Master’s thesis project on the Signal Processing of Single-Molecule measurements in Escherichia coli

Molecular Biology measurement techniques such as the detection of MS2d-GFP tagged mRNA molecules

have enabled the study of mRNA production dynamics at the single molecule level in live cells. This, and

similar single-molecule based methods rely on fluorescent microscopy imaging, which requires

processing of the images for the extraction the statistical data. One problem of the MS2-GFP RNA

tagging method is the determination of the number of RNA molecules in each cell, from the extracted

fluorescence of the spots.

The present method relies on manual techniques of determination of the number of RNAs based

on the histogram of cell (or subcellular spot) intensities. It assumes that the peaks of spots intensities

are concentrated at multiples of the expected intensity of a single RNA. However, from the observation

of histograms it is possible to conclude that this assumption is invalid. First, the intensities of individual,

tagged RNAs differ significantly due to, e.g., being in different locations in the z-plane, or having differing

numbers of MS2-GFP molecules bound to them. Also, because the distribution of number of spots with

a given number of RNAs composing them is not uniform, the quantification of these numbers is not

trivial, i.e., cannot be done by plain rounding. Moreover, for large data sets, manual quantification

becomes excessively laborious and might introduce biases.

In this project, a novel method will be developed to improve RNA quantification from the

images. For that, a mathematical model for the generation of the intensities will be developed. Notably,

this model must not assume any shape of distribution of the RNAs, since this distribution is the subject

of the study. Next, the parameters of the model will be estimated and finally, a classifier is to be

constructed. The classifier will be used to determine the number of RNAs from the intensity of a cluster

of tagged RNAs. Finally, the performance of the classifier in terms of the parameters will be evaluated

and compared the current method, using an independent method of RNA quantification, namely, q-PCR

measurements.

The new method, aside from performing better than the existing method, should also allow fully

automatic quantification of RNA numbers from spot intensities and the estimation of its own accuracy.

Master’s thesis project on Signal Processing and Modeling of Genetic Circuits

Recent studies revealed that the RNA production dynamics in live Escherichia coli cells is highly

dependent on transcription. In particular, these in vivo, single-molecule studies verified that

transcription initiation is the most rate-limiting process in RNA production and that it is composed of

multiple, sequential steps. Also, due to these sequential events, RNA production is a sub-Poissonian

process and its degree of noise can be tuned to some extent, by tuning number and duration of the rate

limiting steps. Relevantly, these steps’ kinetics is both adaptive to environmental conditions and

evolvable, as they are sequence dependent.

In this Masters’ thesis project, using models of genetic circuits with delayed stochastic dynamics, it will

be investigated the effects of the rate-limiting steps in transcription initiation on genetic filter motifs. In

particular, it will be investigated how different promoters, which differ in initiation kinetics, can be used

to tune the behavior of these motifs in optimal conditions. It will also be studied how these promoters

contribute to the degree of adaptability of the circuits to different environmental conditions. For that, it

will be examined how the kinetics of transcription initiation affects the behavior of motifs performing

filtering in amplitude and frequency domain. It will also be investigated how to regulate key features of

these filters, such as the cutoff frequency, by tuning the kinetics of initiation of the constituent

promoters.

This study aims to assess the range of behaviors of genetic motifs as a function of the kinetics of

transcription initiation of the constituent promoters, and thus will aid in tuning of synthetic motifs to

attain specific characteristics without affecting their protein products. Since the kinetics and number of

the steps in transcription initiation are determined by the promoter’s sequence and its induction

kinetics, this project should not only contribute to the knowledge of the range of behaviors of genetic

motifs, but also aid in the future engineering of synthetic circuits with sought characteristics.

Application Development with Perceptual Computing Platform Irek Defee Preceptual computing platform is recently made by Intel to facilitate development of applications relying on multimodal input. The platform includes small camera with depth sensor (in this respect it is similar to Kinect but it is oriented towards desktop environment) and a comprehensive set of software tools including many functions for visual, gesture and speech. Since we jus got the Intel platform the goal of the thesis is first to learn about its operation and performance /comparing to Kinect/ and next develop simple methodology for applications controlling PC user interface using integration of multiple modalities: hand gesture, head movement and voice. For example user makes gesture with hand meaning 'open new task menu ' and at the same time provides speech input 'browser'. People in Photos Irek Defee Many digital cameras available now on the market have proprietary functions related to people and their beahvior. For example detection of face, smile, recognition of known face existing in the database. There is also ongoing public research on identification of groups of people and their arrangement, for example group of photo in two rows, sitting at a table. The goal of the thesis is review, analysis and development of public algorithms for group photo sharing common face

expression. This will be done by combining recent algorithms for face expression detection with algorithms for evaluation of the type of a group in photo. The algorithms will be based on most likely on boosting or SVM for faces and graph matching for groups. Summarization of massive amount of surveillance video data for a forensic analysis Background: Strong demand for security and safety has driven rapid growth in the use of video surveillance during recent years. As a result, an ever-increasing amount of our activities in public spaces are monitored through video surveillance and stored for further possible analysis. Consequently, this video material serves as a common source of evidence for modern law enforcement and forensic investigation. As the amount of surveillance video material increases, the traditional forensic investigation process, where all video material is analyzed from the beginning to the end, becomes more time-consuming. Modern signal processing methods are needed for summarizing the forensically relevant information from the large amount potentially redundant and irrelevant information. Objective: In this project, the student will explore the applicability of existing video summarization algorithms (e.g. scene change detection and synopsis generation algorithms) in the forensic framework and develop new methods for the task. Requirements: Basic knowledge in image/video processing, strong programming skills (C++), prior experience of OpenCV is a plus. External collaborators: Antti Lehmussola, National Bureau of Investigation More information: Pekka Ruusuvuori (pekka.ruusuvuori@tut.fi)

Content-based Feature Synthesis on Local Visual Descriptors

Domain: Data Mining, Evolutionary Algorithms

Contents: The thesis shall address the problem of synthesizing highly descriptive visual features

from the local visual descriptors such as SIFT and SURF that are extracted from a

large number of key-points. The main goal of the theses will be to tackle major

drawbacks of these local descriptors, which are the representation ineffectiveness,

their massive number in a regular image database and the computational complexity.

In other words the features synthesized should aim to narrow the “Semantic Gap”

with a compact representation. The starting point will be the evolutionary feature

synthesis framework that has recently been developed by the MUVIS group and

successfully applied to content-based audio classification and retrieval, [Mak12] and

CBIR.

[Mak12] T. Mäkinen, S. Kiranyaz, J. Raitoharju and M. Gabbouj, "Evolutionary Feature

Generation for Content-based Audio Classification and Retrieval", EURASIP Journal on

Audio, Speech, and Music Processing 2012, 2012.

Evolutionary Wavelet Neural Networks for Content Classification of Dynamic and Large Image

Repositories.

Domain: Machine Intelligence, Data Mining

Contents: The thesis shall focus on evolving Wavelet Neural Networks (WNNs), which

naturally combine the properties of the Wavelet decomposition along with the

characteristics of artificial neural networks (ANNs) and hence promises a superior

performance. The evolutionary process will simultaneously search for the optimal 1)

the wavelet function, 2) the wavelet parameters and 3) the number of wavelets for the

learning problem at hand. The ¿nal distribution of weights (after optimization)

inherits the suitability of wavelets for the best ‘feature detection’ and the result is an

optimal network, which is directly related to the underlying search space of the

problem, and in which all parameters are jointly ¿tted from its data. The starting point

will be the optimization technique recently proposed in a publication of the MUVIS

group [Kir09], which has become one of the most-cited papers in the Journal’s

history. The evolutionary WNNs will then be used as the base classifiers in the

collective network of binary classifier (CNBC) framework that is used for content-

based image classification and retrieval.

[Kir09] S. Kiranyaz, T. Ince, A. Yildirim and M. Gabbouj, “Evolutionary Artificial Neural

Networks by Multi-Dimensional Particle Swarm Optimization”, Neural Networks, vol. 22, pp.

1448 – 1462, Dec. 2009.

Multi-Dimensional TRIBES

Domain: Stochastic Optimization, Evolutionary Algorithms

Contents: The particular Particle Swarm Optimization (PSO) variant, “Tribes”, is basically a

parameter-free PSO algorithm and promises major advantages over the canonical

PSO. Yet it has the same limitation as PSO, which is the capability of searching in a

fixed search (solution) space dimension. The thesis shall address this problem by

proposing a multi-dimension extension, which seeks both positional and dimensional

optima. Furthermore, the methods for avoiding early trappings to local minima will

be investigated. The starting point is the recent optimization technique developed by

the MUVIS group, the Multi-Dimensional PSO (MD PSO) and Fractional Global

Best Formation (FGBF) [Kir10] both of which have successfully been applied to

several problem domains such as dynamic data clustering, machine intelligence, data

mining, and CBIR. Once the optimization technique, Multi-Dimensional TRIBES, is

mature enough, it will be used in many similar applications where the optimalsolution space

dimension is unknown as long as the potential solution is encoded in

the swarm particles accordingly and a proper fitness function is designed.

[Kir10] S. Kiranyaz, T. Ince, A. Yildirim and M. Gabbouj, “Fractional Particle Swarm

Optimization in Multi-Dimensional Search Space”, IEEE Transactions on Systems, Man, and

Cybernetics – Part B, pp. 298 – 319, vol. 40, No. 2, April 2010.

EEG Classification by Scalable, Data-adaptive and Self-evolving Network of Classifiers.

Domain: Machine Intelligence, Bio-Medical Applications

Contents: The electroencephalogram (EEG) is analyzed by physicians in order to detect neural

rhythms and abnormal brain activities such as Epilepsy, which is a chronic disorder of

the central nervous system that predisposes individuals to experiencing recurrent

seizures. The analysis and accurate classification of the EEG signal is quite difficult

and may be infeasible because it is usually a highly dynamic signal with several

pattern variations and also a long term signal which may have several days of

recording. Moreover it is a multichannel recording of the electrical activity generated

by the collections of neurons in the brain and it is generally contaminated with

different noise sources and mixed with other biological signals. The thesis shall

address these limitations and drawbacks of the EEG classification with a “Divide and

Conquer” type approach and contribute to develop a patient-specific EEG

classification framework that will be formed based on a similar approach for ECG

signals proposed earlier in [Inc09]. To accomplish this, a scalable, data-adaptive and

self-evolving network of classifier framework will be developed where such a

massive learning problem (in terms of data size, variations, several features and

multiple channels) will be “divided” into manageable, homogenous chunks using

such a network of classifiers. A reference system for this is the collective network of

(evolutionary) binary classifier (CNBC) framework [Kir12] that has recently been

developed by the MUVIS group.

[Inc09] T. Ince, S. Kiranyaz, and M. Gabbouj, “A Generic and Robust System for

Automated Patient-specific Classification of Electrocardiogram Signals”, IEEE Transactions

on Biomedical Engineering, vol. 56, issue 5, pp. 1415-1426, May 2009.

[Kir12] S. Kiranyaz, T. Ince, S. Uhlmann, and M. Gabbouj, “Collective Network of Binary

Classifier Framework for Polarimetric SAR Image Classification: An Evolutionary

Approach”, IEEE Transactions on Systems, Man, and Cybernetics – Part B, pp. 1169-1186,

August 2012.

Optimal Codebook for Visual Bag of Words

Visual Bag-of-Words has become one of the key tools for “google kind of” image based search. In this project you play with the existing code and data and you writen a machine learning algorithm that will iteratively or randomly generate, test and search optimal codebooks for the BoW based image matching. In particular, the codebooks based on linear filters will be considered.

C/C++ and/or Matlab skills are required. Supervisor: Prof Joni Kamarainen Please contact the supervisor for more details.

Image Alignment Using Pairwise Matching

The main idea of this project is to extend our previous image alignment method using single global seed, to align images pairwise and then build a tree structure which can align each image to any other image via the tree paths. For more details see our BMVC paper.

C/C++ and/or Matlab skills are required. Supervisor: Prof Joni Kamarainen Please contact the supervisor for more details.

3D Interest Points from Stereo Images

Interest points have been the hot topic of computer vision for some time and they are the low level operators for image based search engines. In this project you will step on a cutting-edge topic by developing such interest points further - to be used with a stereo pair images. This means that on the low level you utilise existing interest point detectors, but then you should select only those interest points which match between the left and right view and then add 3D information to them (depth). In this project you learn about stereo imaging and state-of-the-art methods for image-based search.

C/C++ and/or Matlab skills are required. Supervisor: Prof Joni Kamarainen Please contact the supervisor for more details.

What Really is Important in Objects

This work is based on the hypothesis that there are certain local features which are more important than other. Those features can be found from many natural objects and they appear similarly in objects of a same class (car, face, etc.) In this work you will study such important features and make a detector which automatically finds them. First you will use our existing framework to select points which appear similarly in other examples of the same class and then you devise a detector especially for these points.

C++ and/or Matlab skills are required. Supervisor: Prof Joni Kamarainen Please contact the supervisor for more details.

Symbolic Description of 3D Objects

In this work, you learn some cutting edge technologies. You will learn about local features which are used in modern image based search technologies. Moreover, you will learn about stereo images which can now be produced by off-the-shelf cameras. Your task is to describe a 3D object, captured by a stereo camera, using local symbols - a kind of “3D interest points”.

C++ and/or Matlab skills are required. Supervisor: Prof Joni Kamarainen Please contact the supervisor for more details.

Detection of Things from Images

How to detect that there is a “thing” or things in an image. A thing is something which will be interesting for humans and/or important for automatic image retrieval. Humans, cars, buildings are all “things”, but how to automatically detect them. Can you make an object specific detector or even a detector for all things. That will be experimentally investigated in your work. In this work you can do play with a cutting edge problem that also interests certain big companies at the moment.

C++ and/or Matlab skills are required. Supervisor: Prof Joni Kamarainen Please contact the supervisor for more details.

Lost in Probabilities

Probability theory and statistics provide the fundamental background for solving the most difficult problems in computer vision, pattern recognition, machine learning and engineering in general. Do you still feel uncomfortable while encountering probabilities? Do you, however, want to master probabilities and important concepts related them? In this project you will learn about the basic things with probabilities. You will learn what is likelihood, study how to combine likelihoods, how to transform likelihoods to probabilities and play with the concept of “probability score” developed in our laboratory. How the likelihoods can be reliably converted to probability scores and are the probability scores probabilities themselves? Your main task is to briefly review the basic probability theory, spot the most important concepts, review related literature for the selected concepts, explain the main results and program simple examples to explain and verify the theory. The main emphasis is on single and multiple Gaussian densities which have been found very efficient methods for computer vision problems in our laboratory. If you enjoy mathematics and enjoy learning new and powerful things, this could be your project.

Requires Matlab programming (examples) Supervisor: Prof Joni Kämäräinen Please contact the supervisor for more details

Robust Object Class Descriptors

This project is continuum to the previously done master's thesis in our laboratory:

Ville Kangas. Comparison of Local Feature Detectors and Descriptors for Visual Object Categorization, 2011.

In the previous work, it was shown how the existing descriptors perform bad for multiple images of the same class (e.g. motorbike, car etc.) Moreover, an automatic evaluation framework was developed. In this work, you will utilise the existing framework and develop and evaluate new descriptors which would be more suitable for the task. In this work, you will study some of the state-of-the-art technologies of computer vision and image analysis.

Requires programming Supervisor: Prof Joni Kämäräinen Please contact the supervisor for more details

3D Geometric Transformations

This topic will introduce you to the wonderful world of 3D transformations, i.e. how you can manipulate (translate, rotate, scale) 3D graphical objects. Background or interest to computer graphics will be appreciated. With your code you can for example rotate 3D face images or register a set of 3D face images close to each other

Experiments implemented on Matlab or as a combo of Matlab and C graphic libs Supervisor: Prof Joni Kämäräinen Please contact the supervisor for more details

Camera-Projector System Calibration Using a Flat Display

In this work you will learn how to calibrate a digital camera and a projector. You will implement a GUI based calibration system, which uses a display instead of a physical calibration pattern. You will learn about geometry between the world, camera and projector.

Requires programming (mainly Matlab) Supervisor: Prof Joni Kämäräinen Please contact the supervisor for more details

Detecting viruses and bacteria by next generation sequencing of human stool and blood

samples

In clinical research it is very important to be able to detect the viruses and bacteria in the samples from

the patients. The detection and characterization of these pathogens requires many steps in analyzing

the samples in the laboratories. The complexity of the analysis and the lack of automation is an obstacle

for the rapid identification of the pathogens. However, the recent advances on the next

generation

sequencing methods have enabled the detection of the pathogens of the samples. These methods can in

future be utilized more generally in clinical microbiology. Further, by analyzing these metagenomic data,

it is possible to obtain new information on the samples, which can then be associated to the diseases. In

this M.Sc. thesis project, the goal is to review the existing next generation sequencing methods available

for the pathogen analysis, perform a NGS data analysis for stool and blood samples in order to detect

the viruses and bacteria of the samples, and further associate these results to other clinical parameters,

and write a report of the work.

Front-end data processing of new positron emission tomography demonstrator

M.Sc. work begins as soon as the person is selected

M.Sc. work finished: 30.10.2013

Supervisors: Professor Ulla Ruotsalainen and Postdoctoral Researcher Viivi Nuottajärvi

Content of the work

The M.Sc. work will be related to an ongoing TUTLI-project (AvanTomography) of the Department of Signal Processing. The target of the AvanTomography -project is to commercialize new innovative positron emission tomography (PET) technology. Tampere University of Technology has filed a patent application to protect the innovation.

During the ongoing project, we have built a PET-demonstrator that is based on the new AvanTomography -technology. Within the AvanTomography -detector, the scintillating crystals convert the high-energy photons (gamma radiation) to visible light. Visible photons are read by multi-pixel photon counters (also called avalanche photo diodes (APDs)) which supply electrical signal to data acquisition system. This electrical signal is collected as current pulses (charge). The measurement control and data collection of the demonstrator is implemented with Labview -software. Further data analysis is performed by Matlab.

The content of the M.Sc. thesis is related to the further development and testing of the demonstrator. The M.Sc. thesis work will include algorithm development related to the front-end signal processing of the measurement data, like dark-current thresholding and energy windowing. For PET -imaging, very high gain is needed (105 to 106) and APDs are operated with a reverse voltage above the breakdown voltage. In this case, the APD needs to have its signal current pulses limited and quickly diminished. APDs that operate in this high-gain regime are in Geiger mode. This mode is particularly useful for single photon detection provided that the dark count event rate is sufficiently low. However, the dark count rate of the MPPC is approximately of 5 MHz. This low-level dark-count signal should be thresholded from the real measurement data. Different energies of the detected gamma rays are distinguished by the size of the current

pulses (charge). In PET -imaging, the energy of 511 keV is the crucial one. Certain energy region can be distinguished from the signal spectrum by energy windowing.

Efficient front-end signal processing improves the usability and efficiency of the technology. Due to extensive front-end processing, the transfer of useless data and the need for data post-processing can be reduced significantly. The target is to obtain the data in list-mode format, which can be directly utilized for e.g. 3D-image reconstruction. The imaging resolution is limited by the size of the scintillating crystals, detector geometry and sampling of the data. Together with the development of the demonstrator, the proposed signal processing tasks should be optimized to achieve the best imaging resolution and photon detection sensitivity.

Graph cuts for 3-D brain MRI segmentation Markov Random Fields (MRFs) are an elegant technique to incorporate spatial priors in medical image segmentation and, in particular, they are very often an essential component of the methods aiming to brain MR (magnetic resonance) image segmentation. With the help of MRFs, one can derive a spatial prior penalizing the segmentations that are noisy or otherwise unlikely. However, a drawback of MRFs is that their use leads to a complex, non-convex optimization problem. Up until recently, good global optimization algorithms for MRFs have not existed and ineffective local algorithms have been used. However, presently several global optimization schemes for MRFs in image processing have emerged, the most widely used schemes being based on graph cuts (Boykov and Funka-Lea 2006), and the aim of this M.Sc. thesis is to examine if the better optimization leads to better quantitative segmentation accuracy. This has received surprisingly little attention in literature up to date. The topic is significant because nearly every brain study based on anatomical MRI requires automatic image segmentation. Also, graph cuts have emerged as an extremely important technique in image processing and mastering them will be useful in any application requiring image segmentation. The standard databases for MRI segmentation evaluation are used as material and these are already in place (in a pre-processed form) in the Department of Signal Processing. The optimization algorithms will be tested in the framework of the segmentation method described in Tohka et al (2004), which is widely used in the brain imaging community. The task for the thesis worker will be to incorporate improved optimization algorithms to the segmentation pipeline in Tohka et al (2004), run the experiments and interpret the results. This requires strong signal/image processing skills, a good command of Matlab and some knowledge of C/C++ due to the fact that the best implementations of graph cut based optimization algorithms are in C/C++. The thesis can be written either in English or in Finnish. References:

J. Tohka, A. Zijdenbos, and A.C. Evans. Fast and robust parameter estimation for statistical partial volume models in brain MRI. NeuroImage, 23(1):84 - 97, 2004 Yuri Boykov, Gareth Funka-Lea. Graph Cuts and Efficient N-D Image Segmentation. In International Journal of Computer Vision (IJCV), vol. 70, no. 2, pp. 109-131, 2006. Foveated nonlocal imaging Patch-based nonlocal imaging methods rely on the assumption that natural images contain a large number of mutually similar patches at different locations within the image. Patch similarity is typically assessed through the Euclidean distance of the pixel intensities and therefore depends on the patch size: while large patches guarantee stability with respect to degradations such as noise, the mutual similarity that can be verified between pairs of patches tends to reduce as the patch size grows. Thus, a windowed Euclidean distance is commonly used to balance these two conflicting aspects, assigning lower weights to pixels far from the patch center. Patch foveation was proposed as an alternative to windowing in nonlocal imaging. Foveation corresponds to a spatially variant blur operator, characterized by point-spread functions (PSFs) whose bandwidth decreases with the spatial distance from the patch center. In contrast with the conventional windowing, which is only spatially selective and attenuates sharp details and smooth areas in equal way, patch foveation provides selectivity in both space and frequency domain. The approach is inspired by the human visual system (HVS): if we treat the center of the patch point as a fixation point, the foveated distance mimics the inability of the HVS to perceive details at the periphery of the center of attention. Patch similarity can be thus assessed by the Euclidean distance of foveated patches, leading to the concept of foveated self-similarity. Given an arbitrary windowing kernel, we present an explicit construction of a foveation operator yielding a foveated distance that, in terms of expectation under i.i.d. zero-mean Gaussian noise, is guaranteed to be equivalent to the corresponding windowing distance. However, in presence of differences in the signal, the windowed and foveated distances are fundamentally distinct, with the latter providing stronger response with respect to signal structures. This foveated self-similarity can be leveraged in a number of imaging applications, and particularly in image filtering, where foveated self-similarity turns out to be more effective than the windowed

self-similarity in assessing the patch similarity for nonlocal means denoising. The proposed MSc thesis work involves: 1) detailed study of the existing foveated local and nonlocal imaging techniques; 2) study of the connections between foveated imaging and features of the human visual system 3) further development of nonlocal foveated imaging, in particular for the interpolation of grayscale and color images. Supervisor: Alessandro Foi, Academy Research Fellow Distributed Beamforming on an Ad-Hoc Microphone Array: Microphone bearing devices such as mobile phones, laptops, and tablets are present in many social interaction situations. Their location is generally unknown, they have no temporal synchronization. This project investigates the use of traditional signal enhancement methods on a distributed ad hoc microphone array, such as beamforming. Ongoing research in the Audio Research Group has resulted in novel concepts of enabling the use of traditional multichannel signal processing techniques on such arrays, which will be exploited in this work. The project goal, in addition to the MSc thesis, is a scientific publication on the topic. Requirements: Audio and array signal processing, audio measurements, programming skills (Matlab), interest towards reasearch work. Contact: Pasi Pertilä, pasi.pertila@tut.fi Real-time sound event detection

Tuomas Virtanen, tuomas.virtanen@tut.fi

The objective of the project is to develop a real-time sound event detection system for a Linux-

based handheld device platform. The system consists of a sound event detection back-end and a

user interface front-end. The back-end is monitoring surrounding environment continuously,

producing a symbolic representation of the environment by detecting sound events (such as

doorbell, siren, car passing by) and assigning textual labels to them. The user interface front-end

will present the detection results visually to the user.

The project consist of the following tasks: 1) implementation of real-time signal processing and

detection algorithms, and 2) development of a graphical user interface for visualizing the detected

sound events. The project can utilize existing offline algorithms available at TUT [1,2].

The thesis will be supervised by Academy Research Fellow and Adjunct Professor Tuomas

Virtanen and MSc Toni Heittola.

References:

[1] T. Heittola, A. Mesaros, A. Eronen and T. Virtanen. Context-dependent sound event detection.

EURASIP Journal on Audio, Speech, and Music Processing 2013, 2013:1.

[2] CASAbrowser, http://arg.cs.tut.fi/demos/CASAbrowser/index.html

Proposed topic for a diploma engineer thesis by Tapio Saramäki

A modified comb filter structure for decimation

The purpose of this diploma work is to re‐study the material included in

T. Saramäki and T. Ritoniemi, “A modified comb filter structure for decimation,” in Proc. 1997 IEEE

International Symposium on Circuits and Systems (Hong Kong), May 1997, pp. 2353–2356 (see the

attachment “Saramaki_Ritoniemi.pdf”).

This idea has a USA patent (see US_patent.pdf) and it has been used in the VLSI circuit introduced in

T. Ritoniemi, E. Pajarre, S. Ingalsuo, T. Husu, V. Eerola, and T. Saramäki, “A stereo audio sigma‐delta A/D

converter,” IEEE Journal of Solid‐State Circuits, vol. 29, no. 12, pp. 1514–1523, December 1994 (see

solid_state.pdf; stereo_AD_converter_for_audio.pdf shows the upper part of Figure 16 in more detail).

A suggested title for the diploma thesis is “A modified cascaded integrator‐comb decimation filter” and

it has three goals. First, it will be studied whether ideas proposed in

Kwentus, A.; Lee, O.; Willson, A.N., Jr; , "A 250 Msample/sec programmable cascaded integrator‐comb

decimation filter," in Workshop on VLSI Signal Processing, IX, Nov 1996, pp.231‐240.

for reducing the number of bits in the internal evaluations can also be applied to the modified structure.

Second, a MATLAB code will be generated for the coefficient optimization. Third, the original idea has

roughly only 30 citations because it has been published as conference article. Hence, there is a need to

write a journal article for increasing its visibility. It should be emphasized that the proposed cascaded

integrator‐comb decimation filter is very attractive first filter stage after a sigma‐delta modulator.

The use of ubiquitous digital camera in medical imaging Aim: To find out the technical benefits and limitations of ubiquitous digital cameras in medical imaging applications. Background: The digital camera technology has evolved a lot during the last years. These cameras can be used in a wide range of applications in primary and specialist health care. However, the technical limitations of these cameras are seldom analyzed. For example, the cameras are intended for taking visually pleasing images which may not be suitable for computerized analysis. Even for visual use, the complete imaging chain from photography to viewing should be taken into account. Fields: Medical imaging, image processing, photography, human vision (color vision), risk analysis, legislation. Other comments: This thesis involves a varying amount of practical testing. In case the work is done carefully, a scientific paper can be written on the results. There are several possible case studies, e.g., the use of green filters vs. digital image processing in retinopathy screening. Skin photography for allergy testing Aim: To create an arrangement for photographing human skin so that the photograph is as suitable as possible for digital interpretation. Background: Skin photography is required in several medical applications. However, taking a photograph of the human skin is challenging due to different skin colors, texture, and reflections. The photographs should be suitable to be used with digital image processing methods, which sets several requirements for the image quality.

Fields: Medical imaging, practical photography, digital image processing. Other comments: This topic requires a hands-on approach, as different illumination methdos and camera settings have to be tested and evaluated. Also, it is possible to explore the possibilities of light field imaging (3D imaging) within this topic. This topic and topic #3 can be combined. Automatic interpretation of skin reactions in allergy testing Aim: To improve the diagnostic properties of a semi-automatic skin reaction interpretation system developed earlier. Background: The automatization of the most common allergy test - skin pric test - has been studied previously at TUT with reasonably promising results. The camera technology has improved a lot since that study, and there are numerous other improvement possibilities which have been identified. Fields: Medical imaging, digital image processing, photography Other comments: To some extent this topic requires some information from topic #2, but the topics can be combined. The aim of the study is to help provide new scientific information on skin reactions, and thus creating a scientific paper based on this research should not be difficult. This topic requires a solid knowledge of digital image processing.

Error estimation in machine learning

The problem of estimating the test prediction error in classification

and regression problems is usually addressed using traditional cross-validation techniques.

However, recent research results suggest that the traditional techniques may have a poor

performance, especially if the number of training samples is small. This may lead in

false decisions (which classifier to use, which parameters to use, which features to use, etc...)

while developing the classifier. In this thesis, novel techniques for error estimation

will be studied and compared with the traditional ones. The test data is public data

for recent pattern recognition competitions.

Kuvantava kaikuluotain Description: Diplomityö koskee tutkimuskäyttöön tarkoitetun kaikuluotaimen rakentamista ja kuvantamisalgoritmien toteuttamista yhdessä MMDM-ryhmän tutkijoiden kanssa. Työksi voidaan rajata sopiva osuus järjestelmän toteutuksesta perustuen tekijän osaamisprofiiliin. Ohjelmoinnissa käytetään ensisijaisesti MATLAB-ympäristöä. Requirements: Ohjelmointiosaaminen sekä perusteet signaalinkäsittelystä ja mittaustekniikasta; elektroniikkarakentelukokemus/-harrastus on plussaa. Additional information: Juha Jylhä, juha.jylha{at}tut.fi. Robotin paikannus perustuen kameraan Description: Diplomityö koskee testilaitteiston rakentamista ja paikannusalgoritmin toteuttamista. Paikantaminen perustuu robotin ottamien kuvien ja ympäristön assosiointiin, kun ympäristöstä on saatavilla asianmukainen digitaalinen tieto. Tehtävä edellyttää vahvaa itsenäistä ongelmanratkaisukykyä. Ohjelmoinnissa käytetään ensisijaisesti MATLAB-ympäristöä. Requirements: Ohjelmointiosaaminen sekä perusteet kuvaan perustuvasta mittauksesta ja signaalinkäsittelystä; kokemus tai harrastus kameroiden tai elektroniikan parissa on plussaa. Additional information: Juha Jylhä, juha.jylha{at}tut.fi. Tutkan mallintaminen ja simulointi Description: Diplomityö koskee tutkan mallintamisen ja simuloinnin menetelmien kehittämistä huomioiden eri tutkajärjestelmien ominaisuudet ja tutkan mittausympäristö. Mallinnukseen huomioidaan radioaallon eteneminen ja sironta sekä tutkan antenni, elektroniikka ja signaalinkäsittely. Ohjelmoinnissa käytetään ensisijaisesti MATLAB-ympäristöä. Requirements: Hyvä ohjelmontiosaaminen sekä perusteet signaalinkäsittelystä; eduksi katsotaan myös osaaminen tietoliikennetekniikassa, mittaustekniikassa, matematiikassa, fysiikassa ja elektroniikassa. Additional information: Juha Jylhä, juha.jylha{at}tut.fi.

Bat-like audio radar-based navigation for the visually impaired Human hearing is very good at performing spatial analysis of the surrounding space based on the reverberation information. Some visually impaired humans have developed the rare ability to locate objects and move around based on their hearing alone, by producing a constant stream of clicks with their mouth and analyzing the sound as it bounces back from objects and walls. However a drawback of this method is that the produced sound is obstrusive at public places. In this thesis, we develop 1) a simple ultrasound-producing device that is mounted to the head of a subject and 2) an application that maps ultrasound signals form the user's enviroment to the audible frequencies and feeds them to the user's ears. The aim is then to test how well can a normally seeing (or alternatively visually impaired) subject understand the surrounding space based on this bat-like audio radar alone. Musically and perceptually motivated representations for audio processing Time-frequency representations of audio signals are essential for the analysis and manipulation of audio signals. The most commonly used representation is the spectrogram, obtained by applying short-time Fourier transform in successive time frames. However, an important drawback of the Fourier transform is that it has constant frequency resolution across the audible spectrum. This is in sharp contrast with musical and perceptual facts: Human auditory system employs better frequency resolution for low frequencies and better time resolution for high frequencies: the frequency "bins" in hearing are approximately logarighmically spaced. Similarly in music, the note frequencies are logarithmically spaced. This M.Sc. thesis focuses on 1) developing near-constant-Q transforms that mimic the frequency resolution trade-offs in human hearing and 2) their application in audio manipulation, such as pitch shifting of audio and audio coding. Study of intracellular micro environment signals using signal processing method at single cell level

Background:

Unicellular organisms need to be able to respond properly to internal and external signals

by altering the dynamics of its gene expression dynamics. An important feature for the survival

of a population of monoclonal cells is its phenotypic diversity. Several studies have shown how

cell-to-cell variability in some cellular characteristics emerges from biochemical stochasticity.

One of the first observations of phenotypic dependence on the noise in the dynamics of a genetic

circuit was reported in (Neubauerz and Calef, 1970). It is also observed that some bacteria like

Bacillus subtilis can transiently and probabilistically differentiate between two phenotypes, when

under stress (Suel et al., 2006). Factors like intracellular pH, ionic concentration, metabolite

concentration etc are known to play roles in cellular signaling and gene expression. Therefore,

the proposed research project will focus on studying the role of intracellular ionic concentration

and pH on the dynamics of gene expression by observing the transcription dynamics at the single

cell and total population in Escherichia coli. The execution of the project requires knowledge in

experimental biology, computer science and signal processing.

Objectives:

1. Executing cell culturing and microscopy experiments

2. Analysis of microcopy images, development of signal processing tool and interpretation of the

data

Requirements:

For goal (1): Basic knowledge in cell and molecular biology and experience in microbial cell

culture techniques, and microscopy. For goal (2): Programming skills in MATLAB.

Deep neural networks for acoustic pattern recognition

Deep neural networks (DNNs) consisting of multiple layers of simple networks have recently been

shown to produce superior accuracy in comparison to conventional, more simpler network

topologies [1]. Also new learning methods have been proposed to allow estimating the parametrs of

such networks. In audio signal processing. Most of the research related to DNNs has studied

automatic speech recognition [2], but it has potential in any pattern recognition tasks such as image

classification [3].

The thesis will investigate the use of DNNs in automatic sound event recognition in realistic

environments. The work consists of the following tasks: 1) familiarizing oneself with available

DNN implementations, or implementing suitable algorithms in Matlab, and 2) evaluating the

performance of selected DNN classifiers on acoustic event detection. Databases available at TUT

[4] will be used. Also the possibility to use DNNs for front-end feature extraction will be

investigated, so that the methods can be integrated to existing pattern recognition architectures. The

results of the work are aimed to be published in an good-quality peer-reviewed international

conference.

The thesis will be supervised by Academy Research Fellow / Adjunct Professor Tuomas Virtanen.

Another researcher from the audio research team that is familiar with the specific pattern

recognition task may participate in the supervision.

References:

[1] IEEE Transactions on Audio, Speech, and Language Processing, Volume: 20 , Issue: 1, 2012.

[2] H. Hinton et al., Deep Neural Networs for Acoustic Modeling in Speech Recognition, IEEE

Signal Processing Magazine, Volume 29, Number 6, 2012.

[3] D. C. Cireşan et al. Deep, Big, Simple Neural Nets for Handwritten Digit Recognition, Neural

Computation, Vol. 22, No. 12, 2010.

[4] T. Heittola, A. Mesaros, A. Eronen and T. Virtanen. Context-dependent sound event detection.

EURASIP Journal on Audio, Speech, and Music Processing 2013, 2013:1.

Fast multi-view image-based rendering for light-field displays

Background: Several display technologies exist to present viewers with 3D imagery. These include stereoscopic displays with or without glasses, and multiview systems and light field displays. The light-field display technology uses a specially arranged array of optical modules and a holographic screen. Each point of the holographic screen emits light beams of different color and intensity to the various directions. The light beams generated in the optical modules hit the screen points in various angles and the holographic screen makes the necessary optical transformation to compose these beams into a perfectly continuous 3D view.

Light-field displays stand out from the wide range of 3D displays with exceptionally wide field-of-view (FOV) and pixel (light ray) count. These advantages come at a cost, though. Light-field displays as of today are costly and contain a massive amount of identical optical / electrical components working in parallel. Light-field rendering becomes the bottle neck for the wide adoption of light-field displays.

Light-field rendering is about producing wide-baseline / large field-of-view content for light-field displays by using some available light-field or geometrical-scene representation. A dense array of camera views (multi-views) displaced only a few degrees apart is the straightforward and simple but overcomplete and costly representation. Light-field rendering methods employed on respective displays include simple Look Up Table (LUT) based methods with filtering, real-time view-dependent depth estimation and rendering, while others approach the problem with image-domain rendering (warping), avoiding explicit depth estimation.

Thesis task: Study and overview image based rendering methods so far applied to multi-view displays. Select and modify a promising method and develop new one which should be applicable to light-filed displays. Consider a given setup of small number or cameras (input views), develop new view synthesis methods to obtain wide FOV content.

Requirements: Knowledge of basic signal and image processing algorithms (decimation, interpolation, resampling); good matlab skills; knowledge of GPU and related programming (OpenCL, Cuda) is not obligatory by will be considered as beneficial

Further information: Dr. Atanas Gotchev, TE415, atanas.gotchev@tut.fi