Manual Estimation Research Articles

Deep Learning Enables Prostate MRI Segmentation: A Large Cohort Evaluation With Inter-Rater Variability Analysis.

Whole-prostate gland (WPG) segmentation plays a significant role in prostate volume measurement, treatment, and biopsy planning. This study evaluated a previously developed automatic WPG segmentation, deep attentive neural network (DANN), on a large, continuous patient cohort to test its feasibility in a clinical setting. With IRB approval and HIPAA compliance, the study cohort included 3,698 3T MRI scans acquired between 2016 and 2020. In total, 335 MRI scans were used to train the model, and 3,210 and 100 were used to conduct the qualitative and quantitative evaluation of the model. In addition, the DANN-enabled prostate volume estimation was evaluated by using 50 MRI scans in comparison with manual prostate volume estimation. For qualitative evaluation, visual grading was used to evaluate the performance of WPG segmentation by two abdominal radiologists, and DANN demonstrated either acceptable or excellent performance in over 96% of the testing cohort on the WPG or each prostate sub-portion (apex, midgland, or base). Two radiologists reached a substantial agreement on WPG and midgland segmentation (κ = 0.75 and 0.63) and moderate agreement on apex and base segmentation (κ = 0.56 and 0.60). For quantitative evaluation, DANN demonstrated a dice similarity coefficient of 0.93 ± 0.02, significantly higher than other baseline methods, such as DeepLab v3+ and UNet (both p values < 0.05). For the volume measurement, 96% of the evaluation cohort achieved differences between the DANN-enabled and manual volume measurement within 95% limits of agreement. In conclusion, the study showed that the DANN achieved sufficient and consistent WPG segmentation on a large, continuous study cohort, demonstrating its great potential to serve as a tool to measure prostate volume.

Overcoming the distance estimation bottleneck in estimating animal abundance with camera traps

The biodiversity crisis is still accelerating, despite increasing efforts by the international community. Estimating animal abundance is of critical importance to assess, for example, the consequences of land-use change and invasive species on community composition, or the effectiveness of conservation interventions. Various approaches have been developed to estimate abundance of unmarked animal populations. Whereas these approaches differ in methodological details, they all require the estimation of the effective area surveyed in front of a camera trap. Until now camera-to-animal distance measurements are derived by laborious, manual and subjective estimation methods. To overcome this distance estimation bottleneck, this study proposes an automatized pipeline utilizing monocular depth estimation and depth image calibration methods. We are able to reduce the manual effort required by a factor greater than 21. Our system is available.11https://timm.haucke.xyz/publications/distance-estimation-animal-abundance.

Pinot noir Crop Estimation Method Allows Growers to Estimate Yields Earlier than Lag Phase

<h3>Summary</h3> <h3>Goals:</h3> Winegrape growers must estimate yields to prepare for harvest operations and winery processing. Standard practice requires waiting until the lag phase of berry development, which is late for making decisions related to crop thinning, fruit sales, winery inventory, and ordering wine production supplies. Because producers desire improved methods for accurate crop estimation, we evaluated the following questions over six growing seasons for Oregon <i>Vitis vinifera</i> Pinot noir: 1) When does lag phase occur? 2) What cluster weight increase factor should be used with lag phase crop estimation? and 3) Can crop estimation be conducted earlier in the growing season? <h3>Key Findings:</h3> The Pinot noir berry development curve was consistent across six years when based on days post-full bloom (50% capfall). The mid-point of lag phase, the most common time to estimate crop yields, was at 55 days post 50% capfall. Lag phase duration was 12 days. Mean cluster weight increase factor during the entire lag phase over the six-year period was 2.1. Cluster weight increase factor equations were developed based on post-budbreak and bloom thermal times and day counts to allow growers flexibility in timing of crop estimation. <h3>Impact and Significance:</h3> Data was used to improve upon manual lag phase crop estimation protocols commonly used by winegrape producers, including methods to identify berry lag phase occurrence and developing equations for cluster weight increase factors based on berry development. These modifications may improve the accuracy of manual yield estimates and allow growers to obtain yield estimates earlier in the growing season than previously allowed by standard practice. This methodology may be useful in testing increase factors for other grape cultivars.

Proliferation Activity in Canine Gastrointestinal Lymphoma

Gastrointestinal lymphomas are uncommon in dogs and little is known about their distinct subtypes or proliferation rate. The aim of this study was to stratify 33 canine gastrointestinal lymphoma samples according to the latest World Health Organization classification and to determine the Ki67 proliferation index by manual counting, digital image analysis and visual estimation. The Ki67 index was then correlated with subtype, immunophenotype, mitotic index, grade and tumour location. The mitotic index correlated positively with the Ki67 index. A significantly higher number of Ki67-positive cells was found in enteropathy-associated T-cell lymphoma type I and in diffuse large B-cell lymphoma compared with enteropathy-associated T-cell lymphoma type II. There was also a significant difference in Ki67 immunolabelled cells between grade 1 and grade 2 lymphomas. Moderate agreement was found between the Ki67 index as obtained by manual counting and visual estimation, but there was strong agreement between manual counting and digital image analysis. The user-friendly digital imaging system used in this study could have potential for future determination of the Ki67 index in lymphoid neoplasms.

Preliminary study on development of cocoa beans fermentation level measurement based on computer vision and artificial intelligence

The fermentation process is an important indicator of cocoa beans’ quality. The standard method used is the Magra test by splitting the cocoa beans and observing the color of the beans with the naked eye to estimate the degree of fermentation. Although, manual estimation systems require specific expertise, which can lead to inconsistency in predicting cocoa bean fermentation rate. This research aims to develop a classification model of two categories of cocoa, i.e., fermented and unfermented cocoa, using computer vision and a machine learning model. Image analysis has been carried out, and color features have been used to train and compare several classification models. After analyzing the data, it was found out that a model that can quantify the standard and accurate measurement of the degree of fermentation of cocoa beans using artificial neural network models so that it can segment, calculate, and grade classification by using color feature extraction, which is the average value of RGB and L*a*b. The Artificial Neural Network (ANN) Multilayer Perceptron (MLP) has been found to be superior compared to other models achieving training and validation accuracy of 94%.

PEMANFAATAN METODE ACTIVITY BASED COSTING DALAM SISTEM INFORMASI AKUNTANSI PENENTUAN BIAYA PADA BENGKEL MUJI JAYA

The Muji Jaya Workshop in determining the cost of body repair, it still uses a manual estimate model with paper media which will result in losses if the calculation of the cost of body repair that is informed to the customer is not enough so that the body repair costs received are not sufficient for the needs of raw material costs and labor costs in body work. The repair and preparation of financial statements will be disrupted and delayed for a long time if the note paper used is lost or damaged. This results in the owner not being able to make decisions quickly, precisely and accurately.&#x0D; From these various problems, Workshop Muji Jaya implements the use of the activity based costing method in the accounting information system for determining body repair costs in order to make it easier to determine the cost of body repairs to be more accurate and in presenting financial statements to be easier and more efficient because this method is guided by the assignment of costs to products. or services based on all required activities.&#x0D; &#x0D; Keywords: estimation, cost, activity based costing

Estimating Cumulative Radiation Dose to Organs at Risk for Glioblastoma Multiforme Cases via a Radiotherapy Dose Accumulation Routine

Estimating Cumulative Radiation Dose to Organs at Risk for Glioblastoma Multiforme Cases via a Radiotherapy Dose Accumulation Routine

Hist-P-03 - Quantitative IHC analyses for CD30+ cutaneous T-cell lymphoma: chances and challenges

<h3>Objective:</h3> Cutaneous T-cell lymphoma (CTCL) is a malignant manifestation in the skin. The most popular subtype is mycosis fungoides (MF). The straight focus on the molecular background of the malignancy in the last decades lead into new therapeutic possibilities. The subtype CD30+ MF can be nowadays treated with the CD30 antibody conjugate brentuximab vedotin. Diagnostic methods are based on Immunohistochemistry (IHC) followed by manual assessment from pathologists or specialized histologists. Since the manual estimated proportion of CD30+ T cells in the skin is always a subjective calculation, there is a need for objective approaches. QuPath, an open-source software for digital pathology image analysis can meet this need. <h3>Methods:</h3> In this study, 10 samples from CD30+ diagnosed MF patients with different stages of progression were used for CD3 and CD30 IHC staining. For each patient, the CD30/CD3 ratio was assessed independently by three different histologists and QuPath, a computer-assisted image analysis software with included quantitative statement. <h3>Results:</h3> The individual estimates for CD30+/CD3+ ratios varied between individual histologists (mean coefficient of variation 0.46; range 0–0.78). QuPath analysis shows excellent separation between the positive stained cells for CD3 and CD30 IHC and other cells and structures. The QuPath data for CD30+/CD3+ ratios correlate strongly with the mean estimates of the three histologists (Pearson R 0.93). <h3>Conclusion:</h3> In summary, it has been shown that the individual subjective histologist estimates can often be far apart, with the mean of these estimates resembling the QuPath analysis. It can therefore be seen that QuPath image analysis is a good alternative to conventional manual estimation.

Impact of normalization, standardization and pre-fit on the success rate of fitting in electrochemical impedance spectroscopy

Abstract Fitting the data of an electrochemical impedance spectroscopy (EIS) typically requires manual estimation of the initial values before regression algorithms such as complex nonlinear least squares (CNLS) can be applied. This makes the success rate of the fitting dependent on the user input. Furthermore, the Randles circuit consists of parameters with substantially differing magnitudes (e.g. capacitors and resistors), which can also strongly affect the success rate of the fitting due to numerical effects. The aim of this work is to investigate methods addressing the described limitations of fitting. Therefore, a Python implementation performing a fit for EIS is benchmarked with an equivalent open source library. The examined implementation optionally includes the normalization of the parameter values, the standardization of the impedances and a pre-fit. Applying the same equivalent circuit without additional signal processing steps and with fixed initial values defined by midpoints of the value ranges, both implementations were able to fit 46.50% of the simulated database with different spectra. Applying the normalization of the parameter values (76.25%) or the same method with additional pre-fit (97.75%) lead to a significant improvement of the success rate. The standardization of impedances did not affect the success rate.

Familiar size affects the perceived size and distance of real objects even with binocular vision.

Although the familiar size of real-world objects affects size and distance perception, evidence is mixed about whether this is the case when oculomotor cues are available. We examined the familiar size effect (FSE) on both size and distance perception for real objects under two viewing conditions with full or restricted oculomotor cues (binocular viewing, which provides vergence and accommodation cues, and monocular viewing through a 1-mm pinhole, which removes those cues). Familiar objects (a playing die versus a Rubik's cube) were manufactured in their typical (1.6-cm die and 5.7-cm Rubik's cube) and reverse (5.7-cm die and 1.6-cm Rubik's cube) sizes and shown at two distances (25 cm versus 91 cm) in isolation. Small near and large far objects subtended equal retinal angles. Participants provided manual estimates of perceived size and distance. For every combination of size and distance, Rubik's cubes were perceived as larger and farther than the dice, even during binocular viewing at near distances (<1 meter), when oculomotor cues are particularly strong. For size perception but not distance perception, the familiar size effect was significantly stronger under monocular pinhole viewing than binocular viewing. These results suggest that (1) familiar size affects the accuracy of perception, not just the speed; (2) the effect occurs even when oculomotor cues are available; and (3) size and distance perception are not perfectly yoked.

Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method.

Flood depth monitoring is crucial for flood warning systems and damage control, especially in the event of an urban flood. Existing gauge station data and remote sensing data still has limited spatial and temporal resolution and coverage. Therefore, to expand flood depth data source taking use of online image resources in an efficient manner, an automated, low-cost, and real-time working frame called FloodMask was developed to obtain flood depth from online images containing flooded traffic signs. The method was built on the deep learning framework of Mask R-CNN (regional convolutional neural network), trained by collected and manually annotated traffic sign images. Following further the proposed image processing frame, flood depth data were retrieved more efficiently than manual estimations. As the main results, the flood depth estimates from images (without any mirror reflection and other inference problems) have an average error of 0.11 m, when compared to human visual inspection measurements. This developed method can be further coupled with street CCTV cameras, social media photos, and on-board vehicle cameras to facilitate the development of a smart city with a prompt and efficient flood monitoring system. In future studies, distortion and mirror reflection should be tackled properly to increase the quality of the flood depth estimates.

Gaussian Mixture Models and Machine Learning Predict Megakaryocytic Growth and Differentiation Potential Ex Vivo

The ability to analyze single cells via flow cytometry has resulted in a wide range of biological and medical applications, such as monitoring populations of developing immune and hematopoietic cells over time. Currently, there is no established framework to compare and interpret time-series flow cytometry data for cellular engineering applications. Manual analysis of temporal trends is time-consuming and subjective for large-scale datasets. We resolved this bottleneck by developing TEmporal Gaussian Mixture models (TEGM), an unbiased computational strategy to quantify and predict temporal trends of developing cell subpopulations indicative of cellular phenotype. We validated its utility by accurately predicting peak CD41+/CD42+ % of megakaryocyte (MK) cultures derived from human CD34+-selected umbilical cord blood units (CB).TEGM applies Gaussian mixture modeling for feature extraction and gradient boosted trees for prediction. Our modeling strategy allows for data to be gated and clustered on multi-dimensional planes, enabling rapid processing of flow cytometry data. TEGM enables the extraction of subtle features, such as the dispersion and rate of change of surface marker expression for each subpopulation over time. These critical, yet hard-to-discern, features are fed into machine-learning algorithms that predict underlying cellular classes and phenotypes. Our framework can be flexibly applied to conventional flow cytometry sampling schemes, and allows for faster and more consistent processing of time-series flow cytometry data.As proof-of-concept, we applied our method to the analysis of ex vivo MK differentiation and maturation of hematopoietic cells from donors with varying potential to generate CD41+/CD42+ cells. Our computational approach consists of three major steps: preprocessing, feature extraction, and prediction (Figure 1). We illustrate the major steps of the computational approach by predicting peak CD41+/CD42+ % MK maturation of CD34+-selected CB cells from 16 independent donors. Cells were cultured over a 19-day multi-phase differentiation culture, consisting of a pre-expansion phase and a differentiation phase. The novel dataset comprised 720 measurements from 80 perturbations of 16 individual donors, with 9 time-point measurements sampled every 2-5 days for each donor. In the preprocessing step, we filtered out non-uniform events, margin events, and doublets. We then constructed an automated gating strategy to extract surface marker expression of various clusters of DAPIlow/CD41+ MK cells. Notably, we demonstrated that estimation of the CD34+ and CD42+ % was within 1% of manual gating estimates, thus illustrating the consistency and accuracy of the technique. We then performed feature extraction for each flow cytometry time-course dataset on several descriptors, such as growth rate, viability, production, percentage positivity of each surface marker, covariance of mean fluorescence intensity, rate of change, and bifurcation of each subpopulation. A gradient boosted tree model was trained using an explanatory matrix describing early characteristics (Day 0-Day 9) and tested to predict peak CD41+/CD42+ marker expression, which typically occurs on Day 14 to Day 17.Overall, we identified several influential early culture factors that are predictive of peak CD42+ % expression. We showed that CD41+ % on Day 5 and Day 7 is highly predictive of peak CD42+ % expression. Cell viability and CD34+ % were comparatively less predictive of peak CD42+ %. Based on our analysis, we were able to identify the best performing cultures with high sensitivity and specificity (AUROC = 0.92, where 1 denotes perfect accuracy). Predicted and actual CD41+/CD42+ responses were highly correlated (p=7.4e09, R = 0.87) using 3 independently selected partitions of test/training sets of our data. We demonstrated that the resulting model captured apparent cell development processes represented by cell surface markers including CD34, CD41, and CD42, and was able to predict MK differentiation and maturation potential of a given CB unit in culture. Identifying CB units with high and low MK potential early in the 19-day culture process can save expensive resources and time, and provides the potential to intervene during the culture process.Figure 1. Manual gating strategy (left) vs. TEGM gating strategy (right) for time-series flow cytometry data. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Intelligent Fruit Yield Estimation for Orchards Using Deep Learning Based Semantic Segmentation Techniques-A Review.

Smart farming employs intelligent systems for every domain of agriculture to obtain sustainable economic growth with the available resources using advanced technologies. Deep Learning (DL) is a sophisticated artificial neural network architecture that provides state-of-the-art results in smart farming applications. One of the main tasks in this domain is yield estimation. Manual yield estimation undergoes many hurdles such as labor-intensive, time-consuming, imprecise results, etc. These issues motivate the development of an intelligent fruit yield estimation system that offers more benefits to the farmers in deciding harvesting, marketing, etc. Semantic segmentation combined with DL adds promising results in fruit detection and localization by performing pixel-based prediction. This paper reviews the different literature employing various techniques for fruit yield estimation using DL-based semantic segmentation architectures. It also discusses the challenging issues that occur during intelligent fruit yield estimation such as sampling, collection, annotation and data augmentation, fruit detection, and counting. Results show that the fruit yield estimation employing DL-based semantic segmentation techniques yields better performance than earlier techniques because of human cognition incorporated into the architecture. Future directions like customization of DL architecture for smart-phone applications to predict the yield, development of more comprehensive model encompassing challenging situations like occlusion, overlapping and illumination variation, etc., were also discussed.

Methodology for comprehensive cell-level analysis of wound healing experiments using deep learning in MATLAB

BackgroundEndothelial healing after deployment of cardiovascular devices is particularly important in the context of clinical outcome. It is therefore of great interest to develop tools for a precise prediction of endothelial growth after injury in the process of implant deployment. For experimental investigation of re-endothelialization in vitro cell migration assays are routinely used. However, semi-automatic analyses of live cell images are often based on gray value distributions and are as such limited by image quality and user dependence. The rise of deep learning algorithms offers promising opportunities for application in medical image analysis. Here, we present an intelligent cell detection (iCD) approach for comprehensive assay analysis to obtain essential characteristics on cell and population scale.ResultsIn an in vitro wound healing assay, we compared conventional analysis methods with our iCD approach. Therefore we determined cell density and cell velocity on cell scale and the movement of the cell layer as well as the gap closure between two cell monolayers on population scale. Our data demonstrate that cell density analysis based on deep learning algorithms is superior to an adaptive threshold method regarding robustness against image distortion. In addition, results on cell scale obtained with iCD are in agreement with manually velocity detection, while conventional methods, such as Cell Image Velocimetry (CIV), underestimate cell velocity by a factor of 0.5. Further, we found that iCD analysis of the monolayer movement gave results just as well as manual freehand detection, while conventional methods again shows more frayed leading edge detection compared to manual detection. Analysis of monolayer edge protrusion by ICD also produced results, which are close to manual estimation with an relative error of 11.7%. In comparison, the conventional Canny method gave a relative error of 76.4%.ConclusionThe results of our experiments indicate that deep learning algorithms such as our iCD have the ability to outperform conventional methods in the field of wound healing analysis. The combined analysis on cell and population scale using iCD is very well suited for timesaving and high quality wound healing analysis enabling the research community to gain detailed understanding of endothelial movement.

Plaque Size Tool: An automated plaque analysis tool for simplifying and standardising bacteriophage plaque morphology measurements

Bacteriophage plaque size measurement is essential for phage characterisation, but manual size estimation requires a considerable amount of time and effort. In order to ease the work of phage researchers, we have developed an automated command-line application called Plaque Size Tool (PST) that can detect plaques of different morphology on the images of Petri dishes and measure plaque area and diameter. Plaque size measurements using PST showed no difference to those obtained with manual plaque size measurement in Fiji, indicating future results using PST are backwards compatible with prior measurements in the literature. PST can be applied to a range of lytic bacteriophages producing oval-shaped plaques, including bull's-eye and turbid morphology. The application can also be used for titer calculation if most of the plaques are stand-alone. As laboratory automation becomes more commonplace, standardised and flexible open-source analytical tools like PST will be important parts of biofoundry and cloud lab bacteriophage workflows.

Status of Sperm Functionality Assessment in Wildlife Species: From Fish to Primates.

Simple SummaryIn general, wildlife species have been underrepresented, in terms of understanding their reproductive physiology. The artificial propagation of wildlife species, found in aquaculture (e.g., fish) and in protection of endangered species (e.g., black-footed ferret), is pertinent to this discussion. One important approach to addressing this would involve a basic understanding of the structure and function of the gametes of many wildlife species. The focus of this investigation was to provide a better understanding of the physiology of sperm of diverse wildlife species, with special attention given to the assessment of high-quality sperm. Modern approaches using sophisticated microscopy (image analysis) techniques, e.g., computer-aided sperm analysis and sperm flagellar analysis, provided advanced technology to evaluate sperm quality quantitatively. Some of these techniques involved largely automated assessments of many aspects of sperm motility, morphology, vitality, fragmentation and other indirect methods. These modern assessments are fundamental to classify sperm quality. Accordingly, cutting-edge technologies used to define high quality sperm of representative species from most vertebrate animal groups (from fish to primates) were discussed in the present work. These approaches are also important in developing and assessing the best methods to cryopreserve sperm for assisted reproductive technologies in wildlife species.(1) Background: in order to propagate wildlife species (covering the whole spectrum from species suitable for aquaculture to endangered species), it is important to have a good understanding of the quality of their sperm, oocytes and embryos. While sperm quality analyses have mainly used manual assessment in the past, such manual estimations are subjective and largely unreliable. Accordingly, quantitative and cutting-edge approaches are required to assess the various aspects of sperm quality. The purpose of this investigation was to illustrate the latest technology used in quantitative evaluation of sperm quality and the required cut-off points to distinguish the differential grades of fertility potential in a wide range of vertebrate species. (2) Methods: computer-aided sperm analysis (CASA) with an emphasis on sperm motility, 3D tracking and flagellar and sperm tracking analysis (FAST), as well as quantitative assessment of sperm morphology, vitality, acrosome status, fragmentation and many other complimentary technologies. (3) Results: Assessing sperm quality revealed a great deal of species specificity. For example, in freshwater fish like trout, sperm swam in a typical tight helical pattern, but in seawater species sperm motility was more progressive. In amphibian species, sperm velocity was slow, in contrast with some bird species (e.g., ostrich). Meanwhile, in African elephant and some antelope species, fast progressive sperm was evident. In most species, there was a high percentage of morphologically normal sperm, but generally, low percentages were observed for motility, vitality and normal morphology evident in monogamous species. (4) Conclusions: Sperm quality assessment using quantitative methodologies such as CASA motility, FAST analysis, morphology and vitality, as well as more progressive methodologies, assisted in better defining sperm quality—specifically, sperm functionality of high-quality sperm. This approach will assist in the propagation of wildlife species.

Priming of the Sander Parallelogram illusion separates perception from action.

The two-visual stream hypothesis posits that the dorsal stream is less susceptible than the ventral stream to the effects of illusions and visual priming. While previous studies have separately examined these perceptual manipulations, the present study combined the effects of a visual illusion and priming to examine the possibility of dorsally guided actions being susceptible to the perceptual stimuli due to interactions between the two streams. Thirty-four participants were primed with a 'long' or 'short' version of the Sander Parallelogram illusion and were asked to either reach out and grasp or manually estimate the length of a rod placed on a version of the illusion that was on some trials the same as the prime (congruent) and on other trials was the inverse (incongruent). Due to the context-focused nature of ventral processing, we predicted that estimations would be more susceptible to the effects of the illusion and priming than grasps. Results showed that while participants' manual estimations were susceptible to both priming and the illusion, the grasps were only affected by the illusion, not by priming. The influence of the illusion on grip aperture was greater during manual estimations than it was during grasping. These findings support the notion that the functionally distinct dorsal and ventral streams interact under the current experimental paradigm. Outcomes of the study help better understand the nature of stimuli that promote interactions between the dorsal and ventral streams.

Patient BBIs Monitoring System and Movement Analysis using Machine Learning Techniques and IoT

Wireless technology has been developed for different medical fields. The pulse monitoring system is used and in some private medical places only recorded and not utilized properly. However, accessing large medical equipment is inconvenient and expensive. In this, The proposed real-time heart rate monitoring system extract the heart beats measurements and that is stored and retrieved via Arduino microcontroller which connects the heart rate to the internet and sends results to the web server and notifies it to the specialist. Zero crossing in the resulting filter bank output signal represents heart rates that are extracted from BBI’s. The obtained heart rate is classified using the algorithm to denote the condition of the patient to notify it to specialist. Finally, the HRV features are calculated from the extracted BBI’s. If any risk found, it sends the emergency alert notification to the specialist. This system also monitors the patient activity in case of any emergency of patient using the IR sensor connected to the Arduino micro controller which alerts the controller or the specialist by a buzzer. The proposed framework isn't just utilized by the trained professional yet additionally by different clients (User agreeable). It offers speed and exactness in outcomes and with negligible expense. This technique is planned a Heart Rate Monitor System utilizing Arduino and Heartbeat Sensor. The exhibition results are contrasted and ECG signal addressed by oscilloscope and manual heartbeat estimation of heartbeat rate, which gives great arrangement.

Acoustic Identification of the Voicing Boundary during Intervocalic Offsets and Onsets based on Vocal Fold Vibratory Measures.

Methods for automating relative fundamental frequency (RFF)—an acoustic estimate of laryngeal tension—rely on manual identification of voiced/unvoiced boundaries from acoustic signals. This study determined the effect of incorporating features derived from vocal fold vibratory transitions for acoustic boundary detection. Simultaneous microphone and flexible nasendoscope recordings were collected from adults with typical voices (N=69) and with voices characterized by excessive laryngeal tension (N=53) producing voiced–unvoiced–voiced utterances. Acoustic features that coincided with vocal fold vibratory transitions were identified and incorporated into an automated RFF algorithm (“aRFF-APH”). Voiced/unvoiced boundary detection accuracy was compared between the aRFF-APH algorithm, a recently published version of the automated RFF algorithm (“aRFF-AP”), and gold-standard, manual RFF estimation. Chi-square tests were performed to characterize differences in boundary cycle identification accuracy among the three RFF estimation methods. Voiced/unvoiced boundary detection accuracy significantly differed by RFF estimation method for voicing offsets and onsets. Of 7721 productions, 76.0% of boundaries were accurately identified via the aRFF-APH algorithm, compared to 70.3% with the aRFF-AP algorithm and 20.4% with manual estimation. Incorporating acoustic features that corresponded with voiced/unvoiced boundaries led to improvements in boundary detection accuracy that surpassed the gold-standard method for calculating RFF.

Grasping of Real-World Objects Is Not Biased by Ensemble Perception.

The visual system is known to extract summary representations of visually similar objects which bias the perception of individual objects toward the ensemble average. Although vision plays a large role in guiding action, less is known about whether ensemble representation is informative for action. Motor behavior is tuned to the veridical dimensions of objects and generally considered resistant to perceptual biases. However, when the relevant grasp dimension is not available or is unconstrained, ensemble perception may be informative to behavior by providing gist information about surrounding objects. In the present study, we examined if summary representations of a surrounding ensemble display influenced grip aperture and orientation when participants reached-to-grasp a central circular target which had an explicit size but importantly no explicit orientation that the visuomotor system could selectively attend to. Maximum grip aperture and grip orientation were not biased by ensemble statistics during grasping, although participants were able to perceive and provide manual estimations of the average size and orientation of the ensemble display. Support vector machine classification of ensemble statistics achieved above-chance classification accuracy when trained on kinematic and electromyography data of the perceptual but not grasping conditions, supporting our univariate findings. These results suggest that even along unconstrained grasping dimensions, visually-guided behaviors toward real-world objects are not biased by ensemble processing.