DIN-H). Methods: A native Hindi female speaker recorded single digits from 1 to 9 made into triplet combinations, which were binaurally presented to 20 normal hearing subjects having hearing thresholds less than 25 dBHL from 250 to 8000 Hz in the presence of broadband speech-shaped noise. The digit triplets were homogenized using speech intelligibility function for similar difficulty levels across stimuli. The homogenized stimuli were evaluated by obtaining the Speech Recognition Threshold (SRT) of 20 normal hearing subjects. Results: The Mean SRT was − 10.4 and − 11.3 dB before and after homogenization. A strong positive correlation existed between test and retest SRTs (0.78). Mean SRT and slope obtained before and after optimization were comparable to other languages like Korean and English. This test can act as a reliable screening tool for assessing individuals. The test was administered to 106 normal hearing participants. The 95th percentile of the SRT value obtained was − 5.6 dB, which was kept as a cut-off score for the screening test. Conclusion: DIN-H can be used as a screening tool for assessing the integrity of the auditory system on a large scale in less time for the Hindi-speaking population. [ABSTRACT FROM AUTHOR]

Due to the complex and variable nature of the atmospheric conditions, traditional multi-wavelength differential absorption lidar (DIAL) methods often suffer from significant errors when inverting ozone concentrations. As the detection range increases, there is a higher demand for Signal to Noise Ratio (SNR) in lidar signals. Based on this, the paper discusses the impact of different atmospheric factors on the accuracy of ozone concentration inversion. It also compares the advantages and disadvantages of the two-wavelength differential method and the three-wavelength dual-differential method under both noisy and noise-free conditions. Firstly, the errors caused by air molecular extinction, aerosol extinction, and backscatter terms in the inversion using the two-wavelength differential method were simulated. Secondly, the corrected inversion errors were obtained through direct correction and the introduction of a three-wavelength dual differential correction. Finally, addressing the issue of insufficient SNR in practical inversions, the inversion errors of the two correction methods were simulated by constructing lidar parameters and incorporating appropriate noise. The results indicate that the traditional two-wavelength differential algorithm is significantly affected by aerosols, making it more sensitive to aerosol concentration and structural changes. On the other hand, the three-wavelength dual differential algorithm requires a higher SNR in lidar signals. Therefore, we propose a novel strategy for inverting atmospheric ozone concentration, which prioritizes the use of the three-wavelength dual-differential method in regions with high SNR and high aerosol concentration. Conversely, the direct correction method utilizing the two-wavelength differential approach is used. This approach holds the potential for high-precision ozone concentration profile inversion under different atmospheric conditions. [ABSTRACT FROM AUTHOR]

This research paper conducts a thorough comparison of four prominent transmission technologies suitable for mobile robots operating in challenging environments. Emphasizing key factors such as signal strength, noise resistance, and data transfer efficiency, the study aims to identify the optimal communication solution in hostile conditions. The exploration delves into the intricacies of received signal strength indication (RSSI) and signal-tonoise ratio (SNR), revealing distinctive traits and trade-offs among the technologies. Navigating through the complexities of frequency bands, modulation types, and communication topologies, the paper examines the impact of obstacles, energy consumption dynamics, and potential real-world applications. Beyond contributing to the fields of robotics and communication, the study offers practical insights for stakeholders seeking resilient and efficient transmission methods for mobile robotic applications. Advocating for long range (LoRa) as the preferred transmission technology in hostile environments, the paper highlights its unmatched immunity to noise, stability, and minimal energy consumption. These findings provide valuable guidance for technology choices in collaborative mobile robot operations under challenging conditions. This research sets the stage for future developments in robotic communication, underscoring the crucial role of selecting the right transmission means for mission-critical applications in hostile environments. [ABSTRACT FROM AUTHOR]

Recently, superposition coding (SC), Stanford University Interim (SUI) fading channel, user clustering, successive interference cancellation (SIC) mechanism has been incorporated into the power allocation design for attaining good performance. Nonetheless, the existing model works with cluster size up to 4 user per cluster and induce higher control channel overhead. In addressing this paper introduce an effective resource allocation (ERA) design for power domain non-orthogonal multiple access (PDNOMA) system. The ERA, first employ Zadoff Chu (ZC) coding, then power is allocated according to the distance considering 5 user per cluster. Finally, multi-level SIC is done at the receiver end. The ERA performance is studied by varying signal-to-noise ratio (SNR) in terms of bit error rate (BER) considering a maximum of five users per cluster. Further, the ERA performance in terms of BER is tested under different scenario defined in SUI propagation scenario such as higher pathloss, moderate path loss, and low path loss. The throughput performance under varied density and speed is also studied; the ERA achieves much better performance in terms of ERA, SNR and throughput than standard multi-user downlink resource allocation (MUDRA) model. [ABSTRACT FROM AUTHOR]

This paper proposes the Modified Light GBM to classify the Malicious Users (MUs) and legitimate Secondary Users (SUs) in the cognitive-radio network. The proposed method is to avoid the consequences of malicious users in Cooperative Spectrum Sensing (CSS) without the detection of MUs. The method is tested against the occurrence of Always No Malicious User (ANMU), always yes malicious user (AYMU), the Random Malicious User (RMU), and Opposite Malicious User (OMU) transmitting spectrum sensing data to the Fusion Center (FC) by normal secondary users. The efficiency of the proposed method is expressed via simulations and compared with other existing methods. [ABSTRACT FROM AUTHOR]

Small‐signal stability is an important task and key research in electrical engineering for networks. This research article focuses on the implementation of a multi‐objective approach for choosing an optimal location for Phasor Measurement Units (PMUs) to quantify a power system's small‐signal stability by maximizing the signal‐to‐noise ratio (SNR) in the system. The novelty of this research lies in the implementation of Dingo Optimization (DOX) technique along with the Prony Analysis (PA) approach for the assessment of small‐signal stability in standard grid networks. The voltage angle, amplitude and the range of frequencies are measured by the optimal placement of PMUs, which primarily focus on the multi‐signal PA. To achieve the objective of this research, DOX integrated with the multi‐signal PA approach is used to determine the ideal position for PMU placement by considering maximum redundancy and optimizing the signal to noise ratio to a maximum level. The effectiveness of the DOX strategy is established with improved accuracy and fewer disturbances by optimizing the electromechanical oscillations of the system. The implementation of the DOX approach for attaining the best value of the maximized SNR is obtained by analyzing a wide set of conditions, perturbations, and additive noise, which provides an accurate assessment of damping ratio (DR) and frequency (f) of electromechanical oscillations. Numerical results obtained from the standard IEEE test systems (14, 39, 57, 118, and 300 bus systems) are compared with the existing methods in the literature. The statistical indices demonstrate that under the highly limited optimization context selected, the intended optimizer functions satisfactorily. [ABSTRACT FROM AUTHOR]

A new composite model called shadowed Beaulieu-Xie (BX) is used in terahertz and millimeter-wave wireless communication systems. This model characterizes an environment with an arbitrary number of non-line-of-sight and line-of-sight signals. In this paper, an analysis of the average bit error rate (ABER) is done. The analysis is carried out for different coherent and non-coherent modulation techniques. The mathematical expressions of ABER are obtained using the probability density function of the output signal to noise ratio of the shadowed BX fading channel. A new closed-form ABER expression for gray-coded rectangular quadrature amplitude modulation over the shadowed BX channel is also evaluated. The effects of fading and shadowing parameters on the modulation techniques are analyzed and compared. [ABSTRACT FROM AUTHOR]

Purpose: A 128‐channel receive‐only array for brain imaging at 7 T was simulated, designed, constructed, and tested within a high‐performance head gradient designed for high‐resolution functional imaging. Methods: The coil used a tight‐fitting helmet geometry populated with 128 loop elements and preamplifiers to fit into a 39 cm diameter space inside a built‐in gradient. The signal‐to‐noise ratio (SNR) and parallel imaging performance (1/g) were measured in vivo and simulated using electromagnetic modeling. The histogram of 1/g factors was analyzed to assess the range of performance. The array's performance was compared to the industry‐standard 32‐channel receive array and a 64‐channel research array. Results: It was possible to construct the 128‐channel array with body noise–dominated loops producing an average noise correlation of 5.4%. Measurements showed increased sensitivity compared with the 32‐channel and 64‐channel array through a combination of higher intrinsic SNR and g‐factor improvements. For unaccelerated imaging, the 128‐channel array showed SNR gains of 17.6% and 9.3% compared to the 32‐channel and 64‐channel array, respectively, at the center of the brain and 42% and 18% higher SNR in the peripheral brain regions including the cortex. For R = 5 accelerated imaging, these gains were 44.2% and 24.3% at the brain center and 86.7% and 48.7% in the cortex. The 1/g‐factor histograms show both an improved mean and a tighter distribution by increasing the channel count, with both effects becoming more pronounced at higher accelerations. Conclusion: The experimental results confirm that increasing the channel count to 128 channels is beneficial for 7T brain imaging, both for increasing SNR in peripheral brain regions and for accelerated imaging. [ABSTRACT FROM AUTHOR]

In recent days, extensive digital communication process has been performed. Due to this phenomenon, a proper maintenance of authentication, communication without any overhead such as signal attenuation code rate fluctuations during digital communication process can be minimized and optimized by adopting parallel encoder and decoder operations. To overcome the above-mentioned drawbacks by using proposed reconfigurable code rate cooperative (RCRC) and low-density parity check (LDPC) method. The proposed RCRC-LDPC is capable to operate over gigabits/sec data and it effectively performs linear encoding, dual diagonal form, widens the range of code rate and optimal degree distribution of LDPC mother code. The proposed method optimize the transmission rate and it is capable to operate on 0.98 code rate. It is the highest upper bounded code rate as compared to the existing methods. The proposed method optimizes the transmission rate and is capable to operate on a 0.98 code rate. It is the highest upper bounded code rate as compared to the existing methods. the proposed method's implementation has been carried out using MATLAB and as per the simulation result, the proposed method is capable of reaching a throughput efficiency greater than 8.2 (1.9) gigabits per second with a clock frequency of 160 MHz. [ABSTRACT FROM AUTHOR]

The purpose of audio steganography is to cover confidential data in digital audio for confidentiality. To embed the secret image into the audio signal is the objective of the proposed audio steganography. For that purpose, the method utilizes the optimal audio steganography using Adaptive cat swarm optimization and secure XOR (S-XOR) encryption with a dynamic key algorithm. Initially, Discrete Wavelet Transform (DWT) is used to decompose the audio signal into two sub-bands. The novelty of the recommended technique is to optimally select the embedding position using the Adaptive Cat Swarm Optimization algorithm (ACSO). To improve the security of data hiding, secure XOR (S-XOR) encryption along with dynamic key is used here. Before the encryption, the Modified Huffman encoding (MHE) technique is used to compress the secret image; it helps to reduce the storage space of the proposed technique. Using the mean square error (MSE), signal to noise ratio (SNR), and peak signal to noise ratio (PSNR), the quality of stego audio is analyzed. Experimental outcomes demonstrate that the implemented audio steganography method maintains embedding quality with a high PSNR of 52.67 dB. The recommended technique is implemented in the MATLAB working platform. [ABSTRACT FROM AUTHOR]

The efficient operation of the Smart Grid is contingent on the accurate analysis of power signals, which are often compromised by disturbances. These power signals, captured by quality monitors, generate substantial volumes of data, thereby necessitating effective compression strategies to facilitate manageable data transfer and collection. Besides mitigating the costs associated with data storage, transmission, and encryption, these compression techniques must ensure minimal reconstruction error to avoid distortion in the original signal. Moreover, it becomes imperative to eliminate noise for the attainment of high-quality signals, critical for disturbance detection. In this paper, a novel method has been developed employing lower-order wavelets (Db3, Db2, Db2, Db2, and Db1). This method decomposes the signal from the first to fifth level utilizing wavelet Packet Transform, testing the efficacy on Phasor Measurement Unit data. Simulation results demonstrate enhanced data compression and noise reduction compared to previous designs, with the signal being approximately reconstructed. This innovative approach offers a facile, efficient, economical, and time-saving solution for smart grid data management, marking a significant advancement in this field. [ABSTRACT FROM AUTHOR]

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In this paper, Molecular Communication (MC) system, which is likely to be used in a variety of domains including targeted medicine delivery and health monitoring, is investigated over the channel under dynamic scenario. A diffusive mobile MC system with a pair of transmitter and receiver nano- machines suspended in a fluid medium with a uniform flow is considered. It is noteworthy to mention that the Channel Impulse Response (CIR) changing over time and irregular movements of the transmitter as well as receiver nano-machines make MC system most challenging. Establishing reliable communication between mobile nano-machines in such scenario is even more challenging as its statistics change with time. However, Signal to Noise Ratio (SNR) is most suitable parameter which is used to quantify the system reliability. Therefore, this research investigates the effect of mobility of transmitter and/or receiver nano machines on SNR. In particular, SNR under three dynamic scenarios is derived and analysed with the help numerical quantification. In the first scenario, both the transmitter and receiver are in motion. Whereas, in the second scenario, the transmitter is in motion and the receiver is fixed. Finally, in the third scenario, the transmitter is fixed and the receiver is in motion. For each scenarios, effect of different physical parameters such a diffusion coefficient, emission time, concentration of information molecules, radius of receiver and delay time are included in the analysis. The simulation results shows perfect agreement with the theoretical background. Simulation is performed in Maple-18. [ABSTRACT FROM AUTHOR]

Generalized frequency division multiplexing (GFDM) is non orthogonal multicarrier modulation scheme which is suitable for the fifth generation (5G) of wireless network. Pulse shaping filter design in GFDM system have effects on symbol error rate performance due to inter symbol interference. In this paper contribute to symbol error rate performance in GFDM system with additive white Gaussian noise channel, zero forcing channel Rayleigh fading has been analyzed for pulse shaping filter namely raised cosine and root raised cosine filter and also simulation is done and results are reported in terms of symbol error rate, signal to noise ratio, different value of roll off factor and different modulation technique. Comparison of simulation results of this method with existing methods is done and improvements in result are obtained as compared to existing. [ABSTRACT FROM AUTHOR]

For effective utilization of the available spectrum and for preventing the channel interference among the users the sensing of the availability of spectrum becomes an essential task in case of Cognitive Radio Networks. Due to the effect of shadowing, fading, and uncertainty in the receiver, the overall performance of the spectrum sensing technique was compromised. Utilizing the spatial diversity of the nodes the above mentioned issues can be overcome by following a cooperative spectrum sensing approach. This approach slightly increases the overhead and the time consumed for sensing the spectrum. This work introduces a technique to check the availability of spectrum based on cooperative approach wherein the problem of sensing the spectrum is formulated as a classification task. The secondary units transfer the modulated signal present in the channel to the primary unit which estimates the spectrogram of the same and sends it to a trained convolution neural network model to detect whether it is a signal or noise. The efficiency of the cooperative sensing approach is analysed based on the accuracy in detection and the probability of detection under multiple levels of Signal to Noise Ratio levels. [ABSTRACT FROM AUTHOR]

The matched filter is an optimal linear filter for maximizing signal‐to‐noise ratio (SNR) in the presence of additive random noise. Learning‐based matched filter is the proposed work. The proposed model supports (i) maximizing the SNR and (ii) setting the threshold, it is also called an intelligent matched dual tire filter. This improves the SNR by about 20 dB and the corresponding data rate to over 400 MBit/s. The bit error rate for 20 dB is very lower than FEC (10−3) of up to 10−7. The accuracy of the proposed matched learning filter is proved to be above 95%. Indoor visible light communication is one of the applications, and it can also be used for image processing and outdoor communication such as satellite and radar communication. It is obvious for communication as it avoids signal degradation, lobing, and overfitting to achieve a better data rate. This requires funding support and infrastructure facility that can be used to deploy the proposed algorithm in the real‐time scenario in the future. [ABSTRACT FROM AUTHOR]

The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio (SNR). The proposed method utilizes the robust measures of location i.e., the "Median" as well as the measures of variation i.e., "Median absolute deviation (MAD) and Interquartile range (IQR)" in the SNR. By this way, two independent robust signal-to-noise ratios have been proposed. The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio (RSNR). The results obtained via the proposed method are compared with wellknown gene/feature selection methods on the basis of performance metric i.e., classification error rate. A total of 5 gene expression datasets have been used in this study. Different subsets of informative genes are selected by the proposed and all the other methods included in the study, and their efficacy in terms of classification is investigated by using the classifier models such as support vector machine (SVM), Random forest (RF) and k-nearest neighbors (k-NN). The results of the analysis reveal that the proposed method (RSNR) produces minimum error rates than all the other competing feature selection methods in majority of the cases. For further assessment of the method, a detailed simulation study is also conducted. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient deep convolutional neural network with features fusion for recognizing radar signal, which mainly includes data pre-processing, features extraction, multi-features fusion, and classification. Radar signals are first transformed into time-frequency images by using choi-williams distribution and smooth pseudo-wigner-ville distribution, and the image pre-processing methods are used to resize and normalize the time-frequency images. Then, two constructed deep convolutional neural network models are aimed to extract more effective features. Furthermore, a multi-features fusion model is proposed to integrate features extracted from two deep convolutional neural network models, which makes full use of the relationship among different features and further improves the recognition performance. Experimental results shown that the average recognition accuracy of the proposed method is up to 84.38% when the signal to noise ratio is at −12 dB, and even reach to 94.31% at −10 dB, which achieved the superior recognition performance than others, especially at the lower signal to noise ratio. Moreover, the recognition performance of various radar signals can be largely improved, especially for 2FSK, 4FSK and SFM. This work provides a sound experimental foundation for further improving radar signal recognition in modern electronic warfare systems. [ABSTRACT FROM AUTHOR]

• A high-dimensional space coupled bistable stochastic resonance system is proposed. • The influence of system parameters on signal to noise ratio is analyzed. • The proposed model has a higher signal to noise ratio than other stochastic resonance models. • The effectiveness of the proposed method is validated by experiments. As a noise-enhanced signal processing method, stochastic resonance has been widely applied in the field of condition detection and fault diagnosis. To further consummate the application of stochastic resonance in practical engineering, a high-dimensional space model based on coupled bistable stochastic resonance system is proposed in this paper. Firstly, taking the three-dimensional space coupled bistable stochastic resonance system as the research object, the influence of system parameters on the output signal-to-noise ratio is analyzed. Then, the output signal-to-noise ratio of the high-dimensional space coupled bistable stochastic resonance system are analyzed in different dimensions. It is found that the output signals of these coupling ends with the same system parameters are identical, and there is always an optimal dimension for a given input so that the output signal-to-noise ratio reaches the maximum. Moreover, simulation results indicate that the proposed model has good feature enhancement ability and anti-noise robustness. Finally, the proposed approach is employed to diagnose fault signals of two different bearing types. The optimal output of the system is determined by establishing the best dimension value. The bigger output signal-to-noise ratio demonstrates that the proposed method outperforms the one-dimensional bistable stochastic resonance and two-dimensional coupled bistable stochastic resonance in weak fault signal detection, which improves the engineering practicability of stochastic resonance theory. [ABSTRACT FROM AUTHOR]

As an important means of environmental reconnaissance and regional security protection, sound target detection (STD) has been widely studied in the field of machine learning for a long time. Considering the shortcomings of the robustness and generalization performance of existing methods based on machine learning, we proposed a target detection method by an auditory brain-computer interface (BCI). We designed the experimental paradigm according to the actual application scenarios of STD, recorded the changes in Electroencephalogram (EEG) signals during the process of detecting target sound, and further extracted the features used to decode EEG signals through the analysis of neural representations, including Event-Related Potential (ERP) and Event-Related Spectral Perturbation (ERSP). Experimental results showed that the proposed method achieved good detection performance under noisy environment. As the first study of BCI applied to STD, this study shows the feasibility of this scheme in BCI and can serve as the foundation for future related applications. [ABSTRACT FROM AUTHOR]

OFDM is one of the most common multi-carrier modulation schemes implemented for a range of wireless standards. The key drawback of the OFDM method is its frequency offset sensitivity, which contributes to loss of orthogonality. In OFDM system, when the orthogonality is lost ICI occurs which decreases the System Output. The reason for occurrence of Inter Carrier Interference because of the arising carrier frequency offset from the doppler shift from lesser extent. Various methods of Inter Carrier Interference cancellation techniques idea has also been discussed. In this project, the ICI triple cancelation scheme was proposed to minimize inter-carrier interference in a quick time varying environments. In this process, at transmitter section the Inter Carrier Interference self-cancellation modulation is observed and LTV model is modeled for time varying channel to perform ICI dual cancellation scheme and thus ICI self-cancellation demodulation is done at the end of the receiver.. The basic scheme is performed through channel estimation which works more efficient in fast varying systems The outputs obtained by this technique and by comparing MSE in various channel estimations demonstrates that the latest scheme is certainly better than the current scheme and increases the efficiency of the program. [ABSTRACT FROM AUTHOR]

To enable wireless MRI receive arrays, per-channel power consumption must be reduced by a significant factor. To address this, a low-power SiGe alternative to industry standard MRI pre-amplifier blocks has been proposed and its impact on imaging performance evaluated in a benchtop environment. The SiGe amplifier reduces power consumption 28x, but exhibits increased non-linearity and reduced dynamic range relative to industry standard amplifiers. This distorts the images, causing reduced contrast and a blurring of fine features. In conjunction with the amplifier, a semi-blind calibration and compensation framework has been proposed to remove artifacts caused by this non-linearity. Requiring the knowledge of the calibration signal bandwidth, the associated peak transmit powers, and the distorted baseband signals, a second non-linearity is constructed that when cascaded with the receive chain produces a linear response. This method was evaluated for both knee and phantom image datasets of peak input power −20dBm with a −40dBm peak input power image as reference. In the benchtop environment, industry standard amplifiers produced input normalized RMSEs of 0.0199 and 0.0310 for phantom and knee datasets, respectively. The low-power SiGe amplifier resulted in RMSEs of 0.0869 and 0.1130 which were reduced to 0.0158 and 0.0168 following compensation, for phantom and knee images respectively. The ability to effectively compensate for this reduced dynamic range encourages further investigation of low-power SiGe amplifiers for power limited MRI receive arrays. [ABSTRACT FROM AUTHOR]

Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles (MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a calibration scan to measure the system matrix (SM), which is then used to set up an inverse problem to reconstruct images of the MNP distribution during subsequent scans. This calibration enables the reconstruction to sensitively account for various system imperfections. Yet time-consuming SM measurements have to be repeated under notable changes in system properties. Here, we introduce a novel deep learning approach for accelerated MPI calibration based on Transformers for SM super-resolution (TranSMS). Low-resolution SM measurements are performed using large MNP samples for improved signal-to-noise ratio efficiency, and the high-resolution SM is super-resolved via model-based deep learning. TranSMS leverages a vision transformer module to capture contextual relationships in low-resolution input images, a dense convolutional module for localizing high-resolution image features, and a data-consistency module to ensure measurement fidelity. Demonstrations on simulated and experimental data indicate that TranSMS significantly improves SM recovery and MPI reconstruction for up to 64-fold acceleration in two-dimensional imaging. [ABSTRACT FROM AUTHOR]

Powerful solar storms emit plasma that may travel towards the earth. Interactions between the plasma and the earth magnetic field cause geomagnetic disturbances (GMDs), which in turn induce quasi-dc voltage along long conductors in power systems. The induced electric field can be calculated by magnetic field data measured by magnetometers at several stations. In this paper, a set of online processing methods are proposed to improve signal to noise ratio (SNR) of the calculated electric field signals. In the first step, magnetic field signals are denoised inside a sliding window by a wavelet transform. The sliding window width is optimized and the introduced transients are compensated. Time derivative of magnetic field signals are estimated by a continuous wavelet transform in the next step, and the size of the sliding window is minimized for optimal computational speed. Ultimately, a method is proposed to further enhance the SNR by updating the previously processed data points. The magnetic and electric field signals obtained by the online and offline processing methods are compared with each other. It is demonstrated that the online processing methods can decrease the noises to a level comparable to that obtained by the offline methods. The results of this study can be implemented to protect key elements of power systems against severe solar storms. [ABSTRACT FROM AUTHOR]

Minimum energy required to achieve a distortion-noise profile, i.e., a function indicating the maximum allowed distortion value for each channel noise level, is studied for transmission of Gaussian sources over Gaussian channels. A general coding scheme is proposed to upper bound the minimum energy needed for any distortion-noise profile. Conversely, utilizing a family of lower bounds originally derived for broadcast channels with power constraints, the minimum required energy is lower bounded for any profile. As examples, linear, exponential, square-law, and staircase profiles are studied. It is shown that for the linear profile, as well as for any concave profile, uncoded transmission is optimal. As a negative result, it is shown that exponential profiles are not achievable with finite energy. For square-law and staircase profiles, upper bounds and lower bounds are derived and the gaps between them are studied. [ABSTRACT FROM AUTHOR]

In this paper, we study the estimation of a rank-one spiked tensor in the presence of heavy tailed noise. Our results highlight some of the fundamental similarities and differences in the tradeoff between statistical and computational efficiencies under heavy tailed and Gaussian noise. In particular, we show that, for $p$ th order tensors, the tradeoff manifests in an identical fashion as the Gaussian case when the noise has finite $4(p-1)$ th moment. The difference in signal strength requirements, with or without computational constraints, for us to estimate the singular vectors at the optimal rate, interestingly, narrows for noise with heavier tails and vanishes when the noise only has finite fourth moment. Moreover, if the noise has less than fourth moment, tensor SVD, perhaps the most natural approach, is suboptimal even though it is computationally intractable. Our analysis exploits a close connection between estimating the rank-one spikes and the spectral norm of a random tensor with iid entries. In particular, we show that the order of the spectral norm of a random tensor can be precisely characterized by the moment of its entries, generalizing classical results for random matrices. In addition to the theoretical guarantees, we propose estimation procedures for the heavy tailed regime, which are easy to implement and efficient to run. Numerical experiments are presented to demonstrate their practical merits. [ABSTRACT FROM AUTHOR]

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Millimeter wave (mmWave) wireless networks are envisioned to bring a very high degree of spatial reuse, i.e., multiple links can operate simultaneously without interference. The vision, however, is becoming doubtful, as recent studies found that non-negligible interference exists due to imperfect beam patterns. In this paper, we extensively measure the spatial reuse issue in a dense 60 GHz mmWave network consisting of multiple access points (AP) and users. Our measurement quantifies the impact of interference on network performance and finds that the existing prediction based on interference-resolving approaches are insufficient. Motivated by the findings, we propose MDSR, which enhances the spatial reuse in 60 GHz mmWave networks. Instead of relying on interference prediction, MDSR takes a new measurement principle of building a conflict graph that implicitly takes into account the impact of both beam imperfection and reflections. Using the conflict graph, MDSR improves the spatial reuse from three dimensions: AP association, user scheduling, and beam selection, which can determine the optimal AP-user-beam combination and minimize interference in each scheduling cycle. We prototype and evaluate MDSR on the testbed using commodity mmWave radios. The evaluation results demonstrate that MDSR improves network throughput by multi-folds compared with the state-of-the-art one. [ABSTRACT FROM AUTHOR]

Thermal imaging plays a vital role in structural health monitoring of various materials and provides insight into the defect present due to aging, deterioration, and fault during construction. This study investigated the effectiveness of spatial filters during pre-processing of thermal images and a correlation technique in post-processing, as well as exploited its application in non-destructive testing and evaluation of defects in steel structures. Two linear filters (i.e., Gaussian and Window Averaging) and a non-linear filter (i.e., Median) were implemented during pre-processing of a pulsed thermography image sequence. The effectiveness of implemented filters was then assessed using signal to noise ratio as a quality metric. The result of pre-processing revealed that each implemented filter is capable of reducing impulse noise and producing high-quality images; additionally, when comparing the signal to noise ratio, the Gaussian filter dominated both Window Averaging and Median filters. Defect size was determined using a correlation technique on a sequence of pulsed thermography images that had been pre-processed with a Gaussian filter. Finally, it is concluded that the correlation technique could be applied to the fast measurement of defect size, even though the accuracy may depend on the detection limit of thermography and defect size to depth ratio. [ABSTRACT FROM AUTHOR]

To alleviate the extremely intrinsical ill-posedness and nonlinearity of electromagnetic inverse scattering under high contrast and low signal to noise ratio (SNR), we propose a deep unfolding network based on generative adversarial network (GAN) under contrast source inversion (CSI) framework, termed UCSI-GAN. The method solves inverse scattering problems (ISPs) using end-to-end generating confrontation way by incorporating a physical model together with its iterative updating formulation into the internal architecture of GAN. First, the nonlinear iterative scheme is extended to a deep unfolding generator network, and the contrast source and contrast updates are mapped to each module of the generator network. Second, to stabilize the imaging process, we add a refinement network to each variable update. Finally, the discriminator network is employed to ensure the authenticity of reconstructed images. The generator network and discriminator network are alternately trained with a generative adversarial learning strategy to reconstruct the properties of the medium object. Numerical experiments demonstrated that the performance of UCSI-GAN is better than traditional CSI and state-of-the-art learning approaches under high contrast and low SNR condition. [ABSTRACT FROM AUTHOR]

In a Fog Radio Access Network (F-RAN), fog access points (F-APs) are equipped with caches that can store popular files during off-peak hours. Besides, they are densely deployed to have overlapping radio coverage so that requested files can be delivered cooperatively using beamforming. The bottleneck of the network is typically in the bandwidth-limited wireless fronthaul, which connects a cloud server to the F-APs. This work studies index coding design for cooperative caching and delivery in F-RAN to minimize fronthaul traffic and transmit energy. Index coding algorithms are designed considering the cached content at the F-APs and the possibility of beamforming in the access network under coded and uncoded caching schemes. An optimal polynomial-time index coding algorithm for uncoded and repetition caching and an efficient heuristic for Maximum Distance Separable (MDS) coded caching are designed, and their superior performance is verified by simulations. The study is further extended to consider the tradeoff between the traffic load of the fronthaul link and the transmit energy consumed in the access network. At the expense of more fronthaul traffic, beamforming opportunities can be increased, significantly reducing energy consumption. Algorithms to achieve the tradeoff are crafted, and simulation results show that uncoded caching well balances the tradeoff. [ABSTRACT FROM AUTHOR]

Covert communications is critical in many application scenarios for ensuring transmission security and privacy. Two-way protocols are widely adopted in intelligent reflecting surface (IRS) assisted relaying networks to enable covert communications and prevent wireless signals from being overheard. To quantify the performance of covert communications in this special scenario, we derive the closed-form expression of outage probability and its asymptote in this paper. Considering the worst-case of covert communications, the optimal normalized power threshold of warden's detector is analyzed under a complex Gaussian distribution approximation. To meet the requirement of the detection error probability, the ratio of the transmission power between the covert nodes is investigated. Simulation results are provided to validate the theoretical expressions as well as the asymptotic analysis with a small number of reflecting elements. [ABSTRACT FROM AUTHOR]

In this article, a linear precoding weight vector is designed to enhance the minimum signal-to-interference plus noise ratio (SINR) of a space-time line code (STLC)-based multicast users. To solve the intractable minimum SINR maximization problem, the optimal weight vector for each STLC user is derived, and then, the individual optimal weight vectors are linearly combined. The designed linear precoding vector improves the minimum and average SINR of the multicast users as verified by numerical results. Numerical results also verify that the STLC-based multicast is a promising scheme, especially, if a transmitter has a large number of transmit antennas and the additive noises are severe at the users. [ABSTRACT FROM AUTHOR]

We consider a cross-layer packet scheduling problem with hybrid ARQ (HARQ) in fading channels in which the channel state information at the transmitter (CSIT) is known only after one slot delay. Packets arrive according to a Bernoulli process at the transmitter, and each packet is required to be timely-delivered at the receiver, within a delay of $d$ time-slots, and is dropped, if the delay deadline is not met. Since the transmitter has only a delayed CSIT, a HARQ with Chase combining is employed for error recovery. The problem is to decide the transmit-energy in each time-slot such that the timely-throughput is maximum for a given average transmit-energy constraint. We pose this problem as a constrained Markov decision process, and provide an optimum policy based on Lagrangian relaxation. The optimum Lagrangian multiplier is obtained using a subgradient method. We obtain the structure of the optimum policy, based on which we propose a computationally simple policy READER that requires no CSIT. We show that for large Doppler spread (or mobility), the timely-throughput of READER is close to the optimum policy. We also provide two more policies: an optimum policy assuming perfect CSIT with zero delay, and a naive randomization policy, and compare the throughput performance of the proposed policies. [ABSTRACT FROM AUTHOR]

This paper considers the downlink of an ultra-reliable low-latency communication (URLLC) system in which a base station (BS) serves multiple single-antenna users in the short (finite) blocklength (FBL) regime with the assistance of a reconfigurable intelligent surface (RIS). In the FBL regime, the users' achievable rates are complex functions of the beamforming vectors and of the RIS's programmable reflecting elements (PREs). We propose the joint design of the transmit beamformers and PREs to maximize the geometric mean (GM) of these rates (GM-rate) and show that this approach provides fair rate distribution and thus reliable links to all users. A novel computational algorithm is developed, which is based on closed forms to generate improved feasible points. Simulations show the merit of our solution. [ABSTRACT FROM AUTHOR]

Dual-functional radar-communication (DFRC) has been viewed as a promising component in future networks. The considered two-cell interfering DFRC network involves two multi-antenna base stations (BSs), and each BS serves multiple single-antenna users and receives echo signals to detect the target. An optimization framework for the joint transmit beamforming and receive filters design of a coordinated two-cell network is formulated. Specifically, we minimize the transmit power at two BSs subject to the signal-to-interference-and-noise ratio constraints. To solve the formulated non-convex optimization problem, an alternating optimization-based method is invoked. For the transmit beamforming design, a locally optimal successive inner convex approximation (SICA)-based method with fast convergence is firstly proposed. To further reduce the computational complexity, we also provide three zero-forcing (ZF)-based sub-optimal methods for practical consideration. For the receive filters design, a power iterations-based method is proposed. Simulation results validate the effectiveness of our proposed SICA-based locally optimal and ZF-based sub-optimal schemes. [ABSTRACT FROM AUTHOR]

Although Unmanned Aerial Vehicles (UAVs) are capable of significantly improving the information security by detecting the eavesdropper's location, their limited energy motivates our research to propose a secure and energy efficient scheme. Thanks to the common-message philosophy introduced by Rate-Splitting (RS), we no longer have to allocate a portion of the transmit power to radiate Artificial Noise (AN), and yet both the Energy Efficiency (EE) and secrecy can be improved. Hence we define and study the Secrecy Energy Efficiency (SEE) of a multi-carrier multi-UAV network, in which Cooperative Rate-Splitting (CRS) is employed by each multi-antenna UAV Base-Station (UAV-BS) for protecting their downlink transmissions against an external eavesdropper ($Eve$). Furthermore, we consider the challenging scenario in which CRS is employed by each multi-antenna UAV-BS to protect their corresponding downlink transmissions against an external $Eve$. We further consider a difficult scenario in terms of security in which only imperfect channel state information of $Eve$ is available at the Tx. Accordingly, we conceive a robust secure resource allocation algorithm, which maximizes the SEE by jointly optimizing both the user association matrix and the network parameter allocation problem, including the RS precoders, time slot sharing and power allocation. Due to the non-convexity of the problem, it is decoupled into a pair of convex sub-problems. Firstly, new two-tier intra-cell optimization problems are formulated for achieving $\xi$ -optimal solutions by iterative block coordinate decent programming. Then, the power of each sub-channel is optimized by formulating the associated power control problem. [ABSTRACT FROM AUTHOR]

Rate-Splitting Multiple Access (RSMA) is an emerging flexible and powerful multiple access for downlink multi-antenna networks. In this paper, we introduce the concept of RSMA into short-packet downlink communications. We design optimal linear precoders that maximize the sum rate with Finite Blocklength (FBL) constraints. The relations between the sum rate and blocklength of RSMA are investigated for a wide range of network loads and user deployments. Numerical results demonstrate that RSMA can achieve the same transmission rate as Non-Orthogonal Multiple Access (NOMA) and Space Division Multiple Access (SDMA) with smaller blocklengths (and therefore lower latency), especially in overloaded multi-antenna networks. Hence, we conclude that RSMA is a promising multiple access for low-latency communications. [ABSTRACT FROM AUTHOR]

An amplitude weighted cross-correlation detector is proposed for the detection of the Doppler-spread target (DST), based on the frequency modulated continuous waveform (FMCW) radar. The concurrent range-Doppler matrices (RDM) make cross-correlation optimized with higher accumulation gain and significantly reduced computation effort. A novel measurement of signal sparseness is defined. The quantitative analysis of the proposed detector, compared with the energy integration detector, is realized based on the signal sparseness. Two generalizations of proposed detector are discussed for the number of antennas greater than two. Data from a walking pedestrian are got and used to verify the effectiveness of the proposed detector. [ABSTRACT FROM AUTHOR]

With low-cost energy consumption, the reconfigurable intelligent surface (RIS) technique is a potential solution to the real-time data processing for intelligent transportation systems (ITSs). In this paper, an intelligent transmissive surface is introduced into the vehicular communications, enabling vehicle-to-infrastructure (V2I) signals to penetrate the intelligent RIS to access the base station (BS) on the opposite side of the vehicle. Considering that the vehicle-to-vehicle (V2V) communication reuses the spectrum spanned for V2I link, we investigate the ergodic capacity optimization problem for the vehicle performing V2I communications with the assistance of RIS, while meeting the low-latency and high-reliability requirements of the V2V link. The RIS transmission coefficients and power allocation of vehicles are jointly optimized, for the management of the desired and undesired vehicular communication links. Moreover, the expression of optimal phase shifts is derived in a closed-form, which reveals that the performance gain brought by RIS is proportional to the number of intelligent elements, while inversely proportional to the distance from vehicle-to-BS, in a quadratic form. Moreover, in the case of discrete phase shifts, an intelligent algorithm is proposed for the beamforming design at RIS. Afterwards, with the objective to maximize the ergodic capacity of the V2I link, the optimal power allocation is also proposed. Simulation results confirm the accuracy of the proposed resource allocation strategy, and that the system performance in terms of the ergodic V2I capacity can be significantly improved by the RIS. [ABSTRACT FROM AUTHOR]

The new Fifth generation (5G) of wireless communications and beyond 5G (B5G) networks are required to provide connectivity for massive number of devices at high speed and low latency. Within this context, non-orthogonal multiple access (NOMA) has been involved and emerged in recent research since it is considered as an enabling technology for 5G and B5G networks. NOMA allows several users to share the same resources of time and frequency, making it an excellent candidate to provide impressively large connectivity with high spectral efficiency. In this paper, we study the performance of NOMA systems assuming independent but not necessarily to be identical $N$ -Nakagami-m multipath fading channels. This channel model subsumes the double Nakagami-m, which was investigated in the context of intervehicular communications and keyhole multiple input multiple output systems, as special cases. To this end, we derive exact analytical and asymptotic expressions for the probability density function of the overall signal to noise ratio, system capacity, and the pairwise error probability (PEP) for each user. Numerical results are also provided herein to show the effects of fading parameters and number of users on the capacity and the PEP of each user. [ABSTRACT FROM AUTHOR]

The use of 1-bit analog-to-digital converters (ADCs) is seen as a promising approach to significantly reduce the power consumption and hardware cost of multiple-input multiple-output (MIMO) receivers. However, the nonlinear distortion due to 1-bit quantization fundamentally changes the optimal communication strategy and also imposes a capacity penalty to the system. In this paper, the capacity of a Gaussian MIMO channel in which the antenna outputs are processed by an analog linear combiner and then quantized by a set of zero threshold ADCs is studied. A new capacity upper bound for the zero threshold case is established that is tighter than the bounds available in the literature. In addition, we propose an achievability scheme which configures the analog combiner to create parallel Gaussian channels with phase quantization at the output. Under this class of analog combiners, an algorithm is presented that identifies the analog combiner and input distribution that maximize the achievable rate. Numerical results are provided showing that the rate of the achievability scheme is tight in the low signal-to-noise ratio (SNR) regime. Finally, a new 1-bit MIMO receiver architecture which employs analog temporal and spatial processing is proposed. The proposed receiver attains the capacity in the high SNR regime. [ABSTRACT FROM AUTHOR]

In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. This paper studies an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information. At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. In addition, a training strategy which can avoid retraining for different network sizes for a scalable design is proposed. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes. [ABSTRACT FROM AUTHOR]