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In recent years, embedded system technologies and products for sensor networks and wearable devices used for monitoring people's activities and health have become the focus of the global IT industry. In order to enhance the speech recognition capabilities of wearable devices, this article discusses the implementation of audio positioning and enhancement in embedded systems using embedded algorithms for direction detection and mixed source separation. The two algorithms are implemented using different embedded systems: direction detection developed using TI TMS320C6713 DSK and mixed source separation developed using Raspberry Pi 2. For mixed source separation, in the first experiment, the average signal-to-interference ratio (SIR) at 1 m and 2 m distances was 16.72 and 15.76, respectively. In the second experiment, when evaluated using speech recognition, the algorithm improved speech recognition accuracy to 95%.
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Algoritmos , Dispositivos Electrónicos Vestibles , Humanos , Procesamiento de Señales Asistido por Computador , Localización de SonidosRESUMEN
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.
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We report a theoretical investigation of X-ray back diffraction at grazing incidence. Based on the framework of the dynamical theory of X-ray diffraction, the grazing incidence for Si (12 4 0) back diffraction is taken as an example to resolve the eigenvalue problem inside the crystal. The dispersion surface and the resulting diffraction intensities are strongly affected by the miscut angle as well as the diffraction geometry of grazing incidence. The kinematical relationship between the incident angle and the miscut angle is well explained by Snell's law. While only the two-beam diffraction is considered, our treatment can be further extended to include the cases for multiple diffractions as well.
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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.
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Global precipitation is becoming increasingly intense due to the extreme climate. Therefore, creating new technology to manage water resources is crucial. To create a sustainable urban and ecological environment, a water level and water quality control system implementing artificial intelligence is presented in this research. The proposed smart monitoring system consists of four sensors (two different liquid level sensors, a turbidity and pH sensor, and a water oxygen sensor), a control module (an MCU, a motor, a pump, and a drain), and a power and communication system (a solar panel, a battery, and a wireless communication module). The system focuses on low-cost Internet of Things (IoT) devices along with low power consumption and high precision. This proposal collects rainfall from the preceding 10 years in the application region as well as the region's meteorological bureau's weekly weather report and uses artificial intelligence to compute the appropriate water level. More importantly, the adoption of dynamic adjustment systems can reserve and modify water resources in the application region more efficiently. Compared to existing technologies, the measurement approach utilized in this study not only achieves cost savings exceeding 60% but also enhances water level measurement accuracy by over 15% through the successful implementation of water level calibration decisions utilizing multiple distinct sensors. Of greater significance, the dynamic adjustment systems proposed in this research offer the potential for conserving water resources by more than 15% in an effective manner. As a result, the adoption of this technology may efficiently reserve and distribute water resources for smart cities as well as reduce substantial losses caused by anomalous water resources, such as floods, droughts, and ecological concerns.
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Backlight power-saving algorithms can reduce the power consumption of the display by adjusting the frame pixels with optimal clipping points under some tradeoff criteria. However, the computation for the selected clipping points can be complex. In this paper, a novel algorithm is created to reduce the computation time of the state-of-the-art backlight power-saving algorithms. If the current frame is similar to the previous frame, it is unnecessary to execute the backlight power-saving algorithm for the optimal clipping points, and the derived clipping point from the previous frame can be used for the current frame automatically. In this paper, the motion vector information was used as the measurement of the similarity between adjacent frames, where the generation of the motion vector information requires no extra complexity since it is generated to reconstruct the decoded frame pixels before the display. The experiments showed that the proposed work can reduce the running time of the state-of-the-art methods by 25.21% to 64.22%, while the performances are maintained; the differences with the state-of-the-art methods in PSNR are only 0.02~1.91 dB, and those in power are only -0.001~0.008 W.
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An integrated optical chip that minimizes the size of the energy-tuning single-resonance-mode x-ray monochromator system into a 3cm×5cm silicon wafer is proposed. A Fabry-Perot x-ray resonator and two back-reflecting Si mirrors are employed on the wafer as the optical components, where Si(12 4 0) back reflection is used for both Fabry-Perot resonance and re-diffraction of the x-ray beams from the resonator in the incident direction. We can achieve an energy bandwidth of 3.4 meV in single-mode x rays and tune the energy by temperature variation. Such Si chips can be readily employed at the synchrotron beamlines and conventional x-ray laboratories for high-resolution investigations.
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In this paper, a novel self-optimizing water level monitoring methodology is proposed for smart city applications. Considering system maintenance, the efficiency of power consumption and accuracy will be important for Internet of Things (IoT) devices and systems. A multi-step measurement mechanism and power self-charging process are proposed in this study for improving the efficiency of a device for water level monitoring applications. The proposed methodology improved accuracy by 0.16-0.39% by moving the sensor to estimate the distance relative to different locations. Additional power is generated by executing a multi-step measurement while the power self-optimizing process used dynamically adjusts the settings to balance the current of charging and discharging. The battery level can efficiently go over 50% in a stable charging simulation. These methodologies were successfully implemented using an embedded control device, an ultrasonic sensor module, a LORA transmission module, and a stepper motor. According to the experimental results, the proposed multi-step methodology has the benefits of high accuracy and efficient power consumption for water level monitoring applications.
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Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.
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Algoritmos , Infarto del Miocardio , Diagnóstico por Computador , Electrocardiografía , Humanos , Infarto del Miocardio/diagnóstico , Redes Neurales de la ComputaciónRESUMEN
Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorithms are usually designed for the diagnosis (i.e., feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper, we utilized the MIT-BIH AF Database (AFDB), which is composed of data from normal people and patients with AF and onset characteristics, and the AFPDB database (i.e., PAF Prediction Challenge Database), which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF), and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction, we regarded diagnosis and prediction as two classification problems, adopted the traditional support vector machine (SVM) algorithm, and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process, the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases, the sensitivity, specificity, and accuracy measures were 99.2% and 99.2%, 99.2% and 93.3%, and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases, respectively. Moreover, the sensitivity, specificity, and accuracy were 94.2%, 79.7%, and 87.0%, respectively, when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels.
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Fibrilación Atrial , Máquina de Vectores de Soporte , Algoritmos , Inteligencia Artificial , Fibrilación Atrial/diagnóstico , Electrocardiografía , HumanosRESUMEN
Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu's threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.
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Caries Dental , Diente , Inteligencia Artificial , Caries Dental/diagnóstico por imagen , Susceptibilidad a Caries Dentarias , Humanos , Aprendizaje Automático , Redes Neurales de la ComputaciónRESUMEN
Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.
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Redes Neurales de la Computación , Diente , Humanos , Radiografía , Diente/diagnóstico por imagenRESUMEN
Wireless Sensor Networks (WSNs) had been applied in Internet of Things (IoT) and in Industry 4.0. Since a WSN system contains multiple wireless sensor nodes, it is necessary to develop a low-power and multiband wireless communication system that satisfies the specifications of the Federal Communications Commission (FCC) and the Certification European (CE). In a WSN system, many devices are of very small size and can be slipped into a Universal Serial Bus (USB), which is capable of connecting to wireless systems and networks, as well as transferring data. These devices are widely known as USB dongles. This paper develops a planar USB dongle antenna for three frequency bands, namely 2.30-2.69 GHz, 3.40-3.70 GHz, and 5.15-5.85 GHz. This study proposes a novel antenna design that uses four loops to develop the multiband USB dongle. The first and second loops construct the low and intermediate frequency ranges. The third loop resonates the high frequency property, while the fourth loop is used to enhance the bandwidth. The performance and power consumption of the proposed multiband planar USB dongle antenna were significantly improved compared to existing multiband designs.
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This study presents a low-power multi-lead wearable electrocardiogram (ECG) signal sensor system design that can simultaneously acquire the electrocardiograms from three leads, I, II, and V1. The sensor system includes two parts, an ECG test clothing with five electrode patches and an acquisition device. Compared with the traditional 12-lead wired ECG detection instrument, which limits patient mobility and needs medical staff assistance to acquire the ECG signal, the proposed vest-type ECG acquisition system is very comfortable and easy to use by patients themselves anytime and anywhere, especially for the elderly. The proposed study incorporates three methods to reduce the power consumption of the system by optimizing the micro control unit (MCU) working mode, adjusting the radio frequency (RF) parameters, and compressing the transmitted data. In addition, Huffman lossless coding is used to compress the transmitted data in order to increase the sampling rate of the acquisition system. It makes the whole system operate continuously for a long period of time and acquire abundant ECG information, which is helpful for clinical diagnosis. Finally, a series of tests were performed on the designed wearable ECG device. The results have demonstrated that the multi-lead wearable ECG device can collect, process, and transmit ECG data through Bluetooth technology. The ECG waveforms collected by the device are clear, complete, and can be displayed in real-time on a mobile phone. The sampling rate of the proposed wearable sensor system is 250 Hz per lead, which is dependent on the lossless compression scheme. The device achieves a compression ratio of 2.31. By implementing a low power design on the device, the resulting overall operational current of the device is reduced by 37.6% to 9.87 mA under a supply voltage of 2.1 V. The proposed vest-type multi-lead ECG acquisition device can be easily employed by medical staff for clinical diagnosis and is a suitable wearable device in monitoring and nursing the off-ward patients.
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Electrocardiografía Ambulatoria , Electrocardiografía/instrumentación , Monitoreo Fisiológico/métodos , Dispositivos Electrónicos Vestibles , Teléfono Celular , Humanos , Monitoreo Fisiológico/instrumentación , Procesamiento de Señales Asistido por ComputadorRESUMEN
A 16-channel front-end readout chip for a radiation detector is designed for portable or wearable healthcare monitoring applications. The proposed chip reads the signal of the radiation detector and converts it into digital serial-out data by using a nonbinary successive approximation register (SAR) analog-to-digital converter (ADC) that has a 1-MS/s sampling rate and 10-b resolution. The minimum-to-maximum differential and integral nonlinearity are measured as -0.32 to 0.33 and -0.43 to 0.37 least significant bits, respectively. The signal-to-noise-and-distortion ratio and effective number of bits are 57.41 dB and 9.24 bits, respectively, for an input frequency of 500 kHz and a sampling rate of 1 MS/s. The SAR ADC has a 38.9-fJ/conversion step figure of merit at the sampling rate of 1 MS/s. The proposed chip can read input signals with peak currents ranging from 20 to 750 µA and convert the analog signal into a 10-bit serial-output digital signal. The input dynamic range is 2-75 pC. The resolution of the peak current is 208.3 nA. The chip, which has an area of 1.444 mm × 10.568 mm, is implemented using CMOS 0.18-µm 1P6M technology, and the power consumption of each channel is 19 mW. This design is suitable for wearable devices, especially biomedical devices.
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In the field of dentistry, the presence of dental calculus is a commonly encountered issue. If not addressed promptly, it has the potential to lead to gum inflammation and eventual tooth loss. Bitewing (BW) images play a crucial role by providing a comprehensive visual representation of the tooth structure, allowing dentists to examine hard-to-reach areas with precision during clinical assessments. This visual aid significantly aids in the early detection of calculus, facilitating timely interventions and improving overall outcomes for patients. This study introduces a system designed for the detection of dental calculus in BW images, leveraging the power of YOLOv8 to identify individual teeth accurately. This system boasts an impressive precision rate of 97.48%, a recall (sensitivity) of 96.81%, and a specificity rate of 98.25%. Furthermore, this study introduces a novel approach to enhancing interdental edges through an advanced image-enhancement algorithm. This algorithm combines the use of a median filter and bilateral filter to refine the accuracy of convolutional neural networks in classifying dental calculus. Before image enhancement, the accuracy achieved using GoogLeNet stands at 75.00%, which significantly improves to 96.11% post-enhancement. These results hold the potential for streamlining dental consultations, enhancing the overall efficiency of dental services.
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The severity of periodontitis can be analyzed by calculating the loss of alveolar crest (ALC) level and the level of bone loss between the tooth's bone and the cemento-enamel junction (CEJ). However, dentists need to manually mark symptoms on periapical radiographs (PAs) to assess bone loss, a process that is both time-consuming and prone to errors. This study proposes the following new method that contributes to the evaluation of disease and reduces errors. Firstly, innovative periodontitis image enhancement methods are employed to improve PA image quality. Subsequently, single teeth can be accurately extracted from PA images by object detection with a maximum accuracy of 97.01%. An instance segmentation developed in this study accurately extracts regions of interest, enabling the generation of masks for tooth bone and tooth crown with accuracies of 93.48% and 96.95%. Finally, a novel detection algorithm is proposed to automatically mark the CEJ and ALC of symptomatic teeth, facilitating faster accurate assessment of bone loss severity by dentists. The PA image database used in this study, with the IRB number 02002030B0 provided by Chang Gung Medical Center, Taiwan, significantly reduces the time required for dental diagnosis and enhances healthcare quality through the techniques developed in this research.
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To study interactions between host figs and their pollinating wasps and the influence of climatic change on their genetic structures, we sequenced cytoplasmic and nuclear genes and genotyped nuclear microsatellite loci from two varieties of Ficus pumila, the widespread creeping fig and endemic jelly fig, and from their pollinating wasps, Wiebesia pumilae, found in Taiwan and on nearby offshore islands. Great divergence in the mitochondrial cytochrome c oxidase subunit I (mtCOI) with no genetic admixture in nuclear markers indicated that creeping- and jelly-fig wasps are genetically distinct. Compared with creeping-fig wasps, jelly-fig wasps also showed better resistance under cold (20 °C) than warm (25 and 30 °C) conditions in a survival test, indicating their adaptation to a cold environment, which may have facilitated population expansion during the ice age as shown by a nuclear intron and 10 microsatellite loci. An excess of amino acid divergence and a pattern of too many rare mtCOI variants of jelly-fig wasps as revealed by computer simulations and neutrality tests implied the effect of positive selection, which we hypothesize was associated with the cold-adaptation process. Chloroplast DNA of the two fig plants was completely segregated, with signs of genetic admixture in nuclear markers. As creeping- and jelly-fig wasps can pollinate creeping figs, occasional gene flow between the two figs is thus possible. Therefore, it is suggested that pollinating wasps may be playing an active role in driving introgression between different types of host fig.
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Adaptación Fisiológica/genética , ADN de Cloroplastos/genética , ADN Mitocondrial/genética , Ficus , Avispas , Animales , Secuencia de Bases , Ficus/genética , Ficus/fisiología , Flujo Génico , Genética de Población , Repeticiones de Microsatélite/genética , Datos de Secuencia Molecular , Filogenia , Polinización , Taiwán , Avispas/genética , Avispas/fisiologíaRESUMEN
Magnetic nanoparticles (MNPs) have been widely utilized in the biomedical field for numerous years, offering several advantages such as exceptional biocompatibility and diverse applications in biology. However, the existing methods for quantifying magnetic labeled sample assays are scarce. This research presents a novel approach by developing a microfluidic chip system embedded with a giant magnetoresistance (GMR) sensor. The system successfully detects low concentrations of MNPs with magnetic particle velocities of 20 mm/s. The stray field generated by the magnetic subject flowing through the microchannel above the GMR sensor causes variations in the signals. The sensor's output signals are appropriately amplified, filtered, and processed to provide valuable indications. The integration of the GMR microfluidic chip system demonstrates notable attributes, including affordability, speed, and user-friendly operation. Moreover, it exhibits a high detection sensitivity of 10 µg/µL for MNPs, achieved through optimizing the vertical magnetic field to 100 Oe and the horizontal magnetic field to 2 Oe. Additionally, the study examines magnetic labeled RAW264.7 cells. This quantitative detection of magnetic nanoparticles can have applications in DNA concentration detection, protein concentration detection, and other promising areas of research.
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Nanopartículas de Magnetita , Microfluídica , Bioensayo , Campos MagnéticosRESUMEN
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.