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Summary: In this paper, an ultra‐low‐power and area‐efficient demodulator circuit for wireless downlink SCM/CWM modulation schemes is proposed. The proposed demodulator offers high performances and allows simultaneous power and downlink data transmission over a single 13.56‐MHz inductive link. The proposed method requires only two carrier cycles per data bit period, and also, a power index of 75% is obtained. Although the proposed demodulator can be applicable to other wireless systems, here, it is intended for implantable medical devices. To prove the performance of the proposed demodulator circuit, it has been designed and post‐layout simulations are provided using a standard 65‐nm CMOS technology and occupies 27 × 32 μm2 of active area. Post‐layout simulation results indicate that with a 1‐V power supply, the proposed circuit achieves an ultra‐low‐power consumption of 8.51 μW at a data rate of 6.78 Mb/s corresponding to an energy efficiency of 1.25 pJ/bit. [ABSTRACT FROM AUTHOR]

Live video broadcasting is a popular application properly considered in lately developed standard, Versatile Video Coding (H.266/VVC). In live video broadcasting, both the bandwidth and buffer volume are limited, while a high quality level is demanded. In order to solve these problems, a Quantization Parameter Estimation Algorithm (QEA) is proposed. The core of the proposed algorithm is a neuro-fuzzy system that changes the Quantization Parameter (QP) gradually to produce a bandwidth-compliant bit rate and prohibit buffer saturation and starvation while providing high quality. The estimation is conducted according to the proportional, integral, and derivative components of the bit error. In other words, the proposed QEA is a Proportional-Integral-Derivative (PID) controller. The optimal parameters of the neuro-fuzzy system are obtained through the training process. The required data set for the training process is established by taking advantage of dynamic programming. The experiments affirm that the proposed approach achieves the target rate with an average error equal to 1.41% and fully respects the buffering boundaries. This method has at least a 2.48% bit rate reduction rather than other QEAs. Meanwhile, the proposed QEA is faster than other algorithms. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a low‐power transconductor‐capacitor (Gm‐C) filter with programmable cutoff frequency is designed for electroencephalogram (EEG), electrocardiogram (ECG), and surface electromyography (sEMG) biosignals. The second‐order Butterworth low pass filter (LPF) is designed as an interface between an instrumentation amplifier (IA) and an analog‐to‐digital converter (ADC). The proposed Gm‐C filter suppresses out‐of‐band components and also plays an anti‐aliasing role. With the variable transconductance based on flipped voltage follower (FVF), the cutoff frequencies of 100 Hz, 560 Hz, and 1.5 kHz are obtained for EEG, ECG, and sEMG, respectively. This structure is highly accurate by using a robust and low‐power common mode feedback (CMFB). With 0.8 V supply voltage, the total power consumption of the filter is only 56 nW, 100 nW, and 221 nW for cutoff frequencies of 100 Hz, 560 Hz, and 1.5 kHz, respectively. The proposed Gm‐C filter is simulated in TSMC 65 nm CMOS technology and occupies an area of 0.12 mm2. The figure of merit (FOM) of the proposed filter is equal to 1.9 pW/pole. [ABSTRACT FROM AUTHOR]