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Learned image compression (LIC) is currently the cutting-edge method. However, the inherent difference between testing and training images of LIC results in performance degradation to some extent. Especially for out-of-sample, out-of-distribution, or out-of-domain testing images, the performance of LIC dramatically degraded. Classical LIC is a serial image compression (SIC) approach that utilizes an open-loop architecture with serial encoding and decoding units. Nevertheless, according to the theory of automatic control, a closed-loop architecture holds the potential to improve the dynamic and static performance of LIC. Therefore, a circular image compression (CIC) approach with closed-loop encoding and decoding elements is proposed to minimize the gap between testing and training images and upgrade the capability of LIC. The proposed CIC establishes a nonlinear loop equation and proves that steady-state error between reconstructed and original images is close to zero by Taylor series expansion. The proposed CIC method possesses the property of Post-Training and plug-and-play which can be built on any existing advanced SIC methods. Experimental results on five public image compression datasets demonstrate that the proposed CIC outperforms five competing state-of-the-art open-source SIC algorithms in reconstruction capacity. Experimental results further show that the proposed method is suitable for out-of-sample testing images with dark backgrounds, sharp edges, high contrast, grid shapes, or complex patterns.

Classical methods of medical image super-resolution (MISR) utilize open-loop architecture with implicit under-resolution (UR) unit and explicit super-resolution (SR) unit. The UR unit can always be given, assumed, or estimated, while the SR unit is elaborately designed according to various SR algorithms. The closed-loop feedback mechanism is widely employed in current MISR approaches and can efficiently improve their performance. The feedback mechanism may be divided into two categories: local feedback and global feedback. Therefore, this paper proposes a global feedback-based closed-cycle framework, circular MISR (CMISR), with unambiguous UR and advanced SR elements. Mathematical model and closed-loop equation of CMISR are built. Mathematical proof with Taylor-series approximation indicates that CMISR has zero recovery error in steady-state. In addition, CMISR holds plug-and-play characteristic that fuses model-based and learning-based approaches and can be established on any existing MISR algorithms. Five CMISR algorithms are respectively proposed based on the state-of-the-art open-loop MISR algorithms. Experimental results with three scale factors and on three open medical image datasets show that CMISR is superior to MISR in reconstruction performance and is particularly suited to medical images with strong edges or intense contrast.

Closed-loop negative feedback mechanism is extensively utilized in automatic control systems and brings about extraordinary dynamic and static performance. In order to further improve the reconstruction capability of current methods of compressed image super-resolution, a circular Swin2SR (CSwin2SR) approach is proposed. The CSwin2SR contains a serial Swin2SR for initial super-resolution reestablishment and circular Swin2SR for enhanced super-resolution reestablishment. Simulated experimental results show that the proposed CSwin2SR dramatically outperforms the classical Swin2SR in the capacity of super-resolution recovery. On DIV2K test and valid datasets, the average increment of PSNR is greater than 0.18 dB and the related average increment of SSIM is greater than 0.01.

Closed-loop architecture is widely utilized in automatic control systems and attain distinguished performance. However, classical compressive sensing systems employ open-loop architecture with separated sampling and reconstruction units. Therefore, a method of iterative compensation recovery for image compressive sensing (ICRICS) is proposed by introducing closed-loop framework into traditional compresses sensing systems. The proposed method depends on any existing approaches and upgrades their reconstruction performance by adding negative feedback structure. Theory analysis on negative feedback of compressive sensing systems is performed. An approximate mathematical proof of the effectiveness of the proposed method is also provided. Simulation experiments on more than 3 image datasets show that the proposed method is superior to 10 competition approaches in reconstruction performance. The maximum increment of average peak signal-to-noise ratio is 4.36 dB and the maximum increment of average structural similarity is 0.034 on one dataset. The proposed method based on negative feedback mechanism can efficiently correct the recovery error in the existing systems of image compressive sensing.

Autoencoders are composed of coding and decoding units, hence they hold the inherent potential of high-performance data compression and signal compressed sensing. The main disadvantages of current autoencoders comprise the following several aspects: the research objective is not data reconstruction but feature representation; the performance evaluation of data recovery is neglected; it is hard to achieve lossless data reconstruction by pure autoencoders, even by pure deep learning. This paper aims for image reconstruction of autoencoders, employs cascade decoders-based autoencoders, perfects the performance of image reconstruction, approaches gradually lossless image recovery, and provides solid theory and application basis for autoencoders-based image compression and compressed sensing. The proposed serial decoders-based autoencoders include the architectures of multi-level decoders and the related optimization algorithms. The cascade decoders consist of general decoders, residual decoders, adversarial decoders and their combinations. It is evaluated by the experimental results that the proposed autoencoders outperform the classical autoencoders in the performance of image reconstruction.

The full architecture of an electrostatic kinetic energy harvester (KEH) based on the concept of near-limits KEH is reported. This concept refers to the conversion of kinetic energy to electric energy, from environmental vibrations of arbitrary forms, and at rates that target the physical limits set by the device's size and the input excitation characteristics. This is achieved thanks to the synthesis of particular KEH's mass dynamics, that maximize the harvested energy. Synthesizing these dynamics requires little hypotheses on the exact form of the input vibrations. In the proposed architecture, these dynamics are implemented by an adequate mechanical control which is synthesized by the electrostatic transducer. An interface circuit is proposed to carry out the necessary energy transfers between the transducer and the system's energy tank. A computation and finite-state automaton unit controls the interface circuit, based on the external input and on the system's mechanical state. The operation of the reported near-limits KEH is illustrated in simulations that demonstrate proof of concept of the proposed architecture. A figure of $68\%$ of the absolute limit of the KEH's input energy for the considered excitation is attained. This can be further improved by complete system optimization that takes into account the application constraints, the control law, the mechanical design of the transducer, the electrical interface design, and the sensing and computation blocks.

Electrostatic transducers for vibration energy scavenging have been an object to numerous studies, but are still facing major issues relating to their conditioning circuit. One of the most popular ones uses a charge pump and a flyback circuit based on a Buck DC-DC converter (Fig. 1). A commutation between the energy accumulation in the charge pump and the recharge of the buffer capacitor Cres is assured by a switch which is the major bottleneck in the energy harvester circuit. The commutation timing of the switch determines the efficiency of the energy harvesting. In previous papers [1] the switch commutates periodically with some fixed duty ratio. However, this solution is not appropriate when the environment parameters, e.g. the vibration frequency, change. We found that the switching should be ordered by the internal state of the circuit, an not by some fixed timing scenario. We presents how to find the optimal operation mode of the harvester. To validate the study, the system was modeled using a mixed VHDL-AMS - ELDO model., Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/handle/2042/16838)