Dynamic Range Problem Research Articles

Neal Viemeister’s contributions to understanding the dynamic range of the auditory system

Most neurons in the auditory nerve show saturation of their firing rates in response to a tone with a moderate level presented at their characteristic frequency. However, humans can detect changes in intensity for levels up to 120 dB SPL. One explanation of this “dynamic range problem” is that intensity coding at high levels depends on the spread of cochlear excitation. To test this explanation, in 1974 Neal Viemeister measured the intensity discrimination of bursts of noise presented in a band-reject noise of fixed high intensity. He found that Weber’s law held even at high levels. He concluded that spread of excitation is not necessary for the auditory system to maintain its large dynamic range. In 1983 Neal Viemeister showed that, over a wide range of intensities, subjects could detect small differences in the intensity of a high-frequency band of noise (6-14 kHz, a frequency range where phase locking is essentially absent) presented with a complementary band-reject noise, showing that phase locking is not essential for intensity discrimination at high levels. In 1988 Neal Viemeister showed theoretically that a localized rate-based intensity code using a small number of neurons can account for intensity discrimination over a wide range of levels.

Design and Implementation of Cross-hole CT Data Acquisition System

Aiming at the problems of small dynamic range, low signal-to-noise ratio and inaccurate data acquisition of the existing cross-hole CT exploration instrument, the cross-hole CT data acquisition system is designed basing on DSP processor and lownoise high-precision A/D converter. According to signal amplitude range, it can be chosen to do signal amplification and attenuation or straight through processing, control signal to the proper sampling A/D converter, then the FIR low-pass filter is used to filter the signals, and the processed data is transmitted to the PC through the Ethernet port. The test results show that the system has large signal range, high signalto- noise ratio, and high accuracy of data acquisition, the inversion profile coincides with the borehole verification.

Infrared and visible image fusion based on total variation and augmented Lagrangian.

This paper proposes a new algorithm for infrared and visible image fusion based on gradient transfer that achieves fusion by preserving the intensity of the infrared image and then transferring gradients in the corresponding visible one to the result. The gradient transfer suffers from the problems of low dynamic range and detail loss because it ignores the intensity from the visible image. The new algorithm solves these problems by providing additive intensity from the visible image to balance the intensity between the infrared image and the visible one. It formulates the fusion task as an l1-l1-TV minimization problem and then employs variable splitting and augmented Lagrangian to convert the unconstrained problem to a constrained one that can be solved in the framework of alternating the multiplier direction method. Experiments demonstrate that the new algorithm achieves better fusion results with a high computation efficiency in both qualitative and quantitative tests than gradient transfer and most state-of-the-art methods.

Practical High Dynamic Range Imaging of Everyday Scenes: Photographing the world as we see it with our own eyes

High dynamic range (HDR) imaging enables the capture of an extremely wide range of the illumination present in a scene and so produces images that more closely resemble what we see with our own eyes. In this article, we explain the problem of limited dynamic range in the standard imaging pipeline and then present a survey of state-of-the-art research in HDR imaging, including the technology's history, specialized cameras that capture HDR images directly, and algorithms for capturing HDR images using sequential stacks of differently exposed images. Because this last is among the most common methods for capturing HDR images using conventional digital cameras, we also discuss algorithms to address artifacts that occur when using with this method for dynamic scenes. Finally, we consider systems for the capture of HDR video and conclude by reviewing open problems and challenges in HDR imaging.

Technical Pitfalls of Signal Truncation in Perfusion MRI of Glioblastoma.

Dynamic susceptibility contrast (DSC) perfusion-weighted imaging (PWI) is widely used in clinical settings for the radiological diagnosis of brain tumor. The signal change in brain tissue in gradient echo-based DSC PWI is much higher than in spin echo-based DSC PWI. Due to its exquisite sensitivity, gradient echo-based sequence is the preferred method for imaging of all tumors except those near the base of the skull. However, high sensitivity also comes with a dynamic range problem. It is not unusual for blood volume to increase in gene-mediated cytotoxic immunotherapy-treated glioblastoma patients. The increase of fractional blood volume sometimes saturates the MRI signal during first-pass contrast bolus arrival and presents signal truncation artifacts of various degrees in the tumor when a significant amount of blood exists in the image pixels. It presents a hidden challenge in PWI, as this signal floor can be either close to noise level or just above and can go no lower. This signal truncation in the signal intensity time course is a significant issue that deserves attention in DSC PWI. In this paper, we demonstrate that relative cerebral blood volume and relative cerebral blood flow (rCBF) are underestimated due to signal truncation in DSC perfusion, in glioblastoma patients. We propose the use of second-pass tissue residue function in rCBF calculation using least-absolute-deviation deconvolution to avoid the underestimation problem.

HIGH DYNAMIC RANGE IMPLEMENTATION ON ACUTE LEUKEMIA SLIDE IMAGES

Conventional diagnosis of acute leukemia is based on examining the morphology of the blood and bone marrow smear under the microscope and the process can be tedious, time intensive and highly skilled resources dependent. Over recent years, features fusion techniques base on statistical and morphological features had been explored in computer vision studies to enhance the capability of acute leukemia diagnosis task. However, microscopic image capture from the light microscope usually has poor quality due to the limited dynamic range of the camera. Thus, image regions with intensity levels outside the dynamic range captured by the camera sensor suffer from lack of details, appearing either underexposed or overexposed. This paper proposed a High Dynamic Range (HDR) imaging techniques to solve the problem of limited dynamic range and enhance the morphological features of blast cells. The proposed method consists of two main parts: implementing HDR techniques on acute leukemia slide images and comparing the dynamic range between HDR image and original images with different exposure time based on the intensity histograms obtained. The results presented showed that the HDR implementation has enhanced the morphological features of blast cells and increase the dynamic range, hence may benefit in the feature extraction and classification process of acute leukemia.

Calibrating echelle spectrographs with Fabry-Pérot etalons

Over the past decades hollow-cathode lamps have been calibration standards for spectroscopic measurements. Advancing to cm/s radial velocity precisions with the next generation of instruments requires more suitable calibration sources with more lines and less dynamic range problems. Fabry-Perot interferometers provide a regular and dense grid of lines and homogeneous amplitudes making them good candidates for next generation calibrators. We investigate the usefulness of Fabry-Perot etalons in wavelength calibration, present an algorithm to incorporate the etalon spectrum in the wavelength solution and examine potential problems. The quasi periodic pattern of Fabry-Perot lines is used along with a hollow-cathode lamp to anchor the numerous spectral features on an absolute scale. We test our method with the HARPS spectrograph and compare our wavelength solution to the one derived from a laser frequency comb. The combined hollow-cathode lamp/etalon calibration overcomes large distortion (50 m/s) in the wavelength solution of the HARPS data reduction software. Direct comparison to the laser frequency comb bears differences of only maximum 10 m/s. Combining hollow-cathode lamps with Fabry-Perot Interferometers can lead to substantial improvements in the wavelength calibration of echelle spectrographs. Etalons can provide economical alternatives to the laser frequency comb, especially for smaller projects.

Abstract 1562: Proteomic changes observed in colon cancer tumors by label-free differential profiling of paired tumor versus normal FFPE samples

Abstract Colorectal cancer (CRC) is the third most common cancer and if caught early enough has a good prognosis. Mass screening of the adult population is advisable to reduce CRC mortality rate as well as reduce the cost of treating advanced CRC cases. A blood-based biomarker panel with the ability to diagnose CRC as well as determine prognosis and predict the response to drug therapy is desired. However, blood biomarker discovery is hampered by problems of complexity and dynamic range of protein abundance. Formalin-fixed and paraffin-embedded (FFPE) tissue is an ideal sample type for cancer biomarker analysis due to its ubiquitous use in cancer diagnosis in the clinic and consequent availability in oncology hospitals and clinics as well as biobanks. Biomarker discovery to date using FFPE have used only very few patient samples, making clinically relevant biomarker discovery difficult due to large patient-to-patient variation. As a part of our Biospecimen Research Network (BRN) program we funded a proteomic biomarker study of a large number of colorectal cancer and adjacent normal tissue samples. The investigators employed two platforms for biomarker discovery: label-free intensity based discovery using high resolution tandem mass spectrometry and targeted quantitation using multiple reaction monitoring. These two techniques allowed them to dig deeper into the proteome and to profile phosphopeptides. Paired tissue from 40 patients was profiled and of the 2875 proteins quantified, 367 proteins changed significantly in patients whose tumor size was greater than 50%. Large changes in secreted proteins were observed and many of these proteins were also observed in blood, offering promise for a potential blood-based biomarker for colorectal cancer. A blood-based biomarker assay for colorectal cancer could ultimately reduce the number of colonoscopies required, reducing cost and discomfort. Citation Format: Lokesh Agrawal, Fiona McAlister, Sushmita Roy, Florian Unger, Kerstin David, Harmut Juhl, Daniel Chelsky, Helen Moore. Proteomic changes observed in colon cancer tumors by label-free differential profiling of paired tumor versus normal FFPE samples. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1562. doi:10.1158/1538-7445.AM2015-1562

Predicting ecosystem emergent properties at multiple scales.

Predicting ecosystem emergent properties at multiple scales.

A 2-D Interpolation-Based QRD Processor With Partial Layer Mapping for MIMO-OFDM Systems

The rapid growth of wideband communication devices, such as smart phones and tablets, has dramatically increased the throughput requirements of communication systems. Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) has been widely recognized as the potential technology to achieve this requirement. The main implementation bottleneck lies in the high-complexity MIMO detections for thousands of subcarrier symbols in the MIMO-OFDM receiver. The QR decomposition processor is an essential preprocessing module for many MIMO decoders because the QR decomposition helps improve the decoding efficiency significantly by providing the tree-search structure for the MIMO detection algorithms. This paper presents a low-complexity 2-D interpolation-based QR decomposition (2-D-IQRD) with a partial layer mapping scheme to reduce computational complexity. The proposed 2-D-IQRD processor also adopts a scaling technique to reduce the signal dynamic-range problem in the traditional IQRD algorithm. These proposed methods successfully address the limitation of the interpolation-based QRD processor caused by successive multiplications in layer mapping and inverse layer mapping. Therefore, the 2-D interpolation can greatly reduce complexity and improve the BER performance of fast-fading MIMO-OFDM systems. This paper designs and implements the proposed architecture using a 90-nm CMOS technology. The implementation results show that the proposed architecture achieves 45.6 MQRD/s.

Measuring translational diffusion coefficients of peptides and proteins by PFG-NMR using band-selective RF pulses.

Molecular translational self-diffusion, a measure of diffusive motion, provides information on the effective molecular hydrodynamic radius, as well as information on the properties of media or solution through which the molecule diffuses. Protein translational diffusion measured by pulsed-field gradient nuclear magnetic resonance (PFG-NMR) has seen increased application in structure and interaction studies, as structural changes or protein-protein interactions are often accompanied by alteration of their effective hydrodynamic radii. Unlike the analysis of complex mixtures by PFG-NMR, for monitoring changes of protein translational diffusion under various conditions, such as different stages of folding/unfolding, a partial region of the spectrum or even a single resonance is sufficient. We report translational diffusion coefficients measured by PFG-NMR with a modified stimulated echo (STE) sequence where band-selective pulses are employed for all three (1)H RF pulses. Compared with conventional non-selective sequence, e.g. the BPP-LED sequence, the advantage of this modified band-selective excitation short transient (BEST) version of STE (BEST-STE) sequence is multi-fold, namely: (1) potential sensitivity gain as in generalized BEST-based sequences, (2) water suppression is no longer required as the magnetization of solvent water is not perturbed during the measurement, and (3) dynamic range problems due to the presence of intense resonances from molecules other than the protein or peptide of interest, such as non-deuterated detergent micelles, are avoided.

Broadband WET: a novel technique for quantitative characterization of minor components in foods.

NMR analysis of foods frequently suffers from a problem of dynamic range, which limits the detection of minor components due to the huge signals of water and major components such as sugars. In the present study, we propose a new method named as 'broadband WET'. This pulse scheme was applied to persimmon fruit juice for saturating the resonances of water and sugars, which covered a broad bandwidth. In comparison with the conventional solvent suppression methods such as WET and DPFGSE-WATERGATE, it was shown that broadband WET provided highly selective suppression of resonances covering an extensive bandwidth and quantitative signals of minor components without distortion. The proposed method is suitable to detect quantitative signals of the minor components with a high sensitivity.

Analysis of Extracellular Vesicles: New Avenues for Signaling Biology and Biomarker Discovery

Analysis of Extracellular Vesicles: New Avenues for Signaling Biology and Biomarker Discovery

The dynamic range paradox: a central auditory model of intensity change detection.

In this paper we use empirical loudness modeling to explore a perceptual sub-category of the dynamic range problem of auditory neuroscience. Humans are able to reliably report perceived intensity (loudness), and discriminate fine intensity differences, over a very large dynamic range. It is usually assumed that loudness and intensity change detection operate upon the same neural signal, and that intensity change detection may be predicted from loudness data and vice versa. However, while loudness grows as intensity is increased, improvement in intensity discrimination performance does not follow the same trend and so dynamic range estimations of the underlying neural signal from loudness data contradict estimations based on intensity just-noticeable difference (JND) data. In order to account for this apparent paradox we draw on recent advances in auditory neuroscience. We test the hypothesis that a central model, featuring central adaptation to the mean loudness level and operating on the detection of maximum central-loudness rate of change, can account for the paradoxical data. We use numerical optimization to find adaptation parameters that fit data for continuous-pedestal intensity change detection over a wide dynamic range. The optimized model is tested on a selection of equivalent pseudo-continuous intensity change detection data. We also report a supplementary experiment which confirms the modeling assumption that the detection process may be modeled as rate-of-change. Data are obtained from a listening test (N = 10) using linearly ramped increment-decrement envelopes applied to pseudo-continuous noise with an overall level of 33 dB SPL. Increments with half-ramp durations between 5 and 50,000 ms are used. The intensity JND is shown to increase towards long duration ramps (p<10−6). From the modeling, the following central adaptation parameters are derived; central dynamic range of 0.215 sones, 95% central normalization, and a central loudness JND constant of 5.5×10−5 sones per ms. Through our findings, we argue that loudness reflects peripheral neural coding, and the intensity JND reflects central neural coding.

High resolution 19F{1H} nuclear magnetic resonance spectroscopy and liquid chromatography–solid phase extraction–offline 1H nuclear magnetic resonance spectroscopy for conclusive detection and identification of cyanide in water samples

We report herein a new, sensitive and efficient method for detection, identification and quantification of cyanide in water samples. Cyanide was converted (>95% yield) to its versatile and stable derivative, 1-cyano-2,2,2-trifluoro-1-phenylethyl acetate (CTPA).The crude reaction mixture was directly subjected to high resolution fluorine-19 nuclear magnetic resonance (19F{1H} NMR) spectroscopy. In order to do away with signal overlap and dynamic range problems associated with 1H NMR spectroscopy, liquid chromatography with UV detection hyphenated to online solid phase extraction (LC–UV–SPE) was performed. The trapped and enriched CTPA was thereafter subjected to offline 1H NMR spectroscopy. In this way the δ1H, δ19F spectral signatures and LC–UV retention time were used for specific detection and identification and quantification of cyanide. The three techniques (viz. LC–UV and LC–UV–SPE followed by offline 1H NMR and 19F{1H} NMR spectroscopy) demonstrated good linearity (r2>0.99), reproducibility (inter-day RSD: 1.43–1.89%, 2.60–2.80%, 1.42–1.60; intra-day: RSD 1.20–1.38%, 3.21–3.25%, 1.00–1.19%), accuracy (recoveries: 95.1–97.2%, 77.5–82.8%, 96.8–98.9%) and LODs (1.31μg/mL, 4.24μg/mL, and 2.14μg/mL) respectively. Total time required for the analysis was ∼3h. Utility of the method was demonstrated by the detection and identification of spiked water samples. Since the derivative CTPA was volatile, could also be analyzed by GC–MS and GC–FTIR instruments.

Realistic camera noise modeling with application to improved HDR synthesis

Due to the ongoing miniaturization of digital camera sensors and the steady increase of the “number of megapixels”, individual sensor elements of the camera become more sensitive to noise, even deteriorating the final image quality. To go around this problem, sophisticated processing algorithms in the devices, can help to maximally exploit the knowledge on the sensor characteristics (e.g., in terms of noise), and offer a better image reconstruction. Although a lot of research focuses on rather simplistic noise models, such as stationary additive white Gaussian noise, only limited attention has gone to more realistic digital camera noise models. In this article, we first present a digital camera noise model that takes several processing steps in the camera into account, such as sensor signal amplification, clipping, post-processing,.. We then apply this noise model to the reconstruction problem of high dynamic range (HDR) images from a small set of low dynamic range (LDR) exposures of a static scene. In literature, HDR reconstruction is mostly performed by computing a weighted average, in which the weights are directly related to the observer pixel intensities of the LDR image. In this work, we derive a Bayesian probabilistic formulation of a weighting function that is near-optimal in the MSE sense (or SNR sense) of the reconstructed HDR image, by assuming exponentially distributed irradiance values. We define the weighting function as the probability that the observed pixel intensity is approximately unbiased. The weighting function can be directly computed based on the noise model parameters, which gives rise to different symmetric and asymmetric shapes when electronic noise or photon noise is dominant. We also explain how to deal with the case that some of the noise model parameters are unknown and explain how the camera response function can be estimated using the presented noise model. Finally, experimental results are provided to support our findings.

Iterative Superlinear-Convergence SVD Beamforming Algorithm and VLSI Architecture for MIMO-OFDM Systems

In this paper, we propose a singular value decomposition (SVD) algorithm with superlinear-convergence rate, which is suitable for the beamforming mechanism in MIMO-OFDM channels with short coherent time, or short training sequence. The proposed superlinear-convergence SVD (SL-SVD) algorithm has the following features: 1) superlinear-convergence rate; 2) the ability of being extended smaller numbers of transmit and receive antennas; 3) being insensitive to dynamic range problems during the iterative process in hardware implementations; and 4) low computational cost. We verify the proposed design by using the VLSI implementation with CMOS 90 nm technology. The post-layout result of the design has the feature of 0.48 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm mm}^{2}$</tex></formula> core area and 18 mW power consumption. Our design can achieve 7 M channel-matrices/s, and can be extended to deal with different transmit and receive antenna sets.

Closed-loop Operated Time-Based Accelerometer

A high-resolution, high dynamic range capacitive accelerometer based on pull-in time measurement is described in this paper. The high sensitivity of pull-in time can be used to implement high performance accelerometers, but non-linearity and low dynamic range compromise device performance. A closed-loop approach using an electrostatic actuation mechanism to control the duration of the pull-in time is presented, which addresses the low dynamic range problem. Capacitive parallel-plates structures were used and the preliminary experimental results of closed-loop operation under several acceleration values confirm the potential of this technique and the overall accelerometer concept. The results show an accelerometer with high sensitivity (better than 6 μV/μg) and improved linearity over a large dynamic range.

Highly sensitive nano-porous lattice biosensor based on localized surface plasmon resonance and interference

We propose a design for a highly sensitive biosensor based on nanostructured anodized aluminum oxide (AAO) substrates. A gold-deposited AAO substrate exhibits both optical interference and localized surface plasmon resonance (LSPR). In our sensor, application of these disparate optical properties overcomes problems of limited sensitivity, selectivity, and dynamic range seen in similar biosensors. We fabricated uniform periodic nanopore lattice AAO templates by two-step anodizing and assessed their suitability for application in biosensors by characterizing the change in optical response on addition of biomolecules to the AAO template. To determine the suitability of such structures for biosensing applications, we immobilized a layer of C-reactive protein (CRP) antibody on a gold coating atop an AAO template. We then applied a CRP antigen (Ag) atop the immobilized antibody (Ab) layer. The shift in reflectance is interpreted as being caused by the change in refractive index with membrane thickness. Our results confirm that our proposed AAO-based biosensor is highly selective toward detection of CRP antigen, and can measure a change in CRP antigen concentration of 1 fg/ml. This method can provide a simple, fast, and sensitive analysis for protein detection in real-time.

Sample Preparation Techniques for the Untargeted LC‐MS‐Based Discovery of Peptides in Complex Biological Matrices

Although big progress has been made in sample pretreatment over the last years, there are still considerable limitations when it comes to overcoming complexity and dynamic range problems associated with peptide analyses from biological matrices. Being the little brother of proteomics, peptidomics is a relatively new field of research aiming at the direct analysis of the small proteins, called peptides, many of which are not amenable for typical trypsin-based analytics. In this paper, we present an overview of different techniques and methods currently used for reducing a sample's complexity and for concentrating low abundant compounds to enable successful peptidome analysis. We focus on techniques which can be employed prior to liquid chromatography coupled to mass spectrometry for peptide detection and identification and indicate their advantages as well as their shortcomings when it comes to the untargeted analysis of native peptides from complex biological matrices.