In many fields of social life, such as justice, finance, communication and so on, signatures are used for identity recognition. The increasingly convenient and extensive application of technology increases the opportunity for forged signatures. How to effectively identify a forged signature is still a challenge to be tackled by research. Offline static handwriting has a unique structure and strong interpretability, while online handwriting contains dynamic information, such as timing and pressure. Therefore, this paper proposes an authentic signature identification method, integrating dynamic and static features. The dynamic data and structural style of the signature are extracted by dot matrix pen technology, the global and local features, time and space features are fused and clearer and understandable features are applied to signature identification. At the same time, the classification of a forged signature is more detailed according to the characteristics of signature and a variety of machine learning models and a deep learning network structure are used for classification and recognition. When the number of classifications is 5, it is better to identify simple forgery signatures. When the classification number is 15, the accuracy rate is mostly about 96.7% and the highest accuracy reaches 100% on CNN. This paper focuses on feature extraction, incorporates the advantages of dynamic and static features and improves the classification accuracy of signature identification.

The traditional detection method of CO₂ concentration in seed respiration has defects such as low detection accuracy, low detection efficiency, and inability to monitor in real time. In order to solve these problems, we report a seed respiration CO₂ detection system based on wavelength modulation spectroscopy (WMS) techniques in tunable diode laser absorption spectroscopy (TDLAS). This system uses a 2004nm distributed feedback (DFB) laser as the light source, and a double-layer seed respiration device (about 1.5L) is designed based on Herriott cell with an effective optical path of about 21meters. Then, the second harmonic (2f) signal is extracted by the wavelength modulation method for CO₂ concentration inversion. When the ambient temperature and pressure changes greatly, the corrected 2f signal is used for CO₂ concentration inversion to improve the accuracy. A series of verification and comparison experiments have proved that the seed respiration CO₂ detection system has the advantages of strong stability, high sampling frequency, and high detection accuracy. Finally, we used the developed system to measure the respiration intensity and respiration rate of 1g corn seeds. The respiration intensity curves and respiration rate change details show that the seed respiration CO₂ detection system is more suitable for a small amount of seeds than nondispersive infrared (NDIR) CO₂ sensor and gas chromatography in real-time monitoring of the breathing process.

In the field of fiber-optic sensing, effectively reducing the noise of sensing spectra and achieving a high signal-to-noise ratio (SNR) has consistently been a focal point of research. This study proposes a deep-learning-based denoising method for fiber-optic sensors, which involves pre-processing the sensor spectrum into a 2D image and training with a cycle-consistent generative adversarial network (Cycle-GAN) model. The pre-trained algorithm demonstrates the ability to effectively denoise various spectrum types and noise profiles. This study evaluates the denoising performance of simulated spectra obtained from four different types of fiber-optic sensors: fiber Fabry–Perot interferometer (FPI), regular fiber Bragg grating (FBG), chirped FBG, and FBG pair. Compared to traditional denoising algorithms such as wavelet transform (WT) and empirical mode decomposition (EMD), the proposed method achieves an SNR improvement of up to $13.71 \mathrm{dB}$, an RMSE that is up to three times smaller, and a minimum correlation coefficient ($R^2$) of no less than 99.70% with the original high-SNR signals. Additionally, the proposed algorithm was tested for multimode noise reduction, demonstrating an excellent linearity in temperature response with a $R^2$ of 99.95% for its linear fitting and $99.74\%$ for the temperature response obtained from single-mode fiber sensors. The proposed denoising approach effectively reduces the impact of various noises from the sensing system, enhancing the practicality of fiber-optic sensing, especially for specialized fiber applications in research and industrial domains.

Background: Handwriting and speech are served as reliable signatures for detecting cognitive decline, playing a pivotal role in the early diagnosing Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, current unimodal approaches for diagnosing AD and MCI have demonstrated constraints in classification accuracy, potentially overlooking the synergistic value of combining handwriting and speech data. Objective: Presenting an innovative multi-modal screening classification model, that harnesses handwriting and speech analysis to enhance MCI detection, aiming to overcome the constraints of single-modality approaches by integrating data from both modalities, thereby improving diagnostic accuracy. Methods: Proposing a multimodal classification model based on gated recurrent unit (GRU) and attention mechanism, treating handwriting and speech data as sequence inputs. The model was constructed and tested on a dataset of 41 participants, including 20 MCI patients and 21 cognitively normal (CN) individuals. To mitigate the risk of overfitting due to the small sample size, we employed a 10-fold cross-validation strategy to ensure the robustness of the results. Results: Our multimodal classification model achieved an accuracy of 95.2% for MCI versus CN individuals, which shows a significant improvement compared to the results of single-modality. This result indicates the effectiveness of the cross-fusion model in enhancing classification performance, offering a promising approach for the early diagnosis of neurodegenerative diseases. Conclusions: The proposed GRU_CA effectively improves early MCI detection by fusing handwriting and speech data, outperforming a single modality. It shows strong potential for deployment in primary healthcare settings and establishes a foundation for future research on more complex diagnostic tasks, including CN, MCI, and AD classification, as well as longitudinal studies.

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders worldwide, requiring early identification for timely intervention and to slow disease progression. However, existing diagnostic approaches, while effective at later stages, remain limited in detecting early-stage AD. Handwriting analysis has recently emerged as a non-invasive, cost-effective, and ecologically valid digital behavioral biomarker that reflects neurocognitive impairment. This review examines the role of handwriting as a neurocognitive marker for AD, focusing on integrating deep learning methodologies to enhance early diagnostic accuracy. It also elucidates the neurocognitive mechanisms linking handwriting behavior and AD, addressing current methodological and translational challenges. We performed a PRISMA-informed structured literature search and narrative synthesis of handwriting- and drawing-based studies for detecting AD/mild cognitive impairment (MCI), including offline handwriting images and online pen-stroke kinematics captured by digital devices. Task paradigms, data dimensions, preprocessing pipelines, modeling strategies (traditional machine learning and deep learning), evaluation practices, and translational considerations were summarized, and studies were organized by detection purpose and analytic approach. Our findings show that handwriting-based models generally discriminate AD/MCI from healthy controls with accuracy exceeding 80%, while deep learning models (e.g., convolutional neural network and multimodal Transformer fusion) approach 90% in structured tasks like clock drawing and figure copying. Online kinematic markers (e.g., reduced velocity, prolonged in-air time, increased pausing, and pressure instability) recur across studies, and multimodal integration with speech, gait, or facial signals can further improve sensitivity and ecological validity, although most studies are small and single-center.