Chemical and biological reactions of solidification of peat using ordinary portland cement (OPC) and coal ashes

Construction over peat area have often posed a challenge to geotechnical engineers. After decades of study on peat stabilisation techniques, there are still no absolute formulation or guideline that have been established to handle this issue. Some researchers have proposed solidification of peat but a few researchers have also discovered that solidified peat seemed to decrease its strength after a certain period of time. Therefore, understanding the chemical and biological reaction behind the peat solidification is vital to understand the limitation of this treatment technique. In this study, all three types of peat; fabric, hemic and sapric were mixed using Mixing 1 and Mixing 2 formulation which consisted of ordinary Portland cement, fly ash and bottom ash at various ratio. The mixtures of peat-binder-filler were subjected to the unconfined compressive strength (UCS) test, bacterial count test and chemical elemental analysis by using XRF, XRD, FTIR and EDS. Two pattern of strength over curing period were observed. Mixing 1 samples showed a steadily increase in strength over curing period until Day 56 while Mixing 2 showed a decrease in strength pattern at Day 28 and Day 56. Samples which increase in strength steadily have less bacterial count and enzymatic activity with increase quantity of crystallites. Samples with lower strength recorded increase in bacterial count and enzymatic activity with less crystallites. Analysis using XRD showed that pargasite (NaCa2[Mg4Al](Si6Al2)O22(OH)2) was formed in the higher strength samples while in the lower strength samples, pargasite was predicted to be converted into monosodium phosphate and Mg(OH)2 as bacterial consortium was re-activated. The Michaelis�Menten coefficient, Km of the bio-chemical reaction in solidified peat was calculated as 303.60. This showed that reaction which happened during solidification work was inefficient. The kinetics for crystallite formation with enzymatic effect is modelled as 135.42 (1/[S] + 0.44605) which means, when pargasite formed is lower, the amount of enzyme secretes is higher

Разрешавање идентитета и груписање дигиталних доказа о осумњиченима применом технологија препознавања лица и система софтверских интелигентих агената заснованог на неаксиоматском резоновању

The work of criminal police in modern society is characterized by the proliferation of data and information to be processed, greater demands for restrictions on personal data, increased public monitoring, and higher expectations in the efficiency of detecting perpetrators, but still lack resources, both human and material. One of the more complex tasks is to resolve the identity, the change of which seeks to cover up criminal activities, i.e., the perpetrator himself, who is on the run. In order to resolve the identity, it is necessary to group and present all available evidence related to specific persons. The thesis proposes a clustering approach by comparing pairs of face feature vectors extracted from images created in unconstrained conditions and based on reasoning using non-axiomatic logic and graphs. Face clusters will be the central points around which data from various police reports will be grouped. A system model has also been proposed in which software agents will play a significant role, primarily in connecting the distribution environment points formed in practice by police information systems. The clustering approach was experimentally tested with six different face image databases characterized by the fact that they were created in a way that simulates unconstrained conditions. The obtained results of the proposed solution are compared with other state-of-the-art methods. The results showed that the approach gives similar but mostly better results than the others. What gives a notable advantage over other methods is the possibility of using mechanisms from non-axiomatic logic such as revision and deduction, which can be used to acquire new knowledge based on information from different system nodes, or in the local knowledge base, respectively.Рад криминалистичке полиције у савременом друштву одликује пролиферација података и информација које треба обрађивати, већи захтеви за ограничењима у раду са личним подацима, појачани надзор пре свега јавности, већа очекивања у ефикасности откривања извршилаца кривичних дела, али и даље недостатак ресурса, како људских тако и материјалних. Један од сложенијих задатака јесте разрешавање идентитета чијом променом се настоје прикрити криминалне активности, односно сам извршилац који је у бекству. Да би се разрешио идентитет, потребно је груписати и презентовати све расположиве доказе везане за одређене особе. У дисертацији је предложен нови приступ кластеровању поређењем парова вектора одлика лица екстрахованих из слика насталих у неконтролисаним условима, а заснован на резоновању применом неаксиоматске логике и графова. Кластери слика лица представљају централне тачке око којих се групишу подаци из различитих полицијских извештаја. Такође је предложен модел система у коме ће значајну улогу имати софтверски агенти, пре свега у повезивању тачака дистрибуираног окружења које у пракси формирају полицијски информациони системи. Нови приступ кластеровању је експериментално испитан са шест различитих база података лица карактеристичних по томе што су креиране на начин којим се симулирају неконтролисани услови. Добијени резултати предложеног решења су упоређени са осталим врхунским методама. Резултати су показали да приступ даје приближне, али углавном боље резултате од осталих. Оно што даје посебну предност у односу на остале методе јесте могућност коришћења механизама из неаксиоматске логике попут ревизије и дедукције, помоћу којих се могу стицати нова знања на основу информација из различитих нодова система, или у локалној бази знања, респективно

Automatic handwriter identification using advanced machine learning

Handwriter identification a challenging problem especially for forensic investigation. This topic has received significant attention from the research community and several handwriter identification systems were developed for various applications including forensic science, document analysis and investigation of the historical documents. This work is part of an investigation to develop new tools and methods for Arabic palaeography, which is is the study of handwritten material, particularly ancient manuscripts with missing writers, dates, and/or places. In particular, the main aim of this research project is to investigate and develop new techniques and algorithms for the classification and analysis of ancient handwritten documents to support palaeographic studies. Three contributions were proposed in this research. The first is concerned with the development of a text line extraction algorithm on colour and greyscale historical manuscripts. The idea uses a modified bilateral filtering approach to adaptively smooth the images while still preserving the edges through a nonlinear combination of neighboring image values. The proposed algorithm aims to compute a median and a separating seam and has been validated to deal with both greyscale and colour historical documents using different datasets. The results obtained suggest that our proposed technique yields attractive results when compared against a few similar algorithms. The second contribution proposes to deploy a combination of Oriented Basic Image features and the concept of graphemes codebook in order to improve the recognition performances. The proposed algorithm is capable to effectively extract the most distinguishing handwriter’s patterns. The idea consists of judiciously combining a multiscale feature extraction with the concept of grapheme to allow for the extraction of several discriminating features such as handwriting curvature, direction, wrinkliness and various edge-based features. The technique was validated for identifying handwriters using both Arabic and English writings captured as scanned images using the IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting. The results obtained clearly demonstrate the effectiveness of the proposed method when compared against some similar techniques. The third contribution is concerned with an offline handwriter identification approach based on the convolutional neural network technology. At the first stage, the Alex-Net architecture was employed to learn image features (handwritten scripts) and the features obtained from the fully connected layers of the model. Then, a Support vector machine classifier is deployed to classify the writing styles of the various handwriters. In this way, the test scripts can be classified by the CNN training model for further classification. The proposed approach was evaluated based on Arabic Historical datasets; Islamic Heritage Project (IHP) and Qatar National Library (QNL). The obtained results demonstrated that the proposed model achieved superior performances when compared to some similar method

Individual and ensemble functional link neural networks for data classification

This study investigated the Functional Link Neural Network (FLNN) for solving data classification problems. FLNN based models were developed using evolutionary methods as well as ensemble methods. The outcomes of the experiments covering benchmark classification problems, positively demonstrated the efficacy of the proposed models for undertaking data classification problems

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Knowledge and Reasoning for Image Understanding

abstract: Image Understanding is a long-established discipline in computer vision, which encompasses a body of advanced image processing techniques, that are used to locate (“where”), characterize and recognize (“what”) objects, regions, and their attributes in the image. However, the notion of “understanding” (and the goal of artificial intelligent machines) goes beyond factual recall of the recognized components and includes reasoning and thinking beyond what can be seen (or perceived). Understanding is often evaluated by asking questions of increasing difficulty. Thus, the expected functionalities of an intelligent Image Understanding system can be expressed in terms of the functionalities that are required to answer questions about an image. Answering questions about images require primarily three components: Image Understanding, question (natural language) understanding, and reasoning based on knowledge. Any question, asking beyond what can be directly seen, requires modeling of commonsense (or background/ontological/factual) knowledge and reasoning. Knowledge and reasoning have seen scarce use in image understanding applications. In this thesis, we demonstrate the utilities of incorporating background knowledge and using explicit reasoning in image understanding applications. We first present a comprehensive survey of the previous work that utilized background knowledge and reasoning in understanding images. This survey outlines the limited use of commonsense knowledge in high-level applications. We then present a set of vision and reasoning-based methods to solve several applications and show that these approaches benefit in terms of accuracy and interpretability from the explicit use of knowledge and reasoning. We propose novel knowledge representations of image, knowledge acquisition methods, and a new implementation of an efficient probabilistic logical reasoning engine that can utilize publicly available commonsense knowledge to solve applications such as visual question answering, image puzzles. Additionally, we identify the need for new datasets that explicitly require external commonsense knowledge to solve. We propose the new task of Image Riddles, which requires a combination of vision, and reasoning based on ontological knowledge; and we collect a sufficiently large dataset to serve as an ideal testbed for vision and reasoning research. Lastly, we propose end-to-end deep architectures that can combine vision, knowledge and reasoning modules together and achieve large performance boosts over state-of-the-art methods.Dissertation/ThesisDoctoral Dissertation Computer Science 201

A novel approach to handwritten character recognition

A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules

Domain knowledge, uncertainty, and parameter constraints

Ph.D.Committee Chair: Guy Lebanon; Committee Member: Alex Shapiro; Committee Member: Alexander Gray; Committee Member: Chin-Hui Lee; Committee Member: Hongyuan Zh