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The ocean resources have been rapidly depleted in the recent decade, and the complementary role of aquaculture to food security has become more critical than ever before. Water quality is one of the key factors in determining the success of aquaculture and real-time water quality monitoring is an important process for aquaculture. This paper proposes a low-cost and easy-to-build artificial intelligence (AI) buoy system that autonomously measures the related water quality data and instantly forwards them via wireless channels to the shore server. Furthermore, the data provide aquaculture staff with real-time water quality information and also assists server-side AI programs in implementing machine learning techniques to further provide short-term water quality predictions. In particular, we aim to provide a low-cost design by combining simple electronic devices and server-side AI programs for the proposed buoy system to measure water velocity. As a result, the cost for the practical implementation is approximately USD 2015 only to facilitate the proposed AI buoy system to measure the real-time data of dissolved oxygen, salinity, water temperature, and velocity. In addition, the AI buoy system also offers short-term estimations of water temperature and velocity, with mean square errors of 0.021 °C and 0.92 cm/s, respectively. Furthermore, we replaced the use of expensive current meters with a flow sensor tube of only USD 100 to measure water velocity. [ABSTRACT FROM AUTHOR]

In the era of the fifth generation (5G) communication networks, massive multiple-input multiple-output (MIMO) systems demand even lower computation complexity and power consumption while catching up with good detection performance. In this paper, a low-complexity nonlinear detection algorithm is proposed for massive MIMO systems, which is based on partial tree search and successive interference cancellation (SIC). The proposed scheme allows us to expedite the detection process by coping with the transmit symbols group by group. As compared to vertical-Bell laboratories layered space time (V-BLAST), the major breakthrough of computation reduction lies in the fact that the partial tree search can assist the detection process to avoid the inversion of the detection matrix required in each recursion of the SIC process. Both computational complexity analysis and simulation results show that our proposed algorithm not only significantly reduces computational complexity, but also has better bit error rate (BER) performance. [ABSTRACT FROM AUTHOR]

Abstract: We consider the uplink of a multiuser, multiple input multiple output (MIMO), frequency-time-domain spread, multi-carrier (MC), direct sequence code division multiple access (DS-CDMA) system. In this two-domain (2D) spread system, users are arranged in groups. Each user is assigned a unique time-domain (T-domain) signature code and also shares a frequency-domain (F-domain) signature code with users in the same group. Moreover, each user employs his/her individual T-domain signature code and the shared F-domain signature code to spread multiple symbols, and then sends the spreading signals in parallel from multiple transmit antennas to the antenna array at the base station. However, the multiple access interference (MAI) effect, which results from the non-orthogonality of signature codes and is the main performance limitation, still exists in such an MC DS-CDMA system. To mitigate the MAI effect and improve the performance, we propose user-based and group-based layered detection schemes. Specifically, to enable a trade-off between the performance and the computational complexity, the schemes only use one user''s/group''s soft decisions for user-based/group-based layered detection. The results of simulations demonstrate that the proposed schemes outperform existing approaches, and their computational complexity is modest. [Copyright &y& Elsevier]