A 4TH ORDER FULLY INTEGRATED ACTIVE RC COMPLEX FILTER WITH NOVEL AUTOMATIC TUNING SYSTEMS

This letter introduces a 4th order active RC complex filter with 1.SMHz center frequency and 1MHz bandwidth. The total harmonic distortion of the filter is less than -60dB and the image rejection ratio is greater than 60dB. A novel technique is also proposed in this letter to automatically adjust the variation of the time constant. The advantages of the proposed method are its high precision and simplicity. Using 5bits control words, the tuning error is less than ±1.6%.

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

Which is the Best PID Variant for Pneumatic Soft Robots?An Experimental Study

This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots.Fabricated using soft materials,soft robots have gained much attention in academia and industry during recent years because of their inherent safety in human interaction.However,due to structural flexibility and compliance,mathematical models for these soft robots are nonlinear with an infinite degree of freedom(DOF).Therefore,accurate position(or orientation)control and optimization of their dynamic response remains a challenging task.Most existing soft robots currently employed in industrial and rehabilitation applications use model-free control algorithms such as PID.However,to the best of our knowledge,there has been no systematic study on the comparative performance of model-free control algorithms and their ability to optimize dynamic response,i.e.,reduce overshoot and settling time.In this paper,we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results.Additionally,most of the existing work on modelfree control in pneumatic soft-robotic literature use manually tuned parameters,which is a time-consuming,labor-intensive task.We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically.We presented results for both manual tuning and automatic tuning using the Ziegler-Nichols method and proposed algorithm,respectively.We then used experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy.The experiment results show that for soft robots,the PID-controller essentially reduces to the PI controller.This behavior was observed in both manual and automatic tuning experiments;we also discussed a rationale for removing the derivative term.

A CMOS G_m-C complex filter with on-chip automatic tuning for wireless sensor network application

A Gm-C complex filter with on-chip automatic tuning for wireless sensor networks is designed and implemented using 0.18 μm CMOS process. This filter is synthesized from a low-pass 5th-order Chebyshev RLC ladder filter prototype by means of capacitors and fully balanced transconductors. A conventional phase-locked loop is used to realize the on-chip automatic tuning for both center frequency and bandwidth control. The filter is centered at 2 MHz with a bandwidth of 2.4 MHz. The measured results show that the filter provides more than 45 dB image rejection while the ripple in the pass-band is less than 1.2 dB. The complete filter including on-chip tuning circuit consumes 4.9 mA with 1.8 V single supply voltage.

A low power Gm-C filter with on-chip automatic tuning for a WLAN transceiver

A sixth-order Butterworth Gm-C low-pass filter（LPF） with a continuous tuning architecture has been implemented for a wireless LAN（WLAN） transceiver in 0.35μm CMOS technology.An interior node scaling technique has been applied directly to the LPF to improve the dynamic range and the structure of the LPF has been optimized to reduce both the die size and the current consumption.Measurement results show that the filter has 77.5 dB dynamic range,16.3 ns group delay variation,better than 3%cutoff frequency accuracy,and 0 dBm passband IIP3.The whole LPF with the tuning circuit dissipates only 1.42 mA（5 MHz cutoff frequency） or 2.81 mA（10 MHz cutoff frequency） from 2.85 V supply voltage,and only occupies 0.175 mm^2 die size.

A continuously and widely tunable analog baseband chain with digital-assisted calibration for multi-standard DBS applications

This paper presents a continuously and widely tunable analog baseband chain with a digital-assisted calibration scheme implemented on a 0.13μm CMOS technology.The analog baseband is compliant with several digital broadcasting system（DBS） standards,including DVB-S,DVB-S2,and ABS-S.The cut-off frequency of the baseband circuit can be changed continuously from 4.5 to 32 MHz.The gain adjustment range is from 6 to 55.5 dB with 0.5 dB step.The calibration includes automatic frequency tuning（AFT） and automatic DC offset calibration （DCOC） to achieve less than 6%cut-off frequency deviation and 3 mV residual output offset.The out-of-band IIP2 and IIP3 of the overall chain are 45 dBm and 18 dBm respectively,while the input referred noise（IRN） is 17.4 nV/√Hz.All circuit blocks are operated at 2.8 V from LDO and consume current of 20.4 mA in the receiving mode.

Design of a high linearity and high gain accuracy analog baseband circuit for DAB receiver

An analog baseband circuit of high linearity and high gain accuracy for a digital audio broadcasting receiver is implemented in a 0.18-μm RFCMOS process.The circuit comprises a 3rd-order active-RC complex filter（CF） and a programmable gain amplifier（PGA）.An automatic tuning circuit is also designed to tune the CF＇s pass band.Instead of the class-A fully differential operational amplifier（FDOPA） adopted in the conventional CF and PGA design,a class-AB FDOPA is specially employed in this circuit to achieve a higher linearity and gain accuracy for its large current swing capability with lower static current consumption.In the PGA circuit,a novel DC offset cancellation technique based on the MOS resistor is introduced to reduce the settling time significantly.A reformative switching network is proposed,which can eliminate the switch resistor＇s influence on the gain accuracy of the PGA.The measurement result shows the gain range of the circuit is 10-50 dB with a 1-dB step size,and the gain accuracy is less than ±0.3 dB.The OIP3 is 23.3 dBm at the gain of 10 dB.Simulation results show that the settling time is reduced from 100 to 1 ms.The image band rejection is about 40 dB.It only draws 4.5 mA current from a 1.8 V supply voltage.