Spectral Efficiency Maximization of a Single Cell Massive MU-MIMO Down-Link TDD System by Appropriate Resource Allocation

This paper deals with the problem of maximizing the spectral efficiency in a massive multi-user MIMO downlink system, where a base station is equipped with a very large number of antennas and serves single-antenna users simultaneously in the same frequency band, and the beamforming training scheme is employed in the time-division duplex mode. An optimal resource allocation that jointly selects the training duration on uplink transmission, the training signal power on downlink transmission, the training signal power on uplink transmission, and the data signal power on downlink transmission is proposed in such a way that the spectral efficiency is maximized given the total energy budget. Since the spectral efficiency is the main concern of this work, and its calculation using the lower bound on the achievable rate is computationally very intensive, in this paper, we also derive approximate expressions for the lower bound of achievable downlink rate for the maximum ratio transmission (MRT) and zero-forcing (ZF) precoders. The computational simplicity and accuracy of the approximate expressions for the lower bound of achievable downlink rate are validated through simulations. By employing these approximate expressions, experiments are conducted to obtain the spectral efficiency of the massive MIMO downlink time-division duplexing system with the optimal resource allocation and that of the beamforming training scheme. It is shown that the spectral efficiency of the former system using the optimal resource allocation is superior to that yielded by the latter scheme in the cases of both MRT and ZF precoders

Compnet: A New Scheme for Single Image Super Resolution Based on Deep Convolutional Neural Network

The features produced by the layers of a neural network become increasingly more sparse as the network gets deeper and consequently, the learning capability of the network is not further enhanced as the number of layers is increased. In this paper, a novel residual deep network, called CompNet, is proposed for the single image super resolution problem without an excessive increase in the network complexity. The idea behind the proposed network is to compose the residual signal that is more representative of the features produced by the different layers of the network and it is not as sparse. The proposed network is experimented on different benchmark datasets and is shown to outperform the state-of-the-art schemes designed to solve the super resolution problem

A Pipeline VLSI Architecture for High-Speed Computation of the 1-D Discrete Wavelet Transform

In this paper, a scheme for the design of a high-speed pipeline VLSI architecture for the computation of the 1-D discrete wavelet transform (DWT) is proposed. The main focus of the scheme is on reducing the number and period of clock cycles for the DWT computation with little or no overhead on the hardware resources by maximizing the inter- and intrastage parallelisms of the pipeline. The interstage parallelism is enhanced by optimally mapping the computational load associated with the various DWT decomposition levels to the stages of the pipeline and by synchronizing their operations. The intrastage parallelism is enhanced by decomposing the filtering operation equally into two subtasks that can be performed independently in parallel and by optimally organizing the bitwise operations for performing each subtask so that the delay of the critical data path from a partial-product bit to a bit of the output sample for the filtering operation is minimized. It is shown that an architecture designed based on the proposed scheme requires a smaller number of clock cycles compared to that of the architectures employing comparable hardware resources. In fact, the requirement on the hardware resources of the architecture designed by using the proposed scheme also gets improved due to a smaller number of registers that need to be employed. Based on the proposed scheme, a specific example of designing an architecture for the DWT computation is considered. In order to assess the feasibility and the efficiency of the proposed scheme, the architecture thus designed is simulated and implemented on a field-programmable gate-array board. It is seen that the simulation and implementation results conform to the stated goals of the proposed scheme, thus making the scheme a viable approach for designing a practical and realizable architecture for real-time DWT computation

An efficient method of cast shadow removal using multiple features

Features of images are often used for cast shadow removal. A technique based on using only a single feature cannot universally distinguish an object pixel from a shadow pixel of a video frame. On the other hand, the use of multiple features increases the computational cost of a shadow removal technique considerably. In this paper, an efficient yet simple method for cast shadow removal from video sequences with static background using multiple features isdeveloped. The basic idea of the proposed technique is that a simultaneous use of a small number of multiple features, if chosen judiciously, can reduce the similarity between object and shadow pixels without an excessive increase in the computational cost. Using the features of gray levels, color composition and gradients of foreground and background pixels, a method is devised to create a complete object mask. First, based on each of the three features, three individual shadow masks are constructed, from which three corresponding object masks are obtained through a simple subtraction operation. The object masks are then merged together to generate a single object mask. Each of the three shadow masks is created so as to cover as many shadow pixels as possible, even if it results in falsely including in them some of the object pixels. As a result, the subsequent object masks may lose some of these pixels. However, the object pixels missed by one of the object masks should be able to be recovered by at least one of the other two, since they are generated based on features complementary to the one used to construct the first one. The final object mask obtained through a logical OR operation of the three individual masks can, therefore, be expected to include most of the object pixels. The proposed method is applied to a number of video sequences. The simulation results demonstrate that the proposed method provides a mechanism for shadow removal that is superior to some of the recently proposed techniques without imparting an excessive computational cost