Noise Distortion Research Articles

A Pipelined Noise-Shaping SAR ADC Using Ring Amplifier

In this study, a pipelined noise-shaping successive-approximation register analog-to-digital converter (PLNS-SAR ADC) structure was proposed to achieve high resolution and to be free from comparator design requirements. The inter-stage amplifier and integrator of the PLNS-SAR ADC were implemented through a ring amplifier with high gain and speed. The ring amplifier was designed to improve power efficiency and be tolerant to process–voltage–temperature (PVT) variation, and uses a single loop common-mode feedback (CMFB) circuit. By processing residual signals with a single ring amplifier, power efficiency can be maximized, and a low-power system with 30% lower power consumption than that of a conventional PLNS-SAR ADC is implemented. With a high-gain ring amplifier, noise leakage is greatly suppressed, and a structure can be implemented that is tolerant of mismatches between the analog loop and digital correction filters. The measured signal to noise distortion ratio (SNDR) is 70 dB for a 5.15 MHz bandwidth (BW) at a 72 MS/s sampling rate (Fs) with an oversampling ratio (OSR) of 7, and the power consumption is 2.4 mW. The FoMS,SNDR (= SNDR + 10log10BW/Power) is 163.5 dB. The proposed structure in this study can achieve high resolution and wide BW with good power efficiency, without a filter calibration process, through the use of a ring amplifier in the PLNS-SAR ADC.

Distortion measurements of sound pressure level generated by a pistonphone

A supplementary interlaboratory comparison on pistonphone calibration was performed between 2018 and 2020 under the auspices of the Inter-American Metrology System. Seven national metrology institutes took part in this comparison. One pistonphone was circulated among the participants for measurement of the generated sound pressure level, frequency, total distortion + noise and total harmonic distortion. This paper presents the results of distortion measurements and analyzes them. From the stability check, notable variances with respect to reported measurement uncertainty were observed. From reported results, a large difference between the estimated uncertainties by the participants was noted. Convergence between results was found when measurements were performed using one-inch microphone while some divergence between results was found when measurements were performed using half-inch microphone. In addition, these results (using one-inch and half-inch microphones), even when obtained by the same institute, sometimes were different. Reasons for these findings are discussed and suggestions for future comparisons are presented.

SUPPRESSION OF WHITE NOISE FROM THE MIXTURE OF SPEECH AND IMAGE FOR QUALITY ENHANCEMENT

This study proposed a correlation analysis of two recent approaches. The FAST ICA technique is used for the separation of the multimodal data (i.e, mixture of audio, noise and image signal) and the minimum mean-square error (MMSE) is used for the removal of white noise from the audio signal. Initially, multimodal data will be formed by combining all the three signals (i.e. a mixture of audio, noise and image signals). For creating an ideal situation and for SNR comparisons, separation of the signals will be performed using the Fast ICA technique. ICA, Independent element analysis is a recently developed technique in which the goal is to seek a linear interpretation of non-Gaussian knowledge for the elements to be as statistically free as possible. Such representations record the key structure of the data in several applications, including signal quality and signal separation. ICA learns a linear decay of the data. ICA can find the basic elements and sources included in the data found where traditional methods fail. After the separation of the mixed data, denoising will be performed using the MMSE technique. The main purpose of the MMSE technique is to remove White Noise from the unmixed audio signal which will be further used for overall and segmental SNR comparisons for quality enhancement. Based on the designed algorithms, both of these techniques are real-time data-driven programs. These techniques are explored with standard De-noising methods using several different estimation methods like signal-to-noise ratio (SNR). Experimental results prove that the proposed MMSE technique works well for both noise segmentation and overall consideration of noise distortion signals. These statistical techniques can be used in many applications, such as in different communication systems to eliminate background noise and in channels to reduce channel interference between different applications in speech communications

2: Sensorimotor Myoelectric Control Using Surface Based Regenerative Peripheral Nerve Interface (RPNI)

Purpose:Although advanced prosthetic devices have the potential to allow fine-motor movements and extract somatosensory signals via sensitive pressure sensors, an ideal interface to integrate the human nervous system with the prosthetic doesn’t exist. Furthermore, the requirement for the implantation of indwelling electrodes and prohibitive costs limits the application of current technologies. The Regenerative Peripheral Nerve Interface (RPNI) was developed as a stable biologic interface on the notion of providing physiologic end-organ targets for regenerating axons by implantation of a residual nerve into an autogenous free muscle graft. Despite providing intuitive motor control, RPNI sensory feedback is limited and also relies on implantable electrodes for myoelectric signal transmission. To address these challenges, we investigated the placement of RPNIs underneath the defatted skin in rats to capture myoelectric signals using surface electrodes. This strategy simultaneously provides sensory feedback through the sensory reinnervation of the overlying skin.Methods:Utilizing six male F344 rats, the right tibial nerve was transected distally in the thigh before entering the leg’s posterior compartment. Subsequently, the left side extensor digitorum longus (EDL) muscle was harvested and co-apted with the proximal segment of the tibial nerve for RPNI fabrication. The RPNI was placed and secured in between the biceps femoris muscle near the skin while the side opposite to the nerve coaptation was facing the dermis. The overlying skin was defatted and fixed on top of the superficial RPNI (S-RPNI). At two months post-surgery, functional motor reinnervation was evaluated by electrical stimulation of the tibial nerve and compound muscle action potentials (CMAPs) were recorded using surface electrodes. Sensory feedback was assessed by electrical and mechanical stimulation of the skin to respectively record sensory nerve action potentials (SNAPs) and sensory afferent signals. The S-RPNI construct and its overlying skin were subsequently harvested and processed for immunohistochemistry and whole-mount immunostaining.Results:Recording muscle CMAPs from the skin was readily feasible in all animals, showing robust signals with minimal noise distortion (366 µV ± 86.3). Electrical stimulation of the skin on the lateral thigh, which is not innervated by the tibial nerve normally, resulted in classic tri-phasic SNAP generation in this nerve. Brushing the overlying skin using a cotton swab generated synchronized monomorphic afferent signals. Sensory reinnervation of the skin was shown using IHC. Whole-mount staining of the muscle component showed regenerated muscle with new neuromuscular junctions (NMJs) and spatial segregation of sensory fibers toward their end-target organ.Conclusion:Superficially placed RPNI is a viable and an alternate methodology that is simple to implicate and has the potential to transmit simultaneous, real-time, and independent sensory and motor signals between the residual nerve and the prostheses.

Secure Wireless Communication in RIS-Aided MISO System With Hardware Impairments

In practice, residual transceiver hardware impairments inevitably lead to distortion noise which causes the performance loss. In this letter, we study the robust transmission design for a reconfigurable intelligent surface (RIS)-aided secure communication system in the presence of transceiver hardware impairments. We aim for maximizing the secrecy rate while ensuring the transmit power constraint on the active beamforming at the base station and the unit-modulus constraint on the passive beamforming at the RIS. To address this problem, we adopt the alternate optimization method to iteratively optimize one set of variables while keeping the other set fixed. Specifically, the successive convex approximation (SCA) method is used to solve the active beamforming optimization subproblem, while the passive beamforming is obtained by using the semidefinite program (SDP) method. Numerical results illustrate that the proposed transmission design scheme is more robust to the hardware impairments than the conventional non-robust scheme that ignores the impact of the hardware impairments.

A 92 fs<sub>rms</sub> jitter frequency synthesizer based on a multicore class-C voltage-controlled oscillator with digital automatic amplitude control

This letter presents a frequency synthesizer based on a multicore Class-C voltage-controlled oscillator (VCO) with a digital automatic amplitude control (AAC) loop. A novel digital tail current estimation is adopted to mitigate the risks of unexpected VCO oscillation failure, due to current shortage during frequency calibration. Meanwhile, a digital amplitude recalibration is proposed to provide continuous amplitude control, avoiding noise distortion resulted from amplitude drift after a conventional disposable amplitude calibration. The digital AAC loop achieves a specific VCO amplitude with good stability and introduces no extra noise. Fabricated in a 28 nm CMOS process, the presented frequency synthesizer occupies an active area of 1.43 mm2. By measuring a 3 GHz carrier, the open loop VCO phase noise is -132 dBc/Hz at 1 MHz offset and the close loop root mean square jitter is 81 fs.

End-to-end recurrent denoising autoencoder embeddings for speaker identification

Speech 'in-the-wild' is a handicap for speaker recognition systems due to the variability induced by real-life conditions, such as environmental noise and the emotional state of the speaker. Taking advantage of the principles of representation learning, we aim to design a recurrent denoising autoencoder that extracts robust speaker embeddings from noisy spectrograms to perform speaker identification. The end-to-end proposed architecture uses a feedback loop to encode information regarding the speaker into low-dimensional representations extracted by a spectrogram denoising autoencoder. We employ data augmentation techniques by additively corrupting clean speech with real-life environmental noise in a database containing real stressed speech. Our study presents that the joint optimization of both the denoiser and speaker identification modules outperforms independent optimization of both components under stress and noise distortions as well as hand-crafted features.

Time-lapse resistivity imaging: CSEM-data 3-D double-difference inversion and application to the Reykjanes geothermal field

SUMMARY Time-lapse resistivity tomography bring valuable information on the physical changes occurring inside a geological reservoir. In this study, resistivity monitoring from controlled source electromagnetics (CSEM) data is investigated through synthetic and real data. We present three different schemes currently used to perform time-lapse inversions and compare these three methods: parallel, sequential and double difference. We demonstrate on synthetic tests that double difference scheme is the best way to perform time-lapse inversion when the survey parameters are fixed between the different time-lapse acquisitions. We show that double difference inversion allows to remove the imprint of correlated noise distortions, static shifts and most of the non-linearity of the inversion process including numerical noise and acquisition footprint. It also appears that this approach is robust against the baseline resistivity model quality, and even a rough starting resistivity model built from borehole logs or basic geological knowledge can be sufficient to map the time-lapse changes at their right positions. We perform these comparisons with real land time-lapse CSEM data acquired one year apart over the Reykjanes geothermal field.

A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments.

Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential application in more realistic noise environments. Therefore, finding a feasible and accurate handwritten numeral recognition method that is accurate in the more practical noisy environment is crucial. To this end, this paper proposes a new scheme for handwritten numeral recognition using Hybrid orthogonal polynomials. Gradient and smoothed features are extracted using the hybrid orthogonal polynomial. To reduce the complexity of feature extraction, the embedded image kernel technique has been adopted. In addition, support vector machine is used to classify the extracted features for the different numerals. The proposed scheme is evaluated under three different numeral recognition datasets: Roman, Arabic, and Devanagari. We compare the accuracy of the proposed numeral recognition method with the accuracy achieved by the state-of-the-art recognition methods. In addition, we compare the proposed method with the most updated method of a convolutional neural network. The results show that the proposed method achieves almost the highest recognition accuracy in comparison with the existing recognition methods in all the scenarios considered. Importantly, the results demonstrate that the proposed method is robust against the noise distortion and outperforms the convolutional neural network considerably, which signifies the feasibility and the effectiveness of the proposed approach in comparison to the state-of-the-art recognition methods under both clean noise and more realistic noise environments.

A Robust Method for Dehazing of Single Image with Sky Region Detection and Segmentation

In recent times, there has been a tremendous progress in image dehazing for computer vision applications, while the sky region processed by these algorithms tends to degrade by noise and color distortion. In this paper, an improved dark channel prior algorithm is proposed which detects the sky region first and divides the image into sky region and non-sky region and then estimates the transmission of two parts separately, followed by combining with refining step. The proposed algorithm also accurately corrects the transmission of the sky region to avoid noise and color distortion. Experimental results show a greater quality improvement in the output images than the existing strategies.

Automatic Retrieval of Shoeprints Using Modified Multi-Block Local Binary Pattern

A shoeprint is a valuable clue found at a crime scene and plays a significant role in forensic investigations. In this paper, in order to maintain the local features of a shoeprint image and place a pattern in a block, a novel automatic method was proposed, referred to as Modified Multi-Block Local Binary Pattern (MMB-LBP). In this method, shoeprint images are divided into blocks according to two different models. The histograms of all blocks of the first and second models are separately measured and stored in the first and second feature matrices, respectively. The performance evaluations of the proposed method were carried out by comparing with state-of-the-art methods. The evaluation criteria are the successful retrieval rates obtained using the best match score at rank one and cumulative match score for the first five matches. The comparison results indicated that the proposed method performs better than other methods, in terms of retrieval of complete and incomplete shoeprints. That is, the proposed method was able to retrieve 97.63% of complete shoeprints, 96.5% of incomplete toe shoeprints, and 91.18% of incomplete heel shoeprints. Moreover, the experiments showed that the proposed method is significantly resistant to the rotation, salt and pepper noise, and Gaussian white noise distortions in comparison with the other methods.

Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

Conventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

Simultaneous Enhancement and Watermarking of Speech Signals

The paper presents an improvement of the watermark extraction in speech signal watermarking. The noise added to the speech signal during transmission affects the efficiency of the watermark extraction. Removing this noise may aid to enhance the extraction process. There are methods of the speech signal enhancement, which aims to reduce the noise distortion in the speech signal. In this paper, the watermark process is done using the hybrid strategy of the Empirical Mode Decomposition (EMD) and the block-based Singular Value Decomposition (block-based SVD) with chaotic encrypted watermark. The watermark is embedded into the Singular Values matrix (SVs) because of its stability against any disturbance in the speech signal due to the different attacks. The encrypted watermark increases the security level of the watermark. When the watermark extracted at the receiver side, the speech signal will be enhanced first using spectral subtraction, Wiener filter or adaptive Wiener filter enhancement methods. The paper introduces a comparison study to evaluate the performance of each of them. Simulation results indicate that using the enhancement step improve the watermark extraction, especially using the adaptive Wiener filter.

LLISP: Low-Light Image Signal Processing Net via Two-Stage Network

Images taken in extremely low light suffer from various problems such as heavy noise, blur, and color distortion. Assuming the low-light images contain a good representation of the scene content, current enhancement methods focus on finding a suitable illumination adjustment but often fail to deal with heavy noise and color distortion. Recently, some works try to suppress noise and reconstruct low-light images from raw data. But these works apply a network instead of an image signal processing pipeline (ISP) to map the raw data to enhanced results which leads to heavy learning burden for the network and get unsatisfactory results. In order to remove heavy noise, correct color bias and enhance details more effectively, we propose a two-stage Low Light Image Signal Processing Network named LLISP. The design of our network is inspired by the traditional ISP: processing the images in multiple stages according to the attributes of different tasks. In the first stage, a simple denoising module is introduced to reduce heavy noise. In the second stage, we propose a two-branch network to reconstruct the low-light images and enhance texture details. One branch aims at correcting color distortion and restoring image content, while another branch focuses on recovering realistic texture. Experimental results demonstrate that the proposed method can reconstruct high-quality images from low-light raw data and replace the traditional ISP.

Robust Constrained MFMVDR Filters for Single-Channel Speech Enhancement Based on Spherical Uncertainty Set

Aiming at exploiting speech correlation across consecutive time-frames in the short-time Fourier transform domain, the multi-frame minimum variance distortionless response (MFMVDR) filter for single-channel speech enhancement has been proposed. The MFMVDR filter requires an accurate estimate of the normalized speech correlation vector in order to avoid speech distortion and artifacts. In this paper we investigate the potential of using robust MVDR filtering techniques to estimate the normalized speech correlation vector as the vector maximizing the total signal output power within a spherical uncertainty set, which corresponds to imposing a quadratic inequality constraint. Whereas the singly-constrained (SC) MFMVDR filter only considers the quadratic inequality constraint to estimate the (non-normalized) speech correlation vector, the doubly-constrained (DC) MFMVDR filter integrates a linear normalization constraint into the optimization problem to directly estimate the normalized speech correlation vector. To set the upper bound of the quadratic inequality constraint for each time-frequency point, we propose to use a trained non-linear mapping function that depends on the a-priori signal-to-noise ratio (SNR). Experimental results for different speech signals, noise types and SNRs show that the proposed constrained approaches yield a more accurate estimate of the normalized speech correlation vector than a state-of-the-art maximum-likelihood (ML) estimator. An instrumental and a perceptual evaluation show that both constrained MFMVDR filters lead to less speech and noise distortion but a lower noise reduction than the ML-MFMVDR filter, where the DC-MFMVDR filter is preferred in terms of overall quality compared to the SC-MFMVDR and ML-MFMVDR filters.

Evolutionary Detection Accuracy of Secret Data in Audio Steganography for Securing 5G-Enabled Internet of Things

With the unprecedented growing demand for communication between Internet of Things (IoT) devices, the upcoming 5G and 6G technologies will pave the path to a widespread use of ultra-reliable low-latency applications in such networks. However, with most of the sensitive data being transmitted over wireless links, security, privacy and trust management are emerging as big challenges to handle. IoT applications vary, from self-driving vehicles, drone deliveries, online shopping, IoT smart cities, e-healthcare and robotic assisted surgery, with many applications focused on Voice over IP (VoIP) and require securing data from potential eavesdroppers and attackers. One well-known technique is a hidden exchange of secret data between the devices for which security can be achieved with audio steganography. Audio steganography is an efficient, reliable and low-latency mechanism used for securely communicating sensitive data over wireless links. MPEG-1 Audio Layer 3’s (MP3’s) bit rate falls within the acceptable sound quality required for audio. Its low level of noise distortion does not affect its sound quality, which makes it a good carrier medium for steganography and watermarking. The strength of any embedding technique lies with its undetectability measure. Although there are many detection techniques available for both steganography and watermarking, the detection accuracy of secret data has been proven erroneous. It has yet to be confirmed whether different bit rates or a constant sampling rate for embedding eases detection. The accuracy of detecting hidden information in MP3 files drops with the influence of the compression rate or increases. This drop or increase is caused by either the increase in file track size, the sampling rate or the bit rate. This paper presents an experimental study that evaluates the detection accuracy of the secret data embedded in MP3. Training data were used for the embedding and detection of text messages in MP3 files. Several iterations were evaluated. The experimental results show that the used approach was effective in detecting the embedded data in MP3 files. An accuracy rate of 97.92% was recorded when detecting secret data in MP3 files under 128-kbps compression. This result outperformed the previous research work.

Research on the operation condition indicator for centrifugal pump based on sensorless monitoring technology

A centrifugal pump operates under off-design operation conditions would lead to flow instability, additional energy loss or mechanical damage. Early detection is of great importance to its operation efficiency and safety. Sensorless monitoring technology based on motor current signature analysis (MCSA) is a cost-effective and non-intrusive technology to monitor motor-driven devices. However, existing researches on MCSA for centrifugal pump usually realize their monitoring function through complicated time-frequency or energy analysis, they stop short of an indicator that has less calculation cost and definite physical significance compatible with the pump’s operation condition. In response to the limitations, this paper aimed at establishing such indicator for centrifugal pump based on MCSA. Theoretical analysis, CFD simulation and experiments were conducted to study the characteristics of pump torque and motor stator current, whose results suggest that the torque fluctuation frequency characteristics would be transmitted to motor stator current by electromagnetic coupling effect, and distribute as sidebands in current spectrum through power frequency modulation. As a result, torque disturbance caused by off-design operation would be reflected as stronger harmonic and noise distortion of motor stator current, and the distortion intensity could be quantified and employed as the operation condition indicator (OCI) for centrifugal pump. Experimental results show that the OCI has its lowest value under the design operation condition, and gradually increases as the pump’s operation condition getting worse. Such change law agrees well with the efficiency characteristics of centrifugal pump, which provides a new thought for the operation monitoring of centrifugal pump based on MCSA. The proposed indicator could be directly obtained from the current signal and easily calculated, which is more appropriate for application and probably more preferred for industry compared to the former operation monitoring methods.

A novel statistical approach for multiplicative speckle removal using t-locations scale and non-sub sampled shearlet transform

One of the most interesting problems in denoising of images includes despeckling of multiplicative noise. This paper proposes a novel statistical processor in the framework of Non-sub Sampled Shearlet Transform (NSST) to reduce the effect of the multiplicative noise on images given preserving of structural and visual quality of image. First, we indicate that NSST coefficients of logarithmically transformed images can be statistically meaningful modeled by t-location scale (TLS). For designing our processor, we employ Minimum Mean Squared Error (MMSE) estimator to reduce noise distortion. We show by using TLS as the prior distribution, non-linear noise suppression behavior is obtained in test images. Finally, we compare our method by the state-of-the-art algorithms like soft and hard thresholding and also with well-known adaptive filters in this area like Wiener, Frost, Lee and one recent method in shearlet denoising framework.

CDMA Modulation for Communication System Environment using Frequency Hopping Spread Spectrum

In this paper, an overall framework for a joint Special emphasis is placed on the communication segment of the sensing system at 85 GHz. Code division multiplexing using frequency hopping spread spectrum signals is implemented at 85 ghz to take advantage of reduced interference between ambient communication. The framework, which spans the entire chain of signal processing, mat lab is enabled, explained, and simulated using data networking. A template, able to scatter, fraud detection, including radio frequency-block and synchronization non-idealities are built up and analyzed. Also, the implementation of a channel model is into the Win Prop technology and embedded into the simulation of simu link. In the previous paper, they implemented the code division multiple access using a direct sequence spread spectrum at 77ghz for secure communication. Because of using 77ghz for the direct sequence spread spectrum, there will occur noise distortion and interference in the communication system. So that there will be poor system communication between transmitter and receiver. To overcome these problems, our paper explains the implementation of code division multiple access using frequency hopping spread spectrum for better and secure communications. By using this frequency-hopping spread spectrum technology, we can reduce the noise distortion and interference between the transmitter and the receiver. So that our system will be in proper condition to transmit the signals in the same range of frequency without any interference and distortion. FHSS systems can allow a higher aggregate bandwidth for coverage because FHSS provides more channels in the same range of frequencies. In accordance with the bit-error-rate, the module is assessed. By creating white Gaussian additive noise. The attribute is proven to reconcile the theoretical assumptions with the outcomes. By organizing a Rake-Receiver, the system is further boosted with structure configuration without any distortions.

Multiple objective optimization Applied to Speech enhancement problem

Enhancement of speech corrupted by broadband noise is subject of interest in many applications. For several years, the investigation of methods of denoising the vocal signal has yielded very satisfactory results, but certain problems and questions still remain. The term speech quality in speech enhancement is associated with clarity and intelligibility. So, one of these issues is to reach a compromise between noise reduction, signal distortion and musical noise. In this paper, we studied one of the classical techniques based on the spectral subtraction developed by Boll and improved by Berouti where two parameters α and β to control the effects of the distortion and the musical noise are introduced. However, the choice on these parameters (α and β) remains empirical. Our works is to find a compromise between these two parameters to obtain an optimal solution depending on the environment, the unknown noise and its level. Moreover, we propose in this paper, an algorithm based on bi-objective approach precisely Particle Swarm Optimization (PSO) technique in association with speech enhancement technique proposed by Berouti et al. Comparative results show that the performance of our proposed method with several types of noise, depending on the environment and on various noise levels, are better than those of spectral subtraction methods of Boll or Berouti.