Traditional Architecture Research Articles

Data-driven prediction of spinal cord injury recovery: An exploration of current status and future perspectives

Spinal Cord Injury (SCI) presents a significant challenge in rehabilitation medicine, with recovery outcomes varying widely among individuals. Machine learning (ML) is a promising approach to enhance the prediction of recovery trajectories, but its integration into clinical practice requires a thorough understanding of its efficacy and applicability. We systematically reviewed the current literature on data-driven models of SCI recovery prediction. The included studies were evaluated based on a range of criteria assessing the approach, implementation, input data preferences, and the clinical outcomes aimed to forecast. We observe a tendency to utilize routinely acquired data, such as International Standards for Neurological Classification of SCI (ISNCSCI), imaging, and demographics, for the prediction of functional outcomes derived from the Spinal Cord Independence Measure (SCIM) III and Functional Independence Measure (FIM) scores with a focus on motor ability. Although there has been an increasing interest in data-driven studies over time, traditional machine learning architectures, such as linear regression and tree-based approaches, remained the overwhelmingly popular choices for implementation. This implies ample opportunities for exploring architectures addressing the challenges of predicting SCI recovery, including techniques for learning from limited longitudinal data, improving generalizability, and enhancing reproducibility. We conclude with a perspective, highlighting possible future directions for data-driven SCI recovery prediction and drawing parallels to other application fields in terms of diverse data types (imaging, tabular, sequential, multimodal), data challenges (limited, missing, longitudinal data), and algorithmic needs (causal inference, robustness).

Typology of Traditional Domestic Architecture based on Social Stratifications: The Case of Mamasa Tribe, Indonesia

Typologies of traditional domestic architecture across the world are based on numerous criteria. Certainly, social stratification is one among them. Mamasa tribe located in the West Sulawesi Province, which is one of the Indonesian ethnic groups with traditional architecture do so based on the social and economic levels of the community and the caste divisions. Their traditional architectural heritage still maintained to this day is the Mamasa traditional house called banua. The Mamasa community introduced caste divisions based on the social and economic levels of the community which physically influenced the form and typology of the Mamasa traditional house. This study examines the architectural aspects influenced by the social stratification of the Mamasa community: the function, form, space, material, appearance and decorative variety of the Mamasa traditional house. The research employed exploratory qualitative methods. Data collection techniques included direct observations, interviews and literature studies. In-depth interviews were conducted with several traditional leaders related to the Mamasa traditional house. The field data was correlated with data from the literature studies: journals, books, or website sources related to the research study. The findings indicate a strong correlation between the architecture of the traditional Mamasa house and the social stratification that exists in the Mamasa community. The typology based on the social stratification of the Mamasa society is: the first type, Banua Sura' is intended for the tana' bulawan or high nobility class. The second type, Banua Bolong is intended for the tana' bassi or the ordinary nobility class. The third type, Banua Rapa is intended for the tana' karurung or free-people class. The fourth type, Banua Longkarrin is intended for the tana' kua-kua or the slave class. These four types of banua each reflect the social and economic status of the homeowners through their architecture.

High-Performance Method and Architecture for Attention Computation in DNN Inference.

In recent years, The combination of Attention mechanism and deep learning has a wide range of applications in the field of medical imaging. However, due to its complex computational processes, existing hardware architectures have high resource consumption or low accuracy, and deploying them efficiently to DNN accelerators is a challenge. This paper proposes an online-programmable Attention hardware architecture based on compute-in-memory (CIM) marco, which reduces the complexity of Attention in hardware and improves integration density, energy efficiency, and calculation accuracy. First, the Attention computation process is decomposed into multiple cascaded combinatorial matrix operations to reduce the complexity of its implementation on the hardware side; second, in order to reduce the influence of the non-ideal characteristics of the hardware, an online-programmable CIM architecture is designed to improve calculation accuracy by dynamically adjusting the weights; and lastly, it is verified that the proposed Attention hardware architecture can be applied for the inference of deep neural networks through Spice simulation. Based on the 100nm CMOS process, compared with the traditional Attention hardware architectures, the integrated density and energy efficiency are increased by at least 91.38 times, and latency and computing efficiency are improved by about 12.5 times.

APPLICATION OF MODULAR CSIPS TO TRADITIONAL KOREAN HANOK TIMBER STRUCTURES FOR THERMAL AND ENERGY PERFORMANCE

ABSTRACT This study examines the thermal and energy performance capabilities of composite structural insulation panels (CSIPs) for traditional Korean architecture, specifically Hanok. Hanok buildings, renowned for their cultural and architectural significance, often face thermal efficiency and energy consumption challenges. This research aims to explore the environmental potential of CSIPs to improve the thermal performance of Hanok structures while maintaining the traditional aesthetics of these buildings. Simulations were conducted to analyze the thermal behavior, heat flow, and energy use intensity (EUI) by wall cases of Hanok buildings, applying continuous-fiber-reinforced thermoplastic composites as a type of CSIP. The findings demonstrated that applying continuous-fiber-reinforced thermoplastic composites (CFRTCs) to the target Hanok building significantly enhanced the thermal performance, including the thermal bridge and envelope airtightness, as these composites can reduce the thermal conductivity and minimize heat loss, thus improving the insulation capabilities. Furthermore, adopting modular CSIPs in Hanok architecture offers environmental benefits and aligns with sustainability and constructability goals. By reducing reliance on mechanical heating and cooling systems, CSIP-enhanced Hanok buildings can contribute to lower energy consumption and greenhouse gas emissions. This simulation study highlights the significant potential of CSIPs with fiber-glass-reinforced thermoset (FRT) polymer skins to enhance the thermal and energy performance of traditional Korean Hanok buildings. CSIPs as applied to the target Hanok can potentially reduce heating energy consumption by approximately 21% to 29%, leading to overall energy savings of about 4% to 5% in site EUI and CO2 emissions levels per unit area.

CBAM VGG16: An efficient driver distraction classification using CBAM embedded VGG16 architecture

Driver monitoring systems (DMS) are crucial in autonomous driving systems (ADS) when users are concerned about driver/vehicle safety. In DMS, the significant influencing factor of driver/vehicle safety is the classification of driver distractions or activities. The driver’s distractions or activities convey meaningful information to the ADS, enhancing the driver/ vehicle safety in real-time vehicle driving. The classification of driver distraction or activity is challenging due to the unpredictable nature of human driving. This paper proposes a convolutional block attention module embedded in Visual Geometry Group (CBAM VGG16) deep learning architecture to improve the classification performance of driver distractions. The proposed CBAM VGG16 architecture is the hybrid network of the CBAM layer with conventional VGG16 network layers. Adding a CBAM layer into a traditional VGG16 architecture enhances the model’s feature extraction capacity and improves the driver distraction classification results. To validate the significant performance of our proposed CBAM VGG16 architecture, we tested our model on the American University in Cairo (AUC) distracted driver dataset version 2 (AUCD2) for cameras 1 and 2 images. Our experiment results show that the proposed CBAM VGG16 architecture achieved 98.65% classification accuracy for camera 1 and 97.85% for camera 2 AUCD2 datasets. The CBAM VGG16 architecture also compared the driver distraction classification performance with DenseNet121, Xception, MoblieNetV2, InceptionV3, and VGG16 architectures based on the proposed model’s accuracy, loss, precision, F1 score, recall, and confusion matrix. The drivers’ distraction classification results indicate that the proposed CBAM VGG16 has 3.7% classification improvements for AUCD2 camera 1 images and 5% for camera 2 images compared to the conventional VGG16 deep learning classification model. We also tested our proposed architecture with different hyperparameter values and estimated the optimal values for best driver distraction classification. The significance of data augmentation techniques for the data diversity performance of the CBAM VGG16 model is also validated in terms of overfitting scenarios. The Grad-CAM visualization of our proposed CBAM VGG16 architecture is also considered in our study, and the results show that VGG16 architecture without CBAM layers is less attentive to the essential parts of the driver distraction images. Furthermore, we tested the effective classification performance of our proposed CBAM VGG16 architecture with the number of model parameters, model size, various input image resolutions, cross-validation, Bayesian search optimization and different CBAM layers. The results indicate that CBAM layers in our proposed architecture enhance the classification performance of conventional VGG16 architecture and outperform the state-of-the-art deep learning architectures.

Advanced computer architecture optimization for machine learning/deep learning

Abstract The recent progress in Machine Learning (Géron, 2022) and particularly Deep Learning (Goodfellow, 2016) models exposed the limitations of traditional computer architectures. Modern algorithms demonstrate highly increased computational demands and data requirements that most existing architectures cannot handle efficiently. These demands result in training speed, inference latency, and power consumption bottlenecks, which is why advanced methods of computer architecture optimization are required to enable the development of ML/DL-dedicated efficient hardware platforms (Engineers, 2019). The optimization of computer architecture for applications of ML/DL becomes critical, due to the tremendous demand for efficient execution of complex computations by Neural Networks (Goodfellow, 2016). This paper reviewed the numerous approaches and methods utilized to optimize computer architecture for ML/DL workloads. The following sections contain substantial discussion concerning the hardware-level optimizations, enhancements of traditional software frameworks and their unique versions, and innovative explorations of architectures. In particular, we discussed hardware including specialized accelerators, which can improve the performance and efficiency of a computation system using various techniques, specifically describing accelerators like CPUs (multicore) (Hennessy, 2017), GPUs (Hwu, 2015) and TPUs (Contributors, 2017), parallelism in multicore architectures, data movement in hardware systems, especially techniques such as caching and sparsity, compression, and quantization, other special techniques and configurations, such as using specialized data formats, and measurement sparsity. Moreover, this paper provided a comprehensive analysis of current trends in software frameworks, Data Movement optimization strategies (A.Bienz, 2021), sparsity, quantization and compression methods, using ML for architecture exploration, and, DVFS (Hennessy, 2017),, which provides strategies for maximizing hardware utilization and power consumption during training, machine learning, dynamic voltage, and frequency scaling, runtime systems. Finally, the paper discussed research opportunity directions and the possibilities of computer architecture optimization influence in various industrial and academic areas of ML/DL technologies. The objective of implementing these optimization techniques is to largely minimize the current gap between the computational needs of ML/DL algorithms and the current hardware’s capability. This will lead to significant improvements in training times, enable real-time inference for various applications, and ultimately unlock the full potential of cutting-edge machine learning algorithms.

Characteristics of Düzce Traditional Housing Architecture; The Case of Düzce Konuralp

The traditional Turkish house is a type of house that can be described as a cultural heritage, having developed since the moment the Turks appeared on the stage of history and has survived to the present day. Traditional houses are civil architectural works that are built according to the physical conditions and cultural characteristics of the environment they belong to, have become historical, and have become a national symbol. The main idea behind the creation of houses is directly related to the traditional construction rules and building materials of the period to which they belong. In the Western Black Sea Region, where the study was carried out, studies were made on the architecture of traditional houses in the Konuralp District of Düzce province. Konuralp District, a settlement where many historical layers from ancient times to the present day can be seen together, was built on the ancient Roman city of Prusias ad Hypium. In the study, six registered traditional houses, which have survived to the present day with many civil architectural works while preserving their historical texture, were evaluated in terms of building-street relationship, plan typology, facade features, materials, and construction techniques in order to determine their similarity with the architectural features of traditional Turkish houses. It was determined that the traditional houses examined were similar to the concept of Turkish house.

Enhancing stock market prediction accuracy with recurrent deep learning models: A case study on the CAC40 index

This paper explores the application of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for stock price prediction over a 10-day horizon. The study aims to compare the predictive performance of these two deep learning architectures within the context of financial forecasting. Utilizing historical stock data from the CAC40 dataset, which represents a capitalization-weighted measure of the 40 most significant stocks on the Euronext Paris, we train and evaluate RNN and LSTM models to forecast future stock prices. Our results demonstrate the superior performance of LSTM networks in capturing the intricate temporal dependencies inherent in stock price data. Compared to standard RNNs, LSTM models exhibit higher accuracy and provide more reliable forecasts over the 10-day prediction period. The specialized memory cells and gating mechanisms in LSTM networks enable them to effectively identify both short-term changes and long-term patterns in stock prices, thus outperforming traditional RNN architectures. This enhanced ability to model the complex dynamics of stock market data underscores the potential of LSTM networks to improve investment decision-making, risk management, and the overall efficiency of financial markets. The insights gained from this study contribute to the growing body of knowledge on the application of deep learning in finance and investment, offering valuable guidance for practitioners and researchers seeking to harness the power of advanced algorithms for stock market prediction and optimization.

MobileAmcT: A Lightweight Mobile Automatic Modulation Classification Transformer in Drone Communication Systems

With the rapid advancement of wireless communication technology, automatic modulation classification (AMC) plays a crucial role in drone communication systems, ensuring reliable and efficient communication in various non-cooperative environments. Deep learning technology has demonstrated significant advantages in the field of AMC, effectively and accurately extracting and classifying modulation signal features. However, existing deep learning models often have high computational costs, making them difficult to deploy on resource-constrained drone communication devices. To address this issue, this study proposes a lightweight Mobile Automatic Modulation Classification Transformer (MobileAmcT). This model combines the advantages of lightweight convolutional neural networks and efficient Transformer modules, incorporating the Token and Channel Conv (TCC) module and the EfficientShuffleFormer module to enhance the accuracy and efficiency of the automatic modulation classification task. The TCC module, based on the MetaFormer architecture, integrates lightweight convolution and channel attention mechanisms, significantly improving local feature extraction efficiency. Additionally, the proposed EfficientShuffleFormer innovatively improves the traditional Transformer architecture by adopting Efficient Additive Attention and a novel ShuffleConvMLP feedforward network, effectively enhancing the global feature representation and fusion capabilities of the model. Experimental results on the RadioML2016.10a dataset show that compared to MobileNet-V2 (CNN-based) and MobileViT-XS (ViT-based), MobileAmcT reduces the parameter count by 74% and 65%, respectively, and improves classification accuracy by 1.7% and 1.09% under different SNR conditions, achieving an accuracy of 62.93%. This indicates that MobileAmcT can maintain high classification accuracy while significantly reducing the parameter count and computational complexity, clearly outperforming existing state-of-the-art AMC methods and other lightweight deep learning models.

Software-Defined Named Data Networking in Literature: A Review

This paper presents an in-depth review of software-defined named data networking (SD-NDN), a transformative approach in network architectures poised to deliver substantial benefits. By addressing the limitations inherent in traditional host-centric network architectures, SD-NDN offers improvements in network performance, scalability, and efficiency. The paper commences with an overview of named data networking (NDN) and software-defined networking (SDN), the two fundamental building blocks of SD-NDN. It then explores the specifics of integrating NDN with SDN, illustrating examples of various SD-NDN models. These models are designed to leverage SDN for NDN routing, caching, and forwarding. The paper concludes by proposing potential strategies for further integration of SDN and NDN and some open research questions. These proposed strategies aim to stimulate further exploration and innovation in the field of SD-NDN.

Attention-Based Neural Network for Cardiac MRI Segmentation: Application to Strain and Volume Computation

ContextDeep learning algorithms have been widely used for cardiac image segmentation. However, most of these architectures rely on convolutions that hardly model long-range dependencies, limiting their ability to extract contextual information. Moreover, the traditional U-net architecture suffers from the difference of semantic information between feature maps of the encoder and decoder (also known as the semantic gap). Material and methodTo address this issue, a new network architecture relying on attention mechanism was introduced. Swin Filtering Blocks (SFB), that use Swin Transformer blocks in a cross-attention manner, were added between the encoder and the decoder to filter information coming from the encoder based on the feature map from the decoder. Attention was also employed at the lowest resolution in the form of a transformer layer to increase the receptive field of the network.We conducted experiments to assess both generalization capability and to evaluate how training on all frames of the cardiac cycle rather than only the end-diastole and end-systole impacts strain and segmentation performances. Results and conclusionVisual inspection of feature maps suggested that Swin Filtering Blocks contribute to the reduction of the semantic gap. Performing attention between all patches using a transformer layer brought higher performance than convolutions. Training the model with all phases of the cardiac cycle resulted in slightly more accurate segmentations while leading to a more noticeable improvement for strain estimation. A limited decrease in performance was observed when testing on out-of-distribution data, but the gap widens for the most apical slices.

A comparative study to examine principal component analysis and kernel principal component analysis-based weighting layer for convolutional neural networks

ABSTRACT In the recent decay, the focus on processing signal data processing such as time series, images, and videos increased. The purpose of this processing is mainly forecasting, classification, and regression. The development of machine learning methods, on the other hand, caused the introduction of novel machine learning-based signal processor models. Convolutional neural networks (CNNs) are the best-known models widely used for signal processing. This work introduces a novel approach by combining PCA with CNNs for signal processing, offering the potential for improved performance in tasks like forecasting, classification, and regression. A comparative analysis of traditional architectures will be crucial in evaluating the effectiveness of the proposed model.

The Parthian Manor House of Sarab-e Murt in the Context of the Iranian Architectural Tradition

The Parthian Manor House of Sarab-e Murt in the Context of the Iranian Architectural Tradition

Acculturation Phenomenon in Vernacular Architecture between Vietnamese and Chams in the Kingdom of Cochin-China (16th–18th)–Southeast Asia

ABSTRACT This paper presents in general the remaining vernacular houses in the middle region of Vietnam, known as the Kingdom of Cochin-china (16th−18th). Accordingly, there are three common types of vernacular houses. Those are the “Nha Ruong” popular in the north-middle region, the “Nha La Mai” popular in the central and south-middle regions, and the “Nha Roi” popular in both the north and south-middle regions of Vietnam. This study mainly focused on the viewpoint of architectural typology and construction techniques through site investigations, comparative study, and analysis of the remaining vernacular houses. With the approach of indigenous culture, residential planning, spatial arrangement, carpenter’s tools, design methods, and construction techniques, the similarities and differences between the vernacular houses have been determined. In addition, this study also defined the acculturation phenomenon in architecture between the middle region Vietnamese emigrants and the indigenous Chams in the past. The study results are expected to help architects and students to create new architectural products that can be sustainable in the harsh climatic conditions such as the middle region of Vietnam, refining and inheriting the quintessence of traditional architecture to enrich the cultural identity of Vietnam in particular and of Southeast Asia in general.

Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation

Current state-of-the-art medical image segmentation techniques predominantly employ the encoder–decoder architecture. Despite its widespread use, this U-shaped framework exhibits limitations in effectively capturing multi-scale features through simple skip connections. In this study, we made a thorough analysis to investigate the potential weaknesses of connections across various segmentation tasks, and suggest two key aspects of potential semantic gaps crucial to be considered: the semantic gap among multi-scale features in different encoding stages and the semantic gap between the encoder and the decoder. To bridge these semantic gaps, we introduce a novel segmentation framework, which incorporates a Dual Attention Transformer module for capturing channel-wise and spatial-wise relationships, and a Decoder-guided Recalibration Attention module for fusing DAT tokens and decoder features. These modules establish a principle of learnable connection that resolves the semantic gaps, leading to a high-performance segmentation model for medical images. Furthermore, it provides a new paradigm for effectively incorporating the attention mechanism into the traditional convolution-based architecture. Comprehensive experimental results demonstrate that our model achieves consistent, significant gains and outperforms state-of-the-art methods with relatively fewer parameters. This study contributes to the advancement of medical image segmentation by offering a more effective and efficient framework for addressing the limitations of current encoder–decoder architectures. Code: https://github.com/McGregorWwww/UDTransNet.

Bioclimatic Design Strategies For Housing in Areas Affected by Natural Events

Objective: The objective of this study was a housing design with bioclimatic criteria and construction technology with traditional materials to propose an appropriate solution for families that suffered damage from the 2017 earthquakes in Oaxaca, Mexico. Method: A climatic characterization of the site was carried out to obtain bioclimatic strategies for the housing design. Analysis methods were used to validate these strategies such as comfort triangles and Mahoney tables. The housing was designed considering the resulting strategies and prioritized passive ones. Results and conclusions: It was obtained that for the study area the main strategies for the housing design are natural cross ventilation, thermal inertia in walls and ceilings and solar radiation during the cold months. The study highlights the importance of implementing bioclimatic design strategies according to the climatic context that promote the improvement of the habitability level of housing. Implications of the research: The research contributes to the design of a sustainable housing model for families affected in their patrimony by the action of earthquakes in the study area considering the environmental conditions, in addition to promoting the conservation of traditional architecture and construction systems. Originality/value: In the area of sustainability and the environment, a housing design was proposed that adopts practices that minimize the environmental impact and ensures that the new houses are energy efficient and environmentally friendly and adapt to climate change.

Characterizing parameter variations for enhanced performance and adaptability in 3 nm MBCFET technology

With the continuous scaling down of semiconductor devices, traditional transistor architectures face significant challenges in maintaining performance and power efficiency. Multi-bridge channel field-effect transistors (MBCFETs) are promising candidates for next-generation of transistors, enabling significant size reduction while preserving high performance. This paper investigates both n-type and p-type 3-nm MBCFETs with a focus on their behavior under diverse operating conditions. The study examines the influence of doping concentration, sheet thickness, temperature, device width, and the number of sheets, on the device's functionality aiming for high-performance applications. Doping concentrations of acceptors and donors ranging from 1 × 10 15 cm−3 - 9 × 10 17 cm−3 and 1 × 10 17 cm−3 - 9 × 10 19 cm−3, respectively, to observe their impact on device performance. Similarly, sheet thicknesses from 1 nm to 2 nm and device widths from 3 nm to 30 nm are analyzed to understand the scaling effects. The temperature varies from 273.15 K to 573.15 K to simulate different operational environments, while the number of sheets, ranging from 1 to 7, is adjusted to evaluate structural effects on device behavior. By extracting the subthreshold swing (SS), threshold voltage (Vth), ON-current (ION), OFF-current (IOFF), and the ION/IOFF ratio, the study offers valuable insights into the suitability and potential applications of N-MBCFET and P-MBCFET devices at the ultra-scaled 3 nm dimension. At room temperatures, the SS achieves 60 mV dec⁻1 for n-type and 64 mV dec⁻1 for p-type which indicates proper switching speed, with corresponding ON/OFF ratios of 2 × 106 and 1 × 105 for n-type and p-type, respectively. This study makes notable contributions to the field of nanoscale transistor technology, aiding in the design and optimization of future electronic devices.

An In-Memory-Computing Structure with Quantum-Dot Transistor Toward Neural Network Applications: From Analog Circuits to Memory Arrays

The rapid advancements in artificial intelligence (AI) have demonstrated great success in various applications, such as cloud computing, deep learning, and neural networks, among others. However, the majority of these applications rely on fast computation and large storage, which poses significant challenges to the hardware platform. Thus, there is a growing interest in exploring new computation architectures to address these challenges. Compute-in-memory (CIM) has emerged as a promising solution to overcome the challenges posed by traditional computer architecture in terms of data transfer frequency and energy consumption. Non-volatile memory, such as Quantum-dot transistors, has been widely used in CIM to provide high-speed processing, low power consumption, and large storage capacity. Matrix-vector multiplication (MVM) or dot product operation is a primary computational kernel in neural networks. CIM offers an effective way to optimize the performance of the dot product operation by performing it through an intertwining of processing and memory elements. In this paper, we present a novel design and analysis of a Quantum-dot transistor (QDT) based CIM that offers efficient MVM or dot product operation by performing computations inside the memory array itself. Our proposed approach offers energy-efficient and high-speed data processing capabilities that are critical for implementing AI applications on resource-limited platforms such as portable devices.

ADAPTATION OF TRADITIONAL MINAHASA ARCHITECTURE IN MALANG CITY

Geographically and climatically, Minahasa and Malang are similar, being in the highlands and having a two-season climate, but Minahasa is more likely to experience seismicity than Malang. Culturally, the Minahasa tribe upholds mapalus to strengthen kinship. From these conditions, there is an adaptation that adjusts to the new environment which aims for comfort in occupying. This research aims to determine the results of geographical, climatic, and cultural adaptation of traditional Minahasa architecture in Malang City. The research method is descriptive qualitative and interviews by analyzing data in the form of comparing aspects of venustas, firmity, and utility. The findings of this research are that the meaning of the three vertical and horisontal spaces has changed slightly, such as the use of the lower space and placement of stairways into the pores. The knockdown system applied can be identified in the connection between the column and the beams, as well as the concrete column that adapts to the environmental conditions. Some have changed in terms of space, structure, and facade as a result of adaptation to the new environment. However, it does not change the main characteristics of Minahasa architecture so that traditional Minahasa architecture can be accepted in Malang.

In-Sensor Tactile Fusion and Logic for Accurate Intention Recognition.

Touch control intention recognition is an important direction for the future development of human-machine interactions (HMIs). However, the implementation of parallel-sensing functional modules generally requires a combination of different logical blocks and control circuits, which results in regional redundancy, redundant data, and low efficiency. Here, a location-and-pressure intelligent tactile sensor (LPI tactile sensor) unprecedentedly combined with sensing, computing, and logic is proposed, enabling efficient and ultrahigh-resolution action-intention interaction. The LPI tactile sensor eliminates the need for data transfer among the functional units through the core integration design of the layered structure. It actuates in-sensor perception through feature transmission, fusion, and differentiation, thereby revolutionizing the traditional von Neumann architecture. While greatly simplifying the data dimensionality, the LPI tactile sensor achieves outstanding resolution sensing in both location (<400µm) and pressure (75Pa). Synchronous feature fusion and decoding support the high-fidelity recognition of action and combinatorial logic intentions. Benefiting from location and pressure synergy, the LPI tactile sensor demonstrates robust privacy as an encrypted password device and interaction intelligence through pressure enhancement. It can recognize continuous touch actions in real time, map real intentions to target events, and promote accurate and efficient intention-driven HMIs.