Metal Oxide Thin Film Transistors (MO TFTs), known for their flexibility and low cost, are widely used in wearable electronics and biomedical fields. In these applications, capacitive sensors are important for their high sensitivity and low cost, enabling accurate detection of touch and pressure. To make use of these sensor signals, Capacitive-to-Digital Converters (CDCs) are typically used to convert the signals into digital form. Traditional silicon-based CDCs rely on Switched-Capacitor (SC) circuits, with accuracy primarily determined by high-gain Operational Transconductance Amplifiers (OTAs). However, the lack of high-gain OTAs in TFT technique limits SC-based CDC precision. This paper presents a TFT CDC design using an iterative discharge method, avoiding reliance on OTA gain to enhance accuracy and energy efficiency. The proposed design achieves a 6.2-bit Effective Number of Bits (ENOB) for capacitances up to 100pF in 14ms, with a Figure of Merit (FoM) of 690nJ/c-s. To the best of our knowledge, this is the first comprehensive study on TFT CDCs.

Imbalanced image classification faces critical challenges in balancing the quality and diversity of synthetic minority samples. This article proposes the improved estimation distribution algorithm-based latent feature distribution evolution (MEDA_LUDE) algorithm, an evolutionary algorithm-assisted deep distribution learning framework that optimizes latent feature distributions through a multivariate Gaussian mixture (GM) assumption and a novel four-phase training strategy. We introduce a large-margin GM (L-GM) loss to dynamically model covariances for feature learning and design a MEDA that evolves latent features via a similarity-guided fitness function, thus enhancing diversity while preserving synthesis quality. Extensive experiments demonstrate significant improvements: MEDA_LUDE achieves 95.9% accuracy on MNIST (imbalanced ratio-IR:100), surpassing state-of-the-art methods by 1.26% on CIFAR-10. For industrial fabric defect data sets, it elevates accuracy by 1.45% on DHU-FD and 0.92% on ALIYUN-FD, especially with precision and G-mean improvements of 2.5% and 1.17%, respectively, on DHU-FD. Visualizations confirm that MEDA_LUDE generates minority samples with superior quality-diversity tradeoffs. The framework's success in real-world fabric defect classification underscores its practical value in addressing imbalanced learning challenges.

The attention mechanism has become the de facto module in scene text recognition (STR) methods, due to its capability of extracting character-level representations. These methods can be summarized into implicit attention based and supervised attention based, depended on how the attention is computed, i.e., implicit attention and supervised attention are learned from sequence-level text annotations and or character-level bounding box annotations, respectively. Implicit attention, as it may extract coarse or even incorrect spatial regions as character attention, is prone to suffering from an alignment-drifted issue. Supervised attention can alleviate the above issue, but it is character category-specific, which requires extra laborious character-level bounding box annotations and would be memory-intensive when handling languages with larger character categories. To address the aforementioned issues, we propose a novel attention mechanism for STR, self-supervised implicit glyph attention (SICA). SICA delineates the glyph structures of text images by jointly self-supervised text seg-mentation and implicit attention alignment, which serve as the supervision to improve attention correctness without extra character-level annotations. Experimental results demonstrate that SIGA performs consistently and significantly better than previous attention-based STR methods, in terms of both attention correctness and final recognition performance on publicly available context benchmarks and our contributed contextless benchmarks.

Automatic diatom detection refers to the task of identifying and characterizing diatoms based on artificial intelligence. It will replace traditional time-consuming and laborious manual microscopy method of diatom observation to greatly accelerate the process of diatom research and some diatomrelated studies, such as diatom abundance statistics, using diatom properties for environmental monitoring and paleoenvironmental reconstruction. However, complex background interference and the detection of slender diatoms with different integrity are two major challenges for automatic diatom detection. To solve the mentioned-above issues, we propose an oriented object detector for automatic diatom detection based on RepPoints, called OOD-RepPoints. Specifically, for encouraging the network to adaptively capture the feature of slender diatoms, we design a cascaded feature refinement head (CFRH) which consists of points generation stage and points refinement stage, to progressively optimize the extraction of slender diatom features. Furthermore, to fit the shape of diatoms well, especially for slender diatoms, we propose a tailored label assignment strategy for our CFRH, which contains a short side assigner (SSA) for points generation stage and an adaptive IoU thresholds assigner (AITA) for points refinement stage. Besides, we contribute a so called O-Diatom dataset for automatic diatom detection. The dataset has 1711 images which contains 3949 diatoms and provides finely manual oriented bounding box annotations. Extensive experiments demonstrated our method achieve state of the art performance and can reach mAP 89.9% which is highest on O-Diatom, and shows competitive results on slender categories of publicly available datasets (i.e., DOTA and HRSC2016).

In this paper, we propose an Effective and lightweight Transformer for image shadow detection and removal named SpA-Former to recover a shadow-free image from a single shaded image. In contrast to conventional methods that require two stages for shadow detection and then shadow removal, the SpA-Former is a one-stage network capable of learning the mapping function between shadows and no shadows, and does not require a separate shadow detection. SpA-Former is composed of Transformer encoder and CNN decoder, where the CNN decoder contains the GAN network. In the Transformer encoding stage, Gated Feed-Forward Network(GFFN) is devised to control the information flow. In the CNN decoding stage, Two-wheel RNN joint spatial attention(TWRNN) and Fourier transform residual block (FTR) are designed to achieve satisfactory results in shadow removal. The combination of Transformer and CNN is able to feed global features from the Vision Transformer encoder into CNN to enhance the global perception of CNN branches, taking into account the complementarity of local features and the global. The SpA-Former's inference speed is 0.0459s, and the final Parameters and FLOPS are only 0.47MB and 15G, achieving the current lightweight of image shadow removal. The source code of MemoryNet can be obtained from https://github.com/zhangbaijin/SpA-Former-shadow-removal