Research on the Filter Design for Oximetry Monitors

A Performance Test for a Fringe Projection Scanner in Various Ambient Light Conditions

A designing system for adaptively characterizing an audio transmitting system has a white noise generating unit for generating a white noise signal. A speaker radiates the white noise generated by the white noise generating unit into an acoustic space. A microphone is placed at a predetermined position in the acoustic space and collects sound radiated from the speaker. A FIR adaptive filter receives the above white noise signal. An LMS algorithm processing unit updates each tap coefficient of the adaptive filter by using the LMS algorithm. A computation unit calculates the difference between a detection signal output from the microphone and an output of the adaptive filter and outputs the difference as an error signal e. By using a white noise signal having an average power of one, the range of the step size parameter of the LMS algorithm required for stably operating the adaptive filter is fixed. According to the method for characterizing the audio transmitting system and the method for setting an audio filter, the stable operation of the adaptive filters is ensured without increasing the complexity of the overall system.

Active noise control(ANC) has been widely studied for smaller acoustic cavities, like cars and small enclosures, over past several years. One of the key challenges in implementing ANC systems for larger acoustic environments lies in extending the target “quiet” zone for maximizing the noise cancellation. Traditional ANC systems use two “omnidirectional” microphones to access reference noise signal and “residual” error signal at target zone. The target zone around the error microphone remains to be small. Feedback ANC with Ambisonics is aimed to extend the quiet zone by employing specialized encoding and decoding of the noise signal accessed using a single Soundfield microphone. In this paper, a parallel Feedback ANC architecture is fused with the Ambisonics encoding to improve the noise cancellation performance. The proposed ANC system also features an adaptive signal decomposition technique to effectively handle noise types with dominant predictable components, like machinery noise. Noise attenuation level (NAL) is used to compare and quantify the advantage of the proposed method. In this paper, an attempt has been made to explore an improved ANC system for real-time noise control in larger acoustic enclosure such as museums and concert halls.

This paper aims to apply adaptive filtering algorithms in sensor array processing for underwater surveillance and detection systems with linear arrays. Adaptive filters are iterative in nature and continuously update the filter taps to minimize the error signal based upon certain criterion. Adaptive filters are used in beamforming to place the main beam in the direction of desired signal and place nulls in the direction of unwanted signals. Adaptive filters are also used in spectral analysis of acoustic signals for background noise cancellation. This paper evaluates the performance of three adaptive filtering algorithms with respect to sensor array design parameters i.e. number of sensors, sensor spacing, number of incident signals and their angular separation. The three adaptive filtering algorithms namely least mean square (LMS), normalized LMS (NLMS), and sample matrix inversion (SMI) have been compared in the context of linear array beamforming. The paper provides detailed discussions on the simulation results of these adaptive algorithms in terms of convergence speed, beamwidth, null depths and maximum side lobe levels. In addition the paper illustrates a practical example of applying adaptive filters in underwater sensor array processing systems for detection of target’s acoustic signatures.

This paper proposes a multiple adaptive notch filter with the p‐th power average of the estimated error E[ep(n)] ‖as the cost function. The adaptive filter consists of a cascade connection of second‐order adaptive notch filters composed of the allpass filters. In general, the elimination bandwidth of the notch filter is desirably small from the practical point of view. However, as the elimination bandwidth is set smaller, the convergence speed of the tap coefficients of the adaptive filter becomes much slower. From the point of view of convergence theory, the radius of the pole of the second‐order allpass filter needs to be close to 1, so that the elimination bandwidth must be extremely small. Convergence speed must be sacrificed due to the practical and theoretical points of view. In this paper, an adaptive notch filter is studied in which the elimination bandwidth of the notch filter is designed small while the gradient of the tap vector of the cost function used in the adaptive algorithm is made effectively equal to the case in which the elimination bandwidth is rather large. In this way, realization of a multiple adaptive notch filter with a high convergence is possible. The absolute value of the transfer function of the notch filter is close to 0 near the elimination frequency while it is close to 1 at other frequencies. Hence, by setting the power of the absolute value larger, the elimination bandwidth becomes larger and the smoothness near the elimination frequency is increased. By means of the gradient algorithm with a cost function E[ep(n)], this property can be utilized so that excellent convergence speed and steady‐state characteristics can be obtained. Finally, verification of the analytical results and comparison with the conventional method are carried out by computer simulation. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 87(8): 1–8, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10162

Signal de-noising has been sparked and given a great attention by signal processing community since its applications are found in a diverse range of digital signal processing and computer vision problems. To improve signal quality, phase, frequency and power are the important features that should be preserved during the de-noising process. Adaptive filters have been widely used for this purpose due to their ability to cancel out noise signal from the corrupted one precisely. This paper presents a robust adaptive estimator for solving the problem of signal noise cancellation, based on a new adaptive algorithm derived from a new constrained optimization. Simulation results evaluated using MATLAB show that the proposed algorithm is appropriate for several forms of signals contaminated by diverse levels of noise power. The performance of the proposed algorithm is illustrated to be preferable in terms of the power signal to noise ratio, mean square error and time of speed convergence of filter parameters. It is compared to other conventional approaches such as least mean square and normalized least mean square algorithms with various values of white noise power, variance. It exhibits lower steady-state error and faster convergent time than the other implementations. Finally, an efficient performance is achieved comparable with recursive least square and affine projection algorithms.

We explore the automatic adjustment of an artificial light source intensity for the purposes of image-based feature extraction and recognition. Two histogram-based criteria are proposed to achieve this adjustment: a two-class separation measure for 2D features and a Gaussian distribution measure for 2.5D features. To this end, the light source intensity is varied within a fixed interval as a camera captures one image for each intensity variation. The image that best satisfies the criteria for feature extraction is tested on a neural-network based recognition system. The network considers information related to both 2D (contour) and 2.5D shape (local surface curvature) of different objects. Experimental tests performed during different times of the day confirm that the proposed adjustment delivers improved feature extraction, extending the recognition capabilities of the system and adding robustness against changes in ambient light.

Flowers of some Phlox (Phlox x paniculata L.) varieties undergo daily colour shifts, being blue in the early morning, turning red during the day, and returning to blue in the evening. The colour shift, which occurs only in the upper (adaxial) petal surfaces, is due to the daily changes in ambient light. In the laboratory, colour shifts could be induced by 2.5 h of ultraviolet, visible or far‐red light and recorded by reflectance spectrophotometry.There are indications that irradiations with different kinds of light cause qualitatively different colour shifts, and that thus more than one photoreceptor pigment and more than one primary light reaction may be involved. The presence of phytochrome was demonstrated in petals of white Phlox flowers by in vivo transmission spectrophotometry. It is therefore possible that colour shifts in coloured Phlox flowers are mediated by phytochrome. Possibly the movement of ions (e.g. hydrogen ions) into or out of the vacuole (where the visible pigments are located) is affected by light absorption in a pigment in the tonoplast.

Artificial light at night is one of the most apparent environmental changes accompanying anthropogenic habitat change. The global increase in light pollution poses new challenges to wild species, but we still have limited understanding of the temporal and spatial pattern of exposure to light at night. In particular, it has been suggested by several studies that animals exposed to light pollution, such as songbirds, perceive a longer daylength compared with conspecifics living in natural darker areas, but direct tests of such a hypothesis are still lacking. Here, we use a combination of light loggers deployed on individual European blackbirds, as well as automated radio-telemetry, to examine whether urban birds are exposed to a longer daylength than forest counterparts. We first used activity data from forest birds to determine the level of light intensity which defines the onset and offset of daily activity in rural areas. We then used this value as threshold to calculate the subjective perceived daylength of both forest and urban blackbirds. In March, when reproductive growth occurs, urban birds were exposed on average to a 49-min longer subjective perceived daylength than forest ones, which corresponds to a 19-day difference in photoperiod at this time of the year. In the field, urban blackbirds reached reproductive maturity 19 day earlier than rural birds, suggesting that light pollution could be responsible of most of the variation in reproductive timing found between urban and rural dwellers. We conclude that light at night is the most relevant change in ambient light affecting biological rhythms in avian urban-dwellers, most likely via a modification of the perceived photoperiod.

In order to make quantitative statements regarding behavior patterns in animals, it is important to establish whether new observations are statistically consistent with the animal's equilibrium behavior. For example, traumatic stress from the presence of a telemetry transmitter may modify the baseline behavior of an animal, which in turn can lead to a bias in results. From the perspective of information theory such a bias can be interpreted as the amount of information gained from a new measurement, relative to an existing equilibrium distribution. One important concept in information theory is the relative entropy, from which we develop a framework for quantifying time-dependent differences between new observations and equilibrium. We demonstrate the utility of the relative entropy by analyzing observed speed distributions of Pacific bluefin tuna, recorded within a 48-hour time span after capture and release. When the observed and equilibrium distributions are Gaussian, we show that the tuna's behavior is modified by traumatic stress, and that the resulting modification is dominated by the difference in central tendencies of the two distributions. Within a 95% confidence level, we find that the tuna's behavior is significantly altered for approximately 5 hours after release. Our analysis reveals a periodic fluctuation in speed corresponding to the moment just before sunrise on each day, a phenomenon related to the tuna's daily diving pattern that occurs in response to changes in ambient light.

Background Pupillometry has been studied as a physiological marker for quantifying cognitive load since the early 1960s. It has been established that small changes in pupillary size can provide an index of the cognitive load of a participant as he/she performs a mental task. The utility of pupillometry as a measure of expertise is less well established, although recent research in the fields of education, medicine and psychology indicates that differences in pupillary size during domain-specific tasks allows differentiation between experts and novices in appropriately designed experiments. Purpose The goal of this review is to explore the existing body of evidence for the use of pupillometry as a measure of expertise and to identify its strengths and constraints within the context of expertise research in the medical sciences. Results Pupillometry is a robust metric that allows researchers to better understand cognitive load in medical practitioners with varying levels of expertise. In medical expertise research, it has been used to study surgeons, anesthetists and emergency physicians. Its strengths include its ability to provide quantitative and objective outputs, to be measured unobtrusively with new technology and to be precisely computed as cognitive load changes over the course of completion of a task. Constraints associated with this methodology include its potential inaccuracy with changes in ambient light and pupillary accommodation as well as the need for relatively expensive equipment. Conclusion With recent technological advances, pupillometry has become a simple and robust method for quantifying physiological changes attributable to cognitive load and is increasingly being utilized in medical education. It can be used as a reliable marker of mental effort and has been shown to differentiate levels of expertise in medical practitioners.

Classification of soil aggregates: A novel approach based on deep learning

Internet of Things (IoT) is a concept that aims to expand the benefits of continuously connected internet connectivity. This study aims to utilize IoT in home automation and remote light control systems that can be operated with a smartphone application via an internet connection (WiFi). This system uses the NodeMCU ESP8266 module as a microcontroller, a light emitting diode (LDR) sensor as a controller for automating lights according to environmental conditions, and the Blynk smartphone application as a remote control for lights. The light control process can be carried out specifically on certain lamps and can be controlled by changes in ambient light in the morning and evening. The results show that when the LDR sensor gets minimal light, the light will be ON and vice versa the light will be OFF when more light intensity is received by the LDR. In addition, the Blynk application is able to control the lights remotely when connected to the internet network and, in this study, has been tested up to a distance of 2.7 km. It can be concluded that as long as the system is connected to WiFi stably and continuously, this control system can perform the task of turning on and off the lights independently when the owner is not at home.

In low-light environments, the scarcity of visual information makes feature extraction and matching challenging for traditional visual simultaneous localization and mapping (SLAM) systems. Changes in ambient lighting can also reduce the accuracy and recall of loop closure detection. Most existing image enhancement methods tend to introduce noise, artifacts, and color distortions when enhancing images. To address these issues, we propose an innovative low-light visual-inertial (LL-VI) SLAM system, named LL-VI SLAM, which integrates an image enhancement network into the front end of the SLAM system. This system consists of a learning-based low-light enhancement network and an improved visual-inertial odometry. Our low-light enhancement network, composed of a Retinex-based enhancer and a U-Net-based denoiser, enhances image brightness while mitigating the adverse effects of noise and artifacts. Additionally, we incorporate a robust Inertial Measurement Unit initialization process at the front end of the system to accurately estimate gyroscope biases and improve rotational estimation accuracy. Experimental results demonstrate that LL-VI SLAM outperforms existing methods on three datasets, namely LOLv1, ETH3D, and TUM VI, as well as in real-world scenarios. Our approach achieves a peak signal-to-noise ratio of 22.08 dB. Moreover, on the TUM VI dataset, our system reduces localization error by 22.05% compared to ORB-SLAM3, proving the accuracy and robustness of the proposed method in low-light environments.

A common CNS architecture is observed in all chordates, from vertebrates to basal chordates like the ascidian Ciona. Ciona stands apart among chordates in having a complete larval connectome. Starting with visuomotor circuits predicted by the Ciona connectome, we used expression maps of neurotransmitter use with behavioral assays to identify two parallel visuomotor circuits that are responsive to different components of visual stimuli. The first circuit is characterized by glutamatergic photoreceptors and responds to the direction of light. These photoreceptors project to cholinergic motor neurons, via two tiers of cholinergic interneurons. The second circuit responds to changes in ambient light and mediates an escape response. This circuit uses GABAergic photoreceptors which project to GABAergic interneurons, and then to cholinergic interneurons. Our observations on the behavior of larvae either treated with a GABA receptor antagonist or carrying a mutation that eliminates photoreceptors indicate the second circuit is disinhibitory.