Climate change is posing new challenges to crop-related concerns, including food insecurity, supply stability, and economic planning. Accurately predicting crop yields is crucial for addressing these challenges. However, this prediction task is exceptionally complicated since crop yields depend on numerous factors such as weather, land surface, and soil quality, as well as their interactions. In recent years, machine learning models have been successfully applied in this domain. However, these models either restrict their tasks to a relatively small region, or only study over a single or few years, which makes them hard to generalize spatially and temporally. In this paper, we introduce a novel graph-based recurrent neural network for crop yield prediction, to incorporate both geographical and temporal knowledge in the model, and further boost predictive power. Our method is trained, validated, and tested on over 2000 counties from 41 states in the US mainland, covering years from 1981 to 2019. As far as we know, this is the first machine learning method that embeds geographical knowledge in crop yield prediction and predicts crop yields at the county level nationwide. We also laid a solid foundation by comparing our model on a nationwide scale with other well-known baseline methods, including linear models, tree-based models, and deep learning methods. Experiments show that our proposed method consistently outperforms the existing state-of-the-art methods on various metrics, validating the effectiveness of geospatial and temporal information.

This paper describes a methodology for designing a flexible and optimal spherical microphone array for beamforming. Using the approach presented, a spherical microphone array can have very flexible layouts of microphones on the spherical surface, yet optimally approximate a desired beampattern of higher order within a specified robustness constraint. Depending on the specified beampattern order, our approach automatically achieves optimal performances in two cases: when the specified beampattern order is reachable within the robustness constraint we achieve a beamformer with optimal approximation of the desired beampattern; otherwise we achieve a beamformer with maximum directivity, both robustly. For efficient implementation, we also developed an adaptive algorithm for computing the beamformer weights. It converges to the optimal performance quickly while exactly satisfying the specified frequency response and robustness constraint in each step. One application of the method is to allow the building of a real-world system, where microphones may not be placeable on regions, such as near cable outlets and/or a mounting base, while having a minimal effect on the performance. Simulation results are presented

We propose a method to reproduce 3D auditory scenes captured by spherical microphone arrays over headphones. This algorithm employs expansions of the captured sound and the head related transfer function over the sphere and uses the orthonormality of the spherical harmonics. Using a spherical microphone array, we first record the 3D auditory scene, then the recordings are spatially filtered and reproduced through headphones in the orthogonal beam-space of the head related transfer functions (HRTFs). We use the KEMAR HRTF measurements to verify our algorithm. In experiments, we use a hemispherical array for recording. The reproduction results are posted online.

Recently collaborative tagging has received great attention as a public effort to create and share annotations on the Web pages. In this paper, we conducted an empirical study on how this user added descriptions associated with Web resources can be used to enhance the existing Web search paradigm. Comprehensive experiments have been performed on data collected from delicious, one of the most popular collaborative tagging Web site. Some useful conclusions on the usage of different tags have been drawn from our data analysis. Based on these insights, we proposed an innovative tag enhanced exploratory Web search framework

We describe a robust and self-reconfigurable design of a spherical microphone array for beamforming. Our approach achieves a multi-resolution spherical beamformer with performance that is either optimal in the approximation of desired beampattern or is optimal in the directivity achieved, both robustly. Our implementation converges to the optimal performance quickly while exactly satisfying the specified frequency response and robustness constraint in each iteration step without accumulated round-off errors. The advantage of this design lies in its robustness and self-reconfiguration in microphone array reorganization, such as microphone failure, which is highly desirable in online maintenance and anti-terrorism. Design examples and simulation results are presented.

In this paper, we propose a simple yet effective algorithm to design an optimal supply chain network with respect to the cost of physical material flow and task assigning schemes. For clarity, a simplified mathematical model is used here. A slack technique is developed to achieve the degenerate solution while some special properties of this problem are discussed in this paper. A design example is used to demonstrate the effectiveness of our algorithm.

This paper describes an approach to achieving a flexible layout of microphones on the surface of a spherical microphone array for beamforming. Our approach achieves orthonormality of spherical harmonics to higher order for relatively distributed layouts. This gives great flexibility in microphone layout on the spherical surface. One direct advantage is that it makes it much easier to build a real world system, such as those with cable outlets and a mounting base, with minimal effects on the performance. Simulation results are presented.

Virtual reality systems are largely based on computer graphics and vision technologies. However, sound also plays an important role in human's interaction with the surrounding environment, especially for the visually impaired people. In this paper, we develop the theory of recording and reproducing real-world surround auditory scenes in high orders using specially designed microphone and loudspeaker arrays. It is complementary to vision-based technologies in creating mixed and augmented realities. Design examples and simulations are presented.