Orthogonal frequency division multiplexing (OFDM) has become an indispensable part of waveform generation in wideband digital communication since its first appearance in digital audio broadcasting (DAB)and it is indeed in use. These descriptions are simplified version of the detailed descriptions provided by 3gpp. It is a superior technology for the high-speed data rate of wire-line and wireless communication systems. The OFDM has many advantages over other techniques such as its high capacity and immunity against multipath fading channels. However ,one of the main drawbacks of the OFDM system is the high-peak-to-average power ratio (PAPR) that leads the system to produce in-band distortion and out-of-band radiation because of the non-linearity and reduces its efficiency is the distortion of the signal caused at the High Power Amplifier (HPA) of a transmitter . Therefore, it is highly desirable to reduce the PAPR of an OFDM signal. For this, numerous techniques have been proposed to overcome the PAPR problem such as i) selective mapping(SLM) ii) partial transmit sequence (PTS), iii) clipping, iv) clipping and filtering. In this paper, the PTS technique was analytically reviewed as one of the important methods to reduce the high PAPR problem. Simulations are used to analyze the efficiency of the techniques used which signifies OFDM to be providing much better PAPR reduction and a better Bit Error Rate (BER) . From simulation results clipping method shows good PAPR reduction with significant amount of BER degradation. Clipping and filtering method shows slight increase in PAPR with small degradation in BER performance than Clipping method and both methods are computationally less complex. PTS provides good PAPR reduction with high computational complexity.
Crop diseases constitute a big threat to plant existence, but their rapid identification remains difficult in many parts of the planet because of the shortage of the required infrastructure. In computer vision, plant leaf detection made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. employing a public dataset of 4,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to spot one crop species and 4 diseases (or absence thereof). The trained model achieves an accuracy of 97.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of coaching deep learning models on increasingly large and publicly available image datasets presents a transparent path toward smartphoneassisted crop disease diagnosis on a large global scale. After the disease is successfully predicted with a decent confidence level, the corresponding remedy for the disease present is displayed that may be taken as a cure.
Orthogonal frequency division multiplexing (OFDM) has become an important component of waveform generation in wideband transmission. it's a superior technology for the high-speed rate of wired and wireless communication systems. Currently, multiple-input multiple-output orthogonal frequency division multiplexing (MIMO OFDM) systems are crucial wireless communication systems like 4G and 5G networks & tactical communication. The OFDM has many advantages over other techniques like its high capacity and immunity against multipath fading channels. However, one amongst the foremost drawbacks of the OFDM system is that the high-peak-to-average power ratio (PAPR) that leads the system to provide in-band distortion and out-of-band radiation and reduces its efficiency. This problem increases with a rise within the number of transmit antennas. Therefore, it's highly desirable to cut back the PAPR of an OFDM signal. For this, numerous techniques are proposed to beat the PAPR problem like i) Selective mapping (SLM) ii) Partial transmit sequence (PTS), iii) Clipping, iv) Clipping and filtering. All of those are reduced the PAPR by generating alternative subcarrier vector that are statistically independent OFDM symbols for a given data frame and transmitting the symbol with rock bottom peak power. During this paper we also proposed, some hybrid techniques. The hybrid techniques are the technique of clipping is employed in conjunction with SLM and PTS to cut back computational complexity. And also the combination of SLMPTS to scale back PAPR. Simulations are acquainted with analyze the efficiency of the techniques used which signifies OFDM to be providing much better PAPR reduction and a way better Bit Error Rate (BER). it's shown in simulation results that the proposed scheme performs well reducing PAPR. But the proposed scheme is more complex than the prevailing techniques.
The technology of detection within the captured video has implementation within the sort of fields. This emerging technology when implemented over the real-time video feeds could even be beneficial. The supreme good thing about vehicle detection within the real-time streaming video feed is to trace vehicles in busy roads or Bridges like Padma or Jamuna Bridge. An accidents occurred anywhere which may rather be detected. Vehicle detection also called computer vision beholding, basically the scientific methods and ways of how machines see instead of human eyes. This chapter aims to explore the prevailing challenging issue within the planet of unsupervised surveillance and security, Helps traffic police, Maintaining records and Traffic surveillance control. The detection of vehicles is implemented with enhanced algorithms and machine learning libraries like OpenCV, TensorFlow, and others. The varied approaches are accustomed identify and track the particular object through the trained model from the captured video.
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