This paper presents design and simulation of a switchable radiative cooler that exploits phase transition in vanadium dioxide to turn on and off in response to temperature. The cooler consists of an emitter and a solar reflector separated by a spacer. The emitter and the reflector play a role of emitting energy in mid-infrared and blocking incoming solar energy in ultraviolet to near-infrared regime, respectively. Because of the phase transition of doped vanadium dioxide at room temperature, the emitter radiates its thermal energy only when the temperature is above the phase transition temperature. The feasibility of cooling is simulated using real outdoor conditions. We confirme that the switchable cooler can keep a desired temperature, despite change in environmental conditions.

Optical cryptanalysis is essential to the further investigation of more secure optical cryptosystems. Learning-based attack of optical encryption eliminates the need for the retrieval of random phase keys of optical encryption systems but it is limited for practical applications since it requires a large set of plaintext-ciphertext pairs for the cryptosystem to be attacked. Here, we propose a two-step deep learning strategy for ciphertext-only attack (COA) on the classical double random phase encryption (DRPE). Specifically, we construct a virtual DRPE system to gather the training data. Besides, we divide the inverse problem in COA into two more specific inverse problems and employ two deep neural networks (DNNs) to respectively learn the removal of speckle noise in the autocorrelation domain and the de-correlation operation to retrieve the plaintext image. With these two trained DNNs at hand, we show that the plaintext can be predicted in real-time from an unknown ciphertext alone. The proposed learning-based COA method dispenses with not only the retrieval of random phase keys but also the invasive data acquisition of plaintext-ciphertext pairs in the DPRE system. Numerical simulations and optical experiments demonstrate the feasibility and effectiveness of the proposed learning-based COA method.

Optical fiber sensor network has attracted considerable research interests for geoscience applications. However, the sensor capacity and ultra-low frequency noise limits the sensing performance for geoscience data acquisition. To achieve a high-resolution and lager sensing capacity, a strain sensor network is proposed based on phase-sensitive optical time domain reflectometer (φ-OTDR) technology and special packaged fiber with scatter enhanced points (SEPs) array. Specifically, an extra identical fiber with SEPs array which is free of strain is used as the reference fiber, for compensating the ultra-low frequency noise in the φ-OTDR system induced by laser source frequency shift and environment temperature change. Moreover, a hysteresis operator based least square support vector machine (LS-SVM) model is introduced to reduce the compensation residual error generated from the thermal hysteresis nonlinearity between the sensing fiber and reference fiber. In the experiment, the strain sensor network possesses a sensing capacity with 55 sensor elements. The phase bias drift with frequency below 0.1 Hz is effectively compensated by LS-SVM based hysteresis model, and the signal to noise ratio (SNR) of a strain vibration at 0.01 Hz greatly increases by 24 dB compared to that of the sensing fiber for direct compensation. The proposed strain sensor network proves a high dynamic resolution of 10.5 pε·Hz-1/2 above 10 Hz, and ultra-low frequency sensing resolution of 166 pε at 0.001 Hz. It is the first reported a large sensing capacity strain sensor network with sub-nε sensing resolution in mHz frequency range, to the best of our knowledge.