With the rapid growth of medical image data, it has become a current research hotspot that how to realize the large amounts of the real-time upload and storage of medical images with limited network bandwidth and storage space. However, currently, medical image compression technology cannot perform joint optimization of rate (the degree of compression) and distortion (reconstruction effect). Therefore, this study proposed a medical image compression algorithm based on a variational autoencoder. This algorithm takes rate and distortion as the common optimization goal and uses the residual network module to directly transmit information, which alleviates the contradiction between improving the degree of compression and optimizing the reconstruction effect. At the same time, the algorithm also reduces image loss in the medical image compression process by adding the residual network. The experimental results show that, compared with the traditional medical image compression algorithm and the deep learning compression algorithm, the algorithm in this study has smaller distortion, better reconstruction effect, and can obtain higher quality medical images at the same compression rate.

Emerging global privacy mandates enforce strict requirements for biometric data protection, requiring encrypted processing throughout storage and computation phases. While full homomorphic encryption (FHE)-based face recognition ensures security on platforms like Elastic Compute Service (ECS), three critical compliance gaps persist: 1) about 500\(\times\) ciphertext expansion in high-dimensional facial features, exceeding practical deployment thresholds, 2) vulnerabilities in live verification scenarios documented by ENISA (83% failure rate in 2024 audits), and 3) absence of mechanisms satisfying IND-CPA and IND-CCA2 standards. These limitations collectively undermine both regulatory adherence and operational efficiency in practical biometric systems. To address these gaps, this paper proposes a Hybrid Encryption with Facial Data Integrity Verification(HEFDIVS) scheme, which combines dimensionality reduction and hybrid encryption algorithms. Specifically, the scheme first realizes facial feature similarity calculation in the ciphertext domain without decryption. Then, the ISOMAP algorithm is applied to reduce the dimensionality of the facial data, thus alleviating the computational complexity in the ciphertext domain. Finally, based on FHE, a hybrid encryption algorithm combining SM2 and SM4 is introduced to enhance the security of the scheme. Experimental validation on the LFW and Faces94 datasets demonstrates mean recognition accuracy rates of 95.45% and 96.98% respectively, with 89% faster ciphertext computation time (0.028s) compared to pure FHE implementations in the ciphertext domain. The proposed scheme surpasses existing methods in accuracy-efficiency tradeoff while complying with IND-CPA (NIST SP 800-57) and IND-CCA2 (ISO 19772) security standards through hybrid SM2-SM4 authentication mechanisms.

Measurement error occurs if the requirements of undistorted measurements cannot be satisfied by dynamic component characteristics of the sensor. Amplified time constant and longer thermal response time of armored thermocouple is caused by its additional protective cover structure and the inherent thermal inertia of the internal temperature measuring element. Therefore, there is hysteresis dynamic characteristic in actual measurement of sensor. This paper proposes an accurate compensation of armored thermocouple based on fireworks algorithm that balances the contradiction between the accuracy of the existing solution and the complexity of the solution, corrects the measured thermocouple temperature signal. Weight factor is innovatively introduced to design the fitness function of fireworks algorithm, and the iterative process is sectionally executed that achieves the improvement of time constant and thermal response time of armored thermocouple. Finally, results are given in the simulation section. Dynamic compensation time is reduced from 537ms to 130ms, which is 75.79% lower than before compensation result. Moreover, the thermal response time is reduced from 345ms to 110ms, which is 68.16% lower than the before compensation result. From the experimental results, we can observe that the proposed method in this paper can broaden the working bandwidth of the thermocouple sensor through establishing dynamic mathematical model of thermocouple. After compensation, the dynamic characteristics of armored thermocouple are close to bare bead thermocouple without protective sleeve. The proposed method can replace easily damaged bare bead thermocouple to realize accurate measurement in harsh temperature measurement environments without need frequent replacement, which also improves economic benefits.

At present, researchers have made great progress in the research of object detection, however, these studies mainly focus on the object detection of images under normal lighting, ignoring the target detection under low light. And images in the fields of automatic driving at night and surveillance are usually obtained in low-light environments. These images have problems such as poor brightness, low contrast, and obvious noise, which lead to a large amount of information loss in the image. And the performance of object detection in low light is reduced. In this paper, we propose a low-light image enhancement method based on multi-scale network fusion to solve the problems of images in low-light environments. Aiming at the problem that the effective information of low-light images is relatively small, we propose a preprocessing method for image nonlinear transformation and fusion, which improves the amount of available information in the light image. Then, in order to obtain a better enhancement effect, a multi-scale feature fusion method is proposed, which fuses features from different resolution levels in the network. The details of low-light areas in the image are improved, and the problem of feature loss caused by too deep network layers is solved. The experimental results show that our proposed method can achieve better enhancement effects on different datasets compared with the current mainstream methods. The average recall value of the object detection with our method is improved by 38.25%, which shows that our proposed method is effective and can promote the development of autonomous driving, monitoring, and other fields.

Dynamic Compensation of Piezoresistive Pressure Sensor Based on Sparse Domain: In the process of transient test, due to the insufficient bandwidth of the pressure sensor, the test data is inaccurate. Firstly, based on the projection of the shock tube test signal in the sparse domain, the feature expression of the signal sample is obtained. Secondly, the problem of insufficient bandwidth is solved by inverse modeling of sensor dynamic compensation system based on swarm intelligence algorithm. In this paper, the method is used to compensate the shock tube test signals of the 85XX series pressure sensors made by the Endevco company of the United States, the working bandwidth of the sensor is widened obviously, the rise time of the pressure signal can be compensated to 12.5μs, and the overshoot can be reduced to 8.96. The repeatability of dynamic compensation is verified for the actual gun muzzle shock wave test data, the results show that the dynamic compensation can effectively recover the important indexes such as overpressure peak value and positive pressure action time, and the original shock wave signal is recovered from the high resonance data.