Subarray Algorithm Research Articles

Smart Healthcare: A Dynamic Blockchain-Based Trust Management Model Using Subarray Algorithm

Smart Healthcare: A Dynamic Blockchain-Based Trust Management Model Using Subarray Algorithm

An Efficient Interference-Aware Constrained Massive MIMO Beamforming for mm-Wave JSDM

Low-complexity beamformer design with practical constraints is an attractive research area for hybrid analog/digital systems in mm-wave massive multiple-input multiple-output (MIMO). This paper investigates interference-aware pre-beamformer (analog beamformer) design for joint spatial division and multiplexing (JSDM) which is a user-grouping based two-stage beamforming method. Single-carrier frequency domain equalization (SC-FDE) is employed in uplink frequency-selective channels. First, unconstrained slowly changing statistical analog beamformer of each group, namely, generalized eigenbeamformer (GEB) which has strong interference suppression capability is designed by maximizing the mutual information in reduced dimension. Then, constant-modulus constrained approximations of unconstrained beamformer are obtained by utilizing alternating minimization algorithms for fully connected arrays and fixed subarrays. In addition, a dynamic subarray algorithm is proposed where the connections between radio frequency (RF) chains and antennas are changed with changing channel statistics. Convergence of the proposed alternating minimization-based algorithms is provided along with their complexity analysis. It is observed that the additional complexity of proposed algorithms is insignificant for the overall system design. Although most of the interference is suppressed with the help of proposed constrained beamformers, there may be some residual interference after analog beamforming stage. Thus, minimum mean square error (MMSE) criterion based iterative block decision feedback equalization (IB-DFE) method, which takes the residual interference in reduced dimension into account, is promoted for digital beamforming stage. Simulation results verify the superiority of the proposed interference-aware constrained design over existing approaches in terms of beampattern, spectral efficiency, outage capacity, bit-error rate (BER), and channel estimation accuracy.

Maximum subarray algorithms for use in astronomical imaging

The maximum subarray problem is used to identify the subarray of a two-dimensional array, where the sum of elements is maximized. In terms of image processing, the solution has been used to find the brightest region within an image. Two parallel algorithms of the maximum subarray problem solve this problem in O ( n ) and O(log n ) time. A field programmable gate array implementation has verified theoretical maximum performance; however, extensive customization is required, restricting general application. A more convenient platform for this work is a graphics processor unit since it offers a flexible trade-off between hardware customization and performance. Implementation of the maximum subarray algorithm on a graphics processor unit is discussed in this article for rectangular solutions and convex extensions are explored.

Application of the Maximum Convex Sum Algorithm in Determining Environmental Variables that Affect Nigerian Highland Stream Benthic Communities

In this paper, we utilise the newly developed method K-Disjoint Maximum Convex Sum Problem (K-DMCSP) in a real-life application, which investigates the effects of land use changes on benthic stream communities in highland tropical streams of Nigeria. A collaboration between computer scientists and freshwater biologists was established to implement and examine the robustness of this approach compared to a traditional method that used rectangular shape in the K-Disjoint Maximum Sub-Array algorithm (K-DMSA). This novel approach uses the K- DMCSP, which utilises a convex shape as compared to a rectangular shape. In the above comparison the new approach using the convex shape optimises the sum and yields more accurate and precise results. This is because using the rectangular shape is limiting and is therefore not flexible enough to cover various sets of data distributions. The new approach was applied to data that were collated from 55 tropical highland streams on the Mambilla Plateau, Nigeria to investigate the interactions between substrate index or dissolved oxygen and temperature with number of macroinvertebrates (taxa). The K-DMCSP method located the K-maximum threshold values. This was achieved by defining the substrate index or dissolved oxygen associated most with the temperature by maximising the sum of elements of a selected portion of a two dimensional array. The K-DMCSP successfully detected the various temperatures between the different categories of the substrate index or dissolved oxygen, and how those variables affect numbers of macroinvertebrates. Furthermore, applying the algorithm revealed that the number of macroinvertebrates differs according to land use (e.g. forestry and agriculture). The new method used in this paper is encouraging in its capability to find the relationship between various environmental parameters and macroinvertebrate distribution and diversity. This method can potentially be applied to other real-life applications requiring finding associations between different parameters.