Wearable indoor localization can now find applications in a wide spectrum of fields, including the care of children and the elderly, sports motion analysis, rehabilitation medicine, robotics navigation, etc. Conventional inertial measurement unit (IMU)-based position estimation and radio signal indoor localization methods based on WiFi, Bluetooth, ultra-wide band (UWB), and radio frequency identification (RFID) all have their limitations regarding cost, accuracy, or usability, and a combination of the techniques has been considered a promising way to improve the accuracy. This investigation aims to provide a cost-effective wearable sensing solution with data fusion algorithms for indoor localization and real-time motion analysis. The main contributions of this investigation are: (1) the design of a wireless, battery-powered, and light-weight wearable sensing device integrating a low-cost UWB module-DWM1000 and micro-electromechanical system (MEMS) IMU-MPU9250 for synchronized measurement; (2) the implementation of a Mahony complementary filter for noise cancellation and attitude calculation, and quaternions for frame rotation to obtain the continuous attitude for displacement estimation; (3) the development of a data fusion model integrating the IMU and UWB data to enhance the measurement accuracy using Kalman-filter-based time-domain iterative compensations; and (4) evaluation of the developed sensor module by comparing it with UWB- and IMU-only solutions. The test results demonstrate that the average error of the integrated module reached 7.58 cm for an arbitrary walking path, which outperformed the IMU- and UWB-only localization solutions. The module could recognize lateral roll rotations during normal walking, which could be potentially used for abnormal gait recognition.
Palmprint and hand shape, as two kinds of important biometric characteristics, have been widely studied and applied to human identity recognition. The existing research is based mainly on 2D images, which lose the third-dimensional information. The biological features extracted from 2D images are distorted by pressure and rolling, so the subsequent feature matching and recognition are inaccurate. This paper presents a method to acquire accurate 3D shapes of palmprint and hand by projecting full-field composite color sinusoidal fringe patterns and the corresponding color texture information. A 3D imaging system is designed to capture and process the full-field composite color fringe patterns on hand surface. Composite color fringe patterns having the optimum three fringe numbers are generated by software and projected onto the surface of human hand by a digital light processing projector. From another viewpoint, a color CCD camera captures the deformed fringe patterns and saves them for postprocessing. After compensating for the cross talk and chromatic aberration between color channels, three fringe patterns are extracted from three color channels of a captured composite color image. Wrapped phase information can be calculated from the sinusoidal fringe patterns with high precision. At the same time, the absolute phase of each pixel is determined by the optimum three-fringe selection method. After building up the relationship between absolute phase map and 3D shape data, the 3D palmprint and hand are obtained. Color texture information can be directly captured or demodulated from the captured composite fringe pattern images. Experimental results show that the proposed method and system can yield accurate 3D shape and color texture information of the palmprint and hand shape.
At present, the global demand for lithium batteries is still in a high growth state, and the traditional lithium battery pole mill control system is still dominated by ARM (Artificial Intelligence Enhanced Computing), DSP (Digital Signal Processing), and other single-chip control methods. There are problems such as poor anti-interference ability and insufficient real-time online analysis of production data. This paper adopts the dual-chip control system architecture based on "ARM+DSP", starting from the mechanical characteristics and operating signal features of the pole mill. The hardware system adopts a three-unit joint control hardware structure, which separates the control unit from the data processing unit and improves the operation of the system. The software system adopts fuzzy PID algorithm to realize deflection control and tension control, and verifies that the Fuzzy PID (Proportion Integration Differentiation) control algorithm can effectively improve the anti-interference ability of the deflection system and tension system. The results show that the data loss rate is low with the SPI communication between DSP and ARM. The tension error of the "ARM+DSP" control system does not exceed 5%, and the deviation of the correction band is within ±4mm. The dedicated dual-chip hardware architecture effectively improves the robustness and operation efficiency of the pole mill, solves the problem of low tension control accuracy, and provides a theoretical basis for the application of the dual-roll mill.
At 65nm technology node and below, with the ever-smaller process window, it is no longer sufficient to apply traditional model-based verification at only the nominal condition. Full-chip, full process-window verification has started to integrate into the OPC flow at the 65nm production as a way of preventing potentially weak post-OPC designs from reaching the mask making step. Through process-window analysis can be done by way of simulating wafer images at each of the corresponding focus and exposure dose conditions throughout the process window using an accurate and predictive FEM model. Alternatively, due to the strong correlation between the post-OPC design sensitivity to dose variation and aerial image (AI) quality, the study of through-dose behavior of the post-OPC design can also be carried out by carefully analyzing the AI. These types of analysis can be performed at multiple defocus conditions to assess the robustness of the post-OPC designs with respect to focus and dose variations. In this paper, we study the AI based approach for post-OPC verification in detail.For metal layer, the primary metrics for verification are bridging, necking, and via coverage. In this paper we are mainly interested in studying bridging and necking. The minimum AI value in the open space gives an indication of its susceptibility to bridging in an over-dosed situation. Lower minimum intensity indicates less risk of bridging. Conversely, the maximum AI between the metal lines provides indication of potential necking issues in an under-dosed situation.At times, however, in a complex 2D pattern area, the location as to where the AI reaches either maximum or minimum is not obvious. This requires a full-chip, dense image-based approach to fully explore the AI profile of the entire space of the design. We have developed such an algorithm to find the AI maximums and minimums that will bear true relevance to the bridging and necking analysis. In this paper, we apply the full-chip image-based analysis to 65nm metal layers. We demonstrate the capturing of potential bridging or necking issues as identified by the AI analysis. Finally, we show the performance of the full-chip image-based verification. INTRODUCTIONAs the semiconductor industry moves to 65nm and 45nm production, systematic yield loss due to limited process window is getting more and more profound. Due to the tight design rules and CD control, the allowable focus and exposure dose variation is increasingly reduced. It is therefore required that the design and corresponding OPC be able to ensure a good process window in order to achieve good yield. The existence of a few "hot spots" in the OPC design could lead to significant reduction in the overall process window. This calls for through process window verification to be applied for production OPC design so that these "hot spots" can be identified and removed before the masks are manufactured.Traditional model-based verification tool simulates the wafer images from the post OPC design with a process model calibrated to the m...
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