This article proposes a neural network (NN)-based calibration framework via quantization code reconstruction to address the critical limitation of multidimensional NNs (MDNNs) in analog-to-digital converter (ADC) calibration, where performance degrades drastically under varying input frequencies or sampling rates. Conventional MDNN methods suffer from distribution shifts caused by dynamic oversampling ratio (OSR) variations, necessitating repeated retraining. Our innovation lies in a data reconstruction mechanism: for lower OSR scenarios, a third-order cascaded integrator-comb (CIC) filter inserts pseudo-data points between quantization code to match the NN’s input dimension. For higher OSR scenarios, cyclic downsampling decomposes the original sequence into parallel subsequences processed independently by the same network. For multitone signals, a reconstruction–calibration–summation flow hierarchically handles spectral components. Implemented on an FPGA and covalidated with a commercial 14-bit/1 GSPS pipeline ADC, the framework demonstrates robust performance without retraining. Experimental results show: bandwidth expansion from 16.3% to 84.7% of the Nyquist bandwidth. SFDR improvement of 13.8–27.7 dB and ENOB gain of 2.3–4.6 bits across 10.3–160.3 MHz inputs. This work enables consistent high-precision ADC calibration in dynamic signal environments, facilitating deployment in complex application scenarios.

Most collection-based style transfer methods require training a separate model for each individual collection of styles, making the extension to multiple collections of styles less flexible. Besides, the existing collection-based methods are also less flexible in extending to new style collections in a continual manner. To address these issues, we propose a novelMultI-Dictionary Generative Adversarial Network framework (MID-GAN)for multi-collection style transfer. Specifically, we design a multi-dictionary architecture within a GAN, with each dictionary consisting of a set of local style codes for a specific style collection. Benefiting from the local style codes used in the dictionary, a stylization module with aligned skip connections is further proposed, which can better preserve both the local details and the overall image structure. The dictionary design allows a flexible extension to new style collections by readily adding new dictionaries and we propose a continual training strategy that can both preserve the style transfer ability of old styles and achieve good transfer results for newly added styles. Extensive experiments are performed to show that the proposed method is better than existing collection-based style transfer methods. We also demonstrate the proposed method can generate diverse meaningful style transfer results of the same style collection.

This paper proposes an enhanced Branch and Bound (B&B) algorithm for Network-on-Chip (NoC) task mapping. The novelty of the algorithm can be summarized in two aspects. First, a more accurate method is proposed to estimate the lower bound cost. Second, an automatic method to generate the task binding rules is proposed based on the Task Binding Graph (TBG). Both of the two improvements contribute to designing a high speed B&B algorithm with global optimized mapping result, aiming to reduce the communication energy consumption. The experiment results show that the proposed algorithm is nearly 3.5 times faster and the communication energy consumption is 35% less than the state-of-art B&B algorithm in average. Comparing to the Genetic Algorithm, the proposed algorithm is similarly fast and reduce the communication energy consumption by 24% in average. Particularly, as the size of the NoC grows larger, the superiorities of our proposed algorithm become more significant.