462 research outputs found
Budges355: Individual events of a bird's flying motion, along with video clips, annotated images and 3D data (355 clips).
No full textThe dataset consists of birds (Budgerigar) flying motion in a controlled environment. Trajectories of birds flying from one perch to another perch were recorded using video cameras. The dataset contains 355 clips of individual events of a bird's flying motion, along with annotated images and 3D data generated from the events.Only the bird in motion was annotated and 3D trajectories were calculated for all five parts of the bird that were annotated. The annotations were done manually using Computer Vision Annotation Tool (CVAT). For generating the 3D trajectories of the bird's flight motion, Matlab (MathWorks®) was used. The files for all five 3D trajectories are labelled as such; point3D_bird for the bird’s body, point3D_head for the head of the bird, point3D_tail for the tail of the bird, point_3D_left_wing and point_3D_right_wing for the left wing and right wing of the bird respectively. The annotation's file format is JSON, and the format of the annotation is Microsoft COCO . The 3D coordinates for the bird's trajectories are in .mat file format.Dataset folder structure:clip_1-->point3D_bird-->point3D_head-->point3D_tail-->point3D_left_wing-->point3D_right_wing-->left.mp4-->right.mp4-->left-->-->images (includes all the frames of clip 1 left)-->-->annotations (includes .json file for annotation of clip 1 left)-->right-->-->images (includes all the frames of clip 1 right)-->-->annotations (includes .json file for annotation of clip 1 right)...clip_355There are three zip files:1. Budges355 (Random 10 clips) where you can randomly find 10 clips for previewing the dataset.2. Budges355 (clip 1-150).zip - clip 1 to clip 1503. Budges355 (clip 151-355).zip - clip 151 to clip 35
Budges355: Individual events of a bird's flying motion, along with video clips, annotated images and 3D data (355 clips).
No full textThe dataset consists of birds (Budgerigar) flying motion in a controlled environment. Trajectories of birds flying from one perch to another perch were recorded using video cameras. The dataset contains 355 clips of individual events of a bird's flying motion, along with annotated images and 3D data generated from the events.Only the bird in motion was annotated and 3D trajectories were calculated for all five parts of the bird that were annotated. The annotations were done manually using Computer Vision Annotation Tool (CVAT). For generating the 3D trajectories of the bird's flight motion, Matlab (MathWorks®) was used. The files for all five 3D trajectories are labelled as such; point3D_bird for the bird’s body, point3D_head for the head of the bird, point3D_tail for the tail of the bird, point_3D_left_wing and point_3D_right_wing for the left wing and right wing of the bird respectively. The annotation's file format is JSON, and the format of the annotation is Microsoft COCO . The 3D coordinates for the bird's trajectories are in .mat file format.Dataset folder structure:clip_1-->point3D_bird-->point3D_head-->point3D_tail-->point3D_left_wing-->point3D_right_wing-->left.mp4-->right.mp4-->left-->-->images (includes all the frames of clip 1 left)-->-->annotations (includes .json file for annotation of clip 1 left)-->right-->-->images (includes all the frames of clip 1 right)-->-->annotations (includes .json file for annotation of clip 1 right)...clip_355There are two zip files:1. Budges355 (Random 10 clips) where you can randomly find 10 clips for previewing the dataset.2. Budges355 (clip 1-355).zip where you can find full datase
An Analysis of effectiveness of Banglalink’s leave policy
No full textThis internship report is submitted in a partial fulfillment of the requirements for the degree of Bachelor of Business Administration, 2015.Cataloged from PDF version of Internship report.Includes bibliographical references (page 33).In this report I have discussed and define the major activities related with to the HR Operational Activities of Banglalink. Moreover, I tried to discuss the leave policy and implementation of leave policy in Banglalink. In this report I have chosen to write on leave policy as I have directly or indirectly work on this process. The key purpose of the report has been to identify overall condition of HR operational Activities and effectiveness of leave policy in Banglalink. By identifying the overall condition of HR operational Activities and leave policy of Banglalink I can relate my theoretical knowledge to practical implications. Banglalink is an organization which is very practitioner by its nature. It is practiced almost every HR related issue to become more competitive in the market. By making effective policies like leave policy Banglalink tries to make their employees more productive.
To conclude, there is no doubt that the world of work is rapidly changing. As part of an organization, HRM must be equipped to deal with the effects of the changing world of work. HR Operations is a significant part of HR activities at Banglalink. So to perform the operations successfully proper planning and formulation are mandatory. Beside this, now management realizes that effectiveness of their HR functions as well as HR Operations have a substantial impact to achieve expected organizational success. Finally HR operations are responsible for ensuring proper implementation of leave policy at the organizations. My recommendation is Banglalink needs to focus on some elements of the leave policy as the data indicates that the leave policy is working properly, but in the future to avoid any kind of objections some aspects of leave policies should be changed to ensure employee satisfaction.MD. Bashiur Rahman AbirB. Business Administratio
Investigations into source contributions and spatial distribution of airborne pollutants in an urban airshed by the development and application of advanced statistical models
No full textThis thesis developed novel statistical modelling methods to quantify airborne gaseous and particle concentrations and their source contribution in an urban area; in particular, developed a novel Land Use Regression (LUR) model for predicting the daily average concentration of airborne gaseous concentrations, a novel Bayesian modelling approach to quantify airborne ultrafine particle source contribution, and a geostatistical modelling approach for quantifying spatial concentrations of airborne pollutants. Besides, nighttime new particle formation mechanism and their physical properties have been investigated for the first time. The findings have applications in urban planning and management as well as in epidemiological studies
Characterisation and sources of ultrafine particles in an inner city urban area
No full textExposure to atmospheric ultrafine particles (UFPs, D<100 nm) has been an increasingly concern because of their potential impact one health. Motor vehicle emissions are considered as one of the major source of UFPin urban airshed, as the combustion of both petrol and diesel engine leads to emission of particles which are predominantly in this size range (Ban-Weiss et al, 2010; Morawska et al, 2008). New particle formations (NPFs) and major facilities such as airport or seaport has also been identified as major sources of UFPs in urban airshed (Cheung et al, 2010; González et al, 2011; Mazaheri et al, 2013). However, contribution of those urban sources to ambient UFP concentrations has not been comprehensively characterized
Modelling individuals’ longer-term preferences towards autonomous vehicles and their effects on vehicle ownership
No full textAutonomous vehicle (AV) promises to change the transportation landscape such as reducing traffic congestion and emissions. The success of this emerging technology largely depends on how individuals will adopt it. This thesis investigates individuals’ longer-term preferences for AVs, and their effects on vehicle ownership, specifically focusing on the preferences towards different levels of vehicle automation, AV ownership (AVO) and AV sharing (SAV), and vehicle transaction decisions in an AV future. A life history-oriented approach is adopted to examine the effects of historical experiences and changes over the life course, such as historical exposure to technology, the evolution of household characteristics, vehicle ownership history, historical measures of accessibility and built-environment on AV adoption. In addition, variables representing attitudinal factors, travel attributes, and socio-demographics are accommodated in the study. Data for the thesis comes from the retrospective survey conducted for the Okanagan region of British Columbia. In the case of preferences for different levels of vehicle automation, a random parameter rank-ordered logit model is developed to accommodate rank-ordered preferences for vehicular automation levels and capture unobserved heterogeneity. For preferences towards AVO and SAV, a joint bivariate ordered probit model is developed to address the error correlation between decisions. The study confirms that a significant error correlation exists, which indicates that the unobserved factors jointly affect the choice alternatives. A latent class random parameter logit modelling technique is utilized to capture heterogeneity while modelling vehicle transaction decisions in the AV future. The model results confirm the existence of historical deposition effects on individuals’ preferences towards AVs. For example, individuals with historical exposure to vehicle technology, such as availability of lane assist, parking assist, and autonomous emergency stop, have a higher likelihood of adopting higher levels of vehicle automation. Besides, they are more likely to add AV to the current vehicle fleet. Similarly, the AVO and SAV model results suggest that individuals with a higher number of smartphone ownership over the life course are more likely to adopt both. The model results also confirm the existence of heterogeneity. Overall, the findings of the study provide important behavioural insights and have significant policy implications that might be used for targeted marketing to promote AVs.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat
Estimate of main local sources to ambient ultrafine particle number concentrations in an urban area
No full text<b>Highlights</b>\ud
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• Novel statistical model apportions source of particle number concentration (PNC).\ud
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• Nucleated particles play a dominant role in increasing concentrations of urban PNC.\ud
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• Mid-day PNC was up to 32% higher than corresponding peak traffic hours.\ud
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• The majority of mid-day PNC were below 30 nm in diameter.\ud
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<b>Abstract</b>\ud
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Quantifying and apportioning the contribution of a range of sources to ultrafine particles (UFPs, D < 100 nm) is a challenge due to the complex nature of the urban environments. Although vehicular emissions have long been considered one of the major sources of ultrafine particles in urban areas, the contribution of other major urban sources is not yet fully understood. This paper aims to determine and quantify the contribution of local ground traffic, nucleated particle (NP) formation and distant non-traffic (e.g. airport, oil refineries, and seaport) sources to the total ambient particle number concentration (PNC) in a busy, inner-city area in Brisbane, Australia using Bayesian statistical modelling and other exploratory tools. The Bayesian model was trained on the PNC data on days where NP formations were known to have not occurred, hourly traffic counts, solar radiation data, and smooth daily trend. The model was applied to apportion and quantify the contribution of NP formations and local traffic and non-traffic sources to UFPs. The data analysis incorporated long-term measured time-series of total PNC (D ≥ 6 nm), particle number size distributions (PSD, D = 8 to 400 nm), PM<sub>2.5</sub>, PM<sub>10</sub>, NO<sub>x</sub>, CO, meteorological parameters and traffic counts at a stationary monitoring site. The developed Bayesian model showed reliable predictive performances in quantifying the contribution of NP formation events to UFPs (up to 4 × 10<SUP>4</SUP> particles cm− <SUP>3</SUP>), with a significant day to day variability. The model identified potential NP formation and no-formations days based on PNC data and quantified the sources contribution to UFPs. Exploratory statistical analyses show that total mean PNC during the middle of the day was up to 32% higher than during peak morning and evening traffic periods, which were associated with NP formation events. The majority of UFPs measured during the peak traffic and NP formation periods were between 30–100 nm and smaller than 30 nm, respectively. To date, this is the first application of Bayesian model to apportion different sources contribution to UFPs, and therefore the importance of this study is not only in its modelling outcomes but in demonstrating the applicability and advantages of this statistical approach to air pollution studies
Detection of Neural Activity for Cerebrovascular Disease using MRI and EEG
No full textThis thesis is submitted to the Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Electrical and Electronic Engineering, August 2016.Cataloged from PDF Version of Thesis.Includes bibliographical references (pages 60-65).(CVD) such as stroke is the leading cause of long-term
disability and third most common reason of death in the world. Due to the neuronal
deficiency occurred by impaired blood flow to the brain, half of the stroke patients survive a
severe cognitive impairment such as impaired speech, numbness in limbs, immobilization of
limbs, visual impairment and many other observable symptoms. In the recent days, due to the
gradual increase of hypertensive and diabetic patient, the risk of developing stroke is growing
tremendously. The evaluation of actual neuronal status in stroke patient is thus very important
to determine the indication of neurosurgical treatment.
To diagnosis the type, source and location of stroke in the brain, generally, cerebral
blood flow (CBF) test, neuroimaging, and electrical activity test are being used. Among all of
these tests, electroencephalogram (EEG) is a useful tool for acute stroke detection and
monitoring affected tissue owing to its relatively cheap and completely hazardless for
quantitative and statistical analysis. Although a large number of studies have been performed
on the ischemic stroke using EEG, no investigation has been carried out on the interplay
between the infarcted cerebral hemisphere with other healthy part. Moreover, earlier studies
are also limited only age-related changes in EEG activity or memory performance compared
younger people between the ages of about 20 to 30 years with older participants between the
ages of about 60 to 80 years. There is also no study at all for the child below the age of 20
years to show the change and compare the neuronal deterioration of the infarcted part of
brain.
This work focused on the correlation between the extent of infarction and the clinical
effect to monitor the degree of hypo-activity of the affected part through the EEG analysis.
An indication for the assessment of neuronal activity and the degree of severity of stroke
patient has been proposed. A parallel study has also been carried out on healthy volunteers of
under fifteen years to find the comparison of neural activity between different age group.
From the analysis it is found that delta activities of EEG are highly unique of brain
pathophysiology, and preservation of alpha and beta frequencies following stroke is evidently
indicative of neuronal survival and a good prognosis. In order to assess brain
vi
pathophysiology in supratentorial brain lesion patients the delta/alpha ratio (DAR) and deltaplus-
theta to alpha-plus-beta ratio (DTABR) have also been used. It is observed that the DAR
and DTABR values of the left cerebral hemisphere of the patient are much higher than the
right cerebral hemisphere. It is also higher in both cerebral hemispheres than the control. A
threshold value of ~3.7 for DAR and ͂ 3.5 for DTABR has been obtained. It is observed that
DAR and DTABR of left hemisphere of patient are greater than 3.7and 3.5 respectively for
all the measurements indicating severe ischemic stroke in the fronto-temporal region of left
hemisphere. The findings have been further confirmed by a neuroimaging technique such as
magnetic resonance imaging (MRI).This study also show that DAR and DTABR of the
healthier child is~1.It is also found that delta and DAR indices of the old age are two times
more than the child indicating the diminishing of neuronal activity of old age is half of the
child. These results could be important for stroke diagnosis, prognosis, re-habitation
strategies, and proper neurosurgical treatment.G. M. Mahmudur RahmanMaster of Science in Biomedical Engineerin
PCDD and PCDF concentrations in a traffic tunnel environment
No full textIn an effort to understand the fundamental aspects of air quality in traffic tunnel environments, field campaigns were conducted to measure polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs) and other important pollutants within two traffic tunnels in Nam San (NS) and Hong Ji (HJ) in Korea in 2009 and 2010. The mean concentrations of ∑PCDD/Fs (in fg/m(3)) at the two tunnel sites were 1270 (± 880) and 1200 (± 810), respectively. These values were moderately lower than those measured at a non-tunnel urban background site (1350 (± 780) fg/m(3))--selected as a reference in this study. In addition, seasonal patterns of dioxin concentrations were clearly evident at the traffic tunnels like the urban reference site, showing higher levels during the winter (and spring) than the summer (and fall). The observed seasonal variations were driven by changes in the concentrations of ∑PCDF congeners, while ∑PCDD concentrations showed little seasonality. The results of our study suggest that there is no significant difference in source characteristics between the two investigated tunnel sites and urban location, although the role of gasoline and diesel fueled vehicles are considered as the major source in determining the PCDDs and PCDF levels in a tunnel environment. However, given the relative increase in other important ambient pollutant (e.g. PM10) concentrations over ∑PCDD/Fs in tunnel air (compared to urban background air), the balance of sources in tunnels is clearly different from those in urban air overall
Fair and Interpretable Pseudo Value-Based Deep Learning Models for Federated Survival Analysis
No full textSurvival analysis, or time-to-event analysis, aims to predict the time until an event occurs, providing valuable insights into the temporal aspects of various phenomena, such as disease progression. This dissertation addresses the growing need for fair and interpretable machine learning models in survival analysis within the healthcare domain, alongside the necessity for privacy-preserving distributed training methods to enhance generalization and data utilization. In particular, we focus on the following problems: 1) how to efficiently handle censoring, i.e., incomplete survival outcomes; 2) how to make unbiased estimations of the survival analysis quantities in the presence of competing risks and multi-state transitions; 3) how to enhance the interpretability and fairness of survival analysis models; and 4) how to enable privacy-preserving distributed training of survival models to address limited data utilization and lack of generalization due to strict privacy laws such as GDPR and HIPAA. In this dissertation, we provide the following solutions to these problems: 1) To address the censoring challenge, we utilize theoretically consistent pseudo-values from statistical paradigms, simplifying the problem to a regression analysis task. 2) To provide unbiased survival predictions for complex problems like competing risk and multi-state survival analysis, we introduce novel pseudo-value-based deep learning models, DeepPseudo and msPseudo. 3) To enhance interpretability, we propose a pseudo-value-based neural additive model, PseudoNAM, which achieves performance comparable to deep models while offering global and feature-level interpretations. Additionally, we propose the Fair DeepPseudo and Fair PseudoNAM models, incorporating new fairness constraints into a novel pseudo-value-based objective function to ensure equitable and trustworthy survival predictions in the presence of demographic and censoring bias. 4) To enable multi-institution collaboration while preserving data privacy, we introduce federated learning frameworks, FedPseudo for survival analysis and Fedora for competing risk analysis. Furthermore, we introduce a random forest-based federated survival analysis (FSA) framework, FedPRF, to address the communication burden in model exchange and a pioneering fair FSA framework, FairFSA, which integrates fairness through distributionally robust optimization to ensure equitable global survival predictions across clients. We evaluated our approaches on both centralized and decentralized survival datasets, achieving significant improvements over existing methods. These advancements are expected to facilitate better decision-making, optimize healthcare resource allocation, reduce costs, improve treatment interventions and therapy strategies, enhance patient care, and ultimately contribute to improved survival outcomes
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