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.

Precise fish metric estimation is essential in providing intelligent aquaculture farm decisions. Stereo vision has been widely used for size estimation. Still, many factors affect fish metrics accuracy using a low-cost underwater stereo camera, such as distance, ambient lighting, water velocity, and turbidity. Although such a system is affordable and energy-efficient, they are less accurate in estimating depths than its active counterparts. Since power source is always a problem in offshore aquaculture sites, energy-efficient devices are important. To deal with the accuracy problems of the camera, we propose an effective deep-learning-based object matching to optimize the fish metric estimation. In terms of the challenges of the underwater environment, an analysis of the accuracy of the fish 3D position calculation in the aquaculture cage based on the captured stereo camera images is performed. The analysis assumes a known geometrical configuration of the rectified camera system. The critical factor limiting the 3D fish metric estimation accuracy is the resolution of the computed depth maps of fish. An object-based matching is proposed for underwater fish tracking and depth computing to address this issue using reliable convolutional neural networks (CNNs). For each stereo video frame, an object classification and instance segmentation CNN separates the fish objects from their background. The fish objects are then cropped and matched using sub-pixel disparity computation of the video interpolation CNN. The calculated fish disparities and depth values are used for fish metric estimations. We also tracked each fish and computed the metrics across frames. The median metrics are calculated as the final result to reduce the noises introduced by the different gestures of the fish. Furthermore, underwater stereo video datasets with the actual metrics of sampled fish measured by humans are also constructed to verify the effectiveness of our approach. Our proposed method has less than a 5% error rate for fish length estimation.

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.

This letter presents a joint scheme of receive antenna selection and symbol detection for multiple-input,multiple output (MIMO) systems based on the maximum likelihood (ML) criterion. To mitigate the computational complexity, a novel variant of the genetic algorithm (GA) is addressed to solve the nonlinear optimization involved, where a new heterogenous crossover and a new heterogenous mutation are conducted in the chromosome evolution process. Furnished simulations show that the new approach provides superior performance with substantially lower computational load compared with previous works in both correlated and uncorrelated channels.

The utilization of smart IoT devices, commonly referred to as digital twins, is aimed at the digitalization of human knowledge within aquaculture processes. This involves the incorporation of cutting-edge technologies, including information-based management with big data and modeling, to automate machinery and gain comprehensive insights into the aquaculture environment and fish farm conditions. The ultimate objective is to empower farmers to make informed decisions, furnishing them with objective data to enhance their capacity in monitoring and controlling the various factors impacting fish production. As a result, farming decisions can be fine-tuned to enhance fish health and optimize farm output. In the context of large and modern aquaculture farms, technological innovation becomes imperative to automate processes, minimize labor requirements, and streamline fish feeding operations. Remarkably, the literature currently offers limited discussions on the digital transformation of aquaculture through the application of digital twin methodologies. A prior study underscores the critical influence of factors such as market prices and fish survival rates on the profitability of offshore caging culture. In this study, we embark on an analysis of the prerequisites for establishing a digital twin infrastructure tailored to intelligent fish feeding management. This infrastructure is designed to facilitate the integration of technology and data-driven decision-making, ultimately enhancing the efficiency of fish feeding processes. The proposed architecture for the fish feeding digital twin encompasses various digital twin components, encompassing water quality forecasting, fish population assessment, fish metrics estimation, fish feed prediction, and evaluation of fish feeding intensity. Furthermore, we optimize the daily fish feeding process through reinforcement learning algorithms. Finally, we implement a cloud-based AIoT system that provides the runtime environment for executing digital twins and controlling our intelligent fish feeding machinery. Experimental findings underscore the efficacy of the proposed digital twin system in significantly improving traditional fish feeding processes, notably in terms of reducing food costs and labor requirements.

This letter presents an effective space-time multiuser detector (MUD) with the assistance of soft information in multipath code division multiple access (CDMA) channels. The space-time MUD considered is a simple, separable spatial-temporal filter which consists of a single spatial filter and a single temporal filter. Based on the soft-decision outputs determined in the previous iteration, the soft information is then exchanged in the alternating updates of either the spatial filters or the temporal filters. Furnished simulations show that the proposed scheme can offer substantial performance improvement compared with previous works, especially in highly loaded scenarios.