Chemical Characterization of Urban Background Aerosol Using Online And Filter Methods

In order to evaluate the influence of ambient aerosol particles on cloud formation, climate and human health, detailed information about the concentration and composition of ambient aerosol particles is needed. The dura-tion of aerosol formation, growth and removal processes in the atmosphere range from minutes to hours, which highlights the need for high-time-resolution data in order to understand the underlying processes. This thesis focuses on characterization of ambient levels, size distributions and sources of water-soluble organic carbon (WSOC) in ambient aerosols. The results show that in the location of this study typically 50-60 % of organic carbon in fine particles is water-soluble. The amount of WSOC was observed to increase as aerosols age, likely due to further oxidation of organic compounds. In the boreal region the main sources of WSOC were biomass burning during the winter and secondary aerosol formation during the summer. WSOC was mainly attributed to a fine particle mode between 0.1 - 1 μm, although different size distributions were measured for different sources. The WSOC concentrations and size distributions had a clear seasonal variation. Another main focus of this thesis was to test and further develop the high-time-resolution methods for chemical characterization of ambient aerosol particles. The concentrations of the main chemical components (ions, OC, EC) of ambient aerosol particles were measured online during a year-long intensive measurement campaign conducted on the SMEAR III station in Southern Finland. The results were compared to the results of traditional filter collections in order to study sampling artifacts and limitations related to each method. To achieve better a time resolution for the WSOC and ion measurements, a particle-into-liquid sampler (PILS) was coupled with a total organic carbon analyzer (TOC) and two ion chromatographs (IC). The PILS-TOC-IC provided important data about diurnal variations and short-time plumes, which cannot be resolved from the filter samples. In summary, the measurements made for this thesis provide new information on the concentrations, size distribu-tions and sources of WSOC in ambient aerosol particles in the boreal region. The analytical and collection me-thods needed for the online characterization of aerosol chemical composition were further developed in order to provide more reliable high-time-resolution measurements.

Investigating online reading with eye tracking and EEG : The influence of text format, reading task and parafoveal stimuli on reading processes

Research on reading has been successful in revealing how attention guides eye movements when people read single sentences or text paragraphs in simplified and strictly controlled experimental conditions. However, less is known about reading processes in more naturalistic and applied settings, such as reading Web pages. This thesis investigates online reading processes by recording participants eye movements. The thesis consists of four experimental studies that examine how location of stimuli presented outside the currently fixated region (Study I and III), text format (Study II), animation and abrupt onset of online advertisements (Study III), and phase of an online information search task (Study IV) affect written language processing. Furthermore, the studies investigate how the goal of the reading task affects attention allocation during reading by comparing reading for comprehension with free browsing, and by varying the difficulty of an information search task. The results show that text format affects the reading process, that is, vertical text (word/line) is read at a slower rate than a standard horizontal text, and the mean fixation durations are longer for vertical text than for horizontal text. Furthermore, animated online ads and abrupt ad onsets capture online readers attention and direct their gaze toward the ads, and distract the reading process. Compared to a reading-for-comprehension task, online ads are attended to more in a free browsing task. Moreover, in both tasks abrupt ad onsets result in rather immediate fixations toward the ads. This effect is enhanced when the ad is presented in the proximity of the text being read. In addition, the reading processes vary when Web users proceed in online information search tasks, for example when they are searching for a specific keyword, looking for an answer to a question, or trying to find a subjectively most interesting topic. A scanning type of behavior is typical at the beginning of the tasks, after which participants tend to switch to a more careful reading state before finishing the tasks in the states referred to as decision states. Furthermore, the results also provided evidence that left-to-right readers extract more parafoveal information to the right of the fixated word than to the left, suggesting that learning biases attentional orienting towards the reading direction.

Wind loading over the top portions of tall stacks with and without external landing platforms

The variation of the drag force near the top portions of tall stacks with and without external landing platforms, and with the exit open and closed, has been examined by model studies in a wind tunnel at Reynolds numbers of about 10(5). Pressure measurements on three models of different height to diameter ratios have been supplemented by flow visualisation studies. Observations confirm that when there is no platform, significant load enhancement over the top three to four diameters occurs, due to the high suction caused by the sharp separation of the flow over the top from the rim, in the aft regions of the stack. The enhanced loading is found to be greater if the exit is closed. A platform at the top, of less than twice the exit diameter, further increases the drag force near the top, but a still larger platform at the top, of about three times the exit diameter, decreases the drag force to values less than those much further below, effectively nullifying the enhanced drag force. It was found that such a reduction of the enhanced drag force in the top regions can also be achieved by a smaller platform of 1.1 to 1.3 times the local diameter, located at about three to five diameters below the top.

Fusion of Complementary Online and Offline Strategies for Recognition of Handwritten Kannada Characters

This work describes an online handwritten character recognition system working in combination with an offline recognition system. The online input data is also converted into an offline image, and parallely recognized by both online and offline strategies. Features are proposed for offline recognition and a disambiguation step is employed in the offline system for the samples for which the confidence level of the classifier is low. The outputs are then combined probabilistically resulting in a classifier out-performing both individual systems. Experiments are performed for Kannada, a South Indian Language, over a database of 295 classes. The accuracy of the online recognizer improves by 11% when the combination with offline system is used.

Choice of Classifiers in Hierarchical Recognition of Online Handwritten Kannada and Tamil Aksharas

In this paper, we propose a novel dexterous technique for fast and accurate recognition of online handwritten Kannada and Tamil characters. Based on the primary classifier output and prior knowledge, the best classifier is chosen from set of three classifiers for second stage classification. Prior knowledge is obtained through analysis of the confusion matrix of primary classifier which helped in identifying the multiple sets of confused characters. Further, studies were carried out to check the performance of secondary classifiers in disambiguating among the confusion sets. Using this technique we have achieved an average accuracy of 92.6% for Kannada characters on the MILE lab dataset and 90.2% for Tamil characters on the HP Labs dataset.

A current error space vector based hysteresis controller with constant switching frequency and simple online boundary computation for VSI fed IM drive

This paper proposes a simple current error space vector based hysteresis controller for two-level inverter fed Induction Motor (IM) drives. This proposed hysteresis controller retains all advantages of conventional current error space vector based hysteresis controllers like fast dynamic response, simple to implement, adjacent voltage vector switching etc. The additional advantage of this proposed hysteresis controller is that it gives a phase voltage frequency spectrum exactly similar to that of a constant switching frequency space vector pulse width modulated (SVPWM) inverter. In this proposed hysteresis controller the boundary is computed online using estimated stator voltages along alpha and beta axes thus completely eliminating look up tables used for obtaining parabolic hysteresis boundary proposed in. The estimation of stator voltage is carried out using current errors along alpha and beta axes and steady state model of induction motor. The proposed scheme is simple and capable of taking inverter upto six step mode operation, if demanded by drive system. The proposed hysteresis controller based inverter fed drive scheme is simulated extensively using SIMULINK toolbox of MATLAB for steady state and transient performance. The experimental verification for steady state performance of the proposed scheme is carried out on a 3.7kW IM.

An Online Kannada Text Transliteration System for Mobile Phones

Transliteration system for mobile phone is an area that is always in demand given the difficulties and constraints we face in its implementation. In this paper we deal with automatic transliteration system for Kannada which has a non-uniform geometry and inter-character spacing unlike non-oriental language text like English. So it is even more a challenging problem. Working model consists of part of the process taking place on a mobile with remaining on a server. Good results are achieved.

Two Dimensional Principal Component Analysis for Online Tamil Character Recognition

This paper presents a new application of two dimensional Principal Component Analysis (2DPCA) to the problem of online character recognition in Tamil Script. A novel set of features employing polynomial fits and quartiles in combination with conventional features are derived for each sample point of the Tamil character obtained after smoothing and resampling. These are stacked to form a matrix, using which a covariance matrix is constructed. A subset of the eigenvectors of the covariance matrix is employed to get the features in the reduced sub space. Each character is modeled as a separate subspace and a modified form of the Mahalanobis distance is derived to classify a given test character. Results indicate that the recognition accuracy using the 2DPCA scheme shows an approximate 3% improvement over the conventional PCA technique.

Online character recognition using regression techniques

This paper introduces a scheme for classification of online handwritten characters based on polynomial regression of the sampled points of the sub-strokes in a character. The segmentation is done based on the velocity profile of the written character and this requires a smoothening of the velocity profile. We propose a novel scheme for smoothening the velocity profile curve and identification of the critical points to segment the character. We also porpose another method for segmentation based on the human eye perception. We then extract two sets of features for recognition of handwritten characters. Each sub-stroke is a simple curve, a part of the character, and is represented by the distance measure of each point from the first point. This forms the first set of feature vector for each character. The second feature vector are the coeficients obtained from the B-splines fitted to the control knots obtained from the segmentation algorithm. The feature vector is fed to the SVM classifier and it indicates an efficiency of 68% using the polynomial regression technique and 74% using the spline fitting method.

Online Unsupervised Pattern Discovery in Speech Using Parallelization

Segmental dynamic time warping (DTW) has been demonstrated to be a useful technique for finding acoustic similarity scores between segments of two speech utterances. Due to its high computational requirements, it had to be computed in an offline manner, limiting the applications of the technique. In this paper, we present results of parallelization of this task by distributing the workload in either a static or dynamic way on an 8-processor cluster and discuss the trade-offs among different distribution schemes. We show that online unsupervised pattern discovery using segmental DTW is plausible with as low as 8 processors. This brings the task within reach of today's general purpose multi-core servers. We also show results on a 32-processor system, and discuss factors affecting scalability of our methods.

Online Handwriting Recognition of Tamil Script Using Fractal Geometry

We present a fractal coding method to recognize online handwritten Tamil characters and propose a novel technique to increase the efficiency in terms of time while coding and decoding. This technique exploits the redundancy in data, thereby achieving better compression and usage of lesser memory. It also reduces the encoding time and causes little distortion during reconstruction. Experiments have been conducted to use these fractal codes to classify the online handwritten Tamil characters from the IWFHR 2006 competition dataset. In one approach, we use fractal coding and decoding process. A recognition accuracy of 90% has been achieved by using DTW for distortion evaluation during classification and encoding processes as compared to 78% using nearest neighbor classifier. In other experiments, we use the fractal code, fractal dimensions and features derived from fractal codes as features in separate classifiers. While the fractal code is successful as a feature, the other two features are not able to capture the wide within-class variations.

Online Handwritten Kannada Word Recognizer with Unrestricted Vocabulary

In this paper, we propose a novel heuristic approach to segment recognizable symbols from online Kannada word data and perform recognition of the entire word. Two different estimates of first derivative are extracted from the preprocessed stroke groups and used as features for classification. Estimate 2 proved better resulting in 88% accuracy, which is 3% more than that achieved with estimate 1. Classification is performed by statistical dynamic space warping (SDSW) classifier which uses X, Y co-ordinates and their first derivatives as features. Classifier is trained with data from 40 writers. 295 classes are handled covering Kannada aksharas, with Kannada numerals, Indo-Arabic numerals, punctuations and other special symbols like $ and #. Classification accuracies obtained are 88% at the akshara level and 80% at the word level, which shows the scope for further improvement in segmentation algorithm

Unrestricted Kannada online handwritten akshara recognition using SDTW

In this paper, we present an unrestricted Kannada online handwritten character recognizer which is viable for real time applications. It handles Kannada and Indo-Arabic numerals, punctuation marks and special symbols like $, &, # etc, apart from all the aksharas of the Kannada script. The dataset used has handwriting of 69 people from four different locations, making the recognition writer independent. It was found that for the DTW classifier, using smoothed first derivatives as features, enhanced the performance to 89% as compared to preprocessed co-ordinates which gave 85%, but was too inefficient in terms of time. To overcome this, we used Statistical Dynamic Time Warping (SDTW) and achieved 46 times faster classification with comparable accuracy i.e. 88%, making it fast enough for practical applications. The accuracies reported are raw symbol recognition results from the classifier. Thus, there is good scope of improvement in actual applications. Where domain constraints such as fixed vocabulary, language models and post processing can be employed. A working demo is also available on tablet PC for recognition of Kannada words.