arXiv subjects

Tingyi Zhou

Publications and source records attributed to Tingyi Zhou.

5 recordsLinked to original sources

arXiv2023

Time Stretch with Continuous-Wave Lasers

A single-shot measurement technique for ultrafast phenomena with high throughput enables the capture of rare events within a short time scale, facilitating the exploration of rare ultrafast processes. Photonic time stretch stands out as a highly effective method for both detecting rapid events and achieving remarkable speed in imaging and ranging applications. The current time stretch method relies on costly passive mode-locked lasers with continuous and fixed spectra to capture fast transients and dilate their time scale using dispersion. This hinders the broad application of time stretch technology and presents synchronization challenges with ultrafast events for measurement. Here we report the first implementation of time stretch using continuous wave (CW) diode lasers with discrete and tunable spectra that are common in WDM optical communication. This approach offers the potential for more cost-effective and compact time stretch systems and simplifies laser synchronization with the input signal. Two different embodiments in the United States and Japan demonstrate the technique's operation and limitations, and potential applications to time stretch imaging and angular light scattering.

physics.optics↗

arXiv2023

Low Latency Computing for Time Stretch Instruments

Time stretch instruments have been exceptionally successful in discovering single-shot ultrafast phenomena such as optical rogue waves and have led to record-speed microscopy, spectroscopy, lidar, etc. These instruments encode the ultrafast events into the spectrum of a femtosecond pulse and then dilate the time scale of the data using group velocity dispersion. Generating as much as Tbit per second of data, they are ideal partners for deep learning networks which by their inherent complexity, require large datasets for training. However, the inference time scale of neural networks in the millisecond regime is orders of magnitude longer than the data acquisition rate of time stretch instruments. This underscores the need to explore means where some of the lower-level computational tasks can be done while the data is still in the optical domain. The Nonlinear Schrödinger Kernel computing addresses this predicament. It utilizes optical nonlinearities to map the data onto a new domain in which classification accuracy is enhanced, without increasing the data dimensions. One limitation of this technique is the fixed optical transfer function, which prevents training and generalizability. Here we show that the optical kernel can be effectively tuned and trained by utilizing digital phase encoding of the femtosecond laser pulse leading to a reduction of the error rate in data classification.

physics.optics↗

arXiv2021

Nonlinear Schrödinger Kernel for hardware acceleration in machine learning inference

Alternative machine learning approaches that are computationally light with low latency and can work with only a small training dataset are needed for applications where the insatiable demand of deep learning methods for computing power and large training data cannot be met. We show that spectral mapping of data onto femtosecond optical pulses and a projection into an implicit, higher dimensional space using nonlinear optical dynamics increases the accuracy and reduces the latency in data classification by several orders of magnitude. The approach is validated by the classification of various datasets, including brain intracranial pressure, cancer cell imaging, spoken digit recognition, and the classic exclusive OR benchmark for nonlinear classification. The concept is demonstrated by seeding the nonlinear dynamics that are responsible for many fascinating natural phenomena, such as optical rogue waves, with the data before processing the output with a light classifier. A quantitative comparison with a well-known numerical technique is used to provide insight into this physical technique. Single-shot operation is demonstrated using time stretch data acquisition.

physics.optics↗

arXiv2020

Deep Learning Interference Cancellation in Wireless Networks

With the crowding of the electromagnetic spectrum and the shrinking cell size in wireless networks, crosstalk between base stations and users is a major problem. Although hand-crafted functional blocks and coding schemes are proven effective to guarantee reliable data transfer, currently deep learning-based approaches have drawn increasing attention in the communication system modeling. In this paper, we propose a Neural Network (NN) based signal processing technique that works with traditional DSP algorithms to overcome the interference problem in realtime. This technique doesn't require any feedback protocol between the receiver and transmitter which makes it very suitable for low-latency and high data-rate applications such as autonomy and augmented reality. While there has been recent work on the use of Reinforcement Learning (RL) in the control layer to manage and control the interference, our approach is novel in the sense that it introduces a neural network for signal processing at baseband data rate and in the physical layer. We demonstrate this "Deep Interference Cancellation" technique using a convolutional LSTM autoencoder. When applied to QAM-OFDM modulated data, the network produces significant improvement in the symbol error rate (SER). We further discuss the hardware implementation including latency, power consumption, memory requirements, and chip area.

eess.SP↗

arXiv2020

Deep Analog-to-Digital Converter for Wireless Communication

With the advent of the 5G wireless networks, achieving tens of gigabits per second throughputs and low, milliseconds, latency has become a reality. This level of performance will fuel numerous real-time applications, such as autonomy and augmented reality, where the computationally heavy tasks can be performed in the cloud. The increase in the bandwidth along with the use of dense constellations places a significant burden on the speed and accuracy of analog-to-digital converters (ADC). A popular approach to create wideband ADCs is utilizing multiple channels each operating at a lower speed in the time-interleaved fashion. However, an interleaved ADC comes with its own set of challenges. The parallel architecture is very sensitive to the inter-channel mismatch, timing jitter, clock skew between different ADC channels as well as the nonlinearity within individual channels. Consequently, complex post-calibration is required using digital signal processing (DSP) after the ADC. The traditional DSP calibration consumes a significant amount of power and its design requires knowledge of the source and type of errors which are becoming increasingly difficult to predict in nanometer CMOS processes. In this paper, instead of individually targeting each source of error, we utilize a deep learning algorithm to learn the complete and complex ADC behavior and to compensate for it in realtime. We demonstrate this "Deep ADC" technique on an 8G Sample/s 8-channel time-interleaved ADC with the QAM-OFDM modulated data. Simulation results for different QAM symbol constellations and OFDM subcarriers show dramatic improvements of approximately 5 bits in the dynamic range with a concomitant drastic reduction in symbol error rate. We further discuss the hardware implementation including latency, power consumption, memory requirements, and chip area.

eess.SP↗