Detection text detection and extraction from natural scenes (i.e. video or images) can deliver significant information for various applications. To address the issue of text detection, a novel approach for text detection from natural scene image is introduced by developing a joint feature extraction method by considering shape and scale invariant feature transform (SIFT) feature analysis techniques. Shape extraction is improved by applying curvature-based shape analysis model. To construct the feature descriptor, input image is passed through canny edge detection process in which gradients are computed of each image. Later, we perform SIFT analysis and SIFT-based feature matching to formulate the SIFT feature descriptor. Finally, these two descriptors are merged together, and a combined descriptor is presented for text detection. Experimental study is carried out by considering benchmark ICDAR 2003, 2013 and 2015 data sets. Experimental study shows that proposed approach outperforms when compared with stateof-art text detection model.
Detection text detection and extraction from natural scenes (i.e. video or images) can deliver significant information for various applications. To address the issue of text detection, a novel approach for text detection from natural scene image is introduced by developing a joint feature extraction method by considering shape and scale invariant feature transform (SIFT) feature analysis techniques. Shape extraction is improved by applying curvature-based shape analysis model. To construct the feature descriptor, input image is passed through canny edge detection process in which gradients are computed of each image. Later, we perform SIFT analysis and SIFT-based feature matching to formulate the SIFT feature descriptor. Finally, these two descriptors are merged together, and a combined descriptor is presented for text detection. Experimental study is carried out by considering benchmark ICDAR 2003, 2013 and 2015 data sets. Experimental study shows that proposed approach outperforms when compared with stateof-art text detection model.
The increased use of wireless transmission has increased the demand for wireless radio spectrum, which can be used for a variety of social and individual benefits. As a result, radio spectrum allocation and utilization are also critical tasks. Cognitive Radio (CR) is a cutting-edge technology that claims to overcome this problem by allowing unlicensed users to access the radio spectrum without interfering with licensed users.Cognitive radio is a new technique that uses an intelligent Software Defined Radio (SDR) mechanism to assist manage the impending spectrum issue through dynamic spectrum allocation and accommodate growing data traffic. With the use of software defined protocols, SDR avoids frequent changes to the hardware structure.The key innovation of this study is the practical implementation of CR on GNU radio for real-time transmission as a primary user utilizing an energy-based spectrum sensing approach. The goal of this project is to develop a spectrum sensing approach that is best suited for detecting white spaces during SDR transmission.
The fifth generation of wireless mobile network, known as 5G NR (New Radio) is in the process of being designed and deployed. NR follows 3GPP series of standards for wireless network which includes gNB (base station) and UE (user equipment). The connection from User Equipment to gNodeB is known as Uplink, which includes PUCCH channel which is used to convey Control Information to the gNB. The features of the PUCCH in baseband of the UE simulator are modelled using HDL and implemented in FPGA (SoC). This paper proposes a way to develop environment that can be used to capture the functional coverage for the verification. The environment we developed is by using SystemVerilog with UVM framework. We used Python and Perl scripts to automate the process of verification. All the features which are intended to implement based on the specification can be exercised. Which will give 100% coverage, that is closer to the realworld application. This will enables telecommunication industry to make their product more reliable and less time in design cycle. Our work involves the development of the testbench environment and scripts which automatically captures the functional coverage of the feature-based verification process. This can also be helpful in the functional coverage of different channels in any Baseband unit and this environment is not limited to 5G, can be used for 4G LTE or in future 6G.
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