In this paper, we present a new scheme for implementing virtual keyboards, which uses only two to four motion-recognition rings per hand and a two-dimensional keyboard template (e.g., an A4 size paper with printed key positions). It has the benefit of portability, customizability, and low-cost when compared with existing approaches. Essentially, we have shown that wearing two wireless IoT rings on the middle phalanges of two fingers of each hand, users can input the alphabetic characters into a computing device by typing on a flat paper on a desk, and potentially in mid-air. We have demonstrated that two rings are sufficient in capturing the gestures and motions of all fingers in a typing hand for keystrokes recognition. A single wireless IoT ring, which weighs 7.8 grams, consists of a Bluetooth low energy (BLE) unit, a micro inertial measurement unit (mIMU), and a cell battery. The 3-axes attitude angles and the Z-axis acceleration of each ring are adopted as the features for keystroke recognition. The overall keystroke recognition accuracy rate can reach as high as 94.8% when two IoT rings are worn by a user on each hand; this accuracy rate increases to 98.6%, when four rings are worn on each typing hand.

This paper presents a novel method of estimating the altitude of pedestrians who are walking in the indoor environment of a multi-story building. We will show that to achieve pedestrian altitude estimation, a pedestrian only needs to wear a small MEMS-based integrated sensing device consisting of a micro-IMU (i.e., consists of a 3D accelerometer, a 3D gyroscope, and a 3D magnetometer) and a barometer, during indoor activities. High-precision estimates of the pedestrian's position in the vertical direction were obtained by utilizing the acceleration and angular rate data, as well as the height, deduced from barometer data. The inherent drifts of the IMU sensors, which lead to cumulative errors in altitude estimation, were sharply reduced using a complementary filter and an error compensation algorithm. The experimental results demonstrate that this method is effective in reducing estimation errors. When a person walks on stairs with the same step height, the error of the estimated height of each step is within 0.5 cm, and the cumulative height error is about 1.7% over a total height of 2.9 m. This integrated sensing device also exhibits good stability, i.e., three 20-min tests in a 12-h period showed that the cumulative error accounts for about 2% of the total height of 11.23 m. When the stairs have different heights, i.e., heights ranging from 12 to 28 cm, the estimated height error of each step is within 2 cm. With an ability to provide accurate and reliable vertical altitude estimates of pedestrians inside a multi-story building, the sensing device developed through this paper is suitable for use in 3D-space body tracking and pedestrian navigation applications.

The TFT-LCD line generates a large amount of data, in order to fully exploit the value of the yield-related data, thus building a big data platform for yield analysis. By discussing the methods and deficiencies of traditional yield analysis, the ideas and methods of big data implementation are explained. Study the characteristics of TFT-LCD line data and complete the data preparation work of the big data platform. According to the current mainstream big data system architecture, combined with the characteristics of the TFT-LCD line information system, the business view and technical architecture of the big data platform are proposed, and the main business content and technical methods of the big data platform are expounded. Finally, combined with the case, introduce the development process, and realize the big data platform design of yield analysis.

This paper presents the development of human fall protection system based on artificial neural network (ANN) and optimized zero moment point (ZMP) algorithms that can detect and protect falling people in real time. Evaluating the movement data of different parts of the body, the result shows that the double feet and waist make the most contributions. For the sake of monitor the motions of the feet and waist, the inertial MEME sensor-based hardware of the system was designed. The foot pressure measurement units and the Micro Inertial Measurement Units (μIMUs) was applied in this system with Zigbee network. In terms of improving the efficiency and accuracy of fall posture detection, the ZMP algorithm was optimized and combined with Artificial Neural Network. Experimental results showed that when combining the ZMP and artificial neural network algorithms together, the recognition of fall and ADL (Activities of Daily Life), the Sensitivity, the Specificity and the overall accuracy were all better than 98%.

Due to the drift of gyroscope, there is lots of accumulated error in the posture measurement. The gyroscope can't complete a long time and high accurate attitude measurement independently. Therefore, the common method is the multi-sensor information fusion technology. The measurement system based on gyroscope, accelerometer and magnetometer is presented by this paper. The original data is compensated and filtered by extend Kalman filter. And the sensornoise is reduced. The Kalman state equation is established by the gyroscope data calculated by quaternion. The Kalman measurement equation is established by the output of accelerometer and magnetometer. The experiments' results show that the error of the attitude angle is effectively suppressed by this extend Kalman filter.

This paper proposes a new PI controller based on the BP neural network to replace the traditional speed loop in the motor control system. The BP neural network could approach to any nonlinear function and has self-study ability. Through adjusting weights of BP neural network, the model of vector control block could calculate the optimal PI controller parameters in order to accelerate the speed and accuracy of speed controller, thus realizing better effect.

A novel algorithm of multi-redundancy based on 12-position calibration method for electronic compass is presented in this paper. The errors sources of the electronic compass are analyzed. Such as installation misalignment error, hard iron error, soft iron error, scale factor error, non-orthogonal error and offsets error. And this paper simplifies the error model. Firstly, according to the error model and the 12-position calibration method, 12 basic integrated error parameters can be calculated. Then we can use the magnetometer output value after the calibration. Therefore, the heading of the compass has presented by multi-redundancy method. This method can rectify all the parameters of the model through the simulation of the LabVIEW and Matlab. It just requires a special cuboid during the process of the calibration which is made of aluminum. Moreover, the calibration method is convenient and easy to use. The experiment results show that this algorithm is effective and easy to realize. And the precision of heading angle is 1 degree. It is nearly 10 times higher than before without calibration.

This paper presents a novel 16-bit pixel-level analog-to-digital converter (ADC) for the 384*288 Infrared Focal Plane Array (IRFPA). It is a two-step ADC: the first is coarse quantization, and the second is fine quantization. Coarse quantization is to process the most significant 12 bits using a voltage reset method. Fine quantization is to dispose the remaining 4 bits by applying a falling multistep signal on the bottom plate of the integrating capacitor. The ADC is implemented in standard SMIC 0.18μm 1.8V/5V CMOS process, with 100Hz sampling rate.

Magnetometer is widely used to indicate the heading of vehicle by measuring the Earth's magnetic field. In this article, to solve the problem existing in 3D magnetometer, a method is presented for fitting a high precision heading angle. First of all, on the basis of analyzing the sensor working principle and error sources, the error model is established and the compensation algorithms is developed. Then using the ellipsoid fitting method to calibrate the magnetometer and the accelerometer simultaneously. After that, the maximum error of the heading angle of the magnetometer is about 3 degrees. This is due to the non-orthogonal coupling error between the axes of the magnetometer. The BP neural network method is used to fit its nonlinearity. error. The results show that the heading angle error is controlled within 0.8 degrees.

In this paper, we present a new method to calibrate the misalignment error and zero offset of MEMS accelerometer, which applies the Genetic Algorithm (GA) to process measured data and get the error model parameters. This method can effectively eliminate the error caused by the assembly deviation between the sensitive sensor unit and the sensor package shell. Results show that the calibrated output is far more accurate than the raw data obtained by only used factory calibration. The mean squared error (MSE) before calibration is 1.754×10 -3 g 2 which reduces to 2.828×10 -5 g 2 . Furthermore, the proposed procedure shows more advantages than the BP Neural Network method. The genetic algorithm behaves convenient and suitable for the calibration problem of MEMS accelerometer and reduces effect of the misalignment error and zero offset.

Aiming at the typical orthogonal configuration scheme (twelve sensors are orthogonal), the project implementation. With the redundant system and small guide system Navigation computer are linked together, complement a complete micro-miniature redundant strap-down inertial navigation system. Studied the system of fault detection, fault isolation and the system reconstruction technology, and use direct comparison measurement method and the weighted least squares method respectively as the fault detection and system reconstruction project implementation plan, implement the redundant in the system of IMU data collection, fault detection, identification, isolation and system reconfiguration, solving a series of functions such as navigation parameters.

According to the principle of electronic compass, we analyze the error of the E-compass and propose a calibration scheme in view of magnetic deviation, which is based on the self-designed tri-axial magnetometer system. The hardware is made by tri-axial magnetometer and tri-axial accelerometer. Considering the magnetic interference of the external environment will affect the magnetic deviation, we worked out a compensation method based on ellipsoid fitting. The experiment results showed the simplicity and efficiency of the algorithm. The compass deviation fell from 15 degree to within 1 degree. And it can maintain high accuracy even under large magnetic dip.

Nowadays, location-based services (LBS) has become widely used in our daily life. The most famous system is global positioning system (GPS), which is limited to outdoor applications and provide poor locating accuracy. In this paper, we present a positioning systems based on inertial MEMS sensor which includes three-axis accelerometer, three-axis gyroscope and three-axis magnetometer. The system can help people get accurate positioning for indoor environments, also available for outdoors, because of its self-contained character. It is a foot wearable device with wireless network to transmit movement information to computer that can calculate the relative position and show the path walked by. The key concept of the positioning system is inertial navigation and dead reckoning technology. Since it needs twice-integration of the acceleration to get the position, the displacement will drift by time elapse. We make it only drift by distance increasing through gait phase analysis, a method called Zero-Velocity Update (ZVU). As the “stand-still phase” is the key of the system performance, we mainly focus on getting accurate gait phase detection. We used decision tree here and the experimental results showed that we got a gait phase detection accuracy of 99.96% and positioning accuracy of 97.37%.

This paper proposes a position and attitude observer based on INS and GPS. Design and test results of an adaptive dual-rate Extended Kalman Filter(EKF) estimator for fusing data from Global Positioning Systems (GPS) and an Inertial Navigation System (INS) in order to estimate the position, velocity, and attitude. The dual-rate EKF consists of a high-speed filter and a low-speed filter, the high-speed filter fuses data from Real-Time Kinematic (RTK) GPS and INS, the low-speed filter fuses data from pseudorange GPS and INS. This solution designed to isolate the noise from pseudorange and realize the complementary of real-time performance and high precision. The solution yields exponential convergence of the attitude and position estimates. The implementation results show that the proposed method resolves an integer vector identical to that of the original method and achieves state estimation with centimeter global positioning accuracy.

This paper contains the development and analysis of the human motion state and the algorithm of the fall prediction based on the double foot pressure and the micro inertial MEMS sensors. The fall prediction hardware system consists of three parts, the double foot nodes and the waist node and how it was designed, which could measure the foot pressure parameters and the inertial parameters in different human motion states and could detect the falls in real time. What's more, the foot pressure measurement units and the Micro Inertial Measurement Units (µIMUs) were applied in this system with wired network and the fall prediction algorithm was constituted of large numbers of threshold judgments which can detect different falls directly. With this hardware system, the foot pressure data and the motion data can be captured in real time. Then, these data will be dealt with through J48 decision tree classifier. Experiment results showed that the lead time (the time ahead of collision) of fall can be improved to 180ms and different falls can be recognized with different logic trees which were judged through the foot pressure threshold, the angular velocity threshold and the acceleration threshold. Based on the analysis, it can be showed that in the recognition of fall and ADL(Activities of Daily Life), the Sensitivity, the Specificity and the overall accuracy were all over 96%. While in the recognition of different falls, they can all achieve over 92%.

Strapdown inertial navigation system (SINS) is extensively used in many fields especially in military and engineering fields. Traditional processors are unable to meet the accuracy, power consumption and real-time requirements. In this paper, according to the strapdown inertial navigation algorithm, we designed a strap-down inertial navigation calculation ASIC using verilog HDL (Hardware Description Language). Inertial measurement units (IMU) captured the acceleration and angular rate of the carrier and the ASIC circuit calculated the velocity and position of the carrier. We compared the ASIC performance with CPU performance. Experimental results show that computational accuracy of hardware circuit is almost similar to that of software that runs on PC, and computational speed of hardware circuit is faster than software that runs on PC at the same rate.

This paper presents an Always-on buffer cluster implementation based on 28nm chip fabrication process. With the 28 nanometer process, area resources become more deficient than the previous node era in IC physical design. We have encountered serious congestion problems. Traditional method of low power physical design flow cannot make full use of the limited resources of the chip area. Therefore we put forward a low power physical optimization method, Always-on Buffer Clustering. It can save area resources and reduce power consumption dramatically, which can also solve the congestion problem due to lack of routing resources in the chip design process.

This paper presents a physical design for ultra-low power MCU usage based on improved multibit SRPG process. The State Retention Power Gating (SRPG) is used to save static power when the chip turns to sleep mode and its area occupies 50% of the whole standard cells area. The existed multibit SRPG cells occupy too many high metal layers, which lower the utilization of the standard cell region. In this paper, we tuned the layout of the SRPG cells, drastically reduced the number of the high metal. The degree of optimization depends on the experience. Designing the 4-bit SRPG based on the 2-bit SRPG is another important task. And the 4-bit SRPG will be designed to 4-row height comparing with the original one with 2-row height, consequently reducing the width of the layout. After the layout is completed, we extract the LEF file of the SRPG and rerun the synthesis and physical design process. The results turn out that the global area of the die can be reduced 2.3%.

This paper presents the development and analysis of inertial MEMS sensor based system that can detect falls in real time. The system is a major part of mobile human airbag system which prevents the elderly from fall induced fractures. The fall detection system hardware was designed, which could monitor the motions of the feet and waist and detect the falls in real time. Micro Inertial Measurement Units (μ IMUs) was applied in this system with Zigbee network and the fall detection algorithm what was constituted of three sub algorithms also was developed. The system was designed based on data analysis, in order to select the optimal parts for monitoring human motion and verify the algorithm performance, performance for different parts was compared by employing the pattern recognition based sub-algorithm and performance for different combination of human body segments and joints was also compared to get the better result. A wearable motion capture device was utilized to acquire the motion data. The effective extracting features were carried out and the motion classification performance was achieved and compared using the J48 decision tree classifier. Experimental results showed that the waist is the best location for motion monitoring with detection Sensitivity of 95.5%, the Specificity of 98.8% and the overall accuracy of 97.792%. Furthermore, the combination of the waist and feet sensing data was adopted with the Sensitivity of 98.9%, the Specificity of 98.5% and the overall accuracy of 98.565%. Based on the analysis, the system was designed to monitoring the motion of the combination, and the pattern recognition based sub-algorithm was also verified.

Based on parallel processing methodology, a new design of high-performance chip with data flow architecture for accelerating AHRS is presented in this paper. Based gradient descent algorithm, we divided the serial algorithm into four primary pipeline function blocks on chip: Communication Block, Quaternion Initialization Block, Register file Block and Quaternion Update Block. Every block was constructed with several parallel pipeline channels and embedded a number of acceleration units. Acceleration units were the technical-designed cells working for special mathematical operation, high speed reading and writing process, such as floating point operation, multi-dimension vectors computation and high speed cache on chip, which is proven as a great assistance for the data flow process. Compared to the traditional method estimating by serial MCU architecture, this data flow architecture got nearly a 10 times resolution of accuracy. Meanwhile, the speed of estimation could be raised up to over 40 times.