STIRLING ENGINE: FROM DESIGN TO APPLICATION INTO PRACTICE AND EDUCATION

Stirling motor is a type of outside ignition heat motor that can utilize various fuel sources from customary structures (coal, oil, kindling, rice husk, and so forth) to sustainable power sources (sun-oriented energy), climate, squander heat usage, and so forth). The article centers around introducing the fundamental highlights of the improvement history, activity qualities, and plan techniques for certain sorts of Stirling motors, in this way offering useful appropriateness as well as a college preparing for understudies. The understudy studying Thermal Engineering in our nation today.  

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573

Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms

The early detection and diagnosis of breast cancer may increase survival rates and reduce overall treatment costs. The cancer of the breast is a severe and potentially fatal disease that impacts individuals worldwide. Mammography is a widely utilized imaging technique for breast cancer surveillance and diagnosis. However, images produced with mammography frequently contain noise, poor contrast, and other anomalies that hinder radiologists from interpreting the images. This study develops a novel deep-learning technique for breast cancer detection using mammography images. The proposed procedure consists of two primary steps: region-of-interest (ROI) (1) extraction and (2) classification. At the beginning of the procedure, a YOLOX model is utilized to distinguish breast tissue from the background and to identify ROIs that may contain lesions. In the second phase, the EfficientNet or ConvNeXt model is applied to the data to identify benign or malignant ROIs. The proposed technique is validated using a large dataset of mammography images from various institutions and compared to several baseline methods. The pF1 index is used to measure the effectiveness of the technique, which aims to establish a balance between the number of false positives and false negatives, and is a harmonic mean of accuracy and recall. The proposed method outperformed existing methods by an average of 8.0%, obtaining superior levels of precision and sensitivity, and area under the receiver operating characteristics curve (ROC AUC) and the precision–recall curve (PR AUC). In addition, ablation research was conducted to investigate the effects of the procedure’s numerous components. According to the findings, the proposed technique is another choice that could enhance the detection and diagnosis of breast cancer using mammography images

Improvement of the Performance of Scattering Suppression and Absorbing Structure Depth Estimation on Transillumination Image by Deep Learning

The development of optical sensors, especially with regard to the improved resolution of cameras, has made optical techniques more applicable in medicine and live animal research. Research efforts focus on image signal acquisition, scattering de-blur for acquired images, and the development of image reconstruction algorithms. Rapidly evolving artificial intelligence has enabled the development of techniques for de-blurring and estimating the depth of light-absorbing structures in biological tissues. Although the feasibility of applying deep learning to overcome these problems has been demonstrated in previous studies, limitations still exist in terms of de-blurring capabilities on complex structures and the heterogeneity of turbid medium, as well as the limit of accurate estimation of the depth of absorptive structures in biological tissues (shallower than 15.0 mm). These problems are related to the absorption structure’s complexity, the biological tissue’s heterogeneity, the training data, and the neural network model itself. This study thoroughly explores how to generate training and testing datasets on different deep learning models to find the model with the best performance. The results of the de-blurred image show that the Attention Res-UNet model has the best de-blurring ability, with a correlation of more than 89% between the de-blurred image and the original structure image. This result comes from adding the Attention gate and the Residual block to the common U-net model structure. The results of the depth estimation show that the DenseNet169 model shows the ability to estimate depth with high accuracy beyond the limit of 20.0 mm. The results of this study once again confirm the feasibility of applying deep learning in transmission image processing to reconstruct clear images and obtain information on the absorbing structure inside biological tissue. This allows the development of subsequent transillumination imaging studies in biological tissues with greater heterogeneity and structural complexity

Improvement of Ultrasound-Based Localization System Using Sine Wave Detector and CAN Network

This paper presents an improved indoor localization system based on radio frequency (RF) and ultrasonic signals, which we named the SNSH system. This system is composed of a transmitter mounted in a mobile target and a series of receiver nodes that are managed by a coordinator. By measuring the Time Delay of Arrival (TDoA) of RF and ultrasonic signals from the transmitter, the distance from the target to each receiver node is calculated and sent to the coordinator through the CAN network, then all the information is gathered in a PC to estimate the 3D position of the target. A sine wave detector and dynamic threshold filter are applied to provide excellent accuracy in measuring the range from the TDoA results before multilateration algorithms are realized to optimize the accuracy of coordinate determination. Specifically, Linear Least Square (LLS) and Nonlinear Least Square (NLS) techniques are implemented to contrast their performances in target coordinate estimation. RF signal encoding/decoding time, time delay in CAN network and math calculation time are carefully considered to ensure optimal system performance and prepare for field application. Experiments show that the sine wave detector algorithm has greatly improved the accuracy of range measurement, with a mean error of 2.2 mm and maximum error of 6.7 mm for distances below 5 m. In addition, 3D position accuracy is greatly enhanced by multilateration methods, with the mean error in position remaining under 15 mm. Furthermore, there are 90% confidence error values of 23 mm for LLS and 20 mm for NLS. The update in the overall system has been verified in real system operations, with a maximum rate of 25 ms, which is a better result than many other existing studies

Simulating Mangroves Rehabilitation with Cellular Automata

International audienc

BPH Sensor Network Optimization Based on Cellular Automata and Honeycomb Structure

International audienc

A label-free colorimetric sensor based on silver nanoparticles directed to hydrogen peroxide and glucose

A simple method has been developed for preparation of silver nanoparticles (AgNPs) based on the use of graphene quantum dots (GQDs) as a reducing agent and a stabilizer. The synthesized nanocomposites consisting of silver nanoparticles and graphene quantum dots (AgNPs/GQDs) has been characterized by X-ray diffraction (XRD), Transmission Electron Microscopy (TEM), Ultraviolet–visible spectroscopy (UV–Vis), Fourier-Transform Infrared spectroscopy (FT-IR), Energy Dispersive X-ray spectroscopy (EDX) and Dynamic Light Scattering (DLS). Results indicate that monodisperse of AgNPs has been obtained with particles size ca. ∼ 40 nm and specific plasmon peak of silver nanoparticles at 425 nm by UV–Vis spectrum. Using AgNPs/GQDs nanocomposite, we have constructed a colorimetric sensor for hydrogen peroxide (H2O2) and glucose sensors based on the use of AgNPs/GQDs as both probes: capture probe and signal probe. The fabricated sensors perform good sensitivity and selectivity with a low detection limit of 162 nM and 30 μM for H2O2 and glucose sensing, respectively. Moreover, the biosensors have been successfully applied to detect glucose concentrations in human urine. Keywords: Graphene quantum dots, Silver nanoparticles, Hydrogen peroxide (H2O2), Glucose detection, Human urine, Colorimetric senso

Simulating Mangroves Rehabilitation with Cellular Automata(to appear in ACM (ISBN: 978-1-4503-7631-0)

International audienc