Horizon Line Detection: Edge-less and Edge-based Methods

Planetary rover localization is a challenging problem due to unavailability ofconventional localization cues e.g. GPS, architectural landmarks etc. Hori-zon line (boundary segmenting sky and non-sky regions) nds its applicationsfor smooth navigation of UAVs/MAVs, visual geo-localization of mountain-ous images, port security and ship detection and has proven to be a promisingvisual cue for outdoor robot/vehicle localization.Prominent methods for horizon line detection are based on faulty as-sumptions and rely on mere edge detection which is inherently a non-stableapproach due to parameter choices. We investigate the use of supervisedmachine learning for horizon line detection. Specically we propose two dif-ferent machine learning based methods; one relying on edge detection andclassication while other solely based on classication. Given a query image;an edge or classication map is rst built and converted into a multi-stagegraph problem. Dynamic programming is then used to nd a shortest pathwhich conforms to the detected horizon line in the given image. For the rstmethod we provide a detailed quantitative analysis for various texture fea-tures (SIFT, LBP, HOG and their combinations) used to train an SupportVector Machine (SVM) classier and dierent choices (binary edges, classi-ed edge score, gradient score and their combinations) for the nodal costsfor Dynamic Programming. For the second method we investigate the use ofdense classication maps for horizon line detection. We use Support VectorMachines (SVMs) and Convolutional Neural Networks (CNNs) as our classi-er choices and use raw intensity patches as features. Dynamic Programmingis then applied on the resultant dense classier score image to nd the hori-zon line. Both proposed formulations are compared with a prominent edgebased method on three dierent data sets: City (Reno) Skyline, Basalt Hillsand Web data sets and outperform the previous method by a high margin

Robust low-power digital circuit design in nano-CMOS technologies

Device scaling has resulted in large scale integrated, high performance, low-power, and low cost systems. However the move towards sub-100 nm technology nodes has increased variability in device characteristics due to large process variations. Variability has severe implications on digital circuit design by causing timing uncertainties in combinational circuits, degrading yield and reliability of memory elements, and increasing power density due to slow scaling of supply voltage. Conventional design methods add large pessimistic safety margins to mitigate increased variability, however, they incur large power and performance loss as the combination of worst cases occurs very rarely. In-situ monitoring of timing failures provides an opportunity to dynamically tune safety margins in proportion to on-chip variability that can significantly minimize power and performance losses. We demonstrated by simulations two delay sensor designs to detect timing failures in advance that can be coupled with different compensation techniques such as voltage scaling, body biasing, or frequency scaling to avoid actual timing failures. Our simulation results using 45 nm and 32 nm technology BSIM4 models indicate significant reduction in total power consumption under temperature and statistical variations. Future work involves using dual sensing to avoid useless voltage scaling that incurs a speed loss. SRAM cache is the first victim of increased process variations that requires handcrafted design to meet area, power, and performance requirements. We have proposed novel 6 transistors (6T), 7 transistors (7T), and 8 transistors (8T)-SRAM cells that enable variability tolerant and low-power SRAM cache designs. Increased sense-amplifier offset voltage due to device mismatch arising from high variability increases delay and power consumption of SRAM design. We have proposed two novel design techniques to reduce offset voltage dependent delays providing a high speed low-power SRAM design. Increasing leakage currents in nano-CMOS technologies pose a major challenge to a low-power reliable design. We have investigated novel segmented supply voltage architecture to reduce leakage power of the SRAM caches since they occupy bulk of the total chip area and power. Future work involves developing leakage reduction methods for the combination logic designs including SRAM peripherals

Machine Learning based Mountainous Skyline Detection and Visual Geo-Localization

With the ubiquitous availability of geo-tagged imagery and increased computational power, geo-localization has captured a lot of attention from researchers in computer vision and image retrieval communities. Significant progress has been made in urban environments with stable man-made structures and geo-referenced street imagery of frequently visited tourist attractions. However, geo-localization of natural/mountain scenes is more challenging due to changed vegetations, lighting, seasonal changes and lack of geo-tagged imagery. Conventional approaches for mountain/natural geo-localization mostly rely on mountain peaks and valley information, visible skylines and ridges etc. Skyline (boundary segmenting sky and non-sky regions) has been established to be a robust natural feature for mountainous images, which can be matched with the synthetic skylines generated from publicly available terrain maps such as Digital Elevation Models (DEMs). Skyline or visible horizon finds further applications in various other contexts e.g. smooth navigation of Unmanned Aerial Vehicles (UAVs)/Micro Aerial Vehicles (MAVs), port security, ship detection and outdoor robot/vehicle localization.\parProminent methods for skyline/horizon detection are based on non-realistic assumptions and rely on mere edge detection and/or linear line fitting using Hough transform. We investigate the use of supervised machine learning for skyline detection. Specifically we propose two novel machine learning based methods, one relying on edge detection and classification while other solely based on classification. Given a query image, an edge or classification map is first built and converted into a multi-stage graph problem. Dynamic programming is then used to find a shortest path which conforms to the detected skyline in the given image. For the first method, we provide a detailed quantitative analysis for various texture features (Scale Invariant Feature Transform (SIFT), Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG) and their combinations) used to train a Support Vector Machine (SVM) classifier and different choices (binary edges, classified edge score, gradient score and their combinations) for the nodal costs for Dynamic Programming (DP). For the second method, we investigate the use of dense classification maps for horizon line detection. We use Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) as our classifier choices and use normalized intensity patches as features. Both proposed formulations are compared with a prominent edge based method on two different data sets.\par We propose a fusion strategy which boosts the performance of the edge-less approach using edge information. The fusion approach, which has been tested on an additional challenging data set, outperforms each of the two methods alone. Further, we demonstrate the capability of our formulations to detect absence of horizon boundary and detection of partial horizon lines. This could be of great value in applications where a confidence measure of the detection is necessary e.g. localization of planetary rovers/robots. In an extended work, we compare our edge-less skyline detection approach against deep learning networks recently proposed for semantic segmentation on an additional data set. Specifically, we compare our proposed fusion formulation with Fully Convolutional Network (FCN), SegNet and another classical supervised learning based method.\par We further propose a visual geo-localization pipeline based on evolutionary computing; where Particle Swarm Optimization (PSO) is adopted to find/refine an orientation estimate by minimizing the cost function based on horizon-ness probability of pixels. The dense classification score image resulting from our edge-less/fusion approach is used as a fitness measure to guide the particles toward best solution where the rendered horizon from DEM perfectly aligns with the actual horizon from the image without even requiring its explicit detection. The effectiveness of the proposed geo-localization pipeline is evaluated on a decent sized data set

Analyzing the efficiency differences among basic health units in Sargodha District

Pakistan has adequate infrastructure for health services delivery at primary level. The study aims to calculate the technical efficiency of Basic Health Units (BHUs) in Sargodha by using the Data Envelopment Analysis (DEA) with the choice of inputs and outputs being specific to BHUs operation. DEA model results reveals that the mean technical efficiency under, Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) was 0.719 and 0.807 while the mean scale efficiency was 0.88. Study exposed that 77 % BHUs were technically inefficient under CRS while 66 % BHUs were technically inefficient under VRS modal. Overall 76% BHUs were inefficient and destructing the infrastructure. Moreover, findings evidently point to adverse inefficiency of BHUs in health services delivery. Study concluded that existing high level of inefficiency in BHUs needs institutional fascination for scaling up BHUs to meet both regional as well international targets such as Millennium Development Goals (MDGs) and recommended such measures that may curb the waste.Basic Health Units, Technical Efficiency, Data Envelopment Analysis, Pakistan

Mobile Enterprise Resource Planning Adoption and Implementation in Norwegian Organizations A Case Study of RamBase

Master's thesis inIn an age of ongoing technological advancements and mobility, there is an ever-increasing need by the companies to find smart solutions to manage their businesses. Organizations around the world use Enterprise Resource Planning (ERP) system, a systemic technological tool, to increase their performances. Researchers agree upon the fact that mobile information technology (MIT) is an indispensable asset for the longevity of an organization’s innovation practices and economic stability. With the IT revolution, the number of enterprises adopting, implementing, and using mobile information and communications technology has increased. The mobile enterprise generates productivity in small projects and saves costs in the medium to large scale companies giving competitive advantages. RamBase, a straightforward cloud-based Norwegian ERP provider, has a desire to evaluate its current mobile computing application potential in both IOS and Android operating systems and to look for improvement opportunities in this branch. Cloud ERP enhances tracking of incoming raw material and outgoing final products to extend the visibility and control inside and outside the enterprise. As very few studies have been done on the implementation and adoption of a mobile ERP (M-ERP) application so in this thesis, we aim to explore the importance of Mobile ERP (M-ERP) for today’s business environment. We specifically studied the research question: How can RamBase develop a productive M-ERP for its customers while considering the crucial implementation success factors? We applied the qualitative approach by conducting a literature review in addition to a case study. An online survey related to experiences with mobile ERP use, strengths and challenges and opinions on the implementation of mobile ERP was conducted. A questionnaire was formulated to collect data points for the desired variables and was sent out to major firms which were using RamBase’s ERP system. It consisted of both open-ended and closed-ended questions. The questionnaire data were analyzed using descriptive statistics, thematic analysis, and content analytical techniques. Survey results were discussed during a consultation with fellow students to identify key considerations in the implementation of mobile ERP. Our findings suggest that the critical ERP modules for small-medium enterprises (SMEs), which have employees ranging from 10 to 249 and are operating in the manufacturing industry are administration, production, and finance. Previous studies have shown that the access to required modules through the mobile phone enhances the productivity and performance of an organization. The core features of the M-ERP applications include the real-time query of information regardless of location, traceability of information and approval of workflow. The challenges identified by the users and validated by the literature include security, screen size, platform compatibility, training, and user interface. Due to significant complexities in each ERP module, it is not feasible to start developing a mobile application for the whole module. A simple application with the key features of the module will have more usability than a complex whole module application. RamBase should identify the functionality of production, administration, and finance M-ERP modules according to the insights available from their customers. The initial applications should be small with specific functionality for a single group of people. This bottom to top approach will save time and money.submittedVersio

Modelling of discrete extremes through extended versions of discrete generalized Pareto distribution

The statistical modelling of integer-valued extremes such as large avalanche counts has received less attention than their continuous counterparts in the extreme value theory (EVT) literature. One approach to moving from continuous to discrete extremes is to model threshold exceedances of integer random variables by the discrete version of the generalized Pareto distribution. Still, the optimal threshold selection that defines exceedances remains a problematic issue. Moreover, within a regression framework, the treatment of the many data points (those below the chosen threshold) is either ignored or decoupled from extremes. Considering these issues, we extend the idea of using a smooth transition between the two tails (lower and upper) to force large and small discrete extreme values to comply with EVT. In the case of zero inflation, we also develop models with an additional parameter. To incorporate covariates, we extend the Generalized Additive Models (GAM) framework to discrete extreme responses. In the GAM forms, the parameters of our proposed models are quantified as a function of covariates. The maximum likelihood estimation procedure is implemented for estimation purposes. With the advantage of bypassing the threshold selection step, our findings indicate that the proposed models are more flexible and robust than competing models (i.e. discrete generalized Pareto distribution and Poisson distribution).Comment: 32 pages including supplementary materials, 11 figures including supplementary materials figures, 8 Tables including supplementary materials figure

Methane and n-hexane ignition in a newly developed diaphragmless shock tube

Shock tubes have been routinely used to generate reliable chemical kinetic data for gas-phase chemistry. The conventional diaphragm-rupture mode for shock tube operation presents many challenges that may ultimately affect the quality of chemical kinetics data. Numerous diaphragmless concepts have been developed to overcome the drawbacks of using diaphragms. Most of these diaphragmless designs require significant alterations in the driver section of the shock tube and, in some cases, fail to match the performance of the diaphragm-mode of operation. In the present work, an existing diaphragm-type shock tube is retrofitted with a fast-acting valve, and the performance of the diaphragmless shock tube is evaluated for investigating the ignition of methane and n-hexane. The diaphragmless shock tube reported here presents many advantages, such as eliminating the use of diaphragms, avoiding substantial manual effort during experiments, automating the shock tube facility, having good control over driver conditions, and obtaining good repeatability for reliable gas-phase chemical kinetic studies. Ignition delay time measurements have been performed in the diaphragmless shock tube for three methane mixtures and two n-hexane mixtures at $P_5$ = 10 - 20 bar and $T_5$ = 738 - 1537 K. The results obtained for fuel-rich, fuel-lean, and oxygen-rich (undiluted) mixtures show very good agreement with previously reported experimental data and literature kinetic models (AramcoMech 3.0 [1] for methane and Zhang et al. mechanism [2] for n-hexane). The study presents an easy and simple method to upgrade conventional shock tubes to a diaphragmless mode of operation and opens new possibilities for reliable chemical kinetics investigations.Comment: 22 pages, 9 figure

Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection

Horizon or skyline detection plays a vital role towards mountainous visual geo-localization, however most of the recently proposed visual geo-localization approaches rely on \textbf{user-in-the-loop} skyline detection methods. Detecting such a segmenting boundary fully autonomously would definitely be a step forward for these localization approaches. This paper provides a quantitative comparison of four such methods for autonomous horizon/sky line detection on an extensive data set. Specifically, we provide the comparison between four recently proposed segmentation methods; one explicitly targeting the problem of horizon detection\cite{Ahmad15}, second focused on visual geo-localization but relying on accurate detection of skyline \cite{Saurer16} and other two proposed for general semantic segmentation -- Fully Convolutional Networks (FCN) \cite{Long15} and SegNet\cite{Badrinarayanan15}. Each of the first two methods is trained on a common training set \cite{Baatz12} comprised of about 200 images while models for the third and fourth method are fine tuned for sky segmentation problem through transfer learning using the same data set. Each of the method is tested on an extensive test set (about 3K images) covering various challenging geographical, weather, illumination and seasonal conditions. We report average accuracy and average absolute pixel error for each of the presented formulation.Comment: Proceedings of the International Joint Conference on Neural Networks (IJCNN) (oral presentation), IEEE Computational Intelligence Society, 201

Analyzing the Efficiency Differences among Basic Health Units in Sargodha District

Pakistan has adequate infrastructure for health services delivery at primary level. The study aims to calculate the technical efficiency of Basic Health Units (BHUs) in Sargodha by using the Data Envelopment Analysis (DEA) with the choice of inputs and outputs being specific to BHUs operation. DEA model results reveals that the mean technical efficiency under, Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) was 0.719 and 0.807 while the mean scale efficiency was 0.88. Study exposed that 77 % BHUs were technically inefficient under CRS while 66 % BHUs were technically inefficient under VRS modal. Overall 76% BHUs were inefficient and destructing the infrastructure. Moreover, findings evidently point to adverse inefficiency of BHUs in health services delivery. Study concluded that existing high level of inefficiency in BHUs needs institutional fascination for scaling up BHUs to meet both regional as well international targets such as Millennium Development Goals (MDGs) and recommended such measures that may curb the waste