Equalization Filter Research Articles

Numerical Simulation of Second-Order Microfluidic Filter

In this paper, we present a microfluidic filter to improve mixing index and eliminate the pulsatile component within microfluidic devices, which is similar to a second-order RC low-pass filter in electronics. COMSOL Multiphysics, a kind of simulation software, is used to evaluate the analytical method and the efficiency of the device. Compared with the results obtained from the equivalent circuit theory, the simulation method has been verified. In addition, the equivalent circuit theory, in other words the fluid dynamical equivalent of Ohm’s law, is adopted to make the theoretical analysis. This microfluidic filter is equipped with multiple simple channels and microcavities having elastic membrane at the top layer, just like the series–parallel connection of resistors and capacitors. Based on the results, one microcavity is equivalent to first-order filter and two microcavities in series equal second-order filter which shows the better filtering effect. Actually, when the driving frequency exceeds 60 Hz, this device can export almost steady fluid. The mixing effect is improved compared with the simple microfluidic junctions. The combined application of microfluidics and equivalent circuit theory will gain insightful ideas and practical design methods for developing unique microfluidic devices to address a broad range of biological and chemical challenges.

Optimisation of Image Processing Parameters for Flame Image Velocimetry (FIV) Measurement in a Single-Cylinder, Small-Bore Optical Diesel Engine

<div class="section abstract"><div class="htmlview paragraph">High-speed soot luminosity movies are widely used to visualise flame development in optical diesel engines thanks to its simple setup and relatively low cost. Recent studies demonstrated the high-speed soot luminosity movies are not only effective in showing the overall distribution and temporal evolution of sooting flames but also flow fields within the flame through the application of combustion (or flame) image velocimetry. The present study aims to improve this imaging technique by systematically evaluating key image processing parameters based on high-speed soot luminosity movies obtained from a single-cylinder, small-bore optical diesel engine. The raw soot luminosity movies are processed using PIVlab - a Matlab-based open-source code widely used for particle image velocimetry (PIV) applications. The images are pre-processed using the Contrast-Limited Adaptive Histogram Equalization (CLAHE) filter before the velocity vectors are determined through the multi-pass Discrete Fourier Transform (DFT) approach. This is then post processed to find the optimised filter size, interrogation window size, step size and velocity limit for minimal interpolation counts. As a result, a four-step DFT approach with interrogation windows set at 32-32-16-16 pixels, CLAHE filter size at 16 pixels and velocity limit at 3 pixels/frame is determined as optimised. The derived flow fields show that the internal flame pattern change, as demonstrated by previous studies, can be used to obtain the flow vectors within the flame successfully. From a selected example movie, the processed images show the evolution of multiple vortex structures as a result of strong jet-wall and jet-to-jet interactions as well as the swirl effects.</div></div>

Cooperative Operation of Parallel Connected Boost Converters for Low Voltage-High Power Applications: An Experimental Approach

Modern power conversion devices have realized several significant attributes such as high efficiency and reliability for high power (HP) applications. Traditionally, boost converters (BC) are required to operate as an intermediate interface to drive high power loads through available low voltage (LV) sources such as a fuel cell (FC), photovoltaic or battery based energy system. This paper addresses the complementary operation of parallel connected boost converters (PCBC) for high power applications considering wide spectrum of low voltage source conditions. The mutual sharing of load current between parallel connected converters is guaranteed by the appropriate placement of LC based passive equalization filter (LCEF) therefore offloading stress from individual converter and increasing efficiency and reliability drastically. To validate the performance of proposed topology in real-time scenarios, an experimental setup consisting of programmable source, electronic load, boost converters and filter circuit has been synthesized and studied in various operating conditions such as FC energy system based HP and LV application. The experimental results clarifies and validates the necessity of parallel connected converters based power splitting strategy and LC equalization filter topology for high power loads especially with low voltage source conditions.

DIMLOC: Enabling High-Precision Visible Light Localization Under Dimmable LEDs in Smart Buildings

The blooming of Internet of Things enables our modern buildings to become more and more smart, e.g., the intelligent LED lighting system could automatically adjust LEDs’ dimming levels based on the ambient sunlight. However, this brings blurring effects that could significantly affect the effectiveness of visible light localization. In this paper, we propose a high-precision visible light localization system DIMLOC under dimmable LEDs in smart buildings. We first propose to use a novel imaging processing framework that consists of efficient techniques of the second-order polynomial fitting, histogram equalization, and Sobel filter to cope with the blurring effects. We then propose a novel algorithm based on vision analysis and scaling factor that only needs two LEDs for positioning. We prototype our DIMLOC on commercial off-the-shelf devices, and extensive experiments demonstrate that DIMLOC could achieve centimeter precision, e.g., 4.5 cm. Overall, this paper could further broaden application scenarios of visible light localization in the era of smart buildings.

CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm.

Liver tumor segmentation from computed tomography (CT) images is a critical and challenging task. Due to the fuzziness in the liver pixel range, the neighboring organs of the liver with the same intensity, high noise and large variance of tumors. The segmentation process is necessary for the detection, identification, and measurement of objects in CT images. We perform an extensive review of the CT liver segmentation literature. Furthermore, in this paper, an improved segmentation approach based on watershed algorithm, neutrosophic sets (NS), and fast fuzzy c-mean clustering algorithm (FFCM) for CT liver tumor segmentation is proposed. To increase the contrast of the liver CT images, the intensity values are adjusted and high frequencies are removed using histogram equalization and median filter approach. It is followed by transforming the CT image to NS domain, which is described using three subsets (percentage of truth T, the percentage of indeterminacy I, and percentage of falsity F). The obtained NS image is enhanced by adaptive threshold and morphological operators to focus on liver parenchyma. The enhanced NS image passed to a watershed algorithm for post-segmentation process and liver parenchyma is extracted using the connected component algorithm. Finally, the liver tumors are segmented from the segmented liver using fast fuzzy c-mean (FFCM). A quantitative analysis is carried out to evaluate segmentation results using six different indices. The results show that the overall accuracy offered by the employed neutrosophic sets is accurate, less time consuming, less sensitive to noise and performs better on non-uniform CT images.

Thresholds of noise in harmonic series maskers

Numerous studies have investigated the detection of pure tones and harmonic series in noise, but far fewer studied the perception of noise in harmonic series. One such study [Gockel, Moore and Patterson, J. Acoust. Soc. Am., 111(6), 2759–2770 (2002)] demonstrated that the masking thresholds of noise in harmonic series maskers were affected by the fundamental frequency (F0) and the overall level of the series, and by the relative phase of the harmonic components. The maskers used by Gockel et al. comprised unresolved harmonics (10th and higher harmonics below 5 kHz) for which F0 and frequency range covaried. The current study investigated noise detection for three non-overlapping spectral bands of equal auditory filter bandwidths (ERBs). Bands included either resolved harmonics (B1), unresolved harmonics (B3), or both resolved and unresolved harmonics (B2). Masker F0 and overall level were also varied. A Bayesian linear mixed-effects analysis showed that noise detection was better for higher F0s in B1 and B2, and that a higher presentation level resulted in a small improvement in noise detection in B2. Noise detection was better for higher presentation levels in B3. The findings will be discussed in relation to predictions of auditory processing models [Patterson, Allerhand, Giguère, J. Acoust. Soc. Am. 98(4), 1890–1894 (1995)].

MIMO Time-Domain Equalization for High-Speed Continuous Transmission Under Channel Variability

High-speed communication over mutually coupled channels can be severely affected by intersymbol interference (ISI) and crosstalk (XT). Multiple-input multiple-output (MIMO) equalization is known to mitigate both ISI and XT, but requires accurate channel information at the receiver (RX) and/or transmitter (TX), when the channel is stochastic. However, since adjusting the equalization filters to the specific channel realization involves a considerable implementational and computational complexity, it is desirable to keep the equalization filters fixed irrespective of the specific channel realization, especially when the channel variability is relatively small. Therefore, we propose a general MIMO transceiver scheme where the equalization filters depend on the channel statistics rather than the actual channel. More specifically, by considering the time-domain equalization with the fixed linear MIMO pre-equalization and adjustable MIMO decision feedback equalization, the complexity associated with passing channel information from the RX to the TX is avoided, and the computationally intensive and iterative calculations of the fixed pre-equalizer can be performed offline rather than on-chip. The proposed equalization scheme is shown to yield only a small performance degradation compared to fully adjustable equalization schemes, thereby enabling multi-Gbit/s chip-to-chip communication over low-cost electrical interconnects which are prone to manufacturing tolerances.

On Adaptive Length of Temporal Filter for Space-Time Equalization With Cochannel Interference

In this letter, we take the initiative in adaptively determining the optimal temporal filter (TF) length for space-time equalization (STE) schemes, where overfitting may occur under too large TF length. We specifically consider the system without employing cyclic-prefix such that the spectral efficiency could be maintained. Based on the delicately derived connection between the mean square error (MSE) of equalized training symbols and unknown data symbols, the MSE of the latter can be predicated and serves as the decision metric for determining the optimal TF length. Numerical results demonstrate that the proposed STE scheme with TF of adaptively optimized length can work properly, even in the presence of cochannel interference.

Nonlinearity mitigation of intensity modulation and coherent detection systems.

Coherent optical communication systems are still too expensive for metro and short reach networks in the near future. Coherent detection with an intensity modulator replacing a costly dual-polarization in-phase and quadrature modulator could offer a good trade-off between cost and performance for metro networks. The major performance limit of such systems is the nonlinearity. To the best of our knowledge, this Letter offers the first known investigation on nonlinearity of a high speed intensity modulation and coherent detection system using an integrated laser and electro-absorption modulator. Advanced nonlinearity mitigation digital signal processing (DSP) are proposed including a 2×1 multiple-input single-output complex transversal Volterra feed-forward equalizer (FFE) filter, a first proposed post-FFE noise cancellation filter taking into account both pre- and post-cursor noise correlations based on a simple autocorrelation coefficient calculation, and a maximum likelihood sequence estimator. The receiver DSP needs no carrier and phase recovery that is required by a conventional coherent receiver DSP. Results show that, compared to a simple linear FFE equalization, the proposed nonlinear DSP enables over 8 and 4dB improvement in optical signal to noise ratio (OSNR) sensitivity at 7% overhead hard-decision feed-forward error correction (FEC) and 20% overhead soft-decision FEC threshold bit error rates (BERs), respectively, for a 56Gb/s intensity modulation and coherent detection system. To the best of our knowledge, a record 18dB OSNR sensitivity at a hard-decision FEC threshold BER of 3.8×10-3 is also achieved.

Lymphoma images analysis using morphological and non-morphological descriptors for classification

Mantle cell lymphoma, follicular lymphoma and chronic lymphocytic leukemia are the principle subtypes of the non-Hodgkin lymphomas. The diversity of clinical presentations and the histopathological features have made diagnosis of such lymphomas a complex task for specialists. Computer aided diagnosis systems employ computational vision and image processing techniques, which contribute toward the detection, diagnosis and prognosis of digitised images of histological samples. Studies aimed at improving the understanding of morphological and non-morphological features for classification of lymphoma remain a challenge in this area. This work presents a new approach for the classification of information extracted from morphological and non-morphological features of nuclei of lymphoma images. We extracted data of the RGB model of the image and employed contrast limited adaptive histogram equalisation and 2D order-statistics filter methods to enhance the contrast and remove noise. The regions of interest were identified by the global thresholding method. The flood-fill and watershed techniques were used to remove the small false positive regions. The area, extent, perimeter, convex area, solidity, eccentricity, equivalent diameter, minor axis and major axis measurements were computed for the regions detected in the nuclei. In the feature selection stage, we applied the ANOVA, Ansari-Bradley and Wilcoxon rank sum methods. Finally, we employed the polynomial, support vector machine, random forest and decision tree classifiers in order to evaluate the performance of the proposed approach. The non-morphological features achieved higher AUC and AC values for all cases: the obtained rates were between 95% and 100%. These results are relevant, especially when we consider the difficulties of clinical practice in distinguishing the studied groups. The proposed approach is useful as an automated protocol for the diagnosis of lymphoma histological tissue.

Comparison Study of Linear Adaptive Equalization Filters for Image Transmission in OFDM Systems

Comparison Study of Linear Adaptive Equalization Filters for Image Transmission in OFDM Systems

Automatic segmentation of gallbladder using bio-inspired algorithm based on a spider web construction model

In medical image processing, an accurate segmentation and classification are very important and this field still needs an effective computer-based algorithm for accomplishing the task. In gallbladder segmentation, only few automatic segmentation methodologies have been presented. The accuracy is very important in medical image processing for accurate diagnosis of the disease. In this paper, by exploiting the basic web building behavior of spiders, we developed a bio-inspired algorithm based on the spider web construction process for image segmentation process in medical images. The aim of this paper is to detect the shape of the gallbladder and to segment the gallstones and polyps located inside the gallbladder using a computer-based algorithm. It is necessary to apply a suitable preprocessing method in order to eliminate the irregularities presenting in the ultrasound scan images. In the preprocessing stage, histogram equalization and DooG filter are applied to enhance the contrast of the image. After that, the proposed spider web algorithm is applied in the segmentation process. The performance metrics of the proposed method are evaluated by implementing the proposed method to test the input dataset of 60 patients and it is compared with the results obtained from conventional segmentation methods. The values of DSC, OF, OV and PE for images with no lesions are 0.873167, 0.8389, 0.81705 and 0.81452.

Noise Adaptive Kalman Filter for Joint Polarization Tracking and Channel Equalization using Cascaded Covariance Matching

We propose a noise adaptive Kalman filter for joint polarization tracking and channel equalization using cascaded covariance matching. With the process noise covariance (Q) and the measurement noise covariance (R) estimated in a cascaded manner, the proposed Kalman filter scheme can be implemented in adaptive mode. The experimental results demonstrate that in wide range of OSNR and polarization rotation, the scheme can adaptively configure noise covariance parameters and consequently can achieve optimal performance even in long transmission cases. Besides, the scheme is more robust against the initial errors in Q and R compared to nonadaptive filter and R /Q-adaptive algorithms with the allowable initial values of Q and R within 6 and 9 magnitudes, respectively.

A thresholding based technique to extract retinal blood vessels from fundus images

Retinal imaging has become the significant tool among all the medical imaging technology, due to its capability to extract many data which is linked to various eye diseases. So, the accurate extraction of blood vessel is necessary that helps the eye care specialists and ophthalmologist to identify the diseases at the early stages. In this paper, we have proposed a computerized technique for extraction of blood vessels from fundus images. The process is conducted in three phases: (i) pre-processing where the image is enhanced using contrast limited adaptive histogram equalization and median filter, (ii) segmentation using mean-C thresholding to extract retinal blood vessels, (iii) post-processing where morphological cleaning operation is used to remove isolated pixels. The performance of the proposed method is tested on and experimental results show that our method achieve an accuracies of 0.955 and 0.954 on Digital retinal images for vessel extraction (DRIVE) and Child heart and health study in England (CHASE_DB1) databases respectively.

Automatic x-ray image contrast enhancement based on parameter auto-optimization.

PurposeInsufficient image contrast associated with radiation therapy daily setup x‐ray images could negatively affect accurate patient treatment setup. We developed a method to perform automatic and user‐independent contrast enhancement on 2D kilo voltage (kV) and megavoltage (MV) x‐ray images. The goal was to provide tissue contrast optimized for each treatment site in order to support accurate patient daily treatment setup and the subsequent offline review.MethodsThe proposed method processes the 2D x‐ray images with an optimized image processing filter chain, which consists of a noise reduction filter and a high‐pass filter followed by a contrast limited adaptive histogram equalization (CLAHE) filter. The most important innovation is to optimize the image processing parameters automatically to determine the required image contrast settings per disease site and imaging modality. Three major parameters controlling the image processing chain, i.e., the Gaussian smoothing weighting factor for the high‐pass filter, the block size, and the clip limiting parameter for the CLAHE filter, were determined automatically using an interior‐point constrained optimization algorithm.ResultsFifty‐two kV and MV x‐ray images were included in this study. The results were manually evaluated and ranked with scores from 1 (worst, unacceptable) to 5 (significantly better than adequate and visually praise worthy) by physicians and physicists. The average scores for the images processed by the proposed method, the CLAHE, and the best window‐level adjustment were 3.92, 2.83, and 2.27, respectively. The percentage of the processed images received a score of 5 were 48, 29, and 18%, respectively.ConclusionThe proposed method is able to outperform the standard image contrast adjustment procedures that are currently used in the commercial clinical systems. When the proposed method is implemented in the clinical systems as an automatic image processing filter, it could be useful for allowing quicker and potentially more accurate treatment setup and facilitating the subsequent offline review and verification.

An HF and lower VHF spectrum assessment system exploiting instantaneously wideband capture

AbstractWe report on a spectral environment evaluation and recording (SEER) system, for instantaneously wideband spectral capture and characterization in the HF and lower VHF band, utilizing a direct digital receiver coupled to a data recorder. The system is designed to contend with a wide variety of electromagnetic environments and to provide accurately calibrated spectral characterization and display from very short (ms) to synoptic scales. The system incorporates a novel RF front end involving automated gain and equalization filter selection which provides an analogue frequency‐dependent gain characteristic that mitigates the high dynamic range found across the HF and lower VHF spectrum. The system accurately calibrates its own internal noise and automatically subtracts this from low variance, external spectral estimates, further extending the dynamic range over which robust characterization is possible. Laboratory and field experiments demonstrate that the implementation of these concepts has been effective. Sensitivity to varying antenna load impedance of the internal noise reduction process has been examined. Examples of software algorithms to provide extraction and visualization of spectral behavior over narrowband, wideband, short, and synoptic scales are provided. Application in HF noise spectral density monitoring, spectral signal strength assessment, and electromagnetic interference detection is possible with examples provided. The instantaneously full bandwidth collection provides some innovative applications, and this is demonstrated by the collection of discrete lightning emissions, which form fast ionograms called “flashagrams” in power‐delay‐frequency plots.

THE EFFECT OF IMAGE ENHANCEMENT METHODS DURING FEATURE DETECTION AND MATCHING OF THERMAL IMAGES

Abstract. A successful image matching is essential to provide an automatic photogrammetric process accurately. Feature detection, extraction and matching algorithms have performed on the high resolution images perfectly. However, images of cameras, which are equipped with low-resolution thermal sensors are problematic with the current algorithms. In this paper, some digital image processing techniques were applied to the low-resolution images taken with Optris PI 450 382 x 288 pixel optical resolution lightweight thermal camera to increase extraction and matching performance. Image enhancement methods that adjust low quality digital thermal images, were used to produce more suitable images for detection and extraction. Three main digital image process techniques: histogram equalization, high pass and low pass filters were considered to increase the signal-to-noise ratio, sharpen image, remove noise, respectively. Later on, the pre-processed images were evaluated using current image detection and feature extraction methods Maximally Stable Extremal Regions (MSER) and Speeded Up Robust Features (SURF) algorithms. Obtained results showed that some enhancement methods increased number of extracted features and decreased blunder errors during image matching. Consequently, the effects of different pre-process techniques were compared in the paper.

Power delay profiles and temporal vs. spatial degrees of freedom: Predicting and optimizing performance in underwater acoustic communications systems

The performance of underwater acoustic communications systems is often characterized as a function of source to receiver range or the received in-band SNR. However, these measures are incomplete with respect to predicting performance. There is ample field data for which performance improved with increasing range or for which data sets with similar SNRs exhibited markedly different performance. Using field and simulation data, the Primary to Intersymbol Interference (ISI) and Noise Ratio (PINR) is shown to be a more complete predictor of performance than SNR. The results also demonstrate the significant adverse effect of ISI on system performance. With this in mind, the optimal configuration in terms of mitigating the impact of ISI is considered for the receive array and processor structure of equalizers. The role of total degrees of freedom (DOFs), equalizer filter adaptation averaging interval, and the relative stability of the channel's spatial and temporal structures is evaluated. The performance gains attainable with array spatial aperture vs the gains attainable with filter temporal depth is analyzed using closed form expressions as well as simulation and field data.

Face recognition technology development with Gabor, PCA and SVM methodology under illumination normalization condition

Face recognition is a challenging research field in computer sciences, numerous studies have been proposed by many researchers. However, there have been no effective solutions reported for full illumination variation of face images in the facial recognition research field. In this paper, we propose a methodology to solve the problem of full illumination variation by the combination of histogram equalization (HE) and Gaussian low-pass filter (GLPF). In order to process illumination normalization, feature extraction is applied with consideration of both Gabor wavelet and principal component analysis methods. Next, a Support Vector Machine classifier is used for face classification. In the experiments, illustration performance was compared with our proposed approach and the conventional approaches with three different kinds of face databases. Experimental results show that our proposed illumination normalization approach (HE_GLPF) performs better than the conventional illumination normalization approaches, in face images with the full illumination variation problem.

Robust Optimization of SC-FDE-Based Multihop DF Relay Systems With Imperfect CSI

In this paper, we consider the robust transceiver optimization for a single-carrier frequency-domain equalization (SC-FDE)-based multihop half-duplex decode-and-forward (DF) relay system under imperfect channel state information (CSI). The goal is to maximize the system achievable bit rate (ABR) and to minimize the end-to-end bit error rate (BER) subject to a joint node power constraint. Due to the lack of analytical tractable expressions for ABR and BER under imperfect CSI, we resort to lower bounds of the hopwise ABRs and BERs and reformulate the original problem by using two approximated objective functions based on these lower bounds. For the reformulated problems, we show that the optimal equalization filters take the form of robust Wiener filters. Subsequently, we propose two efficient decentralized algorithms to obtain the optimal solutions for the resulting power-allocation problems. Numerical results are provided to confirm the ABR and BER performance of the proposed robust relaying schemes.