We give a brief review of the developments in terahertz time-domain spectroscopy (THz-TDS) systems and microcavity components for probing samples in the University of Shanghai for Science and Technology. The broadband terahertz (THz) radiation sources based on GaAs m-i-n diodes have been investigated by applying high electric fields. Then, the free space THz-TDS and fiber-coupled THz-TDS systems produced in our lab and their applications in drug/cancer detection are introduced in detail. To further improve the signal-to-noise ratio (SNR) and enhance sensitivity, we introduce three general micro-cavity approaches to achieve tiny-volume sample detection, summarizing our previous results about their characteristics, performance, and potential applications.
Over the past two decades, several fluorescence- and non-fluorescence-based optical microscopes have been developed to break the diffraction limited barrier. In this review, the basic principles implemented in microscopy for super-resolution are described. Furthermore, achievements and instrumentation for super-resolution are presented. In addition to imaging, other applications that use super-resolution optical microscopes are discussed.
A grating interferometer, called the “optical encoder,” is a commonly used tool for precise displacement measurements. In contrast to a laser interferometer, a grating interferometer is insensitive to the air refractive index and can be easily applied to multi-degree-of-freedom measurements, which has made it an extensively researched and widely used device. Classified based on the measuring principle and optical configuration, a grating interferometer experiences three distinct stages of development: homodyne, heterodyne, and spatially separated heterodyne. Compared with the former two, the spatially separated heterodyne grating interferometer could achieve a better resolution with a feature of eliminating periodic nonlinear errors. Meanwhile, numerous structures of grating interferometers with a high optical fold factor, a large measurement range, good usability, and multidegree-of-freedom measurements have been investigated. The development of incremental displacement measuring grating interferometers achieved in recent years is summarized in detail, and studies on error analysis of a grating interferometer are briefly introduced.
As a versatile tool for trapping and manipulating neutral particles, optical tweezers have been studied in a broad range of fields such as molecular biology, nanotechnology, and experimentally physics since Arthur Ashkin pioneered the field in the early 1970s. By levitating the “sensor” with a laser beam instead of adhering it to solid components, excellent environmental decoupling is achieved. Furthermore, unlike levitating particles in liquid or air, optical tweezers operating in vacuum are isolated from environmental thermal noise, thus eliminating the primary source of dissipation present for most inertial sensors. This attracted great attention in both fundamental and applied physics. In this paper we review the history and the basic concepts of optical tweezers in vacuum and provide an overall understanding of the field.
We report a high-resolution rapid thermal sensing based on adaptive dual comb spectroscopy interrogated with a phase-shifted fiber Bragg grating (PFBG). In comparison with traditional dual-comb systems, adaptive dual-comb spectroscopy is extremely simplified by removing the requirement of strict phase-locking feedback loops from the dual-comb configuration. Instead, two free-running fiber lasers are adopted as the light sources. Because of good compensation of fast instabilities with adaptive techniques, the optical response of the PFBG is precisely characterized through a fast Fourier transform of the interferograms in the time domain. Single-shot acquisition can be accomplished rapidly within tens of milliseconds at a spectral resolution of 0.1 pm, corresponding to a thermal measurement resolution of 0.01 °C. The optical spectral bandwidth of the measurement also exceeds 14 nm, which indicates a large dynamic temperature range. It shows great potential for thermal sensing in practical outdoor applications with a loose self-control scheme in the adaptive dual-comb system.
In this paper, a decentralized fault-tolerant cooperative control scheme is developed for multiple unmanned aerial vehicles (UAVs) in the presence of actuator faults and a directed communication network. To counteract in-flight actuator faults and enhance formation flight safety, neural networks (NNs) are used to approximate unknown nonlinear terms due to the inherent nonlinearities in UAV models and the actuator loss of control effectiveness faults. To further compensate for NN approximation errors and actuator bias faults, the disturbance observer (DO) technique is incorporated into the control scheme to increase the composite approximation capability. Moreover, the prediction errors, which represent the approximation qualities of the states induced by NNs and DOs to the measured states, are integrated into the developed fault-tolerant cooperative control scheme. Furthermore, prescribed performance functions are imposed on the attitude synchronization tracking errors, to guarantee the prescribed synchronization tracking performance. One of the key features of the proposed strategy is that unknown terms due to the inherent nonlinearities in UAVs and actuator faults are compensated for by the composite approximators constructed by NNs, DOs, and prediction errors. Another key feature is that the attitude synchronization tracking errors are strictly constrained within the prescribed bounds. Finally, simulation results are provided and have demonstrated the effectiveness of the proposed control scheme.
Recently, deep neural networks (DNNs) significantly outperform Gaussian mixture models in acoustic modeling for speech recognition. However, the substantial increase in computational load during the inference stage makes deep models difficult to directly deploy on low-power embedded devices. To alleviate this issue, structure sparseness and low precision fixed-point quantization have been applied widely. In this work, binary neural networks for speech recognition are developed to reduce the computational cost during the inference stage. A fast implementation of binary matrix multiplication is introduced. On modern central processing unit (CPU) and graphics processing unit (GPU) architectures, a 5–7 times speedup compared with full precision floatingpoint matrix multiplication can be achieved in real applications. Several kinds of binary neural networks and related model optimization algorithms are developed for large vocabulary continuous speech recognition acoustic modeling. In addition, to improve the accuracy of binary models, knowledge distillation from the normal full precision floating-point model to the compressed binary model is explored. Experiments on the standard Switchboard speech recognition task show that the proposed binary neural networks can deliver 3–4 times speedup over the normal full precision deep models. With the knowledge distillation from the normal floating-point models, the binary DNNs or binary convolutional neural networks (CNNs) can restrict the word error rate (WER) degradation to within 15.0%, compared to the normal full precision floating-point DNNs or CNNs, respectively. Particularly for the binary CNN with binarization only on the convolutional layers, the WER degradation is very small and is almost negligible with the proposed approach.
Underwater image compression is an important and essential part of an underwater image transmission system. An assessment and prediction method of effectively compressed image quality can assist the system in adjusting its compression ratio during the image compression process, thereby improving the efficiency of the image transmission system. This study first estimates the perceived quality of underwater image compression based on embedded coding compression and compressive sensing, then builds a model based on the mapping between image activity measurement (IAM) and bits per pixel and structural similarity (BPP-SSIM) curves, next obtains model parameters by linear fitting, and finally predicts the perceived quality of the image compression method based on IAM, compression ratio, and compression strategy. Experimental results show that the model can effectively fit the quality curve of underwater image compression. According to the rules of parameters in this model, the perceived quality of underwater compressed images can be estimated within a small error range. The presented method can effectively estimate the perceived quality of underwater compressed images, balance the relationship between the compression ratio and compression quality, reduce the pressure on the data cache, and thus improve the efficiency of the underwater image communication system.
Co-residency of different tenants’ virtual machines (VMs) in cloud provides a good chance for side-channel attacks, which results in information leakage. However, most of current defense suffers from the generality or compatibility problem, thus failing in immediate real-world deployment. VM migration, an inherit mechanism of cloud systems, envisions a promising countermeasure, which limits co-residency by moving VMs between servers. Therefore, we first set up a unified practical adversary model, where the attacker focuses on effective side channels. Then we propose Driftor, a new cloud system that contains VMs of a multi-executor structure where only one executor is active to provide service through a proxy, thus reducing possible information leakage. Active state is periodically switched between executors to simulate defensive effect of VM migration. To enhance the defense, real VM migration is enabled at the same time. Instead of solving the migration satisfiability problem with intractable CIRCUIT-SAT, a greedy-like heuristic algorithm is proposed to search for a viable solution by gradually expanding an initial has-to-migrate set of VMs. Experimental results show that Driftor can not only defend against practical fast side-channel attack, but also bring about reasonable impacts on real-world cloud applications.