With the recent advancement and phenomenal progress in the field of wireless communication technology, there is an ever increasing demand for high data rates and improved quality of service for the end users. In recent times various designs of super wideband antennas (SWB) fulfilling diverse objectives have been proposed for modern wireless networks. Design of compact and wideband antenna for high speed, high capacity, and secure wireless communications presents a challenging task for designers of fixed and mobile wireless communication systems. In this paper, a comprehensive review concerning antenna structures and the technologies adopted for design and analysis of SWB antennas for wireless application is reported. Comparative parameters in terms of electrical dimension, bandwidth, Fractional bandwidth (FB) and Bandwidth Dimension Ratio (BDR) are presented which introduces the researchers to the technical challenges in the design of a compact wideband antenna. This paper contributes to present existing novel approaches along with its adequacy in the design techniques. This review exercise will assist the researchers with valuable support for further research and to achieve better impedance matching, wide bandwidth, high gain and good efficiency along with well directive radiation characteristics.INDEX TERMS Bandwidth dimension ratio, compact design, fractal antenna, monopole antenna, super wideband antenna.

In this article, a compact concentric structured monopole patch antenna for super wideband (SWB) application is proposed and investigated. The essential characteristics of the designed antenna are: (i) to attain super-wide bandwidth characteristics, the proposed antenna is emerged from a traditional circular monopole antenna and has obtained an impedance bandwidth of 38.9:1 (ii) another important characteristic of the presented antenna is its larger bandwidth dimension ratio (BDR) value of 6596 that is accomplished by augmenting the electrical length of the patch. The electrical dimension of the proposed antenna is 0.18λ×0.16λ (λ corresponds to the lower end operating frequency). The designed antenna achieves a frequency range from 1.22 to 47.5 GHz with a fractional bandwidth of 190% and exhibiting S11 < −10 dB in simulation. For validating the simulated outcomes, the antenna model is fabricated and measured. Good conformity is established between measured and simulated results. Measured frequency ranges from 1.25 to 40 GHz with a fractional bandwidth of 188%, BDR of 6523 and S11 < −10 dB. Even though the presented antenna operates properly over the frequency range from 1.22 to 47.5 GHz, the results of the experiment are measured till 40 GHz because of the high-frequency constraint of the existing Vector Network Analyzer (VNA). The designed SWB antenna has the benefit of good gain, concise dimension, and wide bandwidth above the formerly reported antenna structures. Simulated gain varies from 0.5 to 10.3 dBi and measured gain varies from 0.2 to 9.7 dBi. Frequency domain, as well as time-domain characterization, has been realized to guide the relevance of the proposed antenna in SWB wireless applications. Furthermore, an equivalent circuit model of the proposed antenna is developed, and the response of the circuit is obtained. The presented antenna can be employed in L, S, C, X, Ka, K, Ku, and Q band wireless communication systems.

Intracranial tumors are a type of cancer that grows spontaneously inside the skull. Brain tumor is the cause for one in four deaths. Hence early detection of the tumor is important. For this aim, a variety of segmentation techniques are available. The fundamental disadvantage of present approaches is their low segmentation accuracy. With the help of magnetic resonance imaging (MRI), a preventive medical step of early detection and evaluation of brain tumor is done. Magnetic resonance imaging (MRI) offers detailed information on human delicate tissue, which aids in the diagnosis of a brain tumor. The proposed method in this paper is Brain Tumour Detection and Classification based on Ensembled Feature extraction and classification using CNN.

The vision of some particular applications such as robot-guided remote surgery where the image of a patient body will need to be captured by the smart visual sensor and to be sent on a real-time basis through a network of high bandwidth but yet limited. The particular problem considered for the study is to develop a mechanism of a hybrid approach of compression where the Region-of-Interest (ROI) should be compressed with lossless compression techniques and Non-ROI should be compressed with Compressive Sensing (CS) techniques. So the challenge is gaining equal image quality for both ROI and Non-ROI while overcoming optimized dimension reduction by sparsity into Non-ROI. It is essential to retain acceptable visual quality to Non-ROI compressed region to obtain a better reconstructed image. This step could bridge the trade-off between image quality and traffic load. The study outcomes were compared with traditional hybrid compression methods to find that proposed method achieves better compression performance as compared to conventional hybrid compression techniques on the performances parameters e.g. PSNR, MSE, and Compression Ratio.

No abstract