Shoushun Chen

In designing a mm-wave ultra-wideband pulse-Doppler radar IC, one of the main challenges encountered is to reduce the high power consumption of the circuitries in the IC. A direct-RF receiver can be used in the radar’s design to reduce the radar’s power consumption. However, a digitizer that can directly digitize mm-wave ultra-wideband radar pulses is needed to implement such a receiver. This work presents the design of such a digitizer, which comprises several multi-pass sub-ADCs that operate together. Each multi-pass sub-ADC works fundamentally like a flash ADC but comprises one comparator only. A test chip containing a 6-bit prototype of the digitizer is fabricated in a 40 nm CMOS technology. The prototype consumes 19.7 mW when operated at 4 GSa/s and can operate with input signal frequencies of up to 64 GHz while achieving a −1.7/−6 dBFS SNDR of 21.1/22.8 dB at 60 GHz (the corresponding FoM is 532/434 fJ/c-s). The digitizer has a relatively high effective parallel-equivalent input impedance at 60 GHz, which, in a radar IC implementation, allows it to be driven with a relatively low power-consuming 60 GHz RF buffer. These are achieved without the digitizer being fabricated in a much more advanced technology node.

Event camera is an emerging imaging sensor for capturing dynamics of moving objects as events, which motivates our work in estimating 3D human pose and shape from the event signals. Events, on the other hand, have their unique challenges: rather than capturing static body postures, the event signals are best at capturing local motions. This leads us to propose a two-stage deep learning approach, called EventHPE. The first-stage, FlowNet, is trained by unsupervised learning to infer optical flow from events. Both events and optical flow are closely related to human body dynamics, which are fed as input to the ShapeNet in the second stage, to estimate 3D human shapes. To mitigate the discrepancy between image-based flow (optical flow) and shape-based flow (vertices movement of human body shape), a novel flow coherence loss is introduced by exploiting the fact that both flows are originated from the identical human motion. An in-house event-based 3D human dataset is curated that comes with 3D pose and shape annotations, which is by far the largest one to our knowledge. Empirical evaluations on DHP19 dataset and our in-house dataset demonstrate the effectiveness of our approach.

A novel star pattern recognition technique for a “Lost-in-space” mode star tracker is presented in this paper. Firstly, the two-dimensional (2D) vectors connecting the stars are constructed in a rotation-invariant frame. Later, the additive property of 2D vectors is integrated with the rotation-invariant frame to build a vector sequence for star identification. The proposed technique achieves an identification accuracy of 98.7% and has a run-time of only 12ms for real-time testing on star images.

This paper presents a novel star identification algorithm for a "Lost-In-Space" mode star tracker. The spearman correlation approach provides reliable recognition even when the captured images are swayed and biased from the onboard star pattern database. The hamming distance approach provides a shortlisted list of star IDs. Thus, the proposed combination of hamming distance and spearman correlation provides a reliable and fast recognition. The achievable performance is evaluated by testing on simulated and real images.

In this paper, we propose an event-guided support vector machine (ESVM) for tracking high-speed moving objects. Tracking fast-moving objects with low frame rate cameras is always difficult due to motion blur and large displacements. The accuracy problem can be solved by using high frame rate cameras at the expense of tremendous computational cost. For this issue, our ESVM incorporates event-based guiding methods into the traditional structured support vector machine to improve the tracking accuracy at a relatively low-complexity level. The event-based guiding methods include two models, event position guided search localization and event intensity guided sample supplement, which are based on the event features of the CeleX motion sensor. The motion sensor continuously responds to intensity change, which is generally related to object motion. Once it has detected intensity change, the motion sensor outputs event packages, and each of them contains the pixel location, time stamp, and pixel illumination. The generated events are continuous in the temporal domain and thus record the motion trajectory of fast-moving objects, which cannot be fully captured by frame-based cameras. In this paper, we convert high-speed test sequences into sequences of spiking events recorded by the CeleX motion sensor. Our approach presents fairly high computational efficiency, and experiments over sequences from multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our method, compared to the state-of-the-art trackers.

This paper presents an antivibration time-delay integration (TDI) CMOS image sensor (CIS) for small remote imaging systems, introducing a hardware-implemented online deblurring (ODB) algorithm to address the image blur problems caused by vibrations. The proposed sensor has eight TDI stages, column-parallel TDI accumulating and ODB circuits. A 256×8-pixel prototype chip was fabricated using a 0.18-μm CIS technology with a pixel footprint of 6.5 μm × 6.5 μm and a fill factor of 28%. Measurement results show that the sensor can achieve dynamic ranges of 45.1 and 51.8 dB, respectively, with and without enabling the ODB algorithm. Compared with a single-stage line scanner imager, it offers an improvement in signal-to-noise ratios of 1.9 and 8.6 dB, respectively, with and without the ODB algorithm.

The design and radiation characterization of pinned photodiode 4T Active Pixel Sensors (APS) is presented in this paper. Four types of pixel layout are characterized and irradiated by 60Co γ-rays up to 170krad(Si). A variety of radiation hardening techniques are applied in these pixels. The variations between the four pixels allow us to optimize the design parameters for radiation hardness. Our experimental results exhibit increase in dark current and its non-uniformity after radiation. It is also found that Enclosed Layout Transistor (ELT) transfer gate (TX) becomes the major cause of the dark current increase. The dark current increase attributed to the shallow trench isolation (STI) around the photodiode can be mitigated by recessed-STI photodiode structure when the total dose is low. Large floating diffusion (FD) shows more immunity to the radiation-induced leakage current and random noise.

In this paper, we report a novel Time-Delay-Integration (TDI) CMOS image sensor for low-earth orbit (LEO) nano-satellite imaging application, where limited exposure time and unexpected flight fluctuations are major design challenges. The sensor features programmable integration time per stage, dynamic charge transfer path and tunable well capacity. A prototype chip of 1536×8 pixels was implemented using TSMC 0.18µm CMOS image sensor process. Photodiode and other transistors are floor-planned in different arrays, providing small pixel pitch of 3.25µm and high fill factor of 57%.

In this paper we present a low power temporal-difference image sensor with wireless communication capability designed specifically for imaging sensor networks. The event-based image sensor features a 64 × 64 pixel array and can also report standard analog intensity images. An ultra-wide-band radio channel allows to transmit digital temporal difference images wirelessly to a receiver with high rates and reduced power consumption. The sensor can wake up the radio when it detects a specific number of pixels intensity modulation, so that only significant frames are communicated. The prototype chip was implemented using a 2-poly 3-metal AMIS 0.5 μ m CMOS process. Power consumption is 0.9 mW for the sensor and 15 mW for radio transmission to distance of 4 m with rates of 1.3 Mbps and 160 fps.