A digital predistortion (DPD) technique based on an iterative adaptation structure is proposed for linearizing power amplifiers (PAs). To obtain proper DPD parameters, a feedback path that converts the PA's output to a baseband signal is required, and memory is also needed to store the baseband feedback signals. DPD parameters are usually found by an adaptive algorithm by using the transmitted signals and the corresponding feedback signals. However, for the adaptive algorithm to converge to a reliable solution, long feedback samples are required, which increases hardware complexity and cost. Considering that the convergence time of the adaptive algorithm highly depends on the initial condition, we propose a DPD technique that requires relatively shorter feedback samples. Specifically, the proposed DPD iteratively utilizes the short feedback samples in memory while keeping and using the DPD parameters found at the former iteration as the initial condition at the next iteration. Computer simulation shows that the proposed technique performs better than the conventional technique, as the former requires much shorter feedback memory than the latter.
Majority of products and standards that use public-key cryptography for encryption and digital signature use RSA. The key length for secure RSA has increased over recent years ,and this has put heavier processing load on applications using RSA. Recently, a competing system has begun to challenge RSA: Elliptic curve cryptography (ECC).The principle attraction of ECC, compared to RSA, is that it appears to offer equal security for a far smaller key size, thereby reducing processor overhead. Cryptographers are interested only in elliptic curve that belongs to cyclic abelian group. This paper implements cyclic abelian elliptic curve in MATLAB. The properties of abelian group is proved over the coordinates satisfying the curve. Base points of elliptic curve are generated to prove that the elliptic curve belongs to cyclic abelian group.
With the increase in industrial production and human activities, the concentration of atmospheric particulate matter (PM) is substantially increased; due to which fog and haze occur more frequently. Limited visibility is caused by suspended particles in the air, such as fog and haze are a major problem for many applications of computer vision. The captured scenes by such computer vision systems suffer from poor visibility, low contrast, dimmed brightness, low luminance and distorted color. The detection of objects within the scene is more difficult. Therefore visibility improvement, contrast and features enhancement of images and videos captured in bad weather are also called as dehazing, is an inevitable task.The Motion detection is the first essential process within the extraction of data concerning moving objects and makes use of stabilization in purposeful areas, like tracking, classification, recognition, and so on. A total unique and accurate approach to motion detection for the automated video surveillance system has been adopted. Complete detection of moving objects can be achieved by involving three significant projected units, a background modeling (BM) unit, an alarm trigger (AT) unit and an object extraction (OE) module. Intelligent service mechanism development is a crucial and critical issue for human community applications. With the diverse and complicated service desires, the perception and navigation are essential subjects. First of all, a new augmented approach of graph-based optimum estimation derived for concurrent mechanism postures and affecting objective course approximation. Moreover, all the moving object detection issues of a robot's indoor navigation has been solved by divided and conquered via multisensory fusion methodologies.
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