Innovative Architecture Research Articles

TIA-UNet: Transformer-Enhanced Deep Learning for Adolescent Idiopathic Scoliosis Spinal X-Ray Image Segmentation

Abstract Adolescent Idiopathic Scoliosis (AIS) is a common spinal deformity where precise diagnosis is crucial for developing effective treatment strategies. Traditional manual X-ray image analysis is time-consuming and highly dependent on the operator’s expertise, thus constraining diagnostic efficiency and accuracy.This study aimed to develop an automated thoracolumbar spine segmentation method utilizing deep learning to enhance the efficiency and accuracy of AIS diagnosis. We introduced TIA-UNet, an innovative network architecture that combines Convolutional Neural Networks (CNN) with Transformer models.By integrating the IR Block and DA Block, TIA-UNet was optimized for feature extraction and multi-scale information fusion. The model underwent training and validation using our established Adolescent Scoliosis Medical Dataset (ASMD).TIA-UNet attained a Dice similarity coefficient of 90.02%, a Mean Intersection over Union (MIoU) of 81.96%, and a Hausdorff Distance (HD) of 4.09, significantly surpassing current state-of-the-art methods such as UNet and TransUNet. Moreover, TIA-UNet exhibited superior computational efficiency regarding parameter count, inference time, and floatingpoint operations (FLOPs).As an automated medical image segmentation algorithm, TIA-UNet enhanced segmentation accuracy while maintaining high computational efficiency, demonstrating significant potential for clinical diagnostic applications. This study provides compelling evidence supporting the utilization of deep learning
techniques in medical image analysis.

A High Scalability Memory NoC with Shared-Inside Hierarchical-Groupings for Triplet-Based Many-Core Architecture

Innovative processor architecture designs are shifting towards Many-Core Architectures (MCAs) to meet the future demands of high-performance computing as the limits of Moore’s Law have almost been reached. Many-core processors utilize shared memory hierarchies to achieve high-speed memory systems, improving memory access efficiency. However, as the number of cores multiplies, the scalability of this system is significantly constrained by the increased proportion of long-distance and Non-Uniform Memory Access (NUMA). Improving the scalability of MCAs is crucial for achieving large/super-scale general-purpose many-core processors. This work proposes a high scalability memory Network-on-Chip (NoC) for Triplet-Based Many-Core Architecture (TriBA), named TriBA-mNoC. TriBA-mNoC maintains a consistent core-to-core spacing as the network scale increases, effectively preventing increased long-distance memory access latency. Moreover, it leverages an inherent advantage of shared-inside hierarchical-groupings, alleviating common NUMA issues in the NoC design. Evaluations of static network characteristics show that TriBA-mNoC outperforms most classical NoCs in network diameter, average distance, and cost. TriBA-mNoC can be integrated with TriBA in the same silicon die with a tile-like floorplan, forming a novel NoC called TriBA-NoC, which can combine the strengths of both networks to maximize the architecture performance. We evaluated the memory access performance and scalability of TriBA-NoC using the mathematical evaluation models and actual simulations with real traffic (PARSEC 3.0 and SPLASH-2) at different network scales. The mathematical evaluation results indicate that TriBA-NoC achieves an aggregate speedup of approximately 3x compared with 2D-Mesh for a similar number of cores. Furthermore, TriBA-NoC’s single-core speedup efficiency remains stable as the number of cores increases under the same cache hit ratio, while 2D-Mesh experiences a rapid decline, highlighting TriBA-NoC’s exceptional scalability. Finally, the actual traffic simulation results show that TriBA-NoC achieves an average memory access latency and time reduction of 25.90% − 40.50% and 5.61% − 31.69% respectively, compared with 2D-Mesh.

An Innovative Honeypot Architecture for Detecting and Mitigating Hardware Trojans in IoT Devices

The exponential growth and widespread adoption of Internet of Things (IoT) devices have introduced many vulnerabilities. Attackers frequently exploit these flaws, necessitating advanced technological approaches to protect against emerging cyber threats. This paper introduces a novel approach utilizing hardware honeypots as an additional defensive layer against hardware vulnerabilities, particularly hardware Trojans (HTs). HTs pose significant risks to the security of modern integrated circuits (ICs), potentially causing operational failures, denial of service, or data leakage through intentional modifications. The proposed system was implemented on a Raspberry Pi and tested on an emulated HT circuit using a Field-Programmable Gate Array (FPGA). This approach leverages hardware honeypots to detect and mitigate HTs in the IoT devices. The results demonstrate that the system effectively detects and mitigates HTs without imposing additional complexity on the IoT devices. The Trojan-agnostic solution offers full customization to meet specific security needs, providing a flexible and robust layer of security. These findings provide valuable insights into enhancing the security of IoT devices against hardware-based cyber threats, thereby contributing to the overall resilience of IoT networks. This innovative approach offers a promising solution to address the growing security challenges in IoT environments.

Integrated Pockels Modulators on Silicon Photonics Platform

AbstractElectro‐optic (EO) modulators are essential components in various fields, including optical communication, free‐space communication, microwave photonics, sensing, and light detection and ranging. The EO modulation enables the fast conversion of electric signals into optical signals, facilitating the precise manipulation of light. With advancements in fabrication processing techniques, next‐generation integrated EO modulators have demonstrated substantial improvements in modulation efficiency, bandwidth, and footprint. Here, the latest research progress in integrated EO modulation, focusing on the principle of the Pockels effect, key modulation metrics, novel EO thin‐film material platforms, and innovative device architectures is overviewed. Finally, it is evaluated different schemes and provide perspectives on future trends in developing integrated EO modulators, highlighting both the advantages and challenges of integrated EO modulation, including waveguide and electrode engineering, integrated methods, and other applications for large‐scale photonic integrated circuits.

A Plaintext-Related and Ciphertext Feedback Mechanism for Medical Image Encryption Based on a New One-Dimensional Chaotic System

Abstract In recent years, Plaintext-Related Image Encryption (PRIE) algorithms have been introduced, demonstrating a commendable level of plaintext sensitivity to resist chosen plaintext attack (CPA). However, these approaches suffer from several drawbacks, including inability to fully reconstruct the original image, limited practical value and excessive computational demands etc.. Moreover, the exponential expansion of medical data necessitates the formulation of more secure and efficient encryption algorithms. In this paper, firstly, a novel one-dimensional chaotic map, designated as 1D-SAM, which strikes an excellent balance between structural complexity and chaotic performance is proposed. The 1D-SAM achieve a larger chaotic range and an elevated Lyapunov exponent, signifying enhanced dynamical complexity. Subsequently, we devise a lightweight medical image encryption system leveraging the 1D-SAM and an innovative diffusion architecture, termed the plaintext-related and ciphertext feedback mechanism(PRCFM). This encryption system is a symmetric-key cryptosystem, eliminating the need for transmitting supplementary data beyond the secret keys to the recipient. Notably, the encrypted image maintains identical dimensions to its original counterpart and is fully recoverable. Complete simulation experiments were conducted on a personal computer equipped with MATLAB R2021a, OS Windows 11, 2.60GHz CPU and 16GB RAM. The experimental results indicate that our encryption system, employing a single permutation-diffusion round, efficiently encrypts a 512×512 image in approximately 0.2854 seconds. Leveraging the advantages of the PRCFM, our approach demonstrates superior plaintext sensitivity, achieving an average number of pixels changing rate (NPCR) of 99.6051 and a unified average changed intensity (UACI) of 33.4452. In summary, our work addresses key limitations of contemporary encryption frameworks, exhibiting acceptable performance in both encryption speed and security strength.

Mechanical equipment fault diagnosis method based on improved deep residual shrinkage network.

Fault diagnosis of mechanical equipment can effectively reduce property losses and casualties. Bearing vibration signals, as one of the effective sources of diagnostic information, are often overwhelmed by substantial environmental noise. To address this issue, we present a fault diagnosis method, CCSDRSN, which exhibits strong noise resistance. This method enhances the soft threshold function in the traditional deep residual shrinkage network, allowing it to extract useful information from the fault signal to the maximum extent, thus significantly improving diagnostic accuracy. Additionally, we have developed a novel activation function that can nonlinearly transform the time frequency map across multiple dimensions and the entire region. In pursuit of network optimization and parameter reduction, we have strategically incorporated depthwise separable convolutions, effectively replacing conventional convolutional layers. This architectural innovation streamlines the network. By verifying the effectiveness of the proposed method using Case Western Reserve University datasets, the results demonstrate that the proposed method not only possesses strong noise resistance in high noise environments but also achieves high diagnostic accuracy and good generalization performance under different load conditions.

Neural differentiable modeling with diffusion-based super-resolution for two-dimensional spatiotemporal turbulence

Simulating spatiotemporal turbulence with high fidelity remains a cornerstone challenge in computational fluid dynamics (CFD) due to its intricate multiscale nature and prohibitive computational demands. Traditional approaches typically employ closure models, which attempt to represent small-scale features in an unresolved manner. However, these methods often sacrifice accuracy and lose high-frequency/wavenumber information, especially in scenarios involving complex flow physics. In this paper, we introduce an innovative neural differentiable modeling framework designed to enhance the predictability and efficiency of spatiotemporal turbulence simulations. Our approach features differentiable hybrid modeling techniques that seamlessly integrate deep neural networks with numerical PDE solvers within a differentiable programming framework, synergizing deep learning with physics-based CFD modeling. Specifically, a hybrid differentiable neural solver is constructed on a coarser grid to capture large-scale turbulent phenomena, followed by the application of a Bayesian conditional diffusion model that generates small-scale turbulence conditioned on large-scale flow predictions. Two innovative hybrid architecture designs are studied, and their performance is evaluated through comparative analysis against conventional large eddy simulation techniques with physics-based subgrid-scale closures and purely data-driven neural solvers. The findings underscore the potential of the neural differentiable modeling framework to significantly enhance the accuracy and computational efficiency of turbulence simulations. This study not only demonstrates the efficacy of merging deep learning with physics-based numerical solvers but also sets a new precedent for advanced CFD modeling techniques, highlighting the transformative impact of differentiable programming in scientific computing.

Mapping Data to Concepts: Enhancing Quantum Neural Network Transparency with Concept-Driven Quantum Neural Networks

We introduce the concept-driven quantum neural network (CD-QNN), an innovative architecture designed to enhance the interpretability of quantum neural networks (QNNs). CD-QNN merges the representational capabilities of QNNs with the transparency of self-explanatory models by mapping input data into a human-understandable concept space and making decisions based on these concepts. The algorithmic design of CD-QNN is comprehensively analyzed, detailing the roles of the concept generator, feature extractor, and feature integrator in improving and balancing model expressivity and interpretability. Experimental results demonstrate that CD-QNN maintains high predictive accuracy while offering clear and meaningful explanations of its decision-making process. This paradigm shift in QNN design underscores the growing importance of interpretability in quantum artificial intelligence, positioning CD-QNN and its derivative technologies as pivotal in advancing reliable and interpretable quantum intelligent systems for future research and applications.

Hyperspectral Image Classification Algorithm for Forest Analysis Based on a Group-Sensitive Selective Perceptual Transformer

Substantial advancements have been achieved in hyperspectral image (HSI) classification through contemporary deep learning techniques. Nevertheless, the incorporation of an excessive number of irrelevant tokens in large-scale remote sensing data results in inefficient long-range modeling. To overcome this hurdle, this study introduces the Group-Sensitive Selective Perception Transformer (GSAT) framework, which builds upon the Vision Transformer (ViT) to enhance HSI classification outcomes. The innovation of the GSAT architecture is primarily evident in several key aspects. Firstly, the GSAT incorporates a Group-Sensitive Pixel Group Mapping (PGM) module, which organizes pixels into distinct groups. This allows the global self-attention mechanism to function within these groupings, effectively capturing local interdependencies within spectral channels. This grouping tactic not only boosts the model’s spatial awareness but also lessens computational complexity, enhancing overall efficiency. Secondly, the GSAT addresses the detrimental effects of superfluous tokens on model efficacy by introducing the Sensitivity Selection Framework (SSF) module. This module selectively identifies the most pertinent tokens for classification purposes, thereby minimizing distractions from extraneous information and bolstering the model’s representational strength. Furthermore, the SSF refines local representation through multi-scale feature selection, enabling the model to more effectively encapsulate feature data across various scales. Additionally, the GSAT architecture adeptly represents both global and local features of HSI data by merging global self-attention with local feature extraction. This integration strategy not only elevates classification precision but also enhances the model’s versatility in navigating complex scenes, particularly in urban mapping scenarios where it significantly outclasses previous deep learning methods. The advent of the GSAT architecture not only rectifies the inefficiencies of traditional deep learning approaches in processing extensive remote sensing imagery but also markededly enhances the performance of HSI classification tasks through the deployment of group-sensitive and selective perception mechanisms. It presents a novel viewpoint within the domain of hyperspectral image classification and is poised to propel further advancements in the field. Empirical testing on six standard HSI datasets confirms the superior performance of the proposed GSAT method in HSI classification, especially within urban mapping contexts, where it exceeds the capabilities of prior deep learning techniques. In essence, the GSAT architecture markedly refines HSI classification by pioneering group-sensitive pixel group mapping and selective perception mechanisms, heralding a significant breakthrough in hyperspectral image processing.

Pt/ZnO and Pt/few-layer graphene/ZnO Schottky devices with Al ohmic contacts using Atlas simulation and machine learning

The search for highly efficient photodetectors, driven by various applications ranging from environmental monitoring to communication systems and imaging, continues to drive research into novel materials and innovative device architectures. This paper offers an in-depth comparative analysis to optimize the performance of two types of Schottky ultraviolet (UV) photodetectors: platinum (Pt)/zinc oxide (ZnO) and Pt/few-layer graphene (FLG)/ZnO, both featuring aluminum (Al) ohmic contacts. The study systematically examines and compares the electrical and optical characteristics of these two photodetector configurations using the Silvaco TCAD simulation tool and machine learning regression models. The research outcomes demonstrate that the proposed photodetector configurations exhibit remarkable improvements, showcasing their substantial potential for superior performance in UV applications. The Pt/ZnO photodetector demonstrates a dark current density of 8.2 × 10−11 pA/cm2, a photocurrent density of 0.26 μA/cm2, a 3-dB cut-off frequency of 85.4 GHz, an external quantum efficiency of 90.41%, and an external photocurrent responsivity of 0.26A/W, at a bias of −1.0 V. On the other hand, Pt/FLG/ZnO photodetector demonstrates near zero dark current density, a photocurrent density of 0.2 μA/cm2, a 3-dB cut-off frequency of 2.44 THz, an external quantum efficiency of 68.52%, and an external photocurrent responsivity of 0.2 A/W at a bias of −1.0 V. Furthermore, a comprehensive comparative analysis of various machine-learning regression models is conducted, validating the simulation findings and providing a predictive framework for optimizing the photodetector's performance. Each machine-learning regression model is evaluated by getting root mean squared error and R2 values across different test set sizes to assess their accuracy in predicting the photodetector's characteristics. This study underscores the promising role of cutting-edge materials and computational techniques in advancing the development of next-generation optoelectronic devices with enhanced capabilities and performance.

The Impact and Future of Generative Pre-trained Transformers: A Study of GPT in Enhancing Business and Technology

With the development of artificial intelligence (AI), natural language processing (NIP) has clearly become the key to enhancing AI capabilities, especially through the deployment of GPT. This paper explores the major advances and related commercial applications of GPT, as it is at the cutting edge of AI technology with far-reaching capabilities in terms of text generation, language understanding, and human-machine interaction. Developed by OpenAI, GPT uses innovative transformer architecture to make breakthroughs in a variety of language tasks, marking a major evolution in the potential applications of AI in various industries. This research explores the integration of GPT in sectors, highlighting how its complex text processing capabilities can be utilized to improve efficiency and innovative service delivery. The study highlights the transformative impact of GPT on business efficiency, strategic decision-making, and customer engagement, thanks to its scalable and adaptable AI framework. Finally, the document reflects the need for a strong ethical framework and regulatory supervision to guide the deployment of GPT technologies. As the Global Trade Agreement evolves, it is committed to shaping the future of global industry by promoting innovation while calling for a balanced approach to ethical considerations and social impacts.

Andrey Kryachkov. The man who created the city

Andrey Dmitrievich Kryachkov is an outstanding architect, the founder of the Siberian architectural school, whose creative path is closely connected with the foundation of the future administrative and cultural capital of Siberia. The author of most of the iconic historical architectural objects of modern Novosibirsk, Kryachkov largely predetermined the aesthetic code of the young Siberian city at that time, combining traditions and innovations in regional architecture of the first half of the 20th century. The architecture of Kryachkov in the modern appearance of Novosibirsk continues to play a significant city-forming aesthetic role.

DEEP LEARNING APPROACH FOR ASPECT CATEGORY DETECTION: A BIBLIOMETRIC ANALYSIS

This article presents a quantitative and qualitative assessment of current research trends by conducting a bibliometric analysis of the sentiment analysis literature from 2020 to March 2024 using the Scopus database. Our focus is on the review of scientific documents, the arrangement of subject categories, the research trend in aspect category detection, the top 10 scholars that write the most number articles in aspect category detection and keyword trends. Our research shows that specialists in computer science, engineering, mathematics, medicine, decision science, material science, social sciences, business and management accounting, energy, and health are the most common topic groups in this industry. A study of keywords shows that terms like “BERT” and “deep learning” are frequently used together. This highlights the use of sophisticated models like BERT in this field and suggests a tendency to use innovative architecture to achieve better results. Conversely, although terms such as “sentiment analysis” and “aspect-based sentiment analysis” have modest frequency, their link strengths indicate a significant correlation with the main theme, emphasising the relationship between aspect category detection and sentiment analysis in research projects. we also provide the deep-learning technique used by the author for aspect category detection.

Selective and multi-scale fusion Mamba for medical image segmentation

Given the high variability in the morphology and size of lesion areas in medical images, accurate medical image segmentation requires both precise positioning of global contours and careful processing of local boundaries. This emphasizes the importance of fusing multi-scale local and global features. However, existing CNN and Transformer-based models are often limited by high parameter counts and complex calculations, making it difficult to efficiently integrate these features. To address this challenge, we proposed two innovative optimization architectures: Selective Fusion Mamba (SF-Mamba) and Multi-Scale Fusion Mamba (MF-Mamba). SF-Mamba can flexibly and dynamically adjust the fusion strategy of local and global features according to the characteristics of the lesions, effectively handling segmentation tasks with variable morphology. MF-Mamba enhances the model’s segmentation ability for lesions of different sizes by capturing global information across scales. Based on these two structures, we constructed a lightweight SMM-UNet model, which not only significantly reduces the computational burden (with only 0.038M parameters) but also demonstrates excellent generalization ability and can efficiently adapt to various types of medical images. Extensive tests on the ISIC2017, ISIC2018, and BUSI public datasets show that SMM-UNet achieves excellent segmentation performance with an extremely low parameter cost.

HSP-UNet: An Accuracy and Efficient Segmentation Method for Carbon Traces of Surface Discharge in the Oil-Immersed Transformer

Restricted by a metal-enclosed structure, the internal defects of large transformers are difficult to visually detect. In this paper, a micro-robot is used to visually inspect the interior of a transformer. For the micro-robot to successfully detect the discharge level and insulation degradation trend in the transformer, it is essential to segment the carbon trace accurately and rapidly from the complex background. However, the complex edge features and significant size differences of carbon traces pose a serious challenge for accurate segmentation. To this end, we propose the Hadamard production-Spatial coordinate attention-PixelShuffle UNet (HSP-UNet), an innovative architecture specifically designed for carbon trace segmentation. To address the pixel over-concentration and weak contrast of carbon trace image, the Adaptive Histogram Equalization (AHE) algorithm is used for image enhancement. To realize the effective fusion of carbon trace features with different scales and reduce model complexity, the novel grouped Hadamard Product Attention (HPA) module is designed to replace the original convolution module of the UNet. Meanwhile, to improve the activation intensity and segmentation completeness of carbon traces, the Spatial Coordinate Attention (SCA) mechanism is designed to replace the original jump connection. Furthermore, the PixelShuffle up-sampling module is used to improve the parsing ability of complex boundaries. Compared with UNet, UNet++, UNeXt, MALUNet, and EGE-UNet, HSP-UNet outperformed all the state-of-the-art methods on both carbon trace datasets. For dendritic carbon traces, HSP-UNet improved the Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and Class Pixel Accuracy (CPA) of the benchmark UNet by 2.13, 1.24, and 4.68 percentage points, respectively. For clustered carbon traces, HSP-UNet improved MIoU, PA, and CPA by 0.98, 0.65, and 0.83 percentage points, respectively. At the same time, the validation results showed that the HSP-UNet has a good model lightweighting advantage, with the number of parameters and GFLOPs of 0.061 M and 0.066, respectively. This study could contribute to the accurate segmentation of discharge carbon traces and the assessment of the insulation condition of the oil-immersed transformer.

Deep learning-driven ultrasound-assisted diagnosis: optimizing GallScopeNet for precise identification of biliary atresia.

Biliary atresia (BA) is a severe congenital biliary developmental abnormality threatening neonatal health. Traditional diagnostic methods rely heavily on experienced radiologists, making the process time-consuming and prone to variability. The application of deep learning for the automated diagnosis of BA remains underexplored. This study introduces GallScopeNet, a deep learning model designed to improve diagnostic efficiency and accuracy through innovative architecture and advanced feature extraction techniques. The model utilizes data from a carefully constructed dataset of gallbladder ultrasound images. A dataset comprising thousands of ultrasound images was employed, with the majority used for training and validation and a subset reserved for external testing. The model's performance was evaluated using five-fold cross-validation and external assessment, employing metrics such as accuracy and the area under the receiver operating characteristic curve (AUC), compared against clinical diagnostic standards. GallScopeNet demonstrated exceptional performance in distinguishing BA from non-BA cases. In the external test dataset, GallScopeNet achieved an accuracy of 81.21% and an AUC of 0.85, indicating strong diagnostic capabilities. The results highlighted the model's ability to maintain high classification performance, reducing misdiagnosis and missed diagnosis. GallScopeNet effectively differentiates between BA and non-BA images, demonstrating significant potential and reliability for early diagnosis. The system's high efficiency and accuracy suggest it could serve as a valuable diagnostic tool in clinical settings, providing substantial technical support for improving diagnostic workflows.

Разработка архитектуры автономно управляемого роя роботов

Purpose: to create an innovative architecture for an autonomously controlled swadm of robots for effective exploration of smoke-filled rooms in designated areas. The problem of smoke-filled premises is relevant during emergencies such as fires and accidents, where a limited incident makes rescue operations difficult. The use of advanced technologies, including artificial intelligence and innovative methods, can improve the efficiency and safety of research in such settings. A promising direction for the development of this area is the use of artificial intelligence to improve the coordination and autonomy of robots, which will increase the accuracy and speed of task completion. Determine the type of robot and the required characteristics of the designed system include limited autonomous robots with highly sensitive sensors and sensors for cost-effective navigation, as well as self-directed coordination capabilities. Methods: comparison of existing methods for studying smokefilled rooms, as well as existing types. Comparison of the main types of sensors and sensors used for navigation in conditions of limited stability. Results: the type of robot was selected that is most suitable in terms of characteristics for the developed automated system, performing the functions of investigating smoke-filled rooms as efficiently as possible. The robot architecture has been developed. A diagram of the interaction of robot control tasks is presented. Practical importance: the architecture of the robot and the interaction scheme for robot control tasks have been developed. This software code for ensuring the safety and control of robots proposes a modular robot architecture, wide adaptation and flexible change of the system under different conditions, limited savings arising in rescue and search operations. The model being developed includes autonomous robots, methods for performing navigation, detecting obstacles, collecting data on the state of premises and ensuring interaction for coordinated research.

Fuzzy Logic Control with Long Short-Term Memory Neural Network for Hydrogen Production Thermal Control System

In the development of decarbonization technologies and renewable energy, water electrolysis has emerged as a key technology. The efficiency of hydrogen production and its applications are significantly affected by power stability. Enhancing power stability not only improves hydrogen production efficiency and reduces maintenance costs but also ensures long-term reliable system operation. This study proposes a thermal control method that stabilizes hydrogen output by precisely adjusting the temperature of the electrolysis stack, thereby improving hydrogen production efficiency. Fluctuations in the electrolysis stack temperature can lead to instability in the hydrogen output and energy utilization, negatively affecting overall hydrogen production. To address this issue, this study introduces an innovative system architecture and a novel thermal control strategy combining fuzzy logic control with a long short-term memory neural network. This method predicts and adjusts the flow rate of chilled water to maintain the electrolysis stack temperature within a range of ±1 °C while sustaining a constant power output of 10 kW. This approach is crucial for ensuring system stability and maximizing hydrogen production efficiency. Long-term experiments have validated the effectiveness and reliability of this method, demonstrating that this thermal control strategy not only stabilizes the hydrogen production process but also increases the volume of hydrogen generated.

Intelligent Handwritten Identification Using Novel Hybrid Convolutional Neural Networks – Long-short-term Memory Architecture

Handwritten character identification finds broad applications in document analysis, digital forensics, and human-computer interaction. Conventional methods encounter challenges in accurately deciphering a range of handwriting styles and variations. Consequently, investigating intelligent system for handwriting identification becomes crucial to enhance accuracy and efficiency. This paper introduces an innovative hybrid deep learning architecture, seamlessly integrating the strengths of convolutional neural networks (CNN) and long-short term memory (LSTM) within a one single framework. The combination of these structures enables the model to effectively capture both spatial features and temporal dependencies inherent in handwritten strokes, resulting in improved recognition performance. The proposed 2DCNN-LSTM algorithm has been tested on MNIST dataset. The proposed hybrid CNN-LSTM structure has been compared to conventional intelligent machine learning methods, and the results demonstrate the superior performance of the hybrid CNN-LSTM, showcasing heightened accuracy, sensitivity, specificity, and other evaluation metrics.

Reliable resistive switching and synaptic simulation behaviors in ammonium polyphosphate-based memristor with non-inert Al electrode

Abstract Memristive devices that integrate storage and computing capabilities are highly promising candidates for artificial synapses in neuromorphic systems. However, achieving both cost-effectiveness and high-performance in memristors remains a substantial challenge. Ammonium polyphosphate (APP), an all-inorganic ionic polymer, has been utilized in the fabrication of memristive devices due to its distinctive poly-ionic properties and exceptional ion mobility. In this study, a two-terminal APP-based memristor with an Al/APP/ITO structure was fabricated. The experimental results revealed improved bipolar resistive switching behavior, characterized by lower operating voltages, enhanced endurance performance, and extended retention time. Detailed data fitting and chemical bonding analysis suggest that the physical mechanism underlying resistive switching involves a combination of interfacial Schottky barrier and conductive filaments. Furthermore, adjustable device conductance is achieved by applying consecutive positive and negative voltage sweeps. Various synaptic functions, including excitatory postsynaptic current, short-term paired-pulse facilitation, long-term potentiation /depression, and spike-timing-dependent plasticity, are effectively emulated. This study presents an effective approach to enhancing the memristive characteristics of APP-based devices and positions APP as a viable candidate for innovative neuromorphic architectures.