With the advancement of the technological field, day by day, people from around the world are having easier access to internet abled devices, and as a result, video data is growing rapidly. The increase of portable devices such as various action cameras, mobile cameras, motion cameras, etc., can also be considered for the faster growth of video data. Data from these multiple sources need more maintenance to process for various usages according to the needs. By considering these enormous amounts of video data, it cannot be navigated fully by the end-users. Throughout recent times, many research works have been done to generate descriptions from the images or visual scene recordings to address the mentioned issue. This description generation, also known as video captioning, is more complex than single image captioning. Various advanced neural networks have been used in various studies to perform video captioning. In this paper, we propose an attention-based Bi-LSTM and sequential LSTM (Att-BiL-SL) encoder-decoder model for describing the video in textual format. The model consists of two-layer attention-based bi-LSTM and one-layer sequential LSTM for video captioning. The model also extracts the universal and native temporal features from the video frames for smooth sentence generation from optical frames. This paper includes the word embedding with a soft attention mechanism and a beam search optimization algorithm to generate qualitative results. It is found that the architecture proposed in this paper performs better than various existing state of the art models.
With the advancement in technology, decent transfer rate of data for fast communication is an exigency. Different distributions on different wireless communication channels have been used previously to model them and to do performance analysis on the systems. In this work, capacity analysis of identically independently distributed Nakagami-q fading single-input multiple-output (SIMO) wireless communication is presented. The derivation of channel capacity with the analytical solution have been conducted using small limit argument approximation. Where the small limit argument approximation corresponds to the low signal-to-noise ratio (SNR) regime. SIMO channel capacity behavior with respect to number of receiver antennas and with respect to SNR have been explored in depth. The improvement of capacity is depicted rigorously. It has been found that using Nakagami-q distribution, capacity of the system increases as number of receiver antenna increases. It is also found that the capacity of this SIMO wireless system can be further improved through changing of certain parameters.
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