SCALABLE APPROXIMATION OF KERNEL FUZZY C-MEANS

Virtually every sector of business and industry that uses computing, including financial analysis, search engines, and electronic commerce, incorporate Big Data analysis into their business model. Sophisticated clustering algorithms are popular for deducing the nature of data by assigning labels to unlabeled data. We address two main challenges in Big Data. First, by definition, the volume of Big Data is too large to be loaded into a computer’s memory (this volume changes based on the computer used or available, but there is always a data set that is too large for any computer). Second, in real-time applications, the velocity of new incoming data prevents historical data from being stored and future data from being accessed. Therefore, we propose our Streaming Kernel Fuzzy c-Means (stKFCM) algorithm, which reduces both computational complexity and space complexity significantly. The proposed stKFCM only requires O(n2) memory where n is the (predetermined) size of a data subset (or data chunk) at each time step, which makes this algorithm truly scalable (as n can be chosen based on the available memory). Furthermore, only 2n2 elements of the full N × N (where N >> n) kernel matrix need to be calculated at each time-step, thus reducing both the computation time in producing the kernel elements and also the complexity of the FCM algorithm. Empirical results show that stKFCM, even with relatively very small n, can provide clustering performance as accurately as kernel fuzzy c-means run on the entire data set while achieving a significant speedup.

Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

Babxel: búsqueda multilingüe

La Web 2.0 ha tenido un enorme éxito gracias a la posibilidad de una interacción dinámica por parte del usuario, ya no sólo a la hora de participar en elementos colaborativos, como puedan ser los foros, sino en compartir/añadir contenido a la Web. Dos ejemplos claros de este paradigma son YouTube y Flickr. El primero hospeda la mayor parte de los vídeos que podemos encontrar en Internet, y el segundo ha creado la mayor comunidad de fotógrafos existente en la red. Ambos servicios funcionan de una forma similar, el usuario es el que aporta contenidos junto a una información asociada al mismo. Al ser comunidades internacionales, la información añadida por el usuario se realiza en diversos idiomas, por lo que la búsqueda de recursos multimedia en estos sitios es dependiente del idioma de la consulta. En este artículo, presentamos Babxel, un sistema de recuperación de información multimedia y multilingüe, nacido como proyecto de fin de carrera de Ingeniería Informática, como extensión y mejora de FlickrBabel. Babxel aprovecha la capacidad de traducción multilingüe automática para generar más resultados de búsqueda relacionado con la consulta del usuario, resultados que se obtienen de las plataformas mencionadas anteriormente.

A project-oriented course in probability and statistics for undergraduate electrical engineering students

An introductory course in probability and statistics for third-year and fourth-year electrical engineering students is described. The course is centered around several computer-based projects that are designed to achieve two objectives. First, the projects illustrate the course topics and provide hands-on experience for the students. The second and equally important objective of the projects is to convey the relevance and usefulness of probability and statistics to practical problems that undergraduate students can appreciate. The benefit of this course as to motivate electrical engineering students to excel in the study of probability concepts, instead of viewing the subject as one more course requirement toward graduation. The authors co-teach the course, and MATLAB is used for mast of the computer-based projects

Computer analysis of large civil engineering structures

The finite element method is now well established among engineers as being an extremely useful tool in the analysis of problems with complicated boundary conditions. One aim of this thesis has been to produce a set of computer algorithms capable of efficiently analysing complex three dimensional structures. This set of algorithms has been designed to permit much versatility. Provisions such as the use of only those parts of the system which are relevant to a given analysis and the facility to extend the system by the addition of new elements are incorporate. Five element types have been programmed, these are, prismatic members, rectangular plates, triangular plates and curved plates. The 'in and out of plane' stiffness matrices for a curved plate element are derived using the finite element technique. The performance of this type of element is compared with two other theoretical solutions as well as with a set of independent experimental observations. Additional experimental work was then carried out by the author to further evaluate the acceptability of this element. Finally the analysis of two large civil engineering structures, the shell of an electrical precipitator and a concrete bridge, are presented to investigate the performance of the algorithms. Comparisons are made between the computer time, core store requirements and the accuracy of the analysis, for the proposed system and those of another program.

Applying recommendations to aligncompetences, methodology and assessment in Telematics, Computing and Electronic Engineering courses

The alignment between competences, teaching-learning methodologies and assessment is a key element of the European Higher Education Area. This paper presents the efforts carried out by six Telematics, Computer Science and Electronic Engineering Education teachers towards achieving this alignment in their subjects. In a joint work with pedagogues, a set of recommended actions were identified. A selection of these actions were applied and evaluated in the six subjects. The cross-analysis of the results indicate that the actions allow students to better understand the methodologies and assessment planned for the subjects, facilitate (self-) regulation and increase students’ involvement in the subjects.

Benchmarking project of the Department of Electrical Engineering of Lappeenranta University of Technology

Lappeenrannan teknillisen korkeakoulun sähkötekniikan osasto muutti 1.8.2005 sähkötekniikan tutkinnon kaksiportaiseksi ja vastaamaan näin Bologna-prosessia ja Suomen yliopistolainsäädäntöä. Tutkinnonuudistuksen myötä osasto haluaa varmistaa sähkötekniikan tutkintojen laadun ja vertailtavuuden sekä parantaa opiskelijoidensekä henkilökunnan liikkuvuutta. Tutkintojen laatu ja vertailtavuus osoitetaan sähkötekniikan osaston benchmark-projektilla, jossa kerätään tietoja maisteri- ja tohtorintutkintoa tarjoavista eurooppalaisista yliopistoista. Diplomityö käsittää BM-projektin kolmannen vaiheen suunnittelun ja toteutuksen sisältäen teoriaa benchmark-projekteille tyypillisistä toimintatavoista. Hyväksi havaitut menetelmiä, kuten kyselyt ja matriisit, on tässä työssä otettu soveltuvin osin sähkötekniikan osaston BM-projektin työkaluiksi. Diplomityössä analysoidaan työkalujen avulla BM-kumppaneilta kerättyjä tietoja sekä esitetään ratkaisuja, miten sähkötekniikan osastolla voidaan jatkaa parhaiden toimintatapojen löytämistä.

Cardiac tissue engineering by electrical stimulation with subcutaneous and cardiac adipose-tissue derived progenitor cells (ATDPCs)

A major challenge of cardiac tissue engineering is directing cells to establish the physiological structure and function of the myocardium being replaced. In native heart, pacing cells generate electrical stimuli that spread throughout the heartcausing cell membrane depolarization and activation of contractile apparatus. We ought to examine whether electricalstimulation of adipose tissue-derived progenitor cells (ATDPCs) exerts phenotypic and genetic changes that enhance theircardiomyogenic potential.

Site Controller: A System for Computer-Aided Civil Engineering and Construction

A revolution\0\0\0 in earthmoving, a $100 billion industry, can be achieved with three components: the GPS location system, sensors and computers in bulldozers, and SITE CONTROLLER, a central computer system that maintains design data and directs operations. The first two components are widely available; I built SITE CONTROLLER to complete the triangle and describe it here. SITE CONTROLLER assists civil engineers in the design, estimation, and construction of earthworks, including hazardous waste site remediation. The core of SITE CONTROLLER is a site modelling system that represents existing and prospective terrain shapes, roads, hydrology, etc. Around this core are analysis, simulation, and vehicle control tools. Integrating these modules into one program enables civil engineers and contractors to use a single interface and database throughout the life of a project.

Nanocrystalline Ge Flash Memories: Electrical Characterization and Trap Engineering

Conventional floating gate non-volatile memories (NVMs) present critical issues for device scalability beyond the sub-90 nm node, such as gate length and tunnel oxide thickness reduction. Nanocrystalline germanium (nc-Ge) quantum dot flash memories are fully CMOS compatible technology based on discrete isolated charge storage nodules which have the potential of pushing further the scalability of conventional NVMs. Quantum dot memories offer lower operating voltages as compared to conventional floating-gate (FG) Flash memories due to thinner tunnel dielectrics which allow higher tunneling probabilities. The isolated charge nodules suppress charge loss through lateral paths, thereby achieving a superior charge retention time. Despite the considerable amount of efforts devoted to the study of nanocrystal Flash memories, the charge storage mechanism remains obscure. Interfacial defects of the nanocrystals seem to play a role in charge storage in recent studies, although storage in the nanocrystal conduction band by quantum confinement has been reported earlier. In this work, a single transistor memory structure with threshold voltage shift, Vth, exceeding ~1.5 V corresponding to interface charge trapping in nc-Ge, operating at 0.96 MV/cm, is presented. The trapping effect is eliminated when nc-Ge is synthesized in forming gas thus excluding the possibility of quantum confinement and Coulomb blockade effects. Through discharging kinetics, the model of deep level trap charge storage is confirmed. The trap energy level is dependent on the matrix which confines the nc-Ge.