Two efficient solutions are proposed for the localization problem of an object in multiple-input multiple-output (MIMO) radar systems, when the transmitter positions and offsets are unknown. The localization problem is first recast into a convex form by applying the semidefinite relaxation (SDR) technique, the solution of which converges to global optimum. We also propose a closed-form solution warranting global convergence, in which the object position is estimated by two stages. In the stage-one solution, the auxiliary variables are introduced to transform the nonlinear problem into a linear form. The stage-two solution is further designed to refine the estimates obtained from the stage-one solution. The minimum number of receivers and the complexity are also analyzed for the proposed solutions. The simulated results show that the SDR and closed-form solutions provide good estimates for the object position and transmitter positions. Their performance can sufficiently approach the Cramér-Rao Lower Bound (CRLB) accuracy at the high signal-to-noise ratio (SNR).

Biodiversity conservation in urbanized area is an increasing challenge worldwide. A quantitative tool is needed to provide a spatially explicit representation of habitat mosaics and thus afford insight into the ecological consequences of landscape changes. We developed a method for identifying core habitats and generating a cost surface that was used to identify both the permeability of the landscape matrix and the quality of core habitats and then we developed a least-cost model to delimit the links between pairs of core habitats. We defined a graph-theoretic dispersal flow index to measure the dispersal potential of certain ecological processes, i.e. functional connectivity. A network consisting of core habitats and links was assembled in the rapid urbanized Shenzhen City of China. The results showed that the cell-based least-cost distance calculation between the core habitats can avoid the subjectivity of land cover-based approaches that rely on expert experience. A highly connected network can facilitate DF and reduce the ecological risk of extinction due to habitat fragmentation. For saving construction cost in the urban environment, urban planner can efficiently design ecological corridors along the least-cost paths to connect isolated core habitats and promote appropriate animal behaviors. The methodology described here will be useful in making better conservation decisions and predictions regarding the consequences of human activity and thereby promote sustainable development.

Pen-holding postures (PHPs) can significantly affect the speed and quality of writing, and incorrect postures can lead to health problems. This paper presents and experimentally implements a methodology for quickly recognizing and correcting poor writing postures using a digital dot matrix pen. The method first extracts basic handwriting information, including page number, handwriting coordinates, movement trajectory, pen tip pressure, stroke sequence, and pen handling time. This information is then used to generate writing features that are fed into our proposed fusion classification model, which combines a simple parameter-free attention module for convolutional neural networks (CNNs) called NetworkSimAM, CNNs, and an extension of the well-known long short-term memory (LTSM) called Mogrifier LSTM or MLSTM. Finally, the method ends with a classification step (Softmax) to recognize the type of PHP. The implemented method achieves significant results through receiver operating characteristic (ROC) curves and loss functions, including a recognition accuracy of 72%, which is, for example, higher than that of the single-stroke model (i.e., TabNet incorporating SimAM). The obtained results show that a promising solution is provided for accurate and efficient PHP recognition and has the potential to improve writing speed and quality while reducing health problems induced by incorrect postures.