In this paper, we consider a cooperative mobile-to-mobile wireless short-term Weibull fading channel. The source mobile station communicates with the destination via a relay. The channel is considered to be interference-limited, and the interference is present at both relay and the destination. The exact closed-form expressions for the destination mobile station signal-to-interference ratio probability density function and the cumulative distribution function are derived. Approximate numerically efficient closed-form expressions for the level crossing rate and the average fade duration are also derived, using Laplace approximation for a three-fold integral. The influence of the fading channel parameters on the mentioned statistical measures is investigated. The theoretical results are verified by the Monte-Carlo simulation.
In this letter, a wireless communication system with microdiversity and macrodiversity reception in gamma‐shadowed Rician fading channels is considered. Exact and rapidly converging infinite‐series expressions for the average level crossing rate and average fade duration at the output of the system are provided. Numerical results are presented graphically to illustrate the proposed mathematical analysis and to examine the effects of the system's parameters on the quantities considered.
A method for fingerprint enhancement in the frequency domain based on directional log-Gabor filtering is proposed. Experimental results show that the method can successfully enhance even low-quality fingerprint images obtained by the ink method, outperforming standard, well-known techniques including Gabor filtering.Introduction: To utilise automatic fingerprint identification systems (AFIS) in law enforcement agencies, the first step is to digitalise an archive of fingerprints obtained by the ink method. The quality of those fingerprint images greatly depends on the fingerprint acquisition device (equipment and technique) that is used, but there are also a number of factors (postnatal marks, occupational marks, creases, etc.) that contribute to a large number of spurious minutiae detected, decreasing the overall AFIS performance. That is the reason why enhancement is usually performed before minutiae extraction [1]. When dealing with a particular fingerprint image, i.e. latent, we can apply any of many digital techniques and filters, even combining and adjusting them for that particular case. However, in the case of making a database of fingerprint templates for AFIS from an archive of cards of fingerprints usually taken by ink, the process of automatic minutiae extraction is rarely intervened with by a human expert, so an enhancement method needs to be robust and applicable to a wide range of input images. In the literature, several filters have been proposed in the spatial [2] and frequency [3,4] domains. In this Letter, a robust enhancement algorithm in the frequency domain is proposed. It is based first on amplifying the dominant frequencies by a factor of raised power spectrum (noise reduction) and then by appropriate filtering with a log-Gabor filter (directional enhancement). The results prove to be preferable compared to the standard Gaborbased filtering method.
In a criminal investigation, along with processing forensic evidence, different investigative techniques are used to identify the perpetrator of the crime. It includes collecting and analyzing unconstrained face images, mostly with low resolution and various qualities, making identification difficult. Since police organizations have limited resources, in this paper, we propose a novel method that utilizes off-the-shelf solutions (Dlib library Histogram of Oriented Gradients-HOG face detectors and the ResNet faces feature vector extractor) to provide practical assistance in unconstrained face identification. Our experiment aimed to establish which one (if any) of the basic image enhancement techniques should be applied to increase the effectiveness. Results obtained from three publicly available databases and one created for this research (simulating police investigators’ database) showed that resizing the image (especially with a resolution lower than 150 pixels) should always precede enhancement to improve face detection accuracy. The best results in determining whether they are the same or different persons in images were obtained by applying sharpening with a high-pass filter, whereas normalization gives the highest classification scores when a single weight value is applied to data from all four databases.
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