Earticle

To solve the spectrum scarcity problem, dynamic spectrum access (DSA) technology has emerged as a promising solution. Effectively implementing DSA demands accurate and efficient spectrum prediction. However, complex spatiotemporal correlation and heterogeneity in spectrum observations usually make spectral prediction arduous and even ambiguous. In this letter, we propose a spectrum prediction method based on an attention-aided graph convolutional neural network (AttGCN) to capture features in both spatial and temporal dimensions. By leveraging the attention mechanism, the AttGCN adapts its attention weights at different time steps and spatial positions, thus enabling itself to seize changes in spatiotemporal correlations dynamically. Simulation results show that the proposed spectrum prediction method performs better than baseline algorithms in long-term forecasting tasks.

Diabetic Foot Ulcer (DFU) is a complication of diabetes that causes lower limb amputation. In this work, a unique stacked parallel convolution layers-based network (DFU_SPNet) is proposed to perform DFU vs. normal skin classification. The main objective of this work is to design an effective CNN-based classification model, along with proper fine-tuning of optimizer settings. DFU_SPNet consists of 3 blocks of parallel convolution layers with multiple kernel sizes, for local and global feature abstractions. The proposed DFU_SPNet, trained using SGD (with momentum) optimizer with learning rate on the DFUNet dataset, outperformed the current state-of-the-art results with an AUC of 0.974.

In general, it is not a simple task to predict sequences or classify images, and it is even more problematic when both are combined. Nevertheless, biological systems can easily predict sequences and are good at image recognition. For these reasons Long?Short Term Memory and Convolutional Neural Networks were created and were based on the memory and visual systems. These algorithms have shown great properties and shown certain resemblance, yet they are still not the same as their biological counterpart. This article reviews the biological bases and compares them.

Due to the influence of noise in the received signal in non-cooperative communication, it is difficult for existing Automatic modulation classification methods to balance classification accuracy and model complexity. This paper proposes a novel Convolutional Adaptive Noise Reduction (CANR) network, which consists of an Adaptive Noise Reduction (ANR) module and a Convolutional Feature Extraction (CFE) module. The ANR and CFE modules denoise the combined input and capture the spatiotemporal features in the time series. Experiments on benchmark datasets show that the proposed network has the fewest training parameters and state-of-the-art recognition accuracy under the same conditions.

Retinal tissue plays a crucial part in human vision. Infections of retinal tissue and delayed treatment or untreated infection could lead to loss of vision. Additionally, the diagnosis is prone to errors when huge dataset is involved. Therefore, a fully automated model of identification of retinal disease is proposed to reduce human interaction while retaining its high accuracy classification results. This paper introduces an enhanced design of a fully automatic multi-class retina diseases prediction system to assist ophthalmologists in making speedy and accurate investigation. Retinal fundus images, which have been used in this study, were downloaded from the stare website (157 images from five classes: BDR, CRVO, CNV, PDR, and Normal). The five files were categorized according to their annotations conducted by the experienced specialists. The categorized images were first processed with the proposed upgraded contrast-limited adaptive histogram filter for image brightness enhancement, noise reduction, and intensity spectrum normalization. The proposed model was designed with transfer learning method and the fine-tuned pre-trained RESNET50. Eventually, the proposed framework was examined with performance evaluation parameters, recorded a classification rate with 100% sensitivity, 100% specificity, and 100% accuracy. The performance of the proposed model showed a magnificent superiority as compared to the state-of-the-art studies.

Synthetic aperture radar (SAR) is a crucial active imaging technology in remote sensing, offering valuable information for applications like climate monitoring, environmental analysis, and ship surveillance. Ship detection in SAR images remains challenging due to diverse vessel types and environmental interference, especially in inshore areas, despite the proven effectiveness of deep learning-based algorithms. This paper presents an efficient deep learning method named you only look once-shuffle reparameterized blocks with dynamic head (YOLO-SRBD) based on YOLOv8. Additionally, post-processing incorporates the soft non-maximum suppression to enhance precision. Experiments conducted on SAR image datasets demonstrate that the proposed method surpasses the original YOLOv8 both qualitatively and quantitatively, highlighting its feasibility for practical applications. The detection accuracy of the proposed YOLO-SRBD in the high resolution SAR images dataset rose from 89.9% to 91.3%, and the average precision increased from 66.7% to 74.3%, showing significant performance enhancement.

Our objective was to evaluate the CT attenuation coefficient and noise of spatial domain filtering as an alternative to additional image reconstruction using different kernels in abdominal CT. Derived from thin collimated source images was generated using abdomen BIO (very smooth), B20 (smooth), B30 (medium smooth), B40 (medium), B50 (medium sharp), B60 (sharp), B70 (very sharp) and B80 (ultra sharp) kernels. Quantitative CT coefficient and noise measurements provided comparable HU (hounsfield) units in this respect. CT attenuation coefficient (mean HU) values in the abdominal were 60.4 〜 62.2 HU and noise (7.6 〜 63.8 HU) in the liver parenchyma. In the stomach a mean (CT attenuation coefficient) of -2.2 - 0.8 HU and noise (10.1 〜 82.4 HU) was measured. Image reconstructed with a convolution kernel led to an increase in noise, whereas the results for CT attenuation coefficient were comparable. Image medications of image sharpness and noise eliminate the need for reconstruction using different kernels in the future. CT images increase the diagnostic accuracy may be controlled by adjusting CT various kernels, which should be adjusted to take into account the kernels of the CT undeigoing the examination.

주택가격 예측은 개인, 기관, 정부에 이르기까지 다양한 이해관계자들이 관심을 갖는 주요한 주 제이다. 지금까지 통계적 모델을 통해 예측을 해왔지만, 최근 딥러닝 및 머신러닝을 이용하여 예측 하는 연구가 많다. 특히 이미지 정보를 활용한 합성곱신경망(Convolutional Neural Networks)을 적 용한 연구도 진행되고 있다. 그러나 아직 국내에서는 이미지 정보를 이용한 연구는 살펴볼 수 없었 다. 본 연구는 커널밀도추정(Kernel Density Estimation)을 활용하여 인공위성 이미지와 상가 및 편 의시설 밀도 정보를 해당 구역에 표시하고 이를 입력변수로 활용하여 서울시 아파트 가격을 예측하 고자 한다. 본 논문에서는 2021년부터 2022년까지의 주택 거래 기록과 지역소득정보, 인구 밀도, 연령대별 인구 데이터를 수집하였고, 네이버 클라우드 플랫폼의 Static Map API를 통해 인공위성사진을 구역 별로 나누어서 수집했다. 특히 공공데이터포털과 서울 열린데이터 광장에서 수집한 상가, 병원, 공 원, 지하철역, 학교 등 정보는 커널밀도추정을 활용해 밀도 정보로 변환하였다. 이렇게 다양한 형태 로 수집된 데이터는 전처리 과정을 거쳐 면적당 단가로 계산된 아파트 실거래가를 예측하였다. 예 측모델로서는 회귀 모델, 다층 인공신경망 모델, 그리고 합성곱신경망 모델을 이용하였고 이들의 예 측력을 비교하였다. 연구결과는 다음과 같다. 첫째, 회귀 모델 중에서는 인구통계 및 주택관련 변수 에 커널밀도추정으로 구해진 상가 및 편의시설 밀도 정보를 추가한 모델이 예측력이 높았다. 둘째, 다층인공신경망(Multilayer Artificial Neural Network) 모델은 회귀 모델에 비해 상대적으로 높은 성능을 보였다. 셋째, 합성곱신경망과 다층인공신경망을 융합한 모델은 인공위성 이미지 피처, 인구 통계 및 주택 관련 변수, 그리고 커널밀도추정 특성을 모두 사용한 경우 기존 다층 인공신경망과 회귀모델과 비교시 가장 우수한 성능을 보였다. 이 결과는 인공위성 이미지가 주택 가격 예측에 유 용한 정보를 제공하는 것을 시사하며 향후 주택가격예측에 인공위성 이미지, 혹은 커널밀도추정으 로 표시되는 밀도 정보를 합성곱신경망을 이용하는 경우 예측력이 높아졌기에, 향후 이를 개선하는 연구를 기대한다.

House price forecasting is a major topic of interest to a wide range of stakeholders, from individuals to institutions to governments. Until now, predictions have been made using statistical models, but recently, there have been many studies using deep learning and machine learning. In particular, researchers are applying CNN (Convolutional Neural Networks) using image information. However, there have been no studies using image information in Korea. This paper aims to predict apartment prices in Seoul by using Kernel Density Estimation (KDE) to display satellite images and density information of shopping malls and amenities in the area and use them as input variables. In this paper, we collected housing transaction records, local income information, population density, and population data by age group from 2021 to 2022, and collected satellite images divided into zones through Naver Cloud Platform’s Static Map API. In particular, information on shopping malls, hospitals, parks, subway stations, and schools collected from the Open government data portal(www.data.go.kr) and Seoul open data square(data.seoul.go.kr) was converted into density information using KDE. The data collected in various forms was preprocessed to predict the actual transaction price of apartments calculated as a unit price per area. Regression model, multi-layer artificial neural network model, and CNN model were used as prediction models, and their predictive power was compared. The results are as follows. First, among the regression models, the model that adds shopping centre and amenity density information obtained from KDE to demographic and housing-related variables has a high predictive power. Second, the MLP model has a relatively high performance compared to the regression model. Third, the fusion of CNN and MLP performed the best when satellite image features, demographic and housing-related variables, and KDE characteristics were all used, compared to traditional multilayer neural networks and regression models. These results suggest that satellite imagery provides useful information for house price prediction, and we look forward to future research to improve the predictive power of satellite imagery and density information represented by KDE using CNN for house price prediction.

3D object detection is widely applied in robotics and autonomous driving; since 3D scenes in autonomous driving are typically outdoor environments, current methods exhibit substantial computational wastage and significant time delays when using convolution directly in the backbone network. This paper proposes a backbone network based on sparse convolutional spatial-semantic fusion modules to solve this problem. High-level semantic features and low-level spatial features extracted through sub-manifold sparse convolution and sparse convolution are fused to enhance feature representation capabilities. Our proposed backbone network achieves excellent performance on the KITTI dataset.

본 논문에서는 드론을 효과적으로 탐지하기 위해 사용되고 있는 주파수 호핑(Frequency Hopping) 신호 분석 방법과 그 정확도를 비교하였다. 일반적으로 다중 드론의 탐지와 추적을 위해 사용되는 수신 신호의 FFT(Fast Fourier Transform)와 K-평균(K-means) 군집화 기법은 복잡한 운용 환경에서의 간섭 신호와 호핑 시퀀스가 교란(Perturbed)된 상황에서 탐지 정확도가 저하되는 문제가 있다. 본 연구에서는 다수의 드론으로부터 수신되는 신호를 디지털 하향 변환 (Digital Down Conversion), 비교 대상 신호와의 컨볼루션(Convolution) 기반 매칭, 가중치 기반 K-평균(Weighted K-means) 군집화 기법을 사용하여 호핑 신호 탐지 정확도를 비교하였다. 제안된 방법은 낮은 SNR(≤-5dB) 환경에서도 기존 FFT 기법 대비 우수한 탐지 성능을 보였으며, 잡음과 주파수 호핑 시퀀스의 교란(10% perturbed) 상황에서도 우수 한 특성을 나타냈다. 본 연구 결과는 실제 다중 경로 반사 등의 복잡한 드론 운용 환경에서 신뢰성 있는 호핑 신호 탐지 기법으로 활용될 수 있으며, 특히 군용 군집 무인기(UAV) 탐지 및 추적 시스템에 널리 사용될 수 있을 것으로 판단된다.

This paper presents the accuracy of frequency hopping signal analysis techniques used to effectively detect drones. In general, the FFT(Fast Fourier Transform) of the received signals and K-means clustering technique, which are used for multiple drone detection and tracking, have a problem on detection accuracy in complex operating environments with interference signals and perturbed hopping sequences. In this study, we compare the accuracy of hopping signal detection using digital down conversion, convolution-based matching with the reference signals, and weighted K-means clustering techniques for multiple drone signals. The proposed method demonstrates reliable performance in low-SNR(≤-5dB) environments and shows robustness against 10% perturbed hopping signal conditions. The results presented in this study can be utilized as a reliable hopping signal detection technique in such environments involving multipath reflections and other RF interferences. In particular, it is expected to be widely applicable to the military unmanned aerial vehicle (UAV) swarm detection and tracking systems.

Breast cancer remains the foremost cause of cancer-related mortality worldwide. The histopathological diagnosis is impeded by the intricate nature of image interpretation and the presence of inter-observer variability among pathologists. Deep learning (DL) for cancer image understanding has revolutionized accurate breast cancer diagnosis, marking a significant advancement in medical image analysis. Researchers proposed DL-based intelligent models to overcome the challenges of manual observations. However, the existing models suffer from a considerable computational burden, demanding substantial time investments that restrict efficient and scalable breast cancer diagnosis solutions. Our study introduces an automated breast cancer diagnosis system employing a lightweight Convolutional Neural Network (CNN) model, adept at extracting intricate features from histopathological images. Our system has attained superior accuracy through extensive experimentation on a comprehensive breast cancer dataset while employing fewer parameters compared to state-of-theart (SOTA) techniques.

딥러닝을 이용한 객체 검출의 경우 정확도와 실시간성을 모두 요구한다. 그러나, 한정된 자원 환경에서는 수 많은 양의 데이터를 처리하는 딥러닝 모델을 사용하기 어렵다. 이러한 문제 해결을 위해 본 논문에서는 소형 임베디드 장치를 위한 객체 검출을 모델을 제안하였다. 일반적인 검출 모델과 달리 사전 학습된 특징 추출기를 제거한 구조를 사용하여 모델 크기를 최소화하였다. 모델의 구조는 경량화된 컨볼루션 블록을 반복해서 쌓는 구조 로 설계하였다. 또한, 검출 오버헤드를 줄이기 위해 영역 제안 횟수를 크게 줄였다. 제안하는 모델은 공개 데이터 셋인 PASCAL VOC를 사용하여 학습 및 평가하였다. 모델의 정량적 평가를 위해 검출 분야에서 사용하는 average precision으로 검출 성능을 측정하였다. 그리고 실제 임베디드 장치와 유사한 라즈베리 파이에서 검출 속도를 측정하였다. 실험을 통해 기존 검출 방법 대비 향상된 정확도와 빠른 추론 속도를 달성하였다.

In the case of object detection using deep learning, both accuracy and real-time are required. However, it is difficult to use a deep learning model that processes a large amount of data in a limited resource environment. To solve this problem, this paper proposes an object detection model for small embedded devices. Unlike the general detection model, the model size was minimized by using a structure in which the pre-trained feature extractor was removed. The structure of the model was designed by repeatedly stacking lightweight convolution blocks. In addition, the number of region proposals is greatly reduced to reduce detection overhead. The proposed model was trained and evaluated using the public dataset PASCAL VOC. For quantitative evaluation of the model, detection performance was measured with average precision used in the detection field. And the detection speed was measured in a Raspberry Pi similar to an actual embedded device. Through the experiment, we achieved improved accuracy and faster reasoning speed compared to the existing detection method.

본 논문에서는 게임 영상에 대한 색연필 드로잉 렌더링 기법을 제안한다. 제안하는 방법의 키 아이디어는 양방향 회선 필터(bilateral convolution filter)를 이용하여 다양한 스타일의 연필 스트로크를 만드는 것이다. 이 필터는 기존의 컨볼루션 기반 기법들과 달리 경계 흐림 현상을 획기적으로 보완하였다. 더불어 연필 스트로크의 속성들을 직관적으로 제어할 수 있도록 한다. 또한 그릴 물체의 모양으로부터 스트로크 방향을 결정하는 기법을 제안한다. 특징선(feature line)에 가까운 픽셀들에 대해서는 완만한 탄젠트 흐름(smooth tangent flow)을 사용하고, 영역의 내부에는 부분적으로 유사한 흐름을 사용한다. 배경에는 고정된 방향의 흐름을 사용한다. 이처럼 다른 스타일의 스트로크 방향을 사용함으로써, 결과 연필화의 현실성을 증가시킬 수 있다. 제안하는 방법은 사진에 대해 시각적으로 만족스러운 연필 드로잉 효과를 만들어 낸다.

We present a novel approach for the production of color pencil drawing effects from photographs. Our key idea is to use a bilateral convolution filter to produce various styles of pencil strokes. The filter dramatically makes up for smearing affect of conventional techniques. Furthermore, it presents an intuitive control to mimic the properties of pencil strokes. We also present a scheme that determines stroke directions from the shapes to be drawn. Smooth tangent flows are used for the pixels close to feature lines, and partially parallel flows inside regions. The background is rendered using a flow of fixed direction. Using different styles of stroke directions increases the realism of the resulting images. This approach produces visually pleasing pencil drawing effects from photographs.

인간 상호 작용 인식(HIR)은 이미 인간의 행동 및 활동 인식과 동일하게 급속도로 발전하고 있다. HIR에서 우리는 여러 인간 간의 장기적인 상호 관련된 동력을 탐구하여 비디오에서 인간과 인간의 상호 작용 인식 문제를 강조하고 자 한다. HIR 시스템은 인간과 인간의 상호 작용을 정확하게 이해하기 위해서는 동영상을 기반으로 한 강력한 특징 추출 및 선택 방법이 필요하다. 이 논문에서는 비디오에서 인간과 인간의 상호 작용을 현명하게 추적하기 위해 완전 히 연결된 블록에 이어 새로운 3D 컨볼루션 신경망(3D CNN)을 제안한다. 우리는 제안된 15개의 비디오 프레임 시퀀스를 새로운 3D CNN 아키텍처에 공급하여 모든 시퀀스에서 심층 특징을 추출한 다음 해당 시퀀스를 완전히 연결된 블록으로 전달하여 효율성을 높인다. 우리가 제안한 네트워크는 두 개의 벤치마크 데이터 셋인 UT-I와 TV Human Interaction 데이터 셋에서 전체적으로 84% 와 74%의 탁월한 인식 정확도를 달성하고 최신기술을 개선 함으로써 기존 최첨단 방식을 능가하였다. 우리가 제안한 네트워크는 비디오 기반 학습, 서비스 전투, 의료 미래학 자, 대화형 게임 및 감시 시스템과 같은 다양한멀티미디어 콘텐츠 및 보안 응용 프로그램에도 적용할 수 있다.

Human Interaction Recognition(HIR) has already been perceived rapid progress same as human action and activity recognition. In HIR, we intend to highlight the problem of human-to-human interaction recognition in videos by exploring the long term inter-related dynamics between multiple humans. In order to understand the human-to-human interaction precisely, HIR system requires a robust feature extraction and selection method based on videos. In this paper, we propose a novel 3D convolutional neural network(3D CNN) followed by a fully connected block, to wisely trace human to human interactions in videos. We feed our proposed model with 15 sequence of video frames to our novel 3D CNN architecture which extracts deep features from all the sequences and then pass those sequences to the fully connected block to boost our efficiency. Our proposed network outperformed the existing state-of-the-art methods by accomplishing extraordinary recognition accuracy on two benchmark datasets, UT-I and TV Human Interaction dataset i.e., 84% and 74% overall and improved from the state-of-the-art techniques. Our proposed network can also be applicable to other numerous multimedia contents and security applications such as video-based learning, service combats, medical futurists, interactive gaming, and surveillance systems.

본 논문에서는 U-SegNet 을 화재 모듈 및 잔류 컨볼루션과 통합하여 자기 공명 영상(MRI)으로 뇌 조직을 분할하여 U-SegNet 모델을 개선한다. 제안된 인코더-디코더 방법에서, 잔류 연결뿐만 아니라 화재 모듈의 압착 및 확장 컨볼루션 레이어는 뇌 MRI 분할을 위한 더 가볍고 더 효율적인 아키텍처로 이어진다. 잔류 단위는 심층 아키텍처의 원활한 훈련에 도움이 되며, 잔류 컨볼루션 계층에서 얻은 특징은 분할 네트워크에서 더 나은 특징 표현을 보장한다. 또한 더 적은 수의 네트워크 매개 변수로 설계를 보다 효율적으로 만들고 뇌MRI 에 더 나은 분할 정확도를 제공한다. 제안된 아키텍처는 뇌 조직 분할을 위해 공개적으로 이용 가능한 OASIS 데이터 세트에서 평가된다. 제안된 방법의 실험 결과는 주사위 유사도 계수(DSC) 점수가 0.96 이고 자카드 지수(JI)가 0.92 인 뇌 MRI 분할에 대한 기존 방법보다 우수한 성능을 보여준다.

In this paper, we propose an improved U-SegNet model by integrating U-SegNet with fire modules and residual convolutions to segment brain tissues in a magnetic resonance image (MRI). In the proposed encoder-decoder method, the residual connections, as well as squeeze, and expand convolutional layers from the fire module, lead to lighter and more efficient architecture for brain MRI segmentation. The residual unit helps in the smooth training of the deep architecture, and features obtained from residual convolutional layers ensure better feature representation in the segmentation network. It also makes the design more efficient architecture with a fewer number of network parameters and provides better segmentation accuracy for brain MRI. The proposed architecture is evaluated on a publicly available OASIS dataset for brain tissue segmentation. The experimental results of the proposed method exhibit superior performances to conventionalmethods on brain MRI segmentation with a dice similarity coefficient (DSC) score of 0.96 and Jaccard index (JI) of 0.92.

Liquefied hydrogen is attracting attention as an energy source of the future due to its hydrogen storage rate and low risk. However, the disadvantage is that the unit price is high due to technical difficulties in production, transportation, and storage. This study was conducted to improve the design accuracy and development period of needle valves, which are important parts with a wide technical application range among liquefied hydrogen equipment. Since the needle valve must discharge an appropriate flow rate of the liquefied fluid, it is important to determine the needle valve design parameters suitable for the target flow rate. Computational Fluid Dynamics and Artificial Neural Network technology used to determine the design variables of fluid flow were applied to improve the setting and analysis time of the parameter. In addition, procedures and methods for applying the design parameter of needle valves to Convolutional Neural Networks were presented. The procedure and appropriate conditions for selecting parameters and functional conditions of the Convolutional Neural Network were presented, and the accuracy of predicting the flow coefficient according to the design parameter was secured 95%. It is judged that this method can be applied to other structures and machines.