Estimation of bit error rate in 2×2 and 4×4 multi-input multi-output-orthogonal frequency division multiplexing systems

Multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with multiple input antennas and multiple output antennas in dynamic environments face the challenge of channel estimation. To overcome this challenge and to improve the performance and signal-to-noise ratio, in this paper we used the Kalman filter for the correct estimation of the signal in dynamic environments. To obtain the original signal at the receiver end bit error rate factor plays a major role. If the signal to noise ratio is high and the bit error rate is low then signal strength is high, the signal received at the receiver end is almost similar to the ith transmitted signal. The dynamic tracking characteristic of Kalman filter is used to establish a dynamic space-time codeword and a collection of orthogonal pilot sequences to prevent interference among transmissions in this paper. Using the simulation, the Kalman filter method can be compared to the other channel estimation method presented in this paper that can track time-varying channels rapidly

Static Hand Gesture Recognition Using Novel Convolutional Neural Network and Support Vector Machine

Hand tracking and identification through visual means pose a challenging problem. To simplify the identification of hand gestures, some systems have incorporated position markers or colored bands, which are not ideal for controlling robots due to their inconvenience. The motion recognition problem can be solved by combining object identification, recognition, and tracking using image processing techniques. A wide variety of target detection and recognition image processing methods are available. This paper proposes novel CNN-based methods to create a user-free hand gesture detection system. The use of synthetic techniques is recommended to improve recognition accuracy. The proposed method offers several advantages over existing methods, including higher accuracy and real-time hand gesture recognition suitable for sign language recognition and human-computer interaction. The CNN automatically extracts high-level characteristics from the source picture, and the SVM is used to classify these features. This study employed a CNN to automatically extract traits from raw EMG images, which is different from conventional feature extractors. The SVM classifier then determines which hand gestures are being made. Our tests demonstrate that the proposed strategy achieves superior accuracy compared to using only CNN