A third-order low-distortion delta-sigma modulator (DSM), whose third-order noise-shaping ability is achieved by just a single opamp, is proposed. Since only one amplifier is required in the whole circuit, the designed DSM is very power efficient. To realize the adder in front of quantizer without employing the huge-power opamp, a capacitive passive adder, which is the digital-to-analog converter (DAC) array of a successive-approximation-type quantizer, is used. In addition, the feedback path timing is extended from a nonoverlapping interval for the conventional low-distortion structure to half of the clock period, so that the strict operation timing issue with regard to quantization and the dynamic element matching (DEM) logic operation can be solved. In the proposed DSM structure, the features of the unity-gain signal transfer function (STF) and finite-impulse-response (FIR) noise transfer function (NTF) are still preserved, and thus advantages such as a relaxed opamp slew rate and reduced output swing are also maintained, as with the conventional low-distortion DSM. Moreover, the memory effect in the proposed DSM is analyzed when employing the opamp sharing for integrators. The proposed third-order DSM with a 4-bit SAR ADC as the quantizer is implemented in a 90-nm CMOS process. The post-layout simulations show a 79.8-dB signal-to-noise and distortion ratio (SNDR) in the 1.875-MHz signal bandwidth (OSR=16). The active area of the circuit is 0.35mm2 and total power consumption is 2.85mW, resulting in a figure of merit (FOM) of 95 fJ/conversion-step.

Automatic tracking and classification are essential for studying the behaviors of wild animals. Owing to dynamic far-shooting photos, the occlusion problem, protective coloration, the background noise is irregular interference for designing a computerized algorithm for reducing human labeling resources. Moreover, wild dolphin images are hard-acquired by on-the-spot investigations, which takes a lot of waiting time and hardly sets the fixed camera to automatic monitoring dolphins on the ocean in several days. It is challenging tasks to detect well and classify a dolphin from polluted photos by a single famous deep learning method in a small dataset. Therefore, in this study, we propose a generic Cascade Small Object Detection (CSOD) algorithm for dolphin detection to handle small object problems and develop visualization to backbone based classification (V2BC) for removing noise, highlighting features of dolphin and classifying the name of dolphin. The architecture of CSOD consists of the P-net and the F-net. The P-net uses the crude Yolov3 detector to be a core network to predict all the regions of interest (ROIs) at lower resolution images. Then, the F-net, which is more robust, is applied to capture the ROIs from high-resolution photos to solve single detector problems. Moreover, a visualization to backbone based classification (V2BC) method focuses on extracting significant regions of occluded dolphin and design significant post-processing by referencing the backbone of dolphins to facilitate for classification. Compared to the state of the art methods, including faster-rcnn, yolov3 detection and Alexnet, the Vgg, and the Resnet classification. All experiments show that the proposed algorithm based on CSOD and V2BC has an excellent performance in dolphin detection and classification. Consequently, compared to the related works of classification, the accuracy of the proposed designation is over 14% higher. Moreover, our proposed CSOD detection system has 42% higher performance than that of the original Yolov3 architecture.