An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System.

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle's onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle's onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems.

Effects of hydroxy methionine zinc on growth performance, immune response, antioxidant capacity, and intestinal microbiota of red claw crayfish (Procambarus clarkii).

This study aimed to evaluate the effects of varying zinc (Zn) levels on the growth performance, non-specific immune response, antioxidant capacity, and intestinal microbiota of red claw crayfish (Procambarus clarkii (P. clarkii)). Adopting hydroxy methionine zinc (Zn-MHA) as the Zn source, 180 healthy crayfish with an initial body mass of 6.50 ± 0.05 g were randomly divided into the following five groups: X1 (control group) and groups X2, X3, X4, and X5, which were fed the basal feed supplemented with Zn-MHA with 0, 15, 30, 60, and 90 mg kg-1, respectively. The results indicated that following the addition of various concentrations of Zn-MHA to the diet, the following was observed: Specific growth rate (SGR), weight gain rate (WGR), total protein (TP), total cholesterol (TC), the activities of alkaline phosphatase (AKP), phenoloxidase (PO), total antioxidant capacity (T-AOC), total superoxide dismutase (T-SOD) and catalase (CAT), the expression of CTL, GPX, and CuZn-SOD genes demonstrated a trend of rising and then declining-with a maximum value in group X4-which was significantly higher than that in group X1 (P < 0.05). Zn deposition in the intestine and hepatopancreas, the activity of GSH-PX, and the expression of GSH-PX were increased, exhibiting the highest value in group X5. The malonaldehyde (MDA) content was significantly reduced, with the lowest value in group X4, and the MDA content of the Zn-MHA addition groups were significantly lower than the control group (P < 0.05). In the analysis of the intestinal microbiota of P. clarkii, the number of operational taxonomic units in group X4 was the highest, and the richness and diversity indexes of groups X3 and X4 were significantly higher than those in group X1 (P < 0.05). Meanwhile, the dietary addition of Zn-MHA decreased and increased the relative abundance of Proteobacteria and Tenericutes, respectively. These findings indicate that supplementation of dietary Zn-MHA at an optimum dose of 60 mg kg-1 may effectively improve growth performance, immune response, antioxidant capacity, and intestinal microbiota richness and species diversity in crayfish.

Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs.

Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes.

Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film.

As the popularity of dental implants continues to grow at a rate of about 14% per year, so do the risks associated with the procedure. Complications such as sinusitis and nerve damage are not uncommon, and inadequate cleaning can lead to peri-implantitis around the implant, jeopardizing its stability and potentially necessitating retreatment. To address this issue, this research proposes a new system for evaluating the degree of periodontal damage around implants using Periapical film (PA). The system utilizes two Convolutional Neural Networks (CNN) models to accurately detect the location of the implant and assess the extent of damage caused by peri-implantitis. One of the CNN models is designed to determine the location of the implant in the PA with an accuracy of up to 89.31%, while the other model is responsible for assessing the degree of Peri-implantitis damage around the implant, achieving an accuracy of 90.45%. The system combines image cropping based on position information obtained from the first CNN with image enhancement techniques such as Histogram Equalization and Adaptive Histogram Equalization (AHE) to improve the visibility of the implant and gums. The result is a more accurate assessment of whether peri-implantitis has eroded to the first thread, a critical indicator of implant stability. To ensure the ethical and regulatory standards of our research, this proposal has been certified by the Institutional Review Board (IRB) under number 202102023B0C503. With no existing technology to evaluate Peri-implantitis damage around dental implants, this CNN-based system has the potential to revolutionize implant dentistry and improve patient outcomes.

VLSI Design Based on Block Truncation Coding for Real-Time Color Image Compression for IoT.

It has always been a major issue for a hospital to acquire real-time information about a patient in emergency situations. Because of this, this research presents a novel high-compression-ratio and real-time-process image compression very-large-scale integration (VLSI) design for image sensors in the Internet of Things (IoT). The design consists of a YEF transform, color sampling, block truncation coding (BTC), threshold optimization, sub-sampling, prediction, quantization, and Golomb-Rice coding. By using machine learning, different BTC parameters are trained to achieve the optimal solution given the parameters. Two optimal reconstruction values and bitmaps for each 4 × 4 block are achieved. An image is divided into 4 × 4 blocks by BTC for numerical conversion and removing inter-pixel redundancy. The sub-sampling, prediction, and quantization steps are performed to reduce redundant information. Finally, the value with a high probability will be coded using Golomb-Rice coding. The proposed algorithm has a higher compression ratio than traditional BTC-based image compression algorithms. Moreover, this research also proposes a real-time image compression chip design based on low-complexity and pipelined architecture by using TSMC 0.18 µm CMOS technology. The operating frequency of the chip can achieve 100 MHz. The core area and the number of logic gates are 598,880 µm2 and 56.3 K, respectively. In addition, this design achieves 50 frames per second, which is suitable for real-time CMOS image sensor compression.

Probing the outfall-related anomalous Hg levels in the Danshuei Estuarine Coastal, Taiwan.

Seasonal and spatial distributions of total mercury (THg) in the Danshuei Estuary and adjacent coastal areas near the ocean outfall of Taipei, Taiwan, have been successfully investigated from May 2003 to January 2005. We found spatio-temporal variation in THg levels in the Danshuei coastal area was the result of sources and supplies of Hg. High THg concentrations in sediments and seawater were particularly found near the effluent outfall. Average THg levels (avg.: 9-22 ng L-1) were much higher than those in surrounding coastal seawaters (avg.:1-2 ng L-1). Organic carbon contents then played vital roles in controlling water and sedimentary Hg concentrations and occurrences. Hg enrichment factor (EF) as an index of contamination status in surface sediments of the Danshuei coastal area averaged 2.0 ± 0.8 (EFs > 1), indicating an extra non-crustal source from anthropogenic loadings. It implies the Dansheui coastal environment nearby the sewer outfall is facing Hg pollution.