An Improved Network Time Protocol for Industrial Internet of Things.

In the industrial Internet of Things, the network time protocol (NTP) can be used for time synchronization, allowing machines to run in sync so that machines can take critical actions within 1 ms. However, the commonly used NTP mechanism does not take into account that the network packet travel time over a link is time-varying, which causes the NTP to make incorrect synchronization decisions. Therefore, this paper proposed a low-cost modification to NTP with clock skew compensation and adaptive clock adjustment, so that the clock difference between the NTP client and NTP server can be controlled within 1 ms in the wired network environment. The adaptive clock adjustment skips the clock offset calculation when the NTP packet run trip time (RTT) exceeds a certain threshold. The clock skew compensation addresses the inherent issue that different clocks (or oscillators) naturally drift away from each other. Both adaptive clock adjustment and clock skew compensation are environment dependent and device dependent. The measurement result in our experimental environment shows that the when the RTT threshold is set at 1.7 ms, the best synchronization accuracy is achieved.

Design and Implementation of Machine Tool Life Inspection System Based on Sound Sensing.

The main causes of damage to industrial machinery are aging, corrosion, and the wear of parts, which affect the accuracy of machinery and product precision. Identifying problems early and predicting the life cycle of a machine for early maintenance can avoid costly plant failures. Compared with other sensing and monitoring instruments, sound sensors are inexpensive, portable, and have less computational data. This paper proposed a machine tool life cycle model with noise reduction. The life cycle model uses Mel-Frequency Cepstral Coefficients (MFCC) to extract audio features. A Deep Neural Network (DNN) is used to understand the relationship between audio features and life cycle, and then determine the audio signal corresponding to the aging degree. The noise reduction model simulates the actual environment by adding noise and extracts features by Power Normalized Cepstral Coefficients (PNCC), and designs Mask as the DNN's learning target to eliminate the effect of noise. The effect of the denoising model is improved by 6.8% under Short-Time Objective Intelligibility (STOI). There is a 3.9% improvement under Perceptual Evaluation of Speech Quality (PESQ). The life cycle model accuracy before denoising is 76%. After adding the noise reduction system, the accuracy of the life cycle model is increased to 80%.

Low-Voltage OTA-C Filter With an Area- and Power-Efficient OTA for Biosignal Sensor Applications.

This paper presents a systematic method for decreasing the amount of transconductors used by an operational transconductance amplifier-capacitor (OTA-C) filter to decrease the power consumption and the active area. An OTA with a local-feedback linearized technique and a transconductance booster is proposed based on the presented method. The proposed OTA combines current division with source degeneration to enhance linearity and implement low transconductance. This topology enables the proposed OTA to drive multiple integration capacitors without an additional output stage. The OTA-based circuit realizes low power consumption by operating under a weak inversion at a supply voltage of 1 V. Thus, a fifth-order ladder-type low-pass Butterworth OTA-C filter is implemented for the acquisition of electrocardiograph signals. The proposed method is validated using a prototype fabricated through a 1P6M 0.18-µm CMOS process. Results show that in ECG signal acquisition, the proposed filter has a signal bandwidth located within 250 Hz, a dynamic range of 61.2 dB, and a power consumption of 41 nW to achieve a figure-of-merit of 5.4 × 10-13. The active area of the filter is 0.24 mm2.