An integrated mediapipe-optimized GRU model for Indian sign language recognition.

Sign language recognition is challenged by problems, such as accurate tracking of hand gestures, occlusion of hands, and high computational cost. Recently, it has benefited from advancements in deep learning techniques. However, these larger complex approaches cannot manage long-term sequential data and they are characterized by poor information processing and learning efficiency in capturing useful information. To overcome these challenges, we propose an integrated MediaPipe-optimized gated recurrent unit (MOPGRU) model for Indian sign language recognition. Specifically, we improved the update gate of the standard GRU cell by multiplying it by the reset gate to discard the redundant information from the past in one screening. By obtaining feedback from the resultant of the reset gate, additional attention is shown to the present input. Additionally, we replace the hyperbolic tangent activation in standard GRUs with exponential linear unit activation and SoftMax with Softsign activation in the output layer of the GRU cell. Thus, our proposed MOPGRU model achieved better prediction accuracy, high learning efficiency, information processing capability, and faster convergence than other sequential models.

Efficient Fiducial Point Detection of ECG QRS Complex Based on Polygonal Approximation.

Electrocardiogram signal analysis is based on detecting a fiducial point consisting of the onset, offset, and peak of each waveform. The accurate diagnosis of arrhythmias depends on the accuracy of fiducial point detection. Detecting the onset and offset fiducial points is ambiguous because the feature values are similar to those of the surrounding sample. To improve the accuracy of this paper's fiducial point detection, the signal is represented by a small number of vertices through a curvature-based vertex selection technique using polygonal approximation. The proposed method minimizes the number of candidate samples for fiducial point detection and emphasizes these sample's feature values to enable reliable detection. It is also sensitive to the morphological changes of various QRS complexes by generating an accumulated signal of the amplitude change rate between vertices as an auxiliary signal. To verify the superiority of the proposed algorithm, error distribution is measured through comparison with the QT-DB annotation provided by Physionet. The mean and standard deviation of the onset and the offset were stable as - 4.02 ± 7.99 ms and - 5.45 ± 8.04 ms, respectively. The results show that proposed method using small number of vertices is acceptable in practical applications. We also confirmed that the proposed method is effective through the clustering of the QRS complex. Experiments on the arrhythmia data of MIT-BIH ADB confirmed reliable fiducial point detection results for various types of QRS complexes.

PTZ Camera-Based Displacement Sensor System with Perspective Distortion Correction Unit for Early Detection of Building Destruction.

This paper presents a pan-tilt-zoom (PTZ) camera-based displacement measurement system, specially based on the perspective distortion correction technique for the early detection of building destruction. The proposed PTZ-based vision system rotates the camera to monitor the specific targets from various distances and controls the zoom level of the lens for a constant field of view (FOV). The proposed approach adopts perspective distortion correction to expand the measurable range in monitoring the displacement of the target structure. The implemented system successfully obtains the displacement information in structures, which is not easily accessible on the remote site. We manually measured the displacement acquired from markers which is attached on a sample of structures covering a wide geographic region. Our approach using a PTZ-based camera reduces the perspective distortion, so that the improved system could overcome limitations of previous works related to displacement measurement. Evaluation results show that a PTZ-based displacement sensor system with the proposed distortion correction unit is possibly a cost effective and easy-to-install solution for commercialization.

Content-based ultrasound image retrieval using a coarse to fine approach.

One of the current issues for picture archiving and communication systems (PACS) is extending retrieval technologies to deal with multimedia information. This is particularly important for medical applications that assist in diagnostic processes and pathology studies. Accordingly, this paper presents a new approach to content-based image retrieval (CBIR) for a clinical ultrasound image database (DB). The proposed algorithm consists of two stages so as to maximize the retrieval efficiency. In the first stage, a coarse retrieval is performed using the statistical characteristics of the wavelet coefficients that narrow the search by eliminating up to 70% of the total DB images. In the second stage, a fine retrieval is carried out using the Legendre moment of the global histogram pdf on the reduced image set preretrieved by the coarse retrieval. When tested on an abdominal ultrasound image DB and compared with various other methods, the proposed algorithm gave promising results for applying CBIR to clinical ultrasound images.