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This article presents a wide-locking range divide-by-2 quadrature injection-locked frequency divider (QILFD) with capacitive cross-coupled oscillator. The ILFD consists of a quadrature voltage-controlled oscillator (QVCO) and two NMOS switches, which are in parallel with the QVCO resonators for signal injection. The proposed CMOS QILFD has been implemented with the tsmc 0.18-µm CMOS technology and the core power consumption is 20.4mW at the supply voltage of 1V. At the input power of 0dBm, the divide-by-2 locking range is 90.322% from 3.4 GHz to 9.0 GHz. The phase noise of the locked output spectrum is lower than that of free running QILFD in the ÷2 mode for RFI reduction

This presents a differential outputs divide-by-4 with class-A-C injection-locked frequency divider (ILFD) was implemented in the TSMC standard 0.18 μm CMOS process for biomedical sensor and wearable applications. The ILFD is based on a differential VCO with one injection MOSFET for coupling the external signal to the resonator. The locking range of the ILFD is increased by using a new LC resonator. Measurement results show that at the supply voltage of 1.5 V, the divider's free-running frequency is 3.5 GHz, and at the incident power of 0 dBm the locking range is about 3.1GHz (20.4%), from the incident frequency 3.4 to 4.1 GHz. The core power consumption is 12.9mW. The die area is 0.66mm2.

In order to automatically identify the electrical parameters of PMSMs, rather than measure the parameters using instruments such as RLC meters, this paper proposes an approach in which the testing voltage is injected into the motor from the drive. Based on the analysis of equivalent circuits of the PMSMs as well as the phase voltage and phase current information recorded from standstill to sensorless control operation, the RLS method is employed to estimate the values of electrical parameters of the PMSMs. Moreover, the automatic motor parameter identification algorithm developed in this paper is also integrated into the open loop startup procedure of the sensorless control drive. Consequently, the proposed automatic motor parameter identification method has attractive features such as fast identification and being highly convenient. In addition, both the proposed automatic motor parameter identification method and the sensorless control drive developed in this paper are implemented using a low-cost 16-bit microcontroller. Two commercial surface-type and interior-type PMSMs are used in the experiments to assess the performance of the proposed approach. Experimental results verify the effectiveness of the proposed automatic motor parameter identification method.

Due to its attractive features such as high power density, long duration, high efficiency, the Brushless DC Motor (BLDCM) has gradually replaced the conventional brush DC motor and has been widely used in home appliances, office equipments, industrial applications and vehicles, etc. This paper proposes an intellectual property design of functions needed to implement a BLDCM drive, including the algorithms for commutation and control. The developed IP is verified by an Altera FPGA Board. In addition, the IP developed in this paper can be implemented as an Application Specific Integrated Circuit (ASIC) or embedded in SoC IC as a motor drive module. It is cost effective and requires shorter development time compared with the conventional microcontroller drive.

Context-aware applications become more and more popular in today's life. Location-aware information has derived a lot of research issues. This paper presents a precise indoor RF-based WLAN (IEEE 802.11) locating system named precise indoor locating system (PILS). Most proposed location systems acquire well location estimation results but consume high level of manual effort to collect a huge amount of signal data. As a consequence, the system becomes impractical and manpower-wasted. In this paper, we aim to reduce the manual effort in constructing radio map and maintain high accuracy in our system. We propose models for data calibration, interpolating, and location estimation in PILS. In data calibration and location estimation models, we consider of autocorrelation of signal samples to enhance the accuracy. Wireless channel propagation model is also in our concern. Large scale and small scale fading are involved in the wireless channel propagation.

As mobile computing technology becomes more and more mature, people feel great interest on context-aware applications and services. Referring to context, location-aware system is one of the most important components. This paper presents a precise indoor RF-based (IEEE 802.11) locating system named precise indoor locating system (PILS). In order to acquire high level of location estimation result, a large number of training samples should be collected in offline phase. As a result, the system becomes impractical and huge number of man-power is needed. In this paper, we aim to reduce the manual effort in constructing radio map and maintain high accuracy in our system. We propose models for data calibration, interpolating, location estimation, and tracking in PILS. Wireless Channel Propagation model is also in our concern. Large scale and small scale fading are involved in the wireless channel propagation