Entity Recognition via Multimodal Sensor Fusion with Smart Phones

This thesis serves as an exploration that takes the sensors within a cell phone beyond the current state of recognition activities. Current state of the art sensor recognition processes tend to focus on recognizing user activity. Utilizing the same sensors available for user activity classification, this thesis validates the ability to gather data about entities separate from the user carrying the smart phone. With the ability to sense entities, the ability to recognize and classify a multitude of items, situations, and phenomena opens a new realm of possibilities for how devices perceive and react to their environment

Deep Neural Networks and Data for Automated Driving

This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above

Influence of flow variation on fixed-time signal control

The purpose of this thesis is to analyze the effects of fluctuation in traffic flows on the operation of fixed-time control systems. In the development of fixed time plans for area traffic control, traffic volumes reflecting average conditions for the movements at all intersections are normally required. Calculations made on basis of these values commonly overestimate the benefits of the implementation of fixed-time signal control plans because the variability of flows is not taken into account. The day-to-day, or between-days variation of traffic, is not normally considered in fixed-time plans and the daily systematic fluctuation, or within-day variation of traffic, is handled by the adoption of one of several possible procedures. In practice, single observations of flow are commonly used in place of the mean, and flows in different parts of the same network are sometimes observed on different days. The consequences of this practice are analyzed in view of the variability of traffic flows. In this thesis an analysis of the techniques currently available to handle traffic fluctuation by fixed-time control systems is made. Ways are investigated in which some measure of variability of traffic flows can be respected in the calculation of fixed-time signal plans. A method that incorporates the variability of the traffic flows in the calculation of a single fixed-time plan is developed. This was based on theoretical values and validated with real flows, collected in a purpose-specific field survey. The technique developed in this research has been found to improve the efficiency of the fixed-time systems in handling both of these kinds of variation. Using explicit conditions of a variety of traffic flows on each link, this technique is suitable to be used as a standard feature for calculation of fixed-time signal plans which will be used under a range of traffic conditions. The problem of the cycle time selection is analyzed and comparisons are made between the performance of the network, using cycle times suggested by the approximate method incorporated in TRANSYT, and those selected on the basis of full day by day optimisation by this program

10th International Conference on Business, Technology and Innovation 2021

Welcome to IC – UBT 2021 UBT Annual International Conference is the 10th international interdisciplinary peer reviewed conference which publishes works of the scientists as well as practitioners in the area where UBT is active in Education, Research and Development. The UBT aims to implement an integrated strategy to establish itself as an internationally competitive, research-intensive university, committed to the transfer of knowledge and the provision of a world-class education to the most talented students from all background. The main perspective of the conference is to connect the scientists and practitioners from different disciplines in the same place and make them be aware of the recent advancements in different research fields, and provide them with a unique forum to share their experiences. It is also the place to support the new academic staff for doing research and publish their work in international standard level. This conference consists of sub conferences in different fields like: Security Studies Sport, Health and Society Psychology Political Science Pharmaceutical and Natural Sciences Mechatronics, System Engineering and Robotics Medicine and Nursing Modern Music, Digital Production and Management Management, Business and Economics Language and Culture Law Journalism, Media and Communication Information Systems and Security Integrated Design Energy Efficiency Engineering Education and Development Dental Sciences Computer Science and Communication Engineering Civil Engineering, Infrastructure and Environment Architecture and Spatial Planning Agriculture, Food Science and Technology Art and Digital Media This conference is the major scientific event of the UBT. It is organizing annually and always in cooperation with the partner universities from the region and Europe. We have to thank all Authors, partners, sponsors and also the conference organizing team making this event a real international scientific event. Edmond Hajrizi, President of UBT UBT – Higher Education Institutio

Machine Learning Assisted Digital Pathology

Histopatologiset kudosnäytteet sisältävät valtavan määrän tietoa biologisista mekanismeista, jotka vaikuttavat monien tautien ilmenemiseen ja etenemiseen. Tästä syystä histopatologisten näytteiden arviointi on ollut perustana monien tautien diagnostiikassa vuosikymmenien ajan. Perinteinen histopatologinen arviointi on kuitenkin työläs tehtävä ja lisäksi erittäin altis inhimillisille virheille ja voi siten johtaa virheelliseen tai viivästyneeseen diagnoosiin. Viime vuosien teknologinen kehitys on tuonut patologien käyttöön lasiskannerit ja tiedonhallintajarjestelmät ja sitä myötä mahdollistaneet näytelasien digitoinnin ja käynnistäneet koko patologian työnkulun digitalisaation. Histopatologisten näytteiden saatavuus digitaalisina kuvina on puolestaan mahdollistanut älykkäiden algoritmien ja automatisoitujen laskennallisten kuva-analyysityökalujen kehittämisen diagnostiikan tueksi. Koneoppiminen on tekoälyn osa-alue, joka voidaan määritellä datasta oppimiseksi. Kuva-analyysin sovelluksissa, kuvan pikseliarvot muutetaan kvantitatiiviseksi piirre-esitykseksi jonka pohjalta kuva voidaan muuntaa merkitykselliseksi tiedoksi hyvödyntämällä koneoppimista. Vuosien saatossa koneoppimiseen perustuvan kuva-analyysin menetelmät ovat kehittyneet manuaalisesta piirteidenirroituksesta kohti viimevuosien vallitsevia syväoppimiseen pohjautuvia konvoluutioneuroverkkoja. Koneoppimisen hyödyt histopatologisessa arvioinnissa ovat huomattavat, sillä koneoppiminen mahdollistaa kuvien tulkinnan patologiin verrattavalla tarkkuudella ja siten pystyy merkittävästi parantamaan kliinisen patologian diagnostiikan tarkkuutta, toistettavuutta ja tehokkuutta. Tämä väitöstyö esittelee koneoppimiseen pohjautuvia menetelmiä jotka on kehitetty avustamaan kudosnäytteen histopatologista arviointia, vaihetta joka on merkityksellinen niin kliinisessä diagnostiikassa kuin prekliinisissä tutkimuksissa. Työssä esitellään piirteenirroituksen ja koneoppimisen tehokkuus histopatologiseen arviointiin liittyvissä kuva-analyysitehtävissä kuten kudoksen karakterisoinnissa, sekä rintasyövän etäpesäkkeiden, epiteelikudoksen ja tumien tunnistuksessa. Menetelmien lisäksi tässä väitöstyössä on käsitelty keskeisiä haasteita jotka on huomioitava integroitaessa koneoppimismenetelmiä kliiniseen käyttöön. Ennen kaikkea nämä tutkimukset ovat kuitenkin osoittaneet koneoppimisen mahdollisuudet tulevaisuudessa parantaa patologian kliinisten rutiinitehtävien tehokkuutta ja toistettavuutta sekä diagnostiikan laatua.Histopathological tissue samples contain a vast amount of information on underlying biological mechanisms that contribute to disease manifestation and progression. Therefore, diagnosis from histopathological tissue samples has been the gold standard for decades. However, traditional histopathological assessment is a laborious task and prone to human errors, thereby leading to misdiagnosis or delayed diagnosis. The development of whole slide scanners for digitization of tissue glass slides has initiated the transition to a fully digital pathology workflow that allows scanning, interpretation, and management of digital tissue slides. These advances have been the cornerstone for developing intelligent algorithms and automated computational approaches for histopathological assessment and clinical diagnostics. Machine learning is a subcategory of artificial intelligence and can be defined as a process of learning from data. In image analysis tasks, the raw pixel values are transformed into quantitative feature representations. Based on the image data representation, a machine learning model learns a set of rules that can be used to extract meaningful information and knowledge. Over the years, the field of machine learning based image analysis has developed from manually handcrafting complex features to the recent revolution of deep learning and convolutional neural networks. Histopathological assessment can benefit greatly from the ability of machine learning models to discover patterns and connections from the data. Therefore, machine learning holds great promise to improve the accuracy, reproducibility, and efficiency of clinical diagnostics in the field of digital pathology. This thesis is focused on developing machine learning based methods for assisting in the process of histopathological assessment, which is a significant step in clinical diagnostics as well as in preclinical studies. The studies presented in this thesis show the effectiveness of feature engineering and machine learning in histopathological assessment related tasks, such as; tissue characterisation, metastasis detection, epithelial tissue detection, and nuclei detection. Moreover, the studies presented in this thesis address the key challenges related to variation presented in histopathological data as well as the generalisation problem that need to be considered in order to integrate machine learning approaches into clinical practice. Overall, these studies have demonstrated the potential of machine learning for bringing standardization and reproducibility to the process of histopathological assessment

Proceedings of the West Africa Built Environment Research (WABER) Conference 2010

FOREWORD Welcome to this West Africa Built Environment Research (WABER) conference taking place here in Ghana. Thank you for coming and welcome to Accra. The main aims of the WABER conference are: to help young researchers and early-career scholars in West Africa to develop their research work and skills through constructive face-to-face interaction with experienced academics; to provide a platform for networking and collaborative work among senior built environment academics in West Africa; and to serve as a vehicle for developing the field of construction management and economics in Africa. Waber 2009 The WABER event in 2009 was held at the British Council in Accra, Ghana on 2-3 June. The event was a resounding success. It attracted participation from 32 researchers, from 12 different institutions, who presented their work to an audience of approximately 100 people. Each presenter received immediate and constructive feedback from an international panel. The event was opened by Professor K.K. Adarkwa, Vice Chancellor of KNUST, Kumasi, Ghana, with several senior academics and researchers from universities, polytechnics, and other institutions in Ghana and Nigeria in attendance. There was also a significant level of attendance by senior construction practitioners in Ghana. Thank you to the School of Construction Management and Engineering, University of Reading, UK for funding the inaugural event in 2009. We are also grateful to all of you who helped to make the event a success and to those of you who have joined us here today to build upon the success and legacy of WABER 2009. Waber 2010 This year, we have 60+ peer-reviewed papers and presentations on topics relating to Building services and maintenance, Construction costs, Construction design and technology, Construction education, Construction finance, Construction procurement, Contract administration, Contract management, Contractor development, Decision support systems, Dispute resolution, Economic development, Energy efficiency, Environment and sustainability, Health and safety, Human resources, Information technology, Marketing, Materials science, Organisation strategy and business performance, Productivity, Project management, Quantity surveying, Real estate and planning, Solar energy systems, Supply chain management and Urban development. We hope that these papers will generate interest among delagates and stimulate discussion here and beyond the conference into the wider community of academia and industry. The delegates at this conference come from 10 different countries. This provides a rich international and multicultural blend and a perfect platform for networking and developing collaborations. This year we are blessed to have three high profile keynote speakers in the persons of Professor George Ofori (National University of Singapore), Dr Roine Leiringer (University of Reading, UK) and Professor Will Hughes (University of Reading, UK). We are also thankful to Dr Chris Harty (University of Reading, UK) who is facilitating the Research Skills Workshop on ‘Writing a scientific article’. Thank you to Dr Sena Agyepong of our conference organising team for her capable management of local organising arrangements. And above all, thank you to all of you for coming to this conference. Enjoy and have a safe journey back home. Dr Samuel Laryea School of Construction Management and Engineering University of Reading, July 201

Molecular phylogeny of horseshoe crab using mitochondrial Cox1 gene as a benchmark sequence

An effort to assess the utility of 650 bp Cytochrome C oxidase subunit I (DNA barcode) gene in delineating the members horseshoe crabs (Family: xiphosura) with closely related sister taxa was made. A total of 33 sequences were extracted from National Center for Biotechnological Information (NCBI) which include horseshoe crabs, beetles, common crabs and scorpion sequences. Constructed phylogram showed beetles are closely related with horseshoe crabs than common crabs. Scorpion spp were distantly related to xiphosurans. Phylogram and observed genetic distance (GD) date were also revealed that Limulus polyphemus was closely related with Tachypleus tridentatus than with T.gigas. Carcinoscorpius rotundicauda was distantly related with L.polyphemus. The observed mean Genetic Distance (GD) value was higher in 3rd codon position in all the selected group of organisms. Among the horseshoe crabs high GC content was observed in L.polyphemus (38.32%) and lowest was observed in T.tridentatus (32.35%). We conclude that COI sequencing (barcoding) could be used in identifying and delineating evolutionary relatedness with closely related specie

Crab and cockle shells as heterogeneous catalysts in the production of biodiesel

In the present study, the waste crab and cockle shells were utilized as source of calcium oxide to transesterify palm olein into methyl esters (biodiesel). Characterization results revealed that the main component of the shells are calcium carbonate which transformed into calcium oxide upon activated above 700 °C for 2 h. Parametric studies have been investigated and optimal conditions were found to be catalyst amount, 5 wt.% and methanol/oil mass ratio, 0.5:1. The waste catalysts perform equally well as laboratory CaO, thus creating another low-cost catalyst source for producing biodiesel. Reusability results confirmed that the prepared catalyst is able to be reemployed up to five times. Statistical analysis has been performed using a Central Composite Design to evaluate the contribution and performance of the parameters on biodiesel purity