Vasile Lazarescu

A Latent Analysis of Earth Surface Dynamic Evolution Using Change Map Time Series

With a continuous increase in the number of Earth Observation satellites, leading to the development of satellite image time series (SITS), the number of algorithms for land cover analysis and monitoring has greatly expanded. This paper offers a new perspective in dynamic classification for SITS. Four similarity measures (correlation coefficient, Kullback-Leibler divergence, conditional information, and normalized compression distance) based on consecutive image pairs from the data are employed. These measures employ linear dependences, statistical measures, and spatial relationships to compute radiometric, spectral, and texture changes that offer a description for the multitemporal behavior of the SITS. During this process, the original SITS is converted to a change map time series (CMTS), which removes the static information from the data set. The CMTS is analyzed using a latent Dirichlet allocation (LDA) model capable of discovering classes with semantic meaning based on the latent information hidden in the scene. This statistical method was originally used for text classification, thus requiring a word, document, corpus analogy with the elements inside the image. The experimental results were computed using 11 Landsat images over the city of Bucharest and surrounding areas. The LDA model enables us to discover a wide range of scene evolution classes based on the various dynamic behaviors of the land cover. The results are compared with the Corinne Land Cover map. However, this is not a validation method but one that adds static knowledge about the general usage of the analyzed area. In order to help the interpretation of the results, we use several studies on forms of relief, weather forecast, and very high resolution images that can explain the wide range of structures responsible for influencing the dynamic inside the resolution cell.

Novel Approach for Remote Energy Meter Reading Using Mobile Agents

Power or energy meter systems can be incorporated with embedded controllers such as micro Web-servers with Ethernet port to transmit the reader data over the Internet. Such data can be then fed and integrated into existing energy management systems located at power companies and organizations. The problem of efficiently collecting data from a large number of distributed embedded Web-servers in the energy meters is still a challenging problem. Mobile agents are executing programs that migrate during execution and present methods for maintaining and using distributed systems. In this paper, we develop a new abstract approach using mobile agents to solve the problem of efficiently collecting meters readings

New results using the audio watermarking based on wavelet transform

The audio watermarking based on wavelet transform algorithm depends on embedding and extracting bits in the watermark. The original algorithm depends on the idea of determining the signal spectrum computed by detailed and approximated wavelet coefficients and by inserting the watermark in the spectrum (Vieru et al., 2003). The wavelet coefficients for the first level are determined at the beginning. Then the watermark is constructed in a binary form, having 8, 16 bytes. The watermark is added to the first detailed coefficient of high-pass filter. This paper shows new results with a modified version of the original algorithm which uses a larger set of watermark bits (8, 16, 32, 64 bytes) at higher sampling rate and a larger set of the sampled audio signals. Also the results of robustness tests for this algorithm is presented.

Improving Mutation Capabilities in a Real-Coded Genetic Algorithm

This paper introduces a new method of performing mutation in a real-coded Genetic Algorithm (GA), a method driven by Principal Component Analysis (PCA). We present empirical results which show that our mutation operator attains higher levels of diversity in the search space, as compared to other mutation operators, meaning that by employing our mutation operator we maintain diverse populations that increase the chances of finding better solutions during evolution of the GA. The performances of the real-coded GA with PCA-mutation were checked on the problem of designing IIR filters by Deczky method, which is a well known direct design method of IIR filters. Results obtained show that our PCA-mutation GA has been more successful in keeping diverse populations during search, the consequence being the finding of better solutions to the filter design problem, compared to solutions found using GA with classical mutation operators.

Multiprocessor real time edge detection using FPGA IP cores

Real time edge detection is required in many embedded systems where execution speed is critical. This task is intensely computational but highly parallelizable and, in order to satisfy the real-time requirements, using multiprocessor-based embedded systems can be a viable solution. In this paper we propose a MPSoC hardware and software co-design method for real-time Sobel edge detection using FPGA and IP blocks. The experiments we performed show a speed-up of 1.71 in the case of a multiprocessor system with two processors and a speed-up of 3.48 in the case of a multiprocessor system with four processing cores, versus a single processor system.

Extraction of frequent grouped sequential patterns from Satellite Image Time Series

This paper presents an original data mining approach for extracting pixel evolutions and sub-evolutions from Satellite Image Time Series. These patterns, called frequent grouped sequential patterns, represent the (sub-)evolutions of pixels over time, and have to satisfy two constraints: firstly to correspond to at least a given minimum surface and secondly to be shared by pixels that are sufficiently connected. These spatial constraints are actively used to face large data volumes and to select evolutions making sense for end-users. Successful experiments on an optical and a radar SITS are presented.

Pattern segmentation in textile images

This paper presents some results regarding the automatic segmentation of patterns in images of textile samples. The pattern segmentation is necessary for further extraction of a set of shape descriptors to be used in a CBIR database for textiles. Five different filters for removing the fabrics texture have been tested: mean, median, Gaussian, soft wavelet thresholding and a new approach based on Wavelet Transform and Independent Components Analysis. For segmentation, due to the poor color content of the images, the binarization proved to be a satisfactory choice. Three algorithms for the threshold computation have been tested: Otsu, background symmetry and triangle.

A new Hw/Sw co-design method for multiprocessor system on chip applications

Our goal in this paper is to simulate the behavior of multiprocessor system on chip. We used an open virtual platform - OVPSim made by Imperas Company, which offers the possibility of programming and running application on the platforms architectures. With this platform we simulated both hardware architectures and running software applications. We used two types of processors - ARM7 IP core and MIPS32 IP core, shared memory, local memory and BUS for interconnections and simulated three systems on chip models and for each architecture we simulated the running of the same applications.

Knock detection based on power spectrum analysis

A new method based on power spectrum analysis is proposed for knock detection. The method differs from the other existing methods in that the power spectrum is computed for the vibration signals every 1ms. The power spectrum is then analyzed considering the maximum value. When the maximum value of the spectrum varies among the considered segments of analysis, the current cycle is detected as a one containing knock and for the next cycle the engine is protected in order to avoid further appearances of knock.

Efficient threads mapping on multicore architecture

Considering todays hardware performance, in order to obtain best results, a proper programming strategy for optimum mapping of all processes to existing resources is necessary. The presence of multiple cores in a single chip requires applications with a higher level of parallelism. The use of suited mapping algorithms can lead to a great performance improvement considering computing time at a smaller energy consumption. Weve realized a comparison between the parallel computing with an efficient mapping algorithm of threads to specific cores and parallel computing with threads mapping maintained by Linux kernel process scheduler. We observed that a good strategy regarding thread mapping on different processing units, balancing all available cores and allocating a specific amount of work, can lead to improved computational time. In our simulation we used Kubuntu Linux operating system, a system with Intel Core 2 Duo processor and another system with an Intel Quad Core.