• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.3
• /
• pp.91-97
• /
• 2023

Recent research shows that latent directions can be used to image process towards certain attributes. However, controlling the generation process of generative model is very difficult. Though the latent directions are used to image process for certain attributes, many restrictions are required to enhance the attributes received the latent vectors according to certain text and prompts and other attributes largely unaffected. This study presents a generative model having certain restriction to the latent vectors for image generation and manipulation. The suggested method requires only few minutes per manipulation, and the simulation results through Tensorflow Variational Auto-encoder show the effectiveness of the suggested approach with extensive results.

• The Journal of Korea Robotics Society
• /
• v.18 no.4
• /
• pp.427-435
• /
• 2023

This paper proposes a method of estimating the pose of a mobile robot by using a learning model. When estimating the pose of a mobile robot, wheel encoder and inertial measurement unit (IMU) data are generally utilized. However, depending on the condition of the ground surface, slip occurs due to interaction between the wheel and the floor. In this case, it is hard to predict pose accurately by using only encoder and IMU. Thus, in order to reduce pose error even in such conditions, this paper introduces a pose estimation method based on a learning model using data of the wheel encoder and IMU. As the learning model, long short-term memory (LSTM) network is adopted. The inputs to LSTM are velocity and acceleration data from the wheel encoder and IMU. Outputs from network are corrected linear and angular velocity. Estimated pose is calculated through numerically integrating output velocities. Dataset used as ground truth of learning model is collected in various ground conditions. Experimental results demonstrate that proposed learning model has higher accuracy of pose estimation than extended Kalman filter (EKF) and other learning models using the same data under various ground conditions.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.142-146
• /
• 2023

We present a method for generating 3D structures and rendering objects by combining VAE (Variational Autoencoder) and GAN (Generative Adversarial Network). This approach focuses on generating and rendering 3D models with improved quality using residual learning as the learning method for the encoder. We deep stack the encoder layers to accurately reflect the features of the image and apply residual blocks to solve the problems of deep layers to improve the encoder performance. This solves the problems of gradient vanishing and exploding, which are problems when constructing a deep neural network, and creates a 3D model of improved quality. To accurately extract image features, we construct deep layers of the encoder model and apply the residual function to learning to model with more detailed information. The generated model has more detailed voxels for more accurate representation, is rendered by adding materials and lighting, and is finally converted into a mesh model. 3D models have excellent visual quality and accuracy, making them useful in various fields such as virtual reality, game development, and metaverse.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.10 no.2
• /
• pp.155-162
• /
• 2016

The purpose of this study was to determine the walking imbalance using the EMG(electromyogram). To confirm the effectiveness of the proposed encoder and acceleration, EMG sensor based gait imbalance determination system. This experiment was carried out to evaluation with a healthy adult male to 10 people. The Encoder device is attached to the hip and knee joint in order to measure the gait signal. The Accelerometer sensors are attached on the ankle. The EMG sensors are attached on the vastus lateralis and anterior tibialis. SI(Symmetry Index) was used as an index for determining the gait imbalance. To confirm if the judgment has been made correctly, the heel, regarded as the cause of unbalanced ambulation, was adjusted from 0 cm to 6 cm with intervals of 1.5 cm. In the cases of the encoder and the EMG, the difference of 0 cm and 1.5 cm is determined into normal walk but the other difference is distinguished into gait imbalance. In the case of the accelerometer, the difference of 0 cm, 1.5 cm and 3 cm is determined into normal walk but the other difference is distinguished into gait imbalance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.4
• /
• pp.1689-1703
• /
• 2020

When data is transmitted over an unreliable channel, the error of the data packet may result in serious degradation. The multiple description coding (MDC) can solve this problem and save transmission costs. In this paper, we propose a deep multiple description coding network (MDCN) to realize efficient image compression. Firstly, our network framework is based on convolutional auto-encoder (CAE), which include multiple description encoder network (MDEN) and multiple description decoder network (MDDN). Secondly, in order to obtain high-quality reconstructed images at low bit rates, the encoding network and decoding network are integrated into an end-to-end compression framework. Thirdly, the multiple description decoder network includes side decoder network and central decoder network. When the decoder receives only one of the two multiple description code streams, side decoder network is used to obtain side reconstructed image of acceptable quality. When two descriptions are received, the high quality reconstructed image is obtained. In addition, instead of quantization with additive uniform noise, and SSIM loss and distance loss combine to train multiple description encoder networks to ensure that they can share structural information. Experimental results show that the proposed framework performs better than traditional multiple description coding methods.

• Journal of Korea Multimedia Society
• /
• v.14 no.7
• /
• pp.849-857
• /
• 2011

Gaming on demand(GOD) makes people enjoy games by encoding and transmitting game screen at a server side, and decoding the video at a client side. In this paper, we propose a fast game video encoder for multiple users over network with low-powered devices. In the proposed system, the computational complexity of game encoders is reduced by using scene descriptors, which consists of an object motion vector, global motion, and scene change. With additional information from game engines, the proposed encoder does not need to perform various complexity processes such as motion estimation and ratedistortion optimization. The motion estimation and rate-distortion optimization skipped by scene descriptors. We found that the proposed method improved 192 % in terms of FPS, compared with x264 software. With partial assembly code, we also improved coding speed by 86 % in terms of FPS. We found that the proposed fast encoder could encode over 60 FPS for real-time GOD applications.

• The KIPS Transactions:PartB
• /
• v.9B no.6
• /
• pp.715-726
• /
• 2002

In this paper, we construct real-time moving picture encoder based on fractal theory by using general purpose digital signal processors. The constructed encoder is implemented using two fixed-point general DSPs (ADSP2181) and performs image encoding by three stage pipeline structure. In the first pipeline stage, the image grabber acquires image data from NTSC standard image signals and stores digital image into frame memory. In the second stage, the main controller encode image dada using fractal algorithm. The last stage, output controller perform Huffman coding and result the coded data via RS422 port. The performance tests of the constructed encoder shows over 10 frames/sec encoding speed for QCIF data when all the frames are encoded. When we encode the images using the interframe and redundency based on the proposed algorithms, encoding speed increased over 30 frames/sec in average.

• Journal of Broadcast Engineering
• /
• v.12 no.2
• /
• pp.159-176
• /
• 2007

In the paper, we implemented a software system that encodes XML instance documents conforming to a schema document according to the MPEG-7 BiM compression method, and decodes the encoded documents vice versa. We designed software structures of BiM encoder and decoder as class hierarchies, and then implemented the structures. The implemented BiM encoder shows a compression ratio of 9.44% on the average. The BiM encoder is a general-purpose XML compressor that can encode any instance documents conforming to a schema document described in XML Schema language including the MPEG-7 schema. The BiM encoder thus can be used in many application fields including digital broadcasting environment, where encoding XML instance documents is needed.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.4
• /
• pp.774-780
• /
• 2017

This paper proposes an efficient binary arithmetic encoder hardware architecture for CABAC encoding, which is an entropy coding method of HEVC. CABAC is an entropy coding method that is used in HEVC standard. Entropy coding removes statistical redundancy and supports a high compression ratio of images. However, the binary arithmetic encoder causes a delay in real time processing and parallel processing is difficult because of the high dependency between data. The operation of the proposed CABAC BAE hardware structure is to separate the renormalization and process the conventional iterative algorithm in parallel. The new scheme was designed as a four-stage pipeline structure that can reduce critical path optimally. The proposed CABAC BAE hardware architecture was designed with Verilog HDL and implemented in 65nm technology. Its gate count is 8.07K and maximum operating speed of 769MHz. It processes the four bin per clock cycle. Maximum processing speed increased by 26% from existing hardware architectures.

• Journal of IKEEE
• /
• v.23 no.4
• /
• pp.1288-1294
• /
• 2019

The channel-achieving property made the polar code show to advantage as an error-correcting code. However, sufficient error-correction performance shows the asymptotic property that is achieved when the length of the code is long. Therefore, efficient architecture is needed to realize the implementation of very-large-scale integration for the case of long input data. Although the most basic fully parallel encoder is intuitive and easy to implement, it is not suitable for long polar codes because of the high hardware complexity. Complementing this, a partially parallel encoder was proposed which has an excellent result in terms of hardware area. Nevertheless, this method has not been completely generalized and has the disadvantage that different architectures appear depending on the hardware designer. In this paper, we propose a hardware design scheme that applies the proposed systematic approach which is optimized for bit-dimension permutations. By applying this solution, it is possible to design a generalized partially parallel encoder for long polar codes with the same intuitive architecture as a fully parallel encoder.