Dynamic Range Problem Research Articles

High-dimensional inference of radio interferometer beam patterns I: parametric model of the HERA beams

ABSTRACT Accurate modelling of the primary beam is an important but difficult task in radio astronomy. For high dynamic range problems such as 21 cm intensity mapping, small modelling errors in the sidelobes, and spectral structure of the beams can translate into significant systematic errors. Realistic beams exhibit complex spatial and spectral structure, presenting a major challenge for beam measurement and calibration methods. In this paper series, we present a Bayesian framework to infer per-element beam patterns from the interferometric visibilities for large arrays with complex beam structure, assuming a particular (but potentially uncertain) sky model and calibration solution. In this first paper, we develop a compact basis for the beam so that the Bayesian computation is tractable with high-dimensional sampling methods. We use the Hydrogen Epoch of Reionization Array (HERA) as an example, verifying that the basis is capable of describing its single-element E-field beam (i.e. without considering array effects like mutual coupling) with a relatively small number of coefficients. We find that 32 coefficients per feed, incident polarization, and frequency, are sufficient to give percent-level and $\sim$10 per cent errors in the mainlobe and sidelobes, respectively, for the current HERA Vivaldi feeds, improving to $\sim 0.1{{\ \rm per\ cent}}$ and $\sim 1{{\ \rm per\ cent}}$ for 128 coefficients.

A gamma radiation scene image clarification method based on spatiotemporal combination

For the problems of complex noise and color cast in the images captured by complementary metal oxide semiconductor image sensors in the gamma radiation environment, this paper proposed a joint temporal correlation denoising and color cast correction approach for the gamma radiation scene image clarification. Concretely, we first adaptively stretch the difference between noise and background to improve the salience of weak noise, making noise detection results more accurate. Secondly, the temporal transient characteristics of gamma radiation noise are utilized for noise detection, and these noisy pixels are repaired by exploiting the temporal correlation characteristics of background pixels. Lastly, the Bayer-Guided auto white balance method is proposed to solve the problem of dysregulated color in the gamma radiation scene image, and the color level remapping approach is applied to eliminate the problem of insufficient dynamic range. Extensive experiments are carried out on the images captured from the Co60 gamma radiation scene. Compared with the source images, our method improves the peak signal to noise ratio by 6.3(dB) and structural similarity by 0.34. Experimental results demonstrate that the proposed method enjoys state-of-the-art performance in improving the clarity of gamma radiation scene images.

LFIENet: Light Field Image Enhancement Network by Fusing Exposures of LF-DSLR Image Pairs

Plenoptic cameras can record both spatial and angular information of incident rays as 4D light field (LF) images, which have unique advantages in a wide range of computer vision and graphics applications. However, plenoptic cameras usually suffer from image quality degradation due to limited resolution, very small sub-apertures for sub-views, improper exposure and color quantization of image sensors. Raw macro-pixel LF images captured by plenoptic cameras are usually decomposed into an array of sub-views, during which decomposition and improper correction would further damage the quality of LF images. Therefore, the sub-views of an LF image always have tricky problems of low dynamic range, brightness reduction, color deviation and missing textural details in improper exposure areas due to the small sub-aperture for each sub-view. We observed that it is hard to tell that the brightness (tone) ranges of DSLR (Digital Single Lens Reflex) Camera images are always better than that of LF images even captured from the same real-world scenes. Thus, instead of directly taking the accompanying DSLR images as ground truths for enhancing LF images, we propose an unsupervised neural network, called LFIENet, for properly fusing the exposures of LF-DSLR image pairs. With the help of corresponding DSLR images, the enhanced LF images would contain much abundant textural details and have extended dynamic ranges and better contrast. Since histogram equalization enhancement for brightness range is able to extend dynamic range and improve image contrast, we propose a Histogram Equalization Attention Module (HEAM) to discover the over/under-exposed areas for properly fusing LF-DSLR image pairs. In addition, for learning the proposed neural network, we propose a real-world LF-DSLR image pair dataset. Extensive experiments on various challenging real-world LF images demonstrate the effectiveness of our network.

A Method Used to Improve the Dynamic Range of Shack-Hartmann Wavefront Sensor in Presence of Large Aberration.

With the successful application of the Shack–Hartmann wavefront sensor in measuring aberrations of the human eye, researchers found that, when the aberration is large, the local wavefront distortion is large, and it causes the spot corresponding to the sub-aperture of the microlens to shift out of the corresponding range of the sub-aperture. However, the traditional wavefront reconstruction algorithm searches for the spot within the corresponding range of the sub-aperture of the microlens and reconstructs the wavefront according to the calculated centroid, which leads to wavefront reconstruction errors. To solve the problem of the small dynamic range of the Shack–Hartmann wavefront sensor, this paper proposes a wavefront reconstruction algorithm based on the autocorrelation method and a neural network. The autocorrelation centroid extraction method was used to calculate the centroid in the entire spot map in order to obtain a centroid map and to reconstruct the wavefront by matching the centroid with the microlens array through the neural network. This method breaks the limitation of the sub-aperture of the microlens. The experimental results show that the algorithm improves the dynamic range of the first 15 terms of the Zernike aberration reconstruction to varying degrees, ranging from 62.86% to 183.87%.

The Role of Efferent Reflexes in the Efficient Encoding of Speech by the Auditory Nerve.

To avoid information loss, the auditory system must adapt the broad dynamic range of natural sounds to the restricted dynamic range of auditory nerve fibers. How it solves this dynamic range problem is not fully understood. Recent electrophysiological studies showed that dynamic-range adaptation occurs at the auditory nerve level, but the amount of adaptation found was insufficient to prevent information loss. We used the physiological MATLAB Auditory Periphery model to study the contribution of efferent reflexes to dynamic range adaptation. Simulating the healthy human auditory periphery provided adaptation predictions that suggest that the acoustic reflex shifts rate-level functions toward a given context level and the medial olivocochlear reflex sharpens the response of nerve fibers around that context level. A simulator of hearing was created to decode model-predicted firing of the auditory nerve back into an acoustic signal, for use in psychophysical tasks. Speech reception thresholds in noise obtained with a normal-hearing implementation of the simulator were just 1 dB above those measured with unprocessed stimuli. This result validates the simulator for speech stimuli. Disabling efferent reflexes elevated thresholds by 4 dB, reaching thresholds found in mild-to-moderately hearing-impaired individuals. Overall, our studies suggest that efferent reflexes may contribute to overcoming the dynamic range problem. Because specific sensorineural pathologies can be inserted in the model, the simulator can be used to obtain the psychophysical signatures of each pathology, thereby laying a path to differential diagnosis.SIGNIFICANCE STATEMENT The saturation of auditory nerve fibers at moderate sound levels seen in rate-level functions challenges our understanding of how sounds of wide dynamic range are encoded. Our physiologically inspired simulations suggest that efferent reflexes may play a major role in dynamic range adaptation, with the acoustic reflex moving auditory nerve rate-level function toward a given context level and the medial olivocochlear reflex increasing fiber sensitivity around that context level. A psychophysical task using advanced simulations showed how the existence of the efferent system could prevent unrecoverable information loss and severe impairment of speech-in-noise intelligibility. These findings illustrate how important the precise modeling of peripheral compression is to both simulations and the understanding of normal and impaired hearing.

Robust patchmatch HDR image reconstruction for deghosting

• We propose an optimization function to search the optimal patch. • We design a registration scheme to align motion of source images. • We adopt a effective deghosting solution consisting of registration and matching. Aiming at the deghosting problem of High Dynamic Range (HDR) imaging technology, different from the existing algorithms of simply eliminating moving objects or only using a separate pre-registration based on homography transformation or optical flow, we propose an integrated HDR imaging scheme including Registration and Matching based on Patch (HDR-RMP) for deghosting in complex dynamic scenes. In our scheme, the combination of Affine Transformation (AT) and Normalization Mutual Information (NMI) has an implicit registration effect on the input images. Furthermore, NMI and Structural Similarity Index Measurement (SSIM) are fit to establish the final optimal function including the strategies of registration and matching, and the registration accuracy is promoted by this way, thereby the ability to remove ghosts is improved and the time complexity is reduced simultaneously. Finally, the accuracy of image matching is enhanced. The results show that the deghosting effect of HDR-RMP algorithm is remarkable, especially in dynamic scenes with large and mass moving objects, which not only improves the fusion quality, but also reduces the algorithmic complexity.

Tackling the Dynamic Range Problem of Hearing: Diverse Hair Cell Synapses Enable Broad Neural Sound Intensity Coding

Tackling the Dynamic Range Problem of Hearing: Diverse Hair Cell Synapses Enable Broad Neural Sound Intensity Coding

Deep HDR estimation with generative detail reconstruction

AbstractWe study the problem of High Dynamic Range (HDR) image reconstruction from a Standard Dynamic Range (SDR) input with potential clipping artifacts. Instead of building a direct model that maps from SDR to HDR images as in previous work, we decompose an input SDR image into a base (low frequency) and detail layer (high frequency), and treat reconstructing these two layers as two separate problems. We propose a novel architecture that comprises individual components specially designed to handle both tasks. Specifically, our base layer reconstruction component recovers low frequency content and remaps the color gamut of the input SDR, whereas our detail layer reconstruction component, which builds upon prior work on image inpainting, hallucinates missing texture information. The output HDR prediction is produced by a final refinement stage. We present qualitative and quantitative comparisons with existing techniques where our method achieves state‐of‐the‐art performance.

Savitzky–Golay Filtering-Based Fusion of Multiple Exposure Images for High Dynamic Range Imaging

The problem of compositing multiple exposure images has attracted lots of researchers, over the past years. It all began with the problem of High Dynamic Range (HDR) imaging, for capturing scenes with vast differences in their dynamic range. Fine details in all the areas in these scenes cannot be captured with one single exposure setting of the camera aperture. This leads to multiple exposure images with each image containing accurate representation of different regions dimly lit, well lit and brightly lit in the scenes. One can make a combined HDR image out of these multiple exposure shots. This combination of multiple exposure shots leads to an image of a higher dynamic range in a different image format which cannot be represented in the traditional Low Dynamic Range (LDR) formats. Moreover HDR images cannot be displayed in traditional display devices suitable for LDR. So these images have to undergo a process called as tone mapping for further converting them to be suitable enough to be represented on usual LDR displays. An approach based on Savitzky–Golay parametric filtering which preserves edges, is proposed which uses filtered multiple exposure images to generate the alpha matte coefficients required for fusing the input multiple exposure set. The coefficients generated in the proposed approach helps in retaining the weak edges and the fine textures which are lost as a result of the under and over exposures. The proposed approach is similar in nature to the bilateral filter-based compositing approach for multiple exposure images in the literature but it is novel, in exploring the possibility of compositing using a parametric filtering approach. The proposed approach performs the fusion in the LDR domain and the fused output can also be displayed using standard LDR image formats on standard LDR displays. A brief comparison of the results generated by the proposed method and various other approaches, including the traditional exposure fusion, tone mapping-based techniques and bilateral filter-based approach is presented where in the proposed method compares well and fares better in majority of the test cases.

Antenna isolation method using sub-array structure with soft-surface for Tx/Rx separated FDD hybrid beamforming system

The FDD method is normally used for the stability of the Tx and the Rx links in a fast-varying channel. It is required to physically separate the Tx and Rx arrays for independent Tx and Rx beam control. In this system, it is important to reduce the Tx self-interference (SI) that causes the problem of active element saturation and ADC dynamic range in the Rx beamformer. To suppress Tx SI, we proposed two simple techniques. The first is the general soft-surface which attenuates the surface current flow from the Tx tile array to the Rx array. The second is the sub-array structure of the Rx hybrid beamforming system. Based on the Cauchy–Schwartz inequality, the electrical sum by the sub-array acts as a destructive interference of the complex SI signals, and it can be shown as an additional isolation effect. The proposed idea was validated through measurement.

Electromagnetic Shielding Performance Measurement for Braided-Shield Power Cables at Low Frequency Regimes

Abstract Although various measurement techniques have been applied to both qualitative and quantitative evaluation for the electromagnetic shielding performance of braided-shield power cables, the existing measurements cannot directly assess the low-frequency shielding performance (typically below 100 kHz) due to factors such as ground-loop effects and dynamic range problem in measurement. To solve these, an improved shield reduction factor method, based on gain (T/R) rather than scattering parameters, is proposed to evaluate the shielding performance of braided-shield power cables from 25 Hz to 1 MHz. In this work, we highlight the implementation of measurement setup to avoid the effects of ground-loop and stray electromagnetic field. Meanwhile, the test cell is simplified according to the definition of the shield reduction factor in order to obtain the gain (T/R) parameters, which can be used to calculate transfer impedance as well. From the measurements we present more intuitive evaluation of shielding behavior of braided-shield power cables at low frequency regimes, and showcase a detailed comparative discussion between transfer impedance and shield reduction factor. The proposed shield reduction factor method is expected to be a useful way for the evaluation of the low frequency shielding performance of braided-shield cables.

Measurement of the peripheral aberrations of human eyes: A comprehensive review

It has been nearly 50 years since the first glimpse of the relationship between myopia and peripheral refractive errors. According to experiments on both animals and humans, the eyes with hyperopic peripheral vision appear to be at higher risk of developing myopia than those with myopic peripheral refractive errors. Despite the first measurement of peripheral refraction being achieved by a modified manual optometer, the concept of emmetropization triggered a rapidly increasing number of studies on peripheral aberrations. Not only the horizontal off-axis aberrations but also the meridional aberrations at different angles are measured by researchers during the development of peripheral aberrations measuring techniques. According to the differences among the working principles, a variety of techniques have been adopted for performing such measurements. The methods developed to realize the high-performance measurement involve the subject cooperating actively by rotating the head or eyes, the rotation of the whole optical path, and the combination of measurements of many light paths. This paper provides a review of the peripheral aberrations measuring techniques and their current status. This article also highlights the development trend of the measuring techniques of peripheral aberrations and practical applications of peripheral aberration measurements, such as the control of the accommodation, the measuring time, and the dynamic range problem of the wavefront sensor. Although wavefront sensing peripheral measurement is widely recognized for its capability to reveal both lower-order aberrations and higher-order aberrations, the efficiency of an autorefractometer is incomparable. The current study reveals that the most widely used peripheral aberration measurement methods are the use of an open field autorefractometer and Hartman-shack wavefront sensor-based techniques.

Design of Digital Control System for DC/DC Converter of On-Board Charger

Vehicle charging power supply is widely used because of its small size and portability. Aiming at the problems of slow dynamic response, subharmonic, oscillation and limited soft-switching range of phase-shifted full-bridge DC/DC converter, the paper proposed a modified PSFB converter by introducing clamp diodes at the primary side of the transformer to suppress voltage oscillation of the transformer’s secondary side. Also, digital peak current phase-shifting control and slope compensation are introduced to avoid subharmonic oscillation. Dynamic dead-time control technology introduced adjust the dead-time in different load ranges through the dead-time adjustment subroutine. Finally, an experimental platform of on-board charging phase-shifted full-bridge DC/DC converter is established. The experimental results show that the power supply eliminates subharmonic oscillation, achieves a wide range of soft-switching, improves the dynamic performance and antiinterference ability of the system, and optimizes the power efficiency.

Scale-invariant MFCCs for speech/speaker recognition

The feature extraction process is a fundamental part of speech processing. Mel frequency cepstral coefficients (MFCCs) are the most commonly used feature types in the speech/speaker recognition literature. However, the MFCC framework may face numerical issues or dynamic range problems, which decreases their performance. A practical solution to these problems is adding a constant to filter-bank magnitudes before log compression, thus violating the scale-invariant property. In this work, a magnitude normalization and a multiplication constant are introduced to make the MFCCs scale-invariant and to avoid dynamic range expansion of nonspeech frames. Speaker verification experiments are conducted to show the effectiveness of the proposed scheme.

Ultraclean Pure Shift NMR Spectroscopy with Adiabatic Composite Refocusing Pulses: Application to Metabolite Samples

AbstractIn Pure shift NMR (pure shift yielded by chirp pulse excitation (PSYCHE)) Sideband Averaging by Periodic PHase Incrementation of Residual J Evolution (SAPPHIRE) has proved to be a very useful technique for obtaining clean high‐resolution NMR spectra; SAPPHIRE suppresses homo‐decoupling sidebands resulting in high‐quality pure shift spectra. As a consequence, it enables circumventing dynamic range problems for complex mixtures having wide range of concentrations of constituents; in conventional PSYCHE spectra, the signals of low concentration constituents may get masked by the sidebands. However, the SAPPHIRE‐PSYCHE scheme is very sensitive to pulse/power calibrations and does not perform well for miscalibrations. In this background we propose here an improvement employing adiabatic composite refocusing pulses (ad‐SAPPHIRE‐PSYCHE) which has enabled us to obtain ultraclean pure shift NMR spectra with high resolution. The resulting improvements with the new scheme have been demonstrated on metabolite samples, honey and peppermint oil, and we believe that this will become routinely usable in commercial environments using autosamplers and with technically not‐so‐well trained researchers.

Small-protein Enrichment Assay Enables the Rapid, Unbiased Analysis of Over 100 Low Abundance Factors from Human Plasma

Unbiased and sensitive quantification of low abundance small proteins in human plasma (e.g. hormones, immune factors, metabolic regulators) remains an unmet need. These small protein factors are typically analyzed individually and using antibodies that can lack specificity. Mass spectrometry (MS)-based proteomics has the potential to address these problems, however the analysis of plasma by MS is plagued by the extremely large dynamic range of this body fluid, with protein abundances spanning at least 13 orders of magnitude. Here we describe an enrichment assay (SPEA), that greatly simplifies the plasma dynamic range problem by enriching small-proteins of 2-10 kDa, enabling the rapid, specific and sensitive quantification of >100 small-protein factors in a single untargeted LC-MS/MS acquisition. Applying this method to perform deep-proteome profiling of human plasma we identify C5ORF46 as a previously uncharacterized human plasma protein. We further demonstrate the reproducibility of our workflow for low abundance protein analysis using a stable-isotope labeled protein standard of insulin spiked into human plasma. SPEA provides the ability to study numerous important hormones in a single rapid assay, which we applied to study the intermittent fasting response and observed several unexpected changes including decreased plasma abundance of the iron homeostasis regulator hepcidin.

Burst Suppression Segmentation of EEG Using Adaptive Binarization in Time and Frequency Domains

This paper proposes a method for burst suppression segmentation using adaptive EEG binarization in both time and frequency domains. Since the dynamic range of EEG amplitude is very wide and can vary from one recording to another owing to the physical conditions of the subjects and measuring factors, such as electrode types, electrode locations, impedance, and amplifiers, conventional quantitative EEG (qEEG) features for segmentation sensitively vary in proportion to the EEG amplitude and result in erroneous segmentation. EEG binarization was applied to solve the problem of a wide dynamic range. Through the restriction of all different dynamic values of EEG signals to 0 and 1, subsequent signal processing techniques are independent of the dynamic range. Additionally, binarization may reduce the computational time of processing data. We employed the ordered statistic constant false alarm rate (OS-CFAR) algorithm for adaptive binarization. For burst suppression segmentation, maximum likelihood estimation was conducted using Gaussian models for feature values of burst and suppression segments. To investigate the segmentation performance of the proposed method, we evaluated the accordance to visual segmentation and quantified the error of burst suppression ratio estimation. Generally, the proposed binarization method was shown to be beneficial for both increased segmentation accordance and decreased error of estimated burst suppression ratio.

Coherent amplitude modulation imaging based on partially saturated diffraction pattern.

A single-shot phase retrieval algorithm based on a random aperture and partially saturated diffraction pattern is proposed. The diffraction pattern in the saturated area could be retrieved during the iterative process, which circumvents the problem of limited dynamic range of the detector. Besides, the random aperture is easier to be manufactured and if the accuracy of the random aperture is high enough, the design value could be used directly for iterations. It has the potential to be adapted for different wavelengths without additional transmission measurement of the wave modulator. The validity has been demonstrated by simulations and experiment.

Weak-value-amplification metrology without spectral analysis

We propose the difference weak measurement scheme, and illustrate its advantages for measuring small longitude phase-shift in high precision. Compared to the standard interferometry and standard weak measurement schemes, the proposed scheme has much higher resolution in present of various practical imperfections, such as alignment error and light intensity variation error. Moreover, we highlight the advantage of utilizing complex weak value, where its imaginary part can reduce the harmful effect induced by channel decoherence. Finally, we propose closed-loop scenario to solve the narrow dynamic range problem obsessing the current weak measurement schemes. Difference weak measurement scheme simultaneously fulfills the requirements of high precision, wide dynamic range and strong robustness, which makes it a powerfully practical tool for phase-shift measurement and other metrological tasks.

Photon-counting-based diffraction phase microscopy combined with single-pixel imaging

We propose a photon-counting (PC)-based quantitative-phase imaging (QPI) method for use in diffraction phase microscopy (DPM) that is combined with a single-pixel imaging (SPI) scheme (PC-SPI-DPM). This combination of DPM with the SPI scheme overcomes a low optical throughput problem that has occasionally prevented us from obtaining quantitative-phase images in DPM through use of a high-sensitivity single-channel photodetector such as a photomultiplier tube (PMT). The introduction of a PMT allowed us to perform PC with ease and thus solved a dynamic range problem that was inherent to SPI. As a proof-of-principle experiment, we performed a comparison study of analogue-based SPI-DPM and PC-SPI-DPM for a 125-nm-thick indium tin oxide (ITO) layer coated on a silica glass substrate. We discuss the basic performance of the method and potential future modifications of the proposed system.