Time-varying microwave photonic filter for arbitrary waveform signal-to-noise ratio improvement

A time-varying microwave photonic filter (TV-MPF) based on stimulated Brillouin scattering (SBS) is proposed and utilized to suppress the in-band noise of broadband arbitrary microwave waveforms, thereby improving the signal-to-noise ratio (SNR). The filter-controlling signal is designed according to the signal to be filtered and drives the TV-MPF so that the passband of the filter is always aligned with the frequencies of the signal to be filtered. By continuously tracking the signal spectral component, the TV-MPF only retains the spectral components of the signal and filters out the noise other than the spectral component of the signal at the current time, so as to improve the in-band SNR of the signal to be filtered. An experiment is performed. A variety of signals with different formats and in-band SNRs are used to test the noise suppression capability of the TV-MPF, and the waveform mean-square error is calculated to quantify the improvement of the signal, demonstrating the excellent adaptability of the proposed TV-MPF to different kinds of signals.

Personal Mobility With Synchronous Trunk-Knee Passive Exoskeleton: Optimizing Human-Robot Energy Transfer

We present a personal mobility device for lower-body impaired users through a light-weighted exoskeleton on wheels. On its core, a novel passive exoskeleton provides postural transition leveraging natural body postures with support to the trunk on sit-to-stand and stand-to-sit (STS) transitions by a single gas spring as an energy storage unit. We propose a direction-dependent coupling of knees and hip joints through a double-pulley wire system, transferring energy from the torso motion towards balancing the moment load at the knee joint actuator. Herewith, the exoskeleton maximizes energy transfer and the naturalness of the user's movement. We introduce an embodied user interface for hands-free navigation through a torso pressure sensing with minimal trunk rotations, resulting on average $19^{\circ} \pm 13^{\circ}$ on six unimpaired users. We evaluated the design for STS assistance on 11 unimpaired users observing motions and muscle activity during the transitions. Results comparing assisted and unassisted STS transitions validated a significant reduction (up to $68\%$ $p<0.01$) at the involved muscle groups. Moreover, we showed it feasible through natural torso leaning movements of $+12^{\circ}\pm 6.5^{\circ}$ and $- 13.7^{\circ} \pm 6.1^{\circ}$ for standing and sitting, respectively. Passive postural transition assistance warrants further work on increasing its applicability and broadening the user population.

Exploiting Fine-grained Face Forgery Clues via Progressive Enhancement Learning

With the rapid development of facial forgery techniques, forgery detection has attracted more and more attention due to security concerns. Existing approaches attempt to use frequency information to mine subtle artifacts under high-quality forged faces. However, the exploitation of frequency information is coarse-grained, and more importantly, their vanilla learning process struggles to extract fine-grained forgery traces. To address this issue, we propose a progressive enhancement learning framework to exploit both the RGB and fine-grained frequency clues. Specifically, we perform a fine-grained decomposition of RGB images to completely decouple the real and fake traces in the frequency space. Subsequently, we propose a progressive enhancement learning framework based on a two-branch network, combined with self-enhancement and mutual-enhancement modules. The self-enhancement module captures the traces in different input spaces based on spatial noise enhancement and channel attention. The Mutual-enhancement module concurrently enhances RGB and frequency features by communicating in the shared spatial dimension. The progressive enhancement process facilitates the learning of discriminative features with fine-grained face forgery clues. Extensive experiments on several datasets show that our method outperforms the state-of-the-art face forgery detection methods.

A Style and Semantic Memory Mechanism for Domain Generalization

Mainstream state-of-the-art domain generalization algorithms tend to prioritize the assumption on semantic invariance across domains. Meanwhile, the inherent intra-domain style invariance is usually underappreciated and put on the shelf. In this paper, we reveal that leveraging intra-domain style invariance is also of pivotal importance in improving the efficiency of domain generalization. We verify that it is critical for the network to be informative on what domain features are invariant and shared among instances, so that the network sharpens its understanding and improves its semantic discriminative ability. Correspondingly, we also propose a novel "jury" mechanism, which is particularly effective in learning useful semantic feature commonalities among domains. Our complete model called STEAM can be interpreted as a novel probabilistic graphical model, for which the implementation requires convenient constructions of two kinds of memory banks: semantic feature bank and style feature bank. Empirical results show that our proposed framework surpasses the state-of-the-art methods by clear margins.

Transferrable Contrastive Learning for Visual Domain Adaptation

Self-supervised learning (SSL) has recently become the favorite among feature learning methodologies. It is therefore appealing for domain adaptation approaches to consider incorporating SSL. The intuition is to enforce instance-level feature consistency such that the predictor becomes somehow invariant across domains. However, most existing SSL methods in the regime of domain adaptation usually are treated as standalone auxiliary components, leaving the signatures of domain adaptation unattended. Actually, the optimal region where the domain gap vanishes and the instance level constraint that SSL peruses may not coincide at all. From this point, we present a particular paradigm of self-supervised learning tailored for domain adaptation, i.e., Transferrable Contrastive Learning (TCL), which links the SSL and the desired cross-domain transferability congruently. We find contrastive learning intrinsically a suitable candidate for domain adaptation, as its instance invariance assumption can be conveniently promoted to cross-domain class-level invariance favored by domain adaptation tasks. Based on particular memory bank constructions and pseudo label strategies, TCL then penalizes cross-domain intra-class domain discrepancy between source and target through a clean and novel contrastive loss. The free lunch is, thanks to the incorporation of contrastive learning, TCL relies on a moving-averaged key encoder that naturally achieves a temporally ensembled version of pseudo labels for target data, which avoids pseudo label error propagation at no extra cost. TCL therefore efficiently reduces cross-domain gaps. Through extensive experiments on benchmarks (Office-Home, VisDA-2017, Digits-five, PACS and DomainNet) for both single-source and multi-source domain adaptation tasks, TCL has demonstrated state-of-the-art performances.

Short-time Fourier transform based on stimulated Brillouin scattering

In this paper, all-optical short-time Fourier transform (STFT) based on stimulated Brillouin scattering (SBS) is proposed and further used for real-time time-frequency analysis of different radio frequency (RF) signals. In the proposed all-optical STFT system, SBS not only provides a band-pass filter for implementing the window function in conjunction with a periodic frequency-sweep optical signal but also obtains the frequency domain information in different time windows through the generated waveform via frequency-to-time mapping (FTTM). A periodic frequency-sweep optical signal is generated and then modulated at a Mach-Zehnder modulator by the electrical signal under test (SUT). During different sweep periods, the fixed Brillouin gain functions as a bandpass filter to select a specific range of the spectrum, which is equivalent to applying a sliding window function to the corresponding section of the temporal signal with the help of the sweep optical signal. At the same time, after the optical signal is selectively amplified by the SBS gain and converted back to the electrical domain, SBS also implements the real-time FTTM, which can be utilized to obtain the frequency domain information corresponding to different time windows through the generated waveforms via the FTTM. The frequency domain information corresponding to different time windows is formed and spliced to analyze the time-frequency relationship of the SUT in real-time. An experiment is performed. STFTs of a variety of RF signals are carried out in a 12-GHz bandwidth limited only by the equipment, and the dynamic frequency resolution is better than 60 MHz.

Photonics-based de-chirping and leakage cancellation for frequency-modulated continuous-wave radar system

A photonics-based leakage cancellation and echo signal de-chirping approach for frequency-modulated continuous-wave radar systems is proposed based on a dual-drive Mach-Zehnder modulator (DD-MZM), with its performance evaluated by the radar measurement and imaging. The de-chirp reference signal and the leakage cancellation reference signal are combined and applied to the upper arm of the DD-MZM, while the received signal including the leakage signal and echo signals is applied to the lower arm of the DD-MZM. When the amplitudes and delays of the leakage cancellation reference signal and the leakage signal are precisely matched and the DD-MZM is biased at the minimum transmission point, the leakage signal is canceled in the optical domain. The de-chirped signals are obtained after the leakage-free optical signal is detected in a photodetector. An experiment is performed. The cancellation depth of the de-chirped leakage signal is around 23 dB when the center frequency and bandwidth of the linearly frequency-modulated signal are 11.5 and 2 GHz. The leakage cancellation scheme is used in a radar system. When the leakage cancellation is not employed, the leakage signal will seriously affect the imaging results and distance measurement accuracy of the radar system. When the leakage cancellation is applied, the imaging results of multiple targets can be clearly distinguished, and the error of the distance measurement results is significantly reduced to 10 cm.

Digital-assisted photonic analog wideband multipath self-interference cancellation

A digital-assisted photonic analog wideband radio-frequency multipath self-interference cancellation (SIC) and frequency downconversion method based on a dual-drive Mach-Zehnder modulator and the recursive least square (RLS) algorithm is proposed and demonstrated for in-band full-duplex systems. Besides the reference for the direct-path self-interference (SI) signal, the RLS algorithm is used to construct another reference for the residual SI signal from the direct path and the SI signals from the reflection paths. The proposed method can solve the performance limitation in the previously reported SIC methods of constructing the multipath SI signal using a single reference caused by the limited dynamic range of the digital-to-analog converter when the direct-path SI signal is much stronger than the sub-weak reflection-path SI signals. An experiment is performed. When the carrier frequency of the multipath SI signal is 10 GHz and the direct-path SI signal is much stronger than the sub-weak multipath SI signal, the cancellation depths of about 26.7 and 26.1 dB are realized with SI baud rates of 0.5 and 1 Gbaud. When the direct-path SI signal and sub-weak multipath SI signal own closer power, the corresponding cancellation depths are 24.7 and 20.8 dB, respectively.

Time-frequency analysis of microwave signals based on stimulated Brillouin scattering

A novel photonic approach to the time-frequency analysis of microwave signals is proposed based on the stimulated Brillouin scattering (SBS)-assisted frequency-to-time mapping (FTTM). Two types of time-frequency analysis links, namely parallel SBS link and time-division SBS link are proposed. The parallel SBS link can be utilized to perform real-time time-frequency analysis of microwave signal, which provides a promising solution for real-time time-frequency analysis, especially when it is combined with the photonic integration technique. A simulation is made to verify its feasibility by analyzing signals in multiple formats. The time-division SBS link has a simpler and reconfigurable structure, which can realize an ultra-high-resolution time-frequency analysis for periodic signals using the time segmentation and accumulation technique. An experiment is performed for the time-division SBS link. The multi-dimensional reconfigurability of the system is experimentally studied. An analysis bandwidth of 3.9 GHz, an analysis frequency up to 20 GHz, and a frequency resolution of 15 MHz are demonstrated, respectively.

Photonics-assisted wideband RF self-interference cancellation with digital domain amplitude and delay pre-matching

A photonics-based digital and analog self-interference cancellation approach for in-band full-duplex communication systems and frequency-modulated continuous-wave radar systems is reported. One dual-drive Mach-Zehnder modulator is used to implement the analog self-interference cancellation by pre-adjusting the delay and amplitude of the reference signal applied to the dual-drive Mach-Zehnder modulator in the digital domain. The amplitude is determined via the received signal power, while the delay is searched by the cross-correlation and bisection methods. Furthermore, recursive least squared or normalized least mean square algorithms are used to suppress the residual self-interference in the digital domain. Quadrature phase-shift keying modulated signals and linearly frequency-modulated signals are used to experimentally verify the proposed method. The analog cancellation depth is around 20 dB, and the total cancellation depth is more than 36 dB for the 2-Gbaud quadrature phase-shift keying modulated signals. For the linearly frequency-modulated signals, the analog and total cancellation depths are around 19 dB and 34 dB, respectively.