Individual Error Rates Research Articles

Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments

Sound event detection (SED) plays an important role in understanding the sounds in different environments. Recent studies on standardized datasets have shown the growing interest of the scientific community in the SED problem, however, these did not pay sufficient attention to the detection of artificial and natural sound. In order to tackle this issue, the present article uses different features in combination for detection of machine-generated and natural sounds. In this article, we trained and compared a Stacked Convolutional Recurrent Neural Network (S-CRNN), a Convolutional Recurrent Neural Network (CRNN), and an Artificial Neural Network Classifier (ANN) using the DCASE 2017 Task-3 dataset. Relative spectral–perceptual linear prediction (RASTA-PLP) and Mel-frequency cepstrum (MFCC) features are used as input to the proposed multi-model. The performance of monaural and binaural approaches provided to the classifier as an input is compared. In our proposed S-CRNN model, we classified the sound events in the dataset into two sub-classes. When compared with the baseline model, our obtained results show that the PLP-based ANN classifier improves the individual error rate (ER) for each sound event, e.g., the error rate (ER) is improved to 0.23 for heavy vehicle events and 0.32 for people walking, and minor gains are shown in other events as compared to the baseline. Our proposed CRNN performs well when compare to the baseline and to our proposed ANN model. Moreover, in cross-validation trials, the results in the evaluation stage demonstrate a significant improvement compared to the best performance of DCASE 2017 Task-3, reducing the ER to 0.11 and increasing the F1-score by 10% in the evaluation dataset. Erosion and dilation were used during post-processing.

PGT for structural chromosomal rearrangements in 300 couples reveals specific risk factors but an interchromosomal effect is unlikely

Research questionWhat factors affect the proportion of chromosomally balanced embryos in structural rearrangement carriers? Is there any evidence for an interchromosomal effect (ICE)? DesignPreimplantation genetic testing outcomes of 300 couples (198 reciprocal, 60 Robertsonian, 31 inversion and 11 complex structural rearrangement carriers) were assessed retrospectively. Blastocysts were analysed either by array-comparative genomic hybridization or next-generation sequencing techniques. ICE was investigated using a matched control group and sophisticated statistical measurement of effect size (φ). Results300 couples underwent 443 cycles; 1835 embryos were analysed and 23.8% were diagnosed as both normal/balanced and euploid. The overall cumulative clinical pregnancy and live birth rates were 69.5% and 55.8%, respectively. Complex translocations and female age (≥35) were found to be risk factors associated with lower chance of having a transferable embryo (P < 0.001). Based on analysis of 5237 embryos, the cumulative de-novo aneuploidy rate was lower in carriers compared to controls (45.6% versus 53.4%, P < 0.001) but this was a ‘negligible’ association (φ < 0.1). A further assessment of 117,033 chromosomal pairs revealed a higher individual chromosome error rate in embryos of carriers compared to controls (5.3% versus 4.9%), which was also a ‘negligible’ association (φ < 0.1), despite a P-value of 0.007. ConclusionsThese findings suggest that rearrangement type, female age and sex of the carrier have significant impacts on the proportion of transferable embryos. Careful examination of structural rearrangement carriers and controls indicated little or no evidence for an ICE. This study helps to provide a statistical model for investigating ICE and an improved personalized reproductive genetics assessment for structural rearrangement carriers.

Bibliometric analysis of human factors in aviation accident using MKD

AbstractThis study aims to provide a better understanding of human factors and human performance mechanism in aviation accident analysis by visualisation and taxonomy analysis using mapping knowledge domain (MKD). An overview of aviation accident analysis involving human factors is first conducted, followed by an introduction of several conceptual models and human reliability analysis methods. Finally, a specific framework for risk assessment of human factors in aviation is proposed. The human factors analysis and classification system structure combined with the quantitative method is the most frequently used mode in aviation accident analysis. From a macro ergonomic perspective, systematic consideration has been emphasised frequently in existing research. Pilots or individuals are thought to be associated with the organisations and environment they located in. On the other hand, the micro ergonomic aspect prefers a quantitative and probabilistic way to reveal the individual's failure mode or error rate. Based on previous studies, the proposed framework provides a systematic view to help with the evaluation of human performance in aviation, taking into account human issues as well as management and environmental factors.

B-Value Optimization in the Estimation of Intravoxel Incoherent Motion Parameters in Patients with Cervical Cancer.

ObjectiveThis study aimed to find the optimal number of b-values for intravoxel incoherent motion (IVIM) imaging analysis, using simulated and in vivo data from cervical cancer patients.Materials and MethodsSimulated data were generated using literature pooled means, which served as reference values for simulations. In vivo data from 100 treatment-naïve cervical cancer patients with IVIM imaging (13 b-values, scan time, 436 seconds) were retrospectively reviewed. A stepwise b-value fitting algorithm calculated optimal thresholds. Feed forward selection determined the optimal subsampled b-value distribution for biexponential IVIM fitting, and simplified IVIM modeling using monoexponential fitting was attempted. IVIM parameters computed using all b-values served as reference values for in vivo data.ResultsIn simulations, parameters were accurately estimated with six b-values, or three b-values for simplified IVIM, respectively. In vivo data showed that the optimal threshold was 40 s/mm2 for patients with squamous cell carcinoma and a subsampled acquisition of six b-values (scan time, 198 seconds) estimated parameters were not significantly different from reference parameters (individual parameter error rates of less than 5%). In patients with adenocarcinoma, the optimal threshold was 100 s/mm2, but an optimal subsample could not be identified. Irrespective of the histological subtype, only three b-values were needed for simplified IVIM, but these parameters did not retain their discriminative ability.ConclusionSubsampling of six b-values halved the IVIM scan time without significant losses in accuracy and discriminative ability. Simplified IVIM is possible with only three b-values, at the risk of losing diagnostic information.

Is 3D faster and safer than 4K laparoscopic cholecystectomy? A randomised-controlled trial

BackgroundLaparoscopic surgery has well-established benefits for patients; however, laparoscopic procedures have a long and difficult learning curve, in large part due to the lack of stereoscopic depth perception. Developments in high-definition and stereoscopic imaging have attempted to overcome this. Three-dimensional high-definition (3D HD) systems are thought to improve operating times compared to two-dimensional high-definition systems. However their performance against new, ultra-high-definition (‘4K’) systems is not known.MethodsPatients undergoing laparoscopic cholecystectomy were randomised to 3D HD or 4K laparoscopy. Operative videos were recorded, and the time from gallbladder exposure to separation from the liver (minus on table cholangiogram) was calculated. Blinded video assessment was performed to calculate intraoperative error scores.ResultsOne hundred and twenty patients were randomised, of which 109 were analysed (3D HD n = 54; 4K n = 55). No reduction in operative time was detected with 3D HD compared to 4K laparoscopy (median [IQR]; 23.41 min [17.00–37.98] vs 20.90 min [17.67–33.03]; p = 0.91); nor was there any decrease observed in error scores (60 [56–62] vs 58 [56–60]; p = 0.27), complications or reattendance. Stone spillage occurred more frequently with 3D HD, but there were no other differences in individual error rates. Gallbladder grade and operating surgeon had significant effects on time to complete the operation. Gallbladder grade also had a significant effect on the error score.ConclusionsA 3D HD laparoscopic system did not reduce operative time or error scores during laparoscopic cholecystectomy compared with a new 4K imaging system.

Controlling individual and experimentwise error rates in replicated regular two-level factorial experiments

Replicated regular two-level factorial experiments are very useful for industry. The goal of these experiments is to identify active effects that affect the mean and variance of the response. The existing methods give results that are either too anticonservative or conservative in controlling the individual and experimentwise error rates (IER and EER). This paper proposes a Monte Carlo method and an exact-variance method to identify active effects for the mean and variance, respectively, of the response. Simulation studies show that our methods control the IER and EER extremely well. Real data are used to illustrate the performance of the methods.

A comparison of methods for identifying location effects in unreplicated fractional factorials in the presence of dispersion effects

Most methods for identifying location effects in unreplicated fractional factorial designs assume homoscedasticity of the response values. However, dispersion effects in the underlying process may create heteroscedasticity in the response values. This heteroscedasticity may go undetected when identification of location effects is pursued. Indeed, methods for identifying dispersion effects typically require first modeling location effects. Therefore, it is imperative to understand how methods for identifying location effects function in the presence of undetected dispersion effects. We used simulation studies to examine the robustness of four different methods for identifying location effects—Box and Meyer (1986), Lenth (1989), Berk and Picard (1991), and Loughin and Noble (1997)—under models with one, two, or three dispersion effects of varying sizes. We found that the first three methods usually performed acceptably with respect to error rates and power, but the Loughin–Noble method lost control of the individual error rate when moderate-to-large dispersion effects were present.

A proposed method of identifying significant effects in unreplicated factorial experiments

In many areas of research/ production, a lot of factors are combined to obtain a desired product. To be able to analyze which factors (or combinations of factors and at what level) are significant, the experiment has to be replicated. For economic or practical reasons, it may not be feasible to perform the experiment more than once therefore unreplicated factorial designs are often employed. This is especially true in the field of Medicine, Pharmacy and Industrial production units. The traditional method of analysis of variance (ANOVA) cannot be employed in unreplicated factorial designs, therefore many methods have been proposed in literature. In this paper, a new method of analyzing unreplicated factorial designs is proposed and was compared with some of the existing methods. The four existing methods considered were: Lenth, Berk and Picard, Juan and Pena, and Dong. The comparison was performed using Monte Carlo simulation method. The criteria used in evaluating the performances of the methods are Power and Individual Error Rate (IER). Using these criteria of evaluation, the results showed that on overall performance, Dong method is the best among the four existing methods considered and was closely followed by Berk and Picard, Lenth, then Juan and Pena methods in that order. It was also found that not only is the proposed method simpler to compute, it competed favourably with Dong and even performed better than all the others when IER is used for assessment.

Case Study: Assessment of human-conditioned sorting behavior in dairy cows in farm research trials

ABSTRACT Cow–human interactions influence and modulate group and individual behaviors of dairy cows. The objective was to test the effectiveness of human sorting on separating subgroups of dairy cows in on-farm studies and to assess the level of conditioning to this activity. Three sorting methods were compared: (1) human active sorting at the pen gate (AS); (2) human presence as passive sorting (PS); and (3) nonhuman gate sorting (GS). Holstein cows (n = 176) were randomly assigned to 1 of 2 subgroups (A = 91 animals and B = 85 animals) to be sequentially separated by 3 sorting methods. Each method was applied once per day after the morning milking during 5 d, and the proportions of cows correctly allocated in each group were recorded and compared. Additionally, an individual error index rate (number of errors per number of days observed) was calculated for each individual cow. When AS was applied, the total proportion of animals correctly sorted was 99.8%, whereas PS had 94.8% of total sorting accuracy. Cows lost the self-sorting behavior when exposed to nonhuman GS. The daily average of animals correctly placed was greater for AS when compared with PS (175 ± 1.7 vs. 166.6 ± 3.5; P = 0.005). Cows in the group that had longer walking distances to their subpen had greater individual error rate, especially when PS was applied. After a period of training, lactating dairy cows became conditioned to human sorting, which represents an opportunity to perform animal separation without intense human labor or stress for the animals.

A Patient-Independent Significance Test by Means of False-Positive Rates in Selected Correlation Analysis of Brain Multimodal Monitoring Data.

Recently, we introduced a mathematical toolkit called selected correlation analysis (sca) that reliably detects negative and positive correlations between arterial blood pressure (ABP) and intracranial pressure (ICP) data, recorded during multimodal monitoring, in a time-resolved way. As has been shown with the aid of a mathematical model of cerebral perfusion, such correlations reflect impaired autoregulation and reduced intracranial compliance in patients with critical neurological diseases. Sca calculates a Fourier transform-based index called selected correlation (sc) that reflects the strength of correlation between the input data and simultaneously an index called mean Hilbert phase difference (mhpd) that reflects the phasing between the data. To reliably detect pathophysiological conditions during multimodal monitoring, some thresholds for the abovementioned indexes sc and mhpd have to be established that assign predefined significance levels to that thresholds. In this paper, we will present a method that determines the rate of false positives for fixed pairs of thresholds (lsc, lmhpd). We calculate these error rates as a function of the predefined thresholds for each individual out of a patient cohort of 52 patients in a retrospective way. Based on the deviation of the individual error rates, we subsequently determine a globally valid upper limit of the error rate by calculating the predictive interval. From this predictive interval, we deduce a globally valid significance level for appropriate pairs of thresholds that allows the application of sca to every future patient in a prospective, bedside fashion.

Estimating error rates for firearm evidence identifications in forensic science

Estimating error rates for firearm evidence identification is a fundamental challenge in forensic science. This paper describes the recently developed congruent matching cells (CMC) method for image comparisons, its application to firearm evidence identification, and its usage and initial tests for error rate estimation. The CMC method divides compared topography images into correlation cells. Four identification parameters are defined for quantifying both the topography similarity of the correlated cell pairs and the pattern congruency of the registered cell locations. A declared match requires a significant number of CMCs, i.e., cell pairs that meet all similarity and congruency requirements. Initial testing on breech face impressions of a set of 40 cartridge cases fired with consecutively manufactured pistol slides showed wide separation between the distributions of CMC numbers observed for known matching and known non-matching image pairs. Another test on 95 cartridge cases from a different set of slides manufactured by the same process also yielded widely separated distributions. The test results were used to develop two statistical models for the probability mass function of CMC correlation scores. The models were applied to develop a framework for estimating cumulative false positive and false negative error rates and individual error rates of declared matches and non-matches for this population of breech face impressions. The prospect for applying the models to large populations and realistic case work is also discussed. The CMC method can provide a statistical foundation for estimating error rates in firearm evidence identifications, thus emulating methods used for forensic identification of DNA evidence.

Validation Relaxation: A Quality Assurance Strategy for Electronic Data Collection.

BackgroundThe use of mobile devices for data collection in developing world settings is becoming increasingly common and may offer advantages in data collection quality and efficiency relative to paper-based methods. However, mobile data collection systems can hamper many standard quality assurance techniques due to the lack of a hardcopy backup of data. Consequently, mobile health data collection platforms have the potential to generate datasets that appear valid, but are susceptible to unidentified database design flaws, areas of miscomprehension by enumerators, and data recording errors.ObjectiveWe describe the design and evaluation of a strategy for estimating data error rates and assessing enumerator performance during electronic data collection, which we term “validation relaxation.” Validation relaxation involves the intentional omission of data validation features for select questions to allow for data recording errors to be committed, detected, and monitored.MethodsWe analyzed data collected during a cluster sample population survey in rural Liberia using an electronic data collection system (Open Data Kit). We first developed a classification scheme for types of detectable errors and validation alterations required to detect them. We then implemented the following validation relaxation techniques to enable data error conduct and detection: intentional redundancy, removal of “required” constraint, and illogical response combinations. This allowed for up to 11 identifiable errors to be made per survey. The error rate was defined as the total number of errors committed divided by the number of potential errors. We summarized crude error rates and estimated changes in error rates over time for both individuals and the entire program using logistic regression.ResultsThe aggregate error rate was 1.60% (125/7817). Error rates did not differ significantly between enumerators (P=.51), but decreased for the cohort with increasing days of application use, from 2.3% at survey start (95% CI 1.8%-2.8%) to 0.6% at day 45 (95% CI 0.3%-0.9%; OR=0.969; P<.001). The highest error rate (84/618, 13.6%) occurred for an intentional redundancy question for a birthdate field, which was repeated in separate sections of the survey. We found low error rates (0.0% to 3.1%) for all other possible errors.ConclusionsA strategy of removing validation rules on electronic data capture platforms can be used to create a set of detectable data errors, which can subsequently be used to assess group and individual enumerator error rates, their trends over time, and categories of data collection that require further training or additional quality control measures. This strategy may be particularly useful for identifying individual enumerators or systematic data errors that are responsive to enumerator training and is best applied to questions for which errors cannot be prevented through training or software design alone. Validation relaxation should be considered as a component of a holistic data quality assurance strategy.

Group Augmentation in Realistic Visual-Search Decisions via a Hybrid Brain-Computer Interface

Groups have increased sensing and cognition capabilities that typically allow them to make better decisions. However, factors such as communication biases and time constraints can lead to less-than-optimal group decisions. In this study, we use a hybrid Brain-Computer Interface (hBCI) to improve the performance of groups undertaking a realistic visual-search task. Our hBCI extracts neural information from EEG signals and combines it with response times to build an estimate of the decision confidence. This is used to weigh individual responses, resulting in improved group decisions. We compare the performance of hBCI-assisted groups with the performance of non-BCI groups using standard majority voting, and non-BCI groups using weighted voting based on reported decision confidence. We also investigate the impact on group performance of a computer-mediated form of communication between members. Results across three experiments suggest that the hBCI provides significant advantages over non-BCI decision methods in all cases. We also found that our form of communication increases individual error rates by almost 50% compared to non-communicating observers, which also results in worse group performance. Communication also makes reported confidence uncorrelated with the decision correctness, thereby nullifying its value in weighing votes. In summary, best decisions are achieved by hBCI-assisted, non-communicating groups.

Using implicit association tests in age-heterogeneous samples: The importance of cognitive abilities and quad model processes.

Implicit association tests (IATs) are increasingly used to indirectly assess people's traits, attitudes, or other characteristics. In addition to measuring traits or attitudes, IAT scores also reflect differences in cognitive abilities because scores are based on reaction times (RTs) and errors. As cognitive abilities change with age, questions arise concerning the usage and interpretation of IATs for people of different age. To address these questions, the current study examined how cognitive abilities and cognitive processes (i.e., quad model parameters) contribute to IAT results in a large age-heterogeneous sample. Participants (N = 549; 51% female) in an age-stratified sample (range = 12-88 years) completed different IATs and 2 tasks to assess cognitive processing speed and verbal ability. From the IAT data, D2-scores were computed based on RTs, and quad process parameters (activation of associations, overcoming bias, detection, guessing) were estimated from individual error rates. Substantial IAT scores and quad processes except guessing varied with age. Quad processes AC and D predicted D2-scores of the content-specific IAT. Importantly, the effects of cognitive abilities and quad processes on IAT scores were not significantly moderated by participants' age. These findings suggest that IATs seem suitable for age-heterogeneous studies from adolescence to old age when IATs are constructed and analyzed appropriately, for example with D-scores and process parameters. We offer further insight into how D-scoring controls for method effects in IATs and what IAT scores capture in addition to implicit representations of characteristics. (PsycINFO Database Record

Heterogeneity in Trauma Registry Data Quality: Implications for Regional and National Performance Improvement in Trauma

Led by the American College of Surgeons Trauma Quality Improvement Program, performance improvement efforts have expanded to regional and national levels. The American College of Surgeons Trauma Quality Improvement Program recommends 5 audit filters to identify records with erroneous data, and the Georgia Committee on Trauma instituted standardized audit filter analysis in all Level I and II trauma centers in the state. Audit filter reports were performed from July 2013 to September 2014. Records were reviewed to determine whether there was erroneous data abstraction. Percent yield was defined as number of errors divided by number of charts captured. Twelve centers submitted complete datasets. During 15 months, 21,115 patient records were subjected to analysis. Audit filter captured 2,901 (14%) records and review yielded 549 (2.5%) records with erroneous data. Audit filter 1 had the highest number of records identified and audit filter 3 had the highest percent yield. Individual center error rates ranged from 0.4% to 5.2%. When comparing quarters 1 and 2 with quarters 4 and 5, there were 7 of 12 centers with substantial decreases in error rates. The most common missed complications were pneumonia, urinary tract infection, and acute renal failure. The most common missed comorbidities were hypertension, diabetes, and substance abuse. In Georgia, the prevalence of erroneous data in trauma registries varies among centers, leading to heterogeneity in data quality, and suggests that targeted educational opportunities exist at the institutional level. Standardized audit filter assessment improved data quality in the majority of participating centers.

Rapid metagenomic identification of viral pathogens in clinical samples by real-time nanopore sequencing analysis.

We report unbiased metagenomic detection of chikungunya virus (CHIKV), Ebola virus (EBOV), and hepatitis C virus (HCV) from four human blood samples by MinION nanopore sequencing coupled to a newly developed, web-based pipeline for real-time bioinformatics analysis on a computational server or laptop (MetaPORE). At titers ranging from 107–108 copies per milliliter, reads to EBOV from two patients with acute hemorrhagic fever and CHIKV from an asymptomatic blood donor were detected within 4 to 10 min of data acquisition, while lower titer HCV virus (1 × 105 copies per milliliter) was detected within 40 min. Analysis of mapped nanopore reads alone, despite an average individual error rate of 24 % (range 8–49 %), permitted identification of the correct viral strain in all four isolates, and 90 % of the genome of CHIKV was recovered with 97–99 % accuracy. Using nanopore sequencing, metagenomic detection of viral pathogens directly from clinical samples was performed within an unprecedented <6 hr sample-to-answer turnaround time, and in a timeframe amenable to actionable clinical and public health diagnostics.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0220-9) contains supplementary material, which is available to authorized users.

Impact of pharmacist-led educational and error notification interventions on prescribing errors in a family medicine clinic

To determine the rate of prescribing errors in a family medicine clinic and the subsequent impact of pharmacist-led educational and error notification interventions on prescribing errors. Single site, pre-post study design. An outpatient academic family medicine clinic serving pediatric and adult populations in Oklahoma from March 1, 2011, through April 30, 2012. 24 resident physicians who prescribed medications during routine outpatient visits. A prescribing educational program, audit and feedback methods, and weekly newsletter. Percentage of prescription errors and physician error rate before and after intervention among pediatric and adult populations. During the two assessment periods, 24 resident physicians wrote 2,753 prescriptions for 394 pediatric and 899 adult patients. The overall percentage of prescription errors decreased from 18.6% during March 2011 to 14.5% during April 2012 (P = 0.004). Errors were more commonly seen with prescriptions written for pediatric patients (24.9%) than for adult patients (13.9%) (P = 0.001). Individual physician error rates ranged from 5% to 36% (mean ± SD 16.5% ± 8.1). Physicians committed significantly fewer prescribing errors during the postintervention assessment period (14.9%) than during the preintervention assessment period (20.9%) (P = 0.002). Controlling for time, pediatric prescription error rates among physicians who participated in the educational intervention were 36% lower than the error rates among physicians who did not participate (rate ratio 0.64 [95% CI 0.45, 0.91], P = 0.01). The pharmacist-led educational program was effective in reducing pediatric prescribing errors among resident physicians in a family medicine clinic.

Curation accuracy of model organism databases.

Manual extraction of information from the biomedical literature—or biocuration—is the central methodology used to construct many biological databases. For example, the UniProt protein database, the EcoCyc Escherichia coli database and the Candida Genome Database (CGD) are all based on biocuration. Biological databases are used extensively by life science researchers, as online encyclopedias, as aids in the interpretation of new experimental data and as golden standards for the development of new bioinformatics algorithms. Although manual curation has been assumed to be highly accurate, we are aware of only one previous study of biocuration accuracy. We assessed the accuracy of EcoCyc and CGD by manually selecting curated assertions within randomly chosen EcoCyc and CGD gene pages and by then validating that the data found in the referenced publications supported those assertions. A database assertion is considered to be in error if that assertion could not be found in the publication cited for that assertion. We identified 10 errors in the 633 facts that we validated across the two databases, for an overall error rate of 1.58%, and individual error rates of 1.82% for CGD and 1.40% for EcoCyc. These data suggest that manual curation of the experimental literature by Ph.D-level scientists is highly accurate.Database URL: http://ecocyc.org/, http://www.candidagenome.org//

동잡음에 강건한 PPG 신호 측정 방안

본 연구에서는 PPG 신호 측정에서 포함되는 잡음들 중에서 동잡음을 제거하는 신호처리 방안을 제안한다. 측정되는 PPG 신호는 측정 환경 자체의 신호 잡음과 사용자의 동작변화로 발생하는 동잡음 등 여러 잡음이 같이 존재하므로 적절한 신호처리 방법이 요구된다. 본 논문에서는 사용자의 안정된 상태에서 실제 PPG 신호를 측정한 뒤, 이 기준 신호를 바탕으로 여러 채널에서 측정한 다중화된 PPG 신호를 이용하여 동잡음을 제거하는 신호처리 방법을 제안한다. 본 논문의 실험에서는 두 개의 채널을 이용하여 PPG 신호를 측정하며, 각각 20%의 오류율을 가지는 신호를 본 논문에서 제안한 알고리즘으로 처리한 결과 9.56%로 오류율이 감소하였다. In this paper, we propose a methods to eliminate PPG sensor noise resulted from user motion during measurement. Measured PPG signals require approperiate signal processing methods since various types of noises such as a motion noise by user movement and signal noises occurred from the change of measuring environments. This paper suggests a signal processing method that eliminates motion noises by measuring several PPG channels that are based on the stable patterns of the practical users. The PPG signals are measured by the two channels in this experiment. When the individual error rates are 20%, the proposed algorithm reduces the errors to 9.56%.

A stochastic node-failure network with individual tolerable error rate at multiple sinks

Many enterprises consider several criteria during data transmission such as availability, delay, loss, and out-of-order packets from the service level agreements (SLAs) point of view. Hence internet service providers and customers are gradually focusing on tolerable error rate in transmission process. The internet service provider should provide the specific demand and keep a certain transmission error rate by their SLAs to each customer. This paper is mainly to evaluate the system reliability that the demand can be fulfilled under the tolerable error rate at all sinks by addressing a stochastic node-failure network (SNFN), in which each component (edge or node) has several capacities and a transmission error rate. An efficient algorithm is first proposed to generate all lower boundary points, the minimal capacity vectors satisfying demand and tolerable error rate for all sinks. Then the system reliability can be computed in terms of such points by applying recursive sum of disjoint products. A benchmark network and a practical network in the United States are demonstrated to illustrate the utility of the proposed algorithm. The computational complexity of the proposed algorithm is also analyzed.