The following paper evaluates a watermark algorithm designed for digital images by using a perceptive mask and a normalization process, thus preventing human eye detection, as well as ensuring its robustness against common processing and geometric attacks. The Hermite transform is employed because it allows a perfect reconstruction of the image, while incorporating human visual system properties; moreover, it is based on the Gaussian functions derivates. The applied watermark represents information of the digital image proprietor. The extraction process is blind, because it does not require the original image. The following techniques were utilized in the evaluation of the algorithm: peak signal-to-noise ratio, the structural similarity index average, the normalized crossed correlation, and bit error rate. Several watermark extraction tests were performed, with against geometric and common processing attacks. It allowed us to identify how many bits in the watermark can be modified for its adequate extraction.
Shoeprint marks present valuable information for forensic investigators to resolve a crime. These marks can be helpful to find the brand of the shoe and can make the investigation easier. In this paper, we present an associative model-based algorithm to match noisy shoeprint patterns with a brand of shoe. The shoeprints are corrupted with additive, subtractive and mixed noises. A particular case of subtractive noise are partial shoeprints such as toe, heel, left-half and right-half prints. The Morphological Associative Memories (MAMs) were applied. Both memories, max and min, recognize noisy shoeprints corrupted with 98% additive and subtractive noise, respectively, with an effectiveness of 100%. The images corrupted with mixed noise were recognized when the additive or subtractive noise applied was greater than the mixed noise; in this case, the recalling was around 70%, otherwise, both memories failed to recognize the shoeprints.
Electronic sensor devices in geophysical processes are required to measure and automate different tasks. Throughout history, people have created multiple type devices, but acoustics have an important application such as the content form description in deep wells, watersheds, lakes, caves, among others. The acoustic signal is capable of reflecting where other types of signals cannot operate, either by drawbacks or where fluid is displaced. A mathematical model is presented in this paper described in state space as a basic acoustic sensor description. The objective is to adjust the parameters allowing the acoustic device to describe a signal in its trajectory, representing in geophysical manner the cavity form. Therefore, the control is performed on the response of the acoustic sensor model, adjusted with a parameter estimation process. The simulation results counts convergence between the reference and identified signals.
In this work we present a new proposal to initialize the weights in a Backpropagation Neuronal Network using the coefficients from a FIR Low-Pass Filter, to introduce a null in the radiation pattern in a seven-element array of antennas to eliminate interferences in a radar system. A radar system needs to eliminate the directional noise in order to obtain a cleaner signal. The method used to eliminate this kind of noise (jitter) has to be adaptive because the objective is in constant movement, therefore, the adaptation time must be as fast as possible. Our work is based on the window method to reduce the secondary lobes in fixed arrays of antennas. We modify the radiation pattern by introducing a null at 45.5º which corresponds to the secondary lobe where the interference is presented. This is achieved when we create windows from several FIR Low-Pass Filters. The coefficients of these filters are used to initialize the weight vectors of a Backpropagation Neural Network which performs the adaptive process to obtain the final parameters to achieve the noise elimination. For testing our proposal we calculate the Mean Square Error (MSE), the Signal Noise Relation (SNR) and we graphed the Radiation Pattern. In addition we calculated the Cross Correlation Index, in each iteration, between the desired signal and our results. With this method we reduced the number of iterations required by the process.
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