Terrain Classification using Transfer Learning on Hyperspectral Images: A Comparative study

A Hyperspectral image contains much more number of channels as compared to a RGB image, hence containing more information about entities within the image. The convolutional neural network (CNN) and the Multi-Layer Perceptron (MLP) have been proven to be an effective method of image classification. However, they suffer from the issues of long training time and requirement of large amounts of the labeled data, to achieve the expected outcome. These issues become more complex while dealing with hyperspectral images. To decrease the training time and reduce the dependence on large labeled dataset, we propose using the method of transfer learning. The hyperspectral dataset is preprocessed to a lower dimension using PCA, then deep learning models are applied to it for the purpose of classification. The features learned by this model are then used by the transfer learning model to solve a new classification problem on an unseen dataset. A detailed comparison of CNN and multiple MLP architectural models is performed, to determine an optimum architecture that suits best the objective. The results show that the scaling of layers not always leads to increase in accuracy but often leads to overfitting, and also an increase in the training time.The training time is reduced to greater extent by applying the transfer learning approach rather than just approaching the problem by directly training a new model on large datasets, without much affecting the accuracy

Physics-based Computational Modeling of Human Skin using Machine Learning for Physiological Parameter Estimation

The skin in the largest organ in the human body and often subject to the greatest exposure to outside elements throughout one's lifetime. Current data by the World Health Organization suggests that more than 10 people die each hour worldwide due to skin related conditions. Many of these conditions include cancers, such as melanoma, which are growths that originate in the epidermis and if left untreated can spread throughout the body, reducing the chances of survival to less than 1%. If these tumors are detected during the early stages, the chances of survival are over 99%. Unfortunately, there only exist coarse diagnostic metrics, such as evaluations of color, texture, boundaries, and asymmetry, which are not sufficient for early detection of these cancers. In order to develop a screening technology, we require a non-invasive means of measuring the various biological components that make up the layers of the skin, i.e., melanosome concentration, collagen concentration and blood oxygen saturation, amongst others. The temporal analysis of changes in these components can serve as a critical tool in diagnosing the progression of these malignant cancers and in understanding the pathophysiology of cancerous tumors. Quantitative knowledge of these parameters can also be useful in applications such as wound assessment, drug delivery, and point-of-care diagnostics, amongst others. From a systems level perspective, we seek to develop a non-invasive, non-ionizing, and rapid technology that exposes an afflicted area on the skin to light, measures the amount of light that is reflected, transmitted, and/or absorbed, and using this information infers the concentration of each of the materials that make up the skin. Naturally, this inference would require a priori knowledge about the relationship between reflected light and concentration of biological materials. This is the goal of this thesis, the development of a computational model that relates the concentration of biological skin materials to a light reflectance measurement from the surface of the skin. This light reflectance measurement is obtained using hyperspectral imaging (HSI) or reflectance spectrometry. HSI allows for imaging well beyond the visible (VIS) region of the electromagnetic spectrum; past the near-infrared (NIR) and through the short wave infrared (SWIR). HSI allows us to obtain a reflectance measurement for each wavelength (band) spanning from 400 nm (VIS) to 1800 nm (SWIR). Imaging past the VIS can capture characteristic absorptions and other physiological makers typically exhibited by skin components outside the VIS region. In this thesis, we developed a method to estimate human skin parameters, such as melanosome concentration, collagen concentration, oxygen saturation, skin thickness, and blood volume, using hyperspectral radiometric measurements (signatures) obtained from in vivo skin. We developed a computational model based on Kubelka-Munk theory and the Fresnel equations. This model generates a forward mapping (a transformation) between skin parameters and a corresponding HSI reflectance spectra. This is a complex model, and not invertible. Therefore, we used machine learning based regression to generate the inverse mapping (the inverse transformation) between skin parameters and hyperspectral signatures. This yields a transformation (i.e., an inverse transformation) between the skin parameter vector space and the HSI signature vector space. Simply put, using a reflectance signature from a patch of skin, we can estimate the concentration of the biological materials that make up that patch of skin. Another challenge in the field has been that of obtaining ground truth. Methods to estimate skin parameters have been developed by several other studies, but no group has yet to compare their method to actual ground truth. Therefore, there is no direct way to assess the accuracy of the parameter estimation method. A major reason for this has to do with the practical difficulty associated with obtaining this ground truth; it involves biopsies and further biochemical analysis by a pathologist. For some parameters (e.g., melanosome concentration) it is unclear how one would proceed with determining the true concentration. For one skin parameter of dermatological interest, epidermal and dermal thickness, we developed a methodology based on Ultrasound imaging (US) to obtain a proxy ground truth against which to benchmark our machine learning method. For the first time, this provided a direct validation of the performance of the estimation methodology. We tested our methods using synthetic and in vivo skin signatures obtained in the VIS through the SWIR domains from 24 patients of both genders and Caucasian, Asian, and African American ethnicities acquired under IRB approval at the Johns Hopkins Hospital. Performance validation showed promising results: good agreement with the ground truth (average absolute error of 0.05+/-10e-3 percent) and well-established physiological precepts, as well as strong agreement with the gold standard obtained from Ultrasound imaging (mean error of 0.09+/-0.05 mm). Our early results suggested that our methods have potential use in the characterization of skin abnormalities and in non-invasive pre-screening of malignant skin cancers. Thesis Committee: Professor Philippe M. Burlina Professor Jeffrey H. Siewerdsen Professor Jon Meyerl

NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains

In the recent years, Distributed Denial of Service (DDoS) attacks on Internet of Things (IoT) devices have become one of the prime concerns to Internet users around the world. One of the sources of the attacks on IoT ecosystems are botnets. Intruders force IoT devices to become unavailable for its legitimate users by sending large number of messages within a short interval. This study proposes NFDLM, a lightweight and optimised Artificial Neural Network (ANN) based Distributed Denial of Services (DDoS) attack detection framework with mutual correlation as feature selection method which produces a superior result when compared with Long Short Term Memory (LSTM) and simple ANN. Overall, the detection performance achieves approximately 99\% accuracy for the detection of attacks from botnets. In this work, we have designed and compared four different models where two are based on ANN and the other two are based on LSTM to detect the attack types of DDoS.Comment: 7 page

Hypoxia-inducible factor (HIF): fuel for cancer progression

Hypoxia is an integral part of the tumor microenvironment, caused primarily due to rapidly multiplying tumor cells and a lack of proper blood supply. Among the major hypoxic pathways, HIF-1 transcription factor activation is one of the widely investigated pathways in the hypoxic tumor microenvironment (TME). HIF-1 is known to activate several adaptive reactions in response to oxygen deficiency in tumor cells. HIF-1 has two subunits, HIF-1β (constitutive) and HIF-1α (inducible). The HIF-1α expression is largely regulated via various cytokines (through PI3K-ACT-mTOR signals), which involves the cascading of several growth factors and oncogenic cascades. These events lead to the loss of cellular tumor suppressant activity through changes in the level of oxygen via oxygen-dependent and oxygenindependent pathways. The significant and crucial role of HIF in cancer progression and its underlying mechanisms have gained much attention lately among the translational researchers in the fields of cancer and biological sciences, which have enabled them to correlate these mechanisms with various other disease modalities. In the present review, we have summarized the key findings related to the role of HIF in the progression of tumors

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.This study was financially supported by IMDI Grant 104002002 (Brainbox) from ZonMw, the Netherlands Organisation for Health Research and Development, within kind sponsoring by Philips, the University Medical Center Utrecht, and Eindhoven University of Technology. The authors would like to acknowledge the following members of the Utrecht Vascular Cognitive Impairment Study Group who were not included as coauthors of this paper but were involved in the recruitment of study participants and MRI acquisition at the UMC Utrecht (in alphabetical order by department): E. van den Berg, M. Brundel, S. Heringa, and L. J. Kappelle of the Department of Neurology, P. R. Luijten and W. P. Th. M. Mali of the Department of Radiology, and A. Algra and G. E. H. M. Rutten of the Julius Center for Health Sciences and Primary Care. The research of Geert Jan Biessels and the VCI group was financially supported by VIDI Grant 91711384 from ZonMw and by Grant 2010T073 of the Netherlands Heart Foundation. The research of Jeroen de Bresser is financially supported by a research talent fellowship of the University Medical Center Utrecht (Netherlands). The research of Annegreet van Opbroek and Marleen de Bruijne is financially supported by a research grant from NWO (the Netherlands Organisation for Scientific Research). The authors would like to acknowledge MeVis Medical Solutions AG (Bremen, Germany) for providing MeVisLab. Duygu Sarikaya and Liang Zhao acknowledge their Advisor Professor Jason Corso for his guidance. Duygu Sarikaya is supported by NIH 1 R21CA160825-01 and Liang Zhao is partially supported by the China Scholarship Council (CSC).info:eu-repo/semantics/publishedVersio

Bronchiectasis in India:results from the European Multicentre Bronchiectasis Audit and Research Collaboration (EMBARC) and Respiratory Research Network of India Registry

BACKGROUND: Bronchiectasis is a common but neglected chronic lung disease. Most epidemiological data are limited to cohorts from Europe and the USA, with few data from low-income and middle-income countries. We therefore aimed to describe the characteristics, severity of disease, microbiology, and treatment of patients with bronchiectasis in India. METHODS: The Indian bronchiectasis registry is a multicentre, prospective, observational cohort study. Adult patients ( 6518 years) with CT-confirmed bronchiectasis were enrolled from 31 centres across India. Patients with bronchiectasis due to cystic fibrosis or traction bronchiectasis associated with another respiratory disorder were excluded. Data were collected at baseline (recruitment) with follow-up visits taking place once per year. Comprehensive clinical data were collected through the European Multicentre Bronchiectasis Audit and Research Collaboration registry platform. Underlying aetiology of bronchiectasis, as well as treatment and risk factors for bronchiectasis were analysed in the Indian bronchiectasis registry. Comparisons of demographics were made with published European and US registries, and quality of care was benchmarked against the 2017 European Respiratory Society guidelines. FINDINGS: From June 1, 2015, to Sept 1, 2017, 2195 patients were enrolled. Marked differences were observed between India, Europe, and the USA. Patients in India were younger (median age 56 years [IQR 41-66] vs the European and US registries; p<0\ub70001]) and more likely to be men (1249 [56\ub79%] of 2195). Previous tuberculosis (780 [35\ub75%] of 2195) was the most frequent underlying cause of bronchiectasis and Pseudomonas aeruginosa was the most common organism in sputum culture (301 [13\ub77%]) in India. Risk factors for exacerbations included being of the male sex (adjusted incidence rate ratio 1\ub717, 95% CI 1\ub703-1\ub732; p=0\ub7015), P aeruginosa infection (1\ub729, 1\ub710-1\ub750; p=0\ub7001), a history of pulmonary tuberculosis (1\ub720, 1\ub707-1\ub734; p=0\ub7002), modified Medical Research Council Dyspnoea score (1\ub732, 1\ub725-1\ub739; p<0\ub70001), daily sputum production (1\ub716, 1\ub703-1\ub730; p=0\ub7013), and radiological severity of disease (1\ub703, 1\ub701-1\ub704; p<0\ub70001). Low adherence to guideline-recommended care was observed; only 388 patients were tested for allergic bronchopulmonary aspergillosis and 82 patients had been tested for immunoglobulins. INTERPRETATION: Patients with bronchiectasis in India have more severe disease and have distinct characteristics from those reported in other countries. This study provides a benchmark to improve quality of care for patients with bronchiectasis in India. FUNDING: EU/European Federation of Pharmaceutical Industries and Associations Innovative Medicines Initiative inhaled Antibiotics in Bronchiectasis and Cystic Fibrosis Consortium, European Respiratory Society, and the British Lung Foundation

Modellering av spjäll för fordon

A hydraulic damper plays an important role in tuning the handling and comfort characteristicsof a vehicle. Tuning and selecting a damper based on subjective evaluation, by considering theopinions of various users, would be an inefficient method since the comfort requirements of usersvary a lot. Instead, mathematical models of damper and simulation of these models in variousoperating conditions are preferred to standardize the tuning procedure, quantify the comfortlevels and reduce cost of testing. This would require a model, which is good enough to capture thebehaviour of damper in various operating and extreme conditions.The Force-Velocity (FV) curve is one of the most widely used model of a damper. This curve isimplemented either as an equation or as a look-up table. It is a plot between the maximum forceat each peak velocity point. There are certain dynamic phenomena like hysteresis and dependencyon the displacement of damper, which cannot be captured with a FV curve model, but are requiredfor better understanding of the vehicle behaviour.This thesis was conducted in cooperation with Volvo Cars with an aim to improve the existingdamper model which is a Force-Velocity curve. This work focuses on developing a damper model,which is complex enough to capture the phenomena discussed above and simple enough to beimplemented in real time simulations. Also, the thesis aims to establish a standard method toparameterise the damper model and generate the Force-Velocity curve from the tests performedon the damper test rig. A test matrix which includes the standard tests for parameterising andthe extreme test cases for the validation of the developed model will be developed. The final focusis to implement the damper model in a multi body simulation (MBS) software.The master thesis starts with an introduction, where the background for the project is described and then the thesis goals are set. It is followed by a literature review in which fewadvanced damper models are discussed in brief. Then, a step-by-step process of developing thedamper model is discussed along with few more possible options. Later, the construction of a testmatrix is discussed in detail followed by the parameter identification process. Next, the validationof the developed damper model is discussed using the test data from Volvo Hällered ProvingGround (HPG). After validation, implementation of the model in VI CarRealTime and Adams Caralong with the results are presented. Finally the thesis is concluded and the recommendations forfuture work are made on further improving the model.En hydraulisk stötdämpare spelar en viktig roll för att fordonets hantering och komfort. Attjustera och välja en stötdämpare baserat på subjektiv utvärdering, genom att beakta olika användares åsikter, skulle vara en ineffektiv metod eftersom användarnas komfortkrav varierarmycket. Istället föredras matematiska modeller av stötdämpare och simulering av dessa modellerunder olika driftsförhållanden för att standardisera inställningsförfarandet, kvantifiera komfortnivåerna och minska testkostnaden. Detta skulle kräva en modell som är tillräckligt bra för attfånga upp stötdämparens beteende under olika drifts- och extrema förhållanden.Force-Velocity (FV) -kurvan är en av de mest använda stötdämparmodellerna. Denna kurvaimplementeras antingen som en ekvation eller som en uppslagstabell. Det är ett diagram somredovisar den maximala kraften vid varje maxhastighetspunkt. Det finns vissa dynamiskafenomen som hysteres och beroende av stötdämparens förskjutning, som inte kan fångas med enFV-kurvmodell, men som krävs för att bättre förstå fordonets beteende.Denna avhandling genomfördes i samarbete med Volvo Cars i syfte att förbättra den befintligastötdämparmodellen som är en Force-Velocity-kurva. Detta arbete fokuserar på att utveckla enstötdämparmodell, som är tillräckligt komplex för att fånga upp de fenomen som diskuteratsovan och tillräckligt enkel för att implementeras i realtidssimuleringar. Avhandlingen syftarockså till att upprätta en standardmetod för att parametrisera spjällmodellen och generera ForceVelocity-kurvan från de test som utförts på stötdämpartestriggen. En testmatris som innehållerstandardtest för parametrisering och extrema testfall för validering av den utvecklade modellenkommer att utvecklas. Det sista fokuset är att implementera stötdämparmodellen i en multi-bodysimulation (MBS) programvara.Examensarbetet inleds med en introduktion, där bakgrunden för projektet beskrivs ochdärefter definieras målen med arbetet. Det följs av en litteraturöversikt där några avanceradestötdämparmodeller diskuteras i korthet. Därefter diskuteras en steg-för-steg-process för attutveckla stötdämparmodeller tillsammans med några fler möjliga alternativ. Senare diskuteraskonstruktionen av en testmatris i detalj följt av parameteridentifieringsprocessen. Därefterdiskuteras valideringen av den utvecklade stötdämparmodellen med hjälp av testdata från VolvoHällered Proving Ground (HPG). Efter validering presenteras implementeringen av modellen iVI CarRealTime och Adams Car tillsammans med resultaten. Slutligen avslutas rapporten medslutsatser från arbetet och rekommendationer för framtida arbete görs för att ytterligare förbättramodellen

Modellering av spjäll för fordon

A hydraulic damper plays an important role in tuning the handling and comfort characteristicsof a vehicle. Tuning and selecting a damper based on subjective evaluation, by considering theopinions of various users, would be an inefficient method since the comfort requirements of usersvary a lot. Instead, mathematical models of damper and simulation of these models in variousoperating conditions are preferred to standardize the tuning procedure, quantify the comfortlevels and reduce cost of testing. This would require a model, which is good enough to capture thebehaviour of damper in various operating and extreme conditions.The Force-Velocity (FV) curve is one of the most widely used model of a damper. This curve isimplemented either as an equation or as a look-up table. It is a plot between the maximum forceat each peak velocity point. There are certain dynamic phenomena like hysteresis and dependencyon the displacement of damper, which cannot be captured with a FV curve model, but are requiredfor better understanding of the vehicle behaviour.This thesis was conducted in cooperation with Volvo Cars with an aim to improve the existingdamper model which is a Force-Velocity curve. This work focuses on developing a damper model,which is complex enough to capture the phenomena discussed above and simple enough to beimplemented in real time simulations. Also, the thesis aims to establish a standard method toparameterise the damper model and generate the Force-Velocity curve from the tests performedon the damper test rig. A test matrix which includes the standard tests for parameterising andthe extreme test cases for the validation of the developed model will be developed. The final focusis to implement the damper model in a multi body simulation (MBS) software.The master thesis starts with an introduction, where the background for the project is described and then the thesis goals are set. It is followed by a literature review in which fewadvanced damper models are discussed in brief. Then, a step-by-step process of developing thedamper model is discussed along with few more possible options. Later, the construction of a testmatrix is discussed in detail followed by the parameter identification process. Next, the validationof the developed damper model is discussed using the test data from Volvo Hällered ProvingGround (HPG). After validation, implementation of the model in VI CarRealTime and Adams Caralong with the results are presented. Finally the thesis is concluded and the recommendations forfuture work are made on further improving the model.En hydraulisk stötdämpare spelar en viktig roll för att fordonets hantering och komfort. Attjustera och välja en stötdämpare baserat på subjektiv utvärdering, genom att beakta olika användares åsikter, skulle vara en ineffektiv metod eftersom användarnas komfortkrav varierarmycket. Istället föredras matematiska modeller av stötdämpare och simulering av dessa modellerunder olika driftsförhållanden för att standardisera inställningsförfarandet, kvantifiera komfortnivåerna och minska testkostnaden. Detta skulle kräva en modell som är tillräckligt bra för attfånga upp stötdämparens beteende under olika drifts- och extrema förhållanden.Force-Velocity (FV) -kurvan är en av de mest använda stötdämparmodellerna. Denna kurvaimplementeras antingen som en ekvation eller som en uppslagstabell. Det är ett diagram somredovisar den maximala kraften vid varje maxhastighetspunkt. Det finns vissa dynamiskafenomen som hysteres och beroende av stötdämparens förskjutning, som inte kan fångas med enFV-kurvmodell, men som krävs för att bättre förstå fordonets beteende.Denna avhandling genomfördes i samarbete med Volvo Cars i syfte att förbättra den befintligastötdämparmodellen som är en Force-Velocity-kurva. Detta arbete fokuserar på att utveckla enstötdämparmodell, som är tillräckligt komplex för att fånga upp de fenomen som diskuteratsovan och tillräckligt enkel för att implementeras i realtidssimuleringar. Avhandlingen syftarockså till att upprätta en standardmetod för att parametrisera spjällmodellen och generera ForceVelocity-kurvan från de test som utförts på stötdämpartestriggen. En testmatris som innehållerstandardtest för parametrisering och extrema testfall för validering av den utvecklade modellenkommer att utvecklas. Det sista fokuset är att implementera stötdämparmodellen i en multi-bodysimulation (MBS) programvara.Examensarbetet inleds med en introduktion, där bakgrunden för projektet beskrivs ochdärefter definieras målen med arbetet. Det följs av en litteraturöversikt där några avanceradestötdämparmodeller diskuteras i korthet. Därefter diskuteras en steg-för-steg-process för attutveckla stötdämparmodeller tillsammans med några fler möjliga alternativ. Senare diskuteraskonstruktionen av en testmatris i detalj följt av parameteridentifieringsprocessen. Därefterdiskuteras valideringen av den utvecklade stötdämparmodellen med hjälp av testdata från VolvoHällered Proving Ground (HPG). Efter validering presenteras implementeringen av modellen iVI CarRealTime och Adams Car tillsammans med resultaten. Slutligen avslutas rapporten medslutsatser från arbetet och rekommendationer för framtida arbete görs för att ytterligare förbättramodellen