Performance Analysis Of Automatic Dependent Surveillance-Broadcast (ADS-B) And Breakdown Of Anomalies

This thesis work analyzes the performance of Automatic Dependent Surveillance-Broadcast (ADS-B) data received from Grand Forks International Airport, detects anomalies in the data and quantifies the associated potential risk. This work also assesses severity associated anomalous data in Detect and Avoid (DAA) for Unmanned Aircraft System (UAS). The received data were raw and archived in GDL-90 format. A python module is developed to parse the raw data into readable data in a .csv file. The anomaly detection algorithm is based on Federal Aviation Administration\u27s (FAA) ADS-B performance assessment report. An extensive study is carried out on two main types of anomalies, namely dropouts and altitude deviations. A dropout is considered when the update rate exceeds three seconds. Dropouts are of different durations and have a different level of risk depending on how much time ADS-B is unavailable as the surveillance system. Altitude deviation refers to the deviation between barometric and geometric altitude. Deviation ranges from 25 feet to 600 feet have been observed. As of now, barometric altitude has been used for separation and surveillance while geometric altitude can be used in cases where barometric altitude is not available. Many UAS might not have both sensors installed on board due to size and weight constrains. There might be a chance of misinterpretation of vertical separation specially while flying in National Airspace (NAS) if the ownship UAS and intruder manned aircraft use two different altitude sources for separation standard. The characteristics and agreement between two different altitudes is investigated with a regression based approach. Multiple risk matrices are established based on the severity of the DAA well clear. ADS-B is called the Backbone of FAA Next Generation Air Transportation System, NextGen. NextGen is the series of inter-linked programs, systems, and policies that implement advanced technologies and capabilities. ADS-B utilizes the Satellite based Global Positioning System (GPS) technology to provide the pilot and the Air Traffic Control (ATC) with more information which enables an efficient navigation of aircraft in increasingly congested airspace. FAA mandated all aircraft, both manned and unmanned, be equipped with ADS-B out by the year 2020 to fly within most controlled airspace. As a fundamental component of NextGen it is crucial to understand the behavior and potential risk with ADS-B Systems

UAT ADS-B Data Anomalies and the Effect of Flight Parameters on Dropout Occurrences

An analysis of the performance of automatic dependent surveillance-broadcast (ADS-B) data received from the Grand Forks, North Dakota International Airport was carried out in this study. The purpose was to understand the vulnerabilities of the universal access transceiver (UAT) ADS-B system and recognize the effects on present and future air traffic control (ATC) operation. The Federal Aviation Administration (FAA) mandated all the general aviation aircraft to be equipped with ADS-B. The aircraft flying within United States and below the transition altitude (18,000 feet) are more likely to install a UAT ADS-B. At present, unmanned aircraft systems (UAS) and autonomous air traffic control (ATC) towers are being integrated into the aviation industry and UAT ADS-B is a basic sensor for both class 1 and class 2 detect-and-avoid (DAA) systems. As a fundamental component of future surveillance systems, the anomalies and vulnerabilities of the ADS-B system need to be identified to enable a fully-utilized airspace with enhanced situational awareness. The data received was archived in GDL-90 format, which was parsed into readable data. The anomaly detection of ADS-B messages was based on the FAA ADS-B performance assessment report. The data investigation revealed ADS-B message suffered from different anomalies including dropout, missing payload, data jump, low confidence data, and altitude discrepancy. Among those studied, the most severe was dropout and 32.49% of messages suffered from this anomaly. Dropout is an incident where ADS-B failed to update within a specified rate. Considering the potential danger being imposed, an in-depth analysis was carried out to characterize message dropout. Three flight parameters were selected to investigate their effect on dropout. Statistical analysis was carried out and the Friedman Statistical Test identified that altitude affected dropout more than any other flight parameter

Biofilms from agar obtained from an agarophyte of Karachi coast

This study manifests the utilization of red seaweed Gelidium pusillum for the production of biofilms. In this study Gelidium pusillum was collected from Karachi coast to extract agar for making agar biofilm or bioplastic. Universal Testing Machine was used to calculate tensile property of the films. It was also observed that the addition of plasticizers along with agar enhance the strength and elongation of the agar biofilms

A Study on Workload Assessment and Usability of Wind-Aware User Interface for Small Unmanned Aircraft System Remote Operations

This study evaluates pilots' cognitive workload and situational awareness during remote small unmanned aircraft system operations in different wind conditions. To complement the urban air mobility concept that envisions safe, sustainable, and accessible air transportation, we conduct multiple experiments in a realistic wind-aware simulator-user interface pipeline. Experiments are performed with basic and wind-aware displays in several wind conditions to assess how complex wind fields impact pilots' cognitive resources. Post-hoc analysis reveals that providing pilots with real-time wind information improves situational awareness while decreasing cognitive workload

Development of wind-aware piloting interfaces and dynamic quadrotor simulator with spatiotemporally varying wind

With increasing adoption of low-altitude Unmanned Aerial Vehicles (UAVs) in urban environments, alongside present-day research into low-altitude Urban Air Mobility (UAM) vehicles, there exists a need for high quality environmental feedback for pilots operating in dense urban areas. Currently, pilots have minimal awareness as to the wind conditions their aircraft is experiencing beyond natural perception and their previous weather observations. Our work is to develop wind-aware piloting interfaces that modify existing, popular software to provide this feedback in a natural and helpful manner. Furthermore, the development of simulation environments to enhance these interfaces and gather pilot feedback is desirable. Current dynamic quadrotor simulators have sparse support for wind velocities varying with time and space. Our work has resulted in the development of modifications to Microsoft's Airsim simulator to allow support for spatiotemporally varying winds pre-generated by high-fidelity computational fluid dynamics software. By developing improved interfaces and simulation capabilities, this research has the potential to spur further growth in the development of urban flight

Comparison of sampling adequacy between OPD based pipelle biopsy and in- patient conventional D&C, presented with abnormal uterine bleeding

Objective: To determine agreement on adequacy of sample by pipelle biopsy and conventional dilatation and curettage in patients with abnormal uterine bleeding. Study design: Cross sectional studySetting and Duration of Study: Department of Obstetrics and Gyneacology, Islamic International Medical College Trust, Railway Hospital Rawalpindi. Study was carried out over a period of six months (11-07-2012 to 14-01-2013). Patients and Methods: 84 patients presented with abnormal uterine bleeding age 45 years and older, attended Gynecology department of Railway Hospital Rawalpindi. Who qualified the inclusion criteria were enrolled in this study by non-probability consecutive sampling technique. The diagnostic intervention for endometrial sampling was by pipelle device and by conventional D&C. Both procedures were performed in the OT at the same time.First the pipelle sample was taken and was labeled as “A” then conventional D&C was performed and was labeled as “B”. Both samples were sent to the pathologist, who was blinded as to the method of sample collection for histopathology assessment. Adequacy of the sample was assessed as per operational definition. A data base was made in SPSS version 17. Kappa statistics was applied to assess the agreement. Results: Out of 84 patients, 80 (98.8%) of the patients had adequate sample with Pipelle Biopsy as compared to conventional curettage and dilatation (D & C). We therefore recommend the use of pipelle biopsy as a first line tool for endometrial assessment for our setups instead of D&C. Conclusion: Our study concluded that the Pipelle biopsy is a useful and convenient method to the patients and physicians as ompared to D&C performed in the operating theatre. It is useful in obese and high-risk patients with minimum chances of perforation of uterus due to its soft flexible tip

Identifying Optimal Parameters And Their Impact For Predicting Credit Card Defaulters Using Machine-Learning Algorithms

Data mining and Machine learning are the emerging technologies that are rapidly spreading in every field of life due to their beneficial aspects. The financial sector also makes use of these technologies. Many research studies regarding banking data analysis have been performed using machine learning techniques. These research studies also have many Problems as the main focus of these studies was to achieve high accuracy and some of them only perform comparative analysis of different classifier's performance. Another major drawback of these studies was that they do not identify any optimal parameters and their impact. In this research, we have identified optimal parameters. These parameters are valuable for performing the credit scoring process and might also be used to predict credit card defaulters. We also find their impact on the results. We have used feature selection and classification techniques to identify optimal parameters and their impact on credit card defaulters identification. We have introduced three classifiers which are Kstar, SMO and Multilayer perceptron and repeat the process of classification and feature selection for every classifier. First, we apply feature selection techniques to our dataset with each classifier to find out possible optimal parameters and In the next phase, we use classification to find the impact of possible optimal parameters and proved our findings. In each round of classification, we have used different parameters available in the dataset every time we include and exclude some parameters and noted the results of each run of classification with each classifier and in this way, we identify the optimal parameters and their impact on the results Whereas we also analyze the performance of classifiers. To perform this research study, we use the “credit card defaults” dataset which we obtained from UCI Machine learning online repository. We use two feature selection techniques that include ranker approach and evolutionary search method and after that, we also apply classification techniques on the dataset. This research can help to reduce the complexities of the credit scoring process. Through this study, we identify up to six optimal parameters and also find their impact on the performance of classifiers. Further We also identify that multilayer perceptron was the best performing classifier out of three. This research work can also be extended to other fields in the future where we use this mechanism to find out optimal parameters and their impact can help us to predict the  results.  &nbsp

Utilization pattern of drugs among patients attending geriatric outpatient department in a tertiary care hospital in Kashmir

Background: Quality and safety of prescribing in older people remains a global healthcare concern and inappropriate prescribing is a major public health issue because of its direct association with morbidity, mortality and wastage of health resources in this age group. Very limited data is available on the drug utilization pattern in geriatric population and the present study was carried out to see the prescription pattern in geriatric population in this part of the world.Methods: The present study was conducted by the department of pharmacology in outpatient department of geriatrics in a tertiary care centre to look into the prescription pattern among geriatric age group.Results: A total of 237 prescriptions were collected, out of which 108 (45.56%) were males and 129 (54.44%) were females. The majority of the patients were in the age group of 60-69 years (n=141, 59.5%). The most commonly found comorbidity was hypertension (63.29%) and antihypertensive agents (74.68%) were the most frequently prescribed class of drugs. Calcium (37.57%), budesonide (32.91%), thyroxine (27.84%) and pantoprazole (25.31%) were the most common individual drugs prescribed.Conclusions: Like other studies on geriatric population polypharmacy was also observed in the present study and periodic therapeutic audit is essential to ensure rational medicine use

Atypical polypoid adenomyoma with extensive morular metaplasia - Case report

Atypical polypoid adenomyoma (APA) is a rare and benign endometrial polypoid lesion. APA was found in a 38-year-old woman who presented with excessive vaginal bleeding. Histopathological examination of the polyp was consistent with “APA” with extensive morular metaplasia. Immunohistochemical marker CD10 was done to establish the diagnosis of morular metaplasia. Morular metaplasia is dissimilar to squamous metaplasia. It is sometimes misreported as adenosquamous carcinoma. This case illustrates the significance of morular metaplasia as a differential diagnosis