Search Results (30)

Soft sensors are designed to be flexible, making them ideal for wearable devices as they can conform to the human body during motion, capturing pertinent information effectively. However, once these wearable sensors are constructed, modifying them is not straightforward without undergoing a re-prototyping process. In this study, we introduced a novel design for a modular soft sensor unit (M2SU) that incorporates a short, wire-shaped sensory structure made of eutectogel, with magnetic blocks at both ends. This design facilitates the easy assembly and reversible integration of the sensor directly onto a wearable device in situ. Leveraging the piezoresistive properties of eutectogel and the dual conductive and magnetic characteristics of neodymium magnets, our sensor unit acts as both a sensing element and a modular component. To explore the practical application of M2SUs in wearable sensing, we equipped a glove with 8 M2SUs. We evaluated its performance across three common gesture recognition tasks: numeric keypad typing (Task 1), symbol drawing (Task 2), and uppercase letter writing (Task 3). Employing a 1D convolutional neural network to analyze the collected data, we achieved task-specific accuracies of 80.43% (Top 3: 97.68%) for Task 1, 88.58% (Top 3: 96.13%) for Task 2, and 79.87% (Top 3: 91.59%) for Task 3. These results confirm that our modular soft sensor design can facilitate high-accuracy gesture recognition on wearable devices through straightforward, in situ assembly. Full article

Bridge deflection values are significant for their health and safety, but current methods for predicting bridge deflection suffer from problems such as anomalous data and low prediction accuracy. To solve the problems of anomalous bias and loss of short-term trend in traditional smoothing methods, this paper proposes a preprocessing method for cascade residual smoothing. The method firstly uses Gaussian filtering to initially remove the high-frequency noise in the signal and retain the overall trend. Then, the residuals of the initial filtering and the original data are smoothed by quadratic exponential smoothing to extract the short-term trend in the deflection data, which is favorable for the data to have the advantages of both stabilization and retention of small fluctuations. In addition, to simultaneously acquire the temporal dependence and spatial features between long- and short-term temporal signals, this paper proposes a multiscale spatial attention network based on Multiscale Convolutional Neural Networks (MSCNNs), Gated Recurrent Units (GRUs), and self-attention (SA). The method obtains multi-level sensory field spatial information within each period through the MSCNN, focuses on the connection between different time steps using a GRU, and employs SA to automatically focus on the deflection features that have a significant impact and ignore unimportant perturbation variations, thus improving the prediction ability of the model. In this paper, compared with CNN-Attention-LSTM, the MAE is reduced by 25.79%, the RMSE is reduced by 24.69%, and the R2 is increased by 2.36%, which proves the superiority and advancement of the method. Full article

For high-precision navigation, obtaining and maintaining high-precision point-of-interest (POI) data on the road network is crucial. In urban areas with complex terrains, the accuracy of traditional road network POI acquisition methods often falls short. To address this issue, we introduce DualNet-PoiD, a hybrid neural network designed for the efficient recognition of road network POIs in intricate urban environments. This method leverages multimodal sensory data, incorporating both vehicle trajectories and remote sensing imagery. Through an enhanced dual-attention dilated link network (DAD-LinkNet) based on ResNet18, the system extracts static geometric features of roads from remote sensing images. Concurrently, an improved gated recirculation unit (GRU) captures dynamic traffic characteristics implied by vehicle trajectories. The integration of a fully connected layer (FC) enables the high-precision identification of various POIs, including traffic light intersections, gas stations, parking lots, and tunnels. To validate the efficacy of DualNet-PoiD, we collected 500 remote sensing images and 50,000 taxi trajectory data samples covering road POIs in the central urban area of the mountainous city of Chongqing. Through comprehensive area comparison experiments, DualNet-PoiD demonstrated a high recognition accuracy of 91.30%, performing robustly even under conditions of complex occlusion. This confirms the network’s capability to significantly improve POI detection in challenging urban settings. Full article

Infrared small target detection technology plays a crucial role in various fields such as military reconnaissance, power patrol, medical diagnosis, and security. The advancement of deep learning has led to the success of convolutional neural networks in target segmentation. However, due to challenges like small target scales, weak signals, and strong background interference in infrared images, convolutional neural networks often face issues like leakage and misdetection in small target segmentation tasks. To address this, an enhanced U-Net method called MST-UNet is proposed, the method combines multi-scale feature decomposition and fusion and attention mechanisms. The method involves using Haar wavelet transform instead of maximum pooling for downsampling in the encoder to minimize feature loss and enhance feature utilization. Additionally, a multi-scale residual unit is introduced to extract contextual information at different scales, improving sensory field and feature expression. The inclusion of a triple attention mechanism in the encoder structure further enhances multidimensional information utilization and feature recovery by the decoder. Experimental analysis on the NUDT-SIRST dataset demonstrates that the proposed method significantly improves target contour accuracy and segmentation precision, achieving IoU and nIoU values of 80.09% and 80.19%, respectively. Full article

A milk drink flavored with date syrup produced at a lab scale level was evaluated. The production process of date syrup involves a sequence of essential unit operations, commencing with the extraction, filtration, and concentration processes from two cultivars: Sukkary and Khlass. Date syrup was then mixed with cow’s and camel’s milk at four percentages to form a nutritious, natural, sweet, and energy-rich milk drink. The sensory, physical, and chemical characteristics of the milk drinks flavored with date syrup were examined. The objective of this work was to measure the physiochemical properties of date fruits and milk drinks flavored with date syrup, and then to evaluate the physical properties of milk drinks utilizing non-destructive visible–near-infrared spectra (VIS-NIR). The study assessed the characteristics of the milk drink enhanced with date syrup by employing VIS-NIR spectra and utilizing a partial least-square regression (PLSR) and artificial neural network (ANN) analysis. The VIS-NIR spectra proved to be highly effective in estimating the physiochemical attributes of the flavored milk drink. The ANN model outperformed the PLSR model in this context. RMSECV is considered a more reliable indicator of a model’s future predictive performance compared to RMSEC, and the R² value ranged between 0.946 and 0.989. Consequently, non-destructive VIS-NIR technology demonstrates significant promise for accurately predicting and contributing to the entire production process of the product’s properties examined. Full article

This study has designed and developed a smart data glove based on five-channel flexible capacitive stretch sensors and a six-axis inertial measurement unit (IMU) to recognize 25 static hand gestures and ten dynamic hand gestures for amphibious communication. The five-channel flexible capacitive sensors are fabricated on a glove to capture finger motion data in order to recognize static hand gestures and integrated with six-axis IMU data to recognize dynamic gestures. This study also proposes a novel amphibious hierarchical gesture recognition (AHGR) model. This model can adaptively switch between large complex and lightweight gesture recognition models based on environmental changes to ensure gesture recognition accuracy and effectiveness. The large complex model is based on the proposed SqueezeNet-BiLSTM algorithm, specially designed for the land environment, which will use all the sensory data captured from the smart data glove to recognize dynamic gestures, achieving a recognition accuracy of 98.21%. The lightweight stochastic singular value decomposition (SVD)-optimized spectral clustering gesture recognition algorithm for underwater environments that will perform direct inference on the glove-end side can reach an accuracy of 98.35%. This study also proposes a domain separation network (DSN)-based gesture recognition transfer model that ensures a 94% recognition accuracy for new users and new glove devices. Full article

Optimizing the performance of heating, ventilation, and air-conditioning (HVAC) systems is critical in today’s energy-conscious world. Fan coil units (FCUs) play a critical role in providing comfort in various environments as an important component of HVAC systems. However, FCUs often experience failures that affect their efficiency and increase their energy consumption. In this context, deep learning (DL)-based fault detection offers a promising solution. By detecting faults early and preventing system failures, the efficiency of FCUs can be improved. This paper explores DL models as fault detectors for FCUs to enable smarter and more energy-efficient hotel buildings. We tested three contemporary DL modeling approaches: convolutional neural network (CNN), long short-term memory network (LSTM), and a combination of CNN and gated recurrent unit (GRU). The random forest model (RF) was additionally developed as a baseline benchmark. The fault detectors were tested on a real-world dataset obtained from the sensory measurement system installed in a hotel and additionally supplemented with simulated data via a physical model developed in TRNSYS. Three representative FCU faults, namely, a stuck valve, a reduction in airflow, and an FCU outage, were simulated with a much larger dataset than is typically utilized in similar studies. The results showed that the hybrid model, integrating CNN and GRU, performed best for all three observed faults. DL-based fault detectors outperformed the baseline RF model, confirming these solutions as viable components for energy-efficient hotels. Full article

Ancient villages are a unique landscape of cultural heritage with both tangible and intangible culture, which provide rich ecosystem services for human beings. Assessment of society’s perceptions on cultural heritage landscapes can improve the integration of cultural heritage values into decision-making processes that affect landscapes, thereby contributing to maximizing the benefits people receive from cultural ecosystem services. Based on this premise, a new sense-based hierarchical assessment framework for a cultural landscape of ancient villages in China from the perspectives of experts and the public was developed in this study. Field research was conducted by the experts to preliminarily extract the evaluation indicators by identifying and refining the characteristics of the landscape perception units based on the classification of village’s landscape resources. The public indicators as supplements were determined by the semantic and social networks generated with ROSTCM tool post-processing, which followed crawling public comments on the tourism platforms with Python. The findings indicated that visual stimulation (57.36%) is the strongest, while touch perception is the weakest (3.56%). The proportion of hearing, smell, and taste was 21.52%, 12.05%, and 5.53%, respectively. Furthermore, people consider variety, historicity, culture, and localism as the core themes of perception in their landscape experiences. The value and usefulness of the sensory experiences for cultural landscape assessment and for decision-making in the context of cultural ecosystem services are discussed. Full article

Different types of buildings in different climate zones have their own design specifications and specific user populations. Generally speaking, these populations have similar sensory feedbacks in their perception of environmental thermal comfort. Existing thermal comfort models do not incorporate personal thermal comfort models for specific populations. In terms of an algorithm, the existing work constructs machine learning models based on an established human thermal comfort database with variables such as indoor temperature, clothing insulation, et al., and has achieved satisfactory classification results. More importantly, such thermal comfort models often lack scientific interpretability. Therefore, this study selected a specific population as the research object, adopted the 0-order Takagi–Sugeno–Kang (TSK) fuzzy classifier as the base training unit, and constructed a shared feature-guided new TSK fuzzy classification algorithm with extra feature compensation (SFG-TFC) to explore the perception features of the population in the thermal environment of buildings and to improve the classification performance and interpretability of the model. First, the shared features of subdatasets collected in different time periods were extracted. Second, the extra features of each subdataset were independently trained, and the rule outputs corresponding to the key shared features were reprojected into the corresponding fuzzy classifiers. This strategy not only highlights the guiding role of shared features but also considers the important compensation effect of extra features; thereby, improving the classification performance of the entire classification model. Finally, the least learning machine (LLM) was used to solve the parameters of the “then” part of each basic training unit, and these output weights were integrated to enhance the generalization performance of the model. The experimental results demonstrate that SFG-TFC has better classification performance and interpretability than the classic nonfuzzy algorithms support vector machine (SVM) and deep belief network (DBN), the 0-order TSK, and the multilevel optimization and fuzzy approximation algorithm QI-TSK. Full article

The developing entorhinal–hippocampal system is embedded within a large-scale bottom-up network, where spontaneous myoclonic movements, presumably via somatosensory feedback, trigger hippocampal early sharp waves (eSPWs). The hypothesis, that somatosensory feedback links myoclonic movements with eSPWs, implies that direct somatosensory stimulation should also be capable of evoking eSPWs. In this study, we examined hippocampal responses to electrical stimulation of the somatosensory periphery in urethane-anesthetized, immobilized neonatal rat pups using silicone probe recordings. We found that somatosensory stimulation in ~33% of the trials evoked local field potential (LFP) and multiple unit activity (MUA) responses identical to spontaneous eSPWs. The somatosensory-evoked eSPWs were delayed from the stimulus, on average, by 188 ms. Both spontaneous and somatosensory-evoked eSPWs (i) had similar amplitude of ~0.5 mV and half-duration of ~40 ms, (ii) had similar current-source density (CSD) profiles, with current sinks in CA1 strata radiatum, lacunosum-moleculare and DG molecular layer and (iii) were associated with MUA increase in CA1 and DG. Our results indicate that eSPWs can be triggered by direct somatosensory stimulations and support the hypothesis that sensory feedback from movements is involved in the association of eSPWs with myoclonic movements in neonatal rats. Full article

We live in the age of the 4th industrial revolution. The leading technologies of this revolution are Cloud computing, Big Data and the Internet of Things (IoT). The vast majority of IoT technologies are characterized by the fact that we collect data with the help of sensors using the Internet. The project MOVIR also implements such an IoT technology. The main goal of the project was the development of a sensor unit. Such sensor units form a network that protects a specific area. This network forms an autonomous electronic area or space protection system. To create this network, we need to define the place we want to protect and the placement of sensor units within this area. Our work is about the mathematical and digital definition of such an area and the placement of sensor units. One of our articles on air traffic control gave the idea of digital modeling the protected area. Here we define the area of interest using significant points. Points are given using GPS coordinates. With the help of a spatial coordinate system, these significant points and a projection, we define a coordinate system to define and model our protected area and the network of sensor units. Here, a digital raster terrain model where significant points are located is required as input data. The digital model of area is defined using a matrix whose elements indicate the height of points in space. The row and column indices of the matrix determine the details of the area. We can use several height layers to describe different obstacles. The accuracy of this theoretical mathematical terrain model depends on the description and details of the accuracy of the terrain. The mathematical model of the area of interest is a 3D polygon. The network of sensor units model is also a 3D polygon located within the area of interest. Full article

Neurons and glia of the peripheral nervous system are derived from progenitor cell populations, originating from embryonic neural crest. The neural crest and vasculature are intimately associated during embryonic development and in the mature central nervous system, in which they form a neurovascular unit comprised of neurons, glia, pericytes, and vascular endothelial cells that play important roles in health and disease. Our group and others have previously reported that postnatal populations of stem cells originating from glia or Schwann cells possess neural stem cell qualities, including rapid proliferation and differentiation into mature glia and neurons. Bone marrow receives sensory and sympathetic innervation from the peripheral nervous system and is known to contain myelinating and unmyelinating Schwann cells. Herein, we describe a population of neural crest-derived Schwann cells residing in a neurovascular niche of bone marrow in association with nerve fibers. These Schwann cells can be isolated and expanded. They demonstrate plasticity in vitro, generating neural stem cells that exhibit neurogenic potential and form neural networks within the enteric nervous system in vivo following transplantation to the intestine. These cells represent a novel source of autologous neural stem cells for the treatment of neurointestinal disorders. Full article

With the rapid development of artificial intelligence and the Internet of Things, there is an explosion of available data for processing and analysis in any domain. However, signal processing efficiency is limited by the Von Neumann structure for the conventional computing system. Therefore, the design and construction of artificial synapse, which is the basic unit for the hardware-based neural network, by mimicking the structure and working mechanisms of biological synapses, have attracted a great amount of attention to overcome this limitation. In addition, a revolution in healthcare monitoring, neuro-prosthetics, and human–machine interfaces can be further realized with a flexible device integrating sensing, memory, and processing functions by emulating the bionic sensory and perceptual functions of neural systems. Until now, flexible artificial synapses and related neuromorphic systems, which are capable of responding to external environmental stimuli and processing signals efficiently, have been extensively studied from material-selection, structure-design, and system-integration perspectives. Moreover, low-dimensional materials, which show distinct electrical properties and excellent mechanical properties, have been extensively employed in the fabrication of flexible electronics. In this review, recent progress in flexible artificial synapses and neuromorphic systems based on low-dimensional materials is discussed. The potential and the challenges of the devices and systems in the application of neuromorphic computing and sensory systems are also explored. Full article

Caenorhabditis elegans (C. elegans) exhibits sophisticated chemotaxis behavior with a unique locomotion pattern using a simple nervous system only and is, therefore, well suited to inspire simple, cost-effective robotic navigation schemes. Chemotaxis in C. elegans involves two complementary strategies: klinokinesis, which allows reorientation by sharp turns when moving away from targets; and klinotaxis, which gradually adjusts the direction of motion toward the preferred side throughout the movement. In this study, we developed an autonomous search model with undulatory locomotion that combines these two C. elegans chemotaxis strategies with its body undulatory locomotion. To search for peaks in environmental variables such as chemical concentrations and radiation in directions close to the steepest gradients, only one sensor is needed. To develop our model, we first evolved a central pattern generator and designed a minimal network unit with proprioceptive feedback to encode and propagate rhythmic signals; hence, we realized realistic undulatory locomotion. We then constructed adaptive sensory neuron models following real electrophysiological characteristics and incorporated a state-dependent gating mechanism, enabling the model to execute the two orientation strategies simultaneously according to information from a single sensor. Simulation results verified the effectiveness, superiority, and realness of the model. Our simply structured model exploits multiple biological mechanisms to search for the shortest-path concentration peak over a wide range of gradients and can serve as a theoretical prototype for worm-like navigation robots. Full article

Nowadays, one of the most important objectives in health research is the improvement of the living conditions and well-being of the elderly, especially those who live alone. These people may experience undesired or dangerous situations in their daily life at home due to physical, sensorial or cognitive limitations, such as forgetting their medication or wrong eating habits. This work focuses on the development of a database in a home, through non-intrusive technology, where several users are residing by combining: a set of non-intrusive sensors which captures events that occur in the house, a positioning system through triangulation using beacons and a system for monitoring the user’s state through activity wristbands. Two months of uninterrupted measurements were obtained on the daily habits of 2 people who live with a pet and receive sporadic visits, in which 18 different types of activities were labelled. In order to validate the data, a system for the real-time recognition of the activities carried out by these residents was developed using different current Deep Learning (DL) techniques based on neural networks, such as Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM) or Gated Recurrent Unit networks (GRU). A personalised prediction model was developed for each user, resulting in hit rates ranging from 88.29% to 90.91%. Finally, a data sharing algorithm has been developed to improve the generalisability of the model and to avoid overtraining the neural network. Full article