Vladimir Mladenovic

Electronic tour guide for Android mobile platform with multimedia travel book

In this paper, a new system for positioning and multimedia reporting, named electronic tour guide, is presented. The mobile device of the system communicates with the server, from which it receives electronic tourist maps, and the GPS satellites. Each location on the map with predetermined irregular shape avoids overlap with other areas and thus it increases the accuracy of the visited location. The device is guiding the tourist using the coordinates received from the GPS signals and the objects that are preloaded as electronic maps. The main functions of the system are: (a) it is displaying the content according to users movement, position and preferred selections; (b) it generates reports for guiding using existing date stored from the server, the current position, the previous movement and the paid services; (c) it reports and prepares the multimedia travel book based on the paid services; (d) it prepares the daily trip report from images, text and audio files downloaded from server or recorded by user; (e) it submits the recorded data for future individual, group or public usage. This paper presents the main characteristics and advantages of the new system as well as benefits for commercial usage.

Multi-separable dictionary learning

Abstract As the extensive applications of sparse representation, the methods of dictionary learning have received widespread attentions. In this paper, we propose a multi-separable dictionary learning (MSeDiL) algorithm for sparse representation, which is based on the Lagrange Multiplier and the QR decomposition. Different with the traditional dictionary learning methods, the training samples are clustered firstly. Then the separable dictionaries for each cluster are optimized by the QR decomposition. The efficiency of the reconstruction process is improved in our algorithm because of the under-determinedness of the dictionaries for each cluster. Experimental results show that with the similar PSNR (Peak Signal to Noise Ratio) and SSIM (Structure Similarity Index), the reconstruction speed of our algorithm is much faster than other dictionary learning methods, especially when the size of samples is large.

SURF binarization and fast codebook construction for image retrieval

Abstract A new framework for image retrieval/object search is proposed based on the VLAD model and SURF descriptors to improve the codebook construction speed, the image matching accuracy, and the online retrieval speed and to reduce the data storage. First, SURF binarization and dimensionality reduction methods are proposed to convert a 64-dimensional SURF descriptor into an 8-dimensional descriptor. Second, a two-step clustering algorithm is proposed for codebook construction to significantly reduce the computational cost of clustering while maintaining the accuracy of the clustering results. Moreover, for object search, a scalable overlapping partition method is proposed to segment an image into 65 patches with different sizes so that the object can be matched quickly and efficiently. Finally, a feature fusion strategy is employed to compensate the performance degradation caused by the information loss of our proposed dimensionality reduction method. Experiments on the Holidays and Oxford datasets demonstrate the effectiveness and efficiency of the proposed algorithms.

Automated proving properties of expectation-maximization algorithm using symbolic tools

In many analysis based on estimation the parameters of probability distribution functions, the algorithms are developing for unknown probabilities. Some algorithms are derived starting from previous solutions and algorithms. One very popular algorithm is the EM (Expectation-Maximization) algorithm. The EM algorithm is a starting point for developing other advanced algorithms. Features of EM and other algorithms are observed with the traditional numerical approach. In this paper, we present a new approach of analysis EM algorithm using symbolic processing (specific Mathematica). We automatically derive properties of the algorithm. The knowledge embedded in the symbolic expressions was used to simulate an example system and EM algorithm to generate the implementation code of some critical parts of analysis.

The direct execution of the expectation-maximization algorithm using symbolic processing

This paper surveys a short study about using and applying symbolic processing to direct execution of the expectation-maximization algorithm. Formulas are derived in the manner defined by the expectation-maximization algorithm and how there are applied. Symbolic processing uses the set of equations on the same way that describe expectation maximization algorithm without any adaptation and loops of computation as usually done. The methodology of processing is described step-by-step for direct execution of expectation-maximization algorithm. Some of the advantages of symbolic processing are described in regard to numerical processing. Finally, numerical data are applied on the complete model and results are displayed.

An Operation Reduction Using Fast Computation of an Iteration-Based Simulation Method with Microsimulation-Semi-Symbolic Analysis

This paper presents a method for shortening the computation time and reducing the number of math operations required in complex calculations for the analysis, simulation, and design of processes and systems. The method is suitable for education and engineering applications. The efficacy of the method is illustrated with a case study of a complex wireless communication system. The computer algebra system (CAS) was applied to formulate hypotheses and define the joint probability density function of a certain modulation technique. This innovative method was used to prepare microsimulation-semi-symbolic analyses to fully specify the wireless system. The development of an iteration-based simulation method that provides closed form solutions is presented. Previously, expressions were solved using time-consuming numerical methods. Students can apply this method for performance analysis and to understand data transfer processes. Engineers and researchers may use the method to gain insight into the impact of the parameters necessary to properly transmit and detect information, unlike traditional numerical methods. This research contributes to this field by improving the ability to obtain closed form solutions of the probability density function, outage probability, and considerably improves time efficiency with shortened computation time and reducing the number of calculation operations.

Spatio-temporal image inpainting for video applications

Video inpainting or completion is a vital video improvement technique used to repair or edit digital videos. This paper describes a framework for temporally consistent video completion. The proposed method allows to remove dynamic objects or restore missing or tainted regions presented in a video sequence by utilizing spatial and temporal information from neighboring scenes. Masking algorithm is used for detection of scratches or damaged portions in video frames. The algorithm iteratively performs the following operations: achieve frame; update the scene model; update positions of moving objects; replace parts of the frame occupied by the objects marked for remove by using a background model. In this paper, we extend an image inpainting algorithm based texture and structure reconstruction by incorporating an improved strategy for video. Our algorithm is able to deal with a variety of challenging situations which naturally arise in video inpainting, such as the correct reconstruction of dynamic textures, multiple moving objects and moving background. Experimental comparisons to state-of-the-art video completion methods demonstrate the effectiveness of the proposed approach. It is shown that the proposed spatio-temporal image inpainting method allows restoring a missing blocks and removing a text from the scenes on videos.

Post-processing with advanced symbolic simulation and design using SchematicSolver and mathematica

The development of modern electronic devices require usage of very sophisticated and complex algorithms that combine expertise in the field of signal processing and software engineering. Classic computer tools that are based only on the numeric computations may be ineffective because the design space is almost unlimited. In this paper, we present the possibility of computer algebra systems (CAS) for symbolic processing, analysis and design of data obtained by numeric software tools. The symbolic processing using CAS is called post-processing.

An Operation – Reduced Calculation Method for Solving Complex Communication Systems

Calculation and design of complex communication systems are usually based on expert knowledge in telecommunications and through the usage of numeric software tools. According to various requests and initial conditions, the software procedure can be time consuming due to a large number of operations. This paper incorporates a new approach focused on a novel concept of calculation in optimisation procedures of complex communication systems, which reduces the number of operations. The key point is defining an auxiliary function and modelling a new optimised function suitable for further manipulation and calculation. We evaluate the hypothesis that a fast yet accurate approximation of an error probability is possible using a parametric function. It is used for calculation and analysis of error probability using an optimised function. The method is verified and validated on PC and Raspberry pi.

Graphical user interface for electrical engineering systems using Wolfram language

New graphical user interface is presented that is intended for the design, simulation and teaching in electrical engineering. The software is written using Wolfram language. It has properties of similar software simulators, but it is open for further automated symbolic manipulations. The main feature is fast drawing of systems with the appropriate visual assistance.