• Journal of Communications and Networks
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• v.14 no.2
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• pp.151-161
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• 2012

This paper considers a two-user Gaussian interference channel with energy harvesting transmitters. Different than conventional battery powered wireless nodes, energy harvesting transmitters have to adapt transmission to availability of energy at a particular instant. In this setting, the optimal power allocation problem to maximize the sum throughput with a given deadline is formulated. The convergence of the proposed iterative coordinate descent method for the problem is proved and the short-term throughput maximizing offline power allocation policy is found. Examples for interference regions with known sum capacities are given with directional water-filling interpretations. Next, stochastic data arrivals are addressed. Finally, online and/or distributed near-optimal policies are proposed. Performance of the proposed algorithms are demonstrated through simulations.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.109-118
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• 2008

We present two estimators for discrete non-Gaussian and nonstationary probability density estimation based on a dynamic Bayesian network (DBN). The first estimator is for off line computation and consists of a DBN whose transition distribution is represented in terms of kernel functions. The estimator parameters are the weights and shifts of the kernel functions. The parameters are determined through a recursive learning algorithm using maximum likelihood (ML) estimation. The second estimator is a DBN whose parameters form the transition probabilities. We use an asymptotically convergent, recursive, on-line algorithm to update the parameters using observation data. The DBN calculates the state probabilities using the estimated parameters. We provide examples that demonstrate the usefulness and simplicity of the two proposed estimators.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.55.2-55
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region efface candidate. The feature information in the region of face candidate is used to detect a face region. In the recognition step, as a tested, the 360 images of 30 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression, Input variables of the neural networks are the feature information that comes from the eigenface spaces. The simulation results of 30 persons show that the proposed method yields high recognition rates.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.5
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• pp.113-119
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• 2008

Conventional 1-1-1 cooperative protocol offers path-loss gain as advantage of multi-hop and spatial diversity which is equivalent to MIMO system. This protocol is enable to get higher reliability and reduction of power consumption than those of the single-hop or multi-hop. But the 1-1-1 cooperative protocol get only the diversity order 2 and limited path-loss reduction gain because this protocol has a single cooperative relay. We propose 1-2-1 cooperative protocol using two cooperative relays R1, R2. The 1-2-1 cooperative protocol can improve path-loss reduction and increase diversity order 3. Moreover, the cooperative relay R2 attains diversity order 2. The signaling method in transmission uses DF (Decode and Forward) or DR (Decode and Reencode) and 1-2-1 DF/DR cooperative protocol are applied to clustering based wireless sensor networks (WSNs). Simulations are performed to evaluate the performance of the protocols under Rayleigh fading channel plus AWGN (Additive White Gaussian Noise).

• Journal of Communications and Networks
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• v.14 no.1
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• pp.1-9
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• 2012

We consider a receiving node, located at the origin, and a Poisson point process (PPP) that models the locations of the desired transmitter as well as the interferers. Interference is known to be non-Gaussian in this scenario. The capacity bounds for additive non-Gaussian channels depend not only on the power of interference (i.e., up to second order statistics) but also on its entropy power which is influenced by higher order statistics as well. Therefore, a complete statistical characterization of interference is required to obtain the capacity bounds. While the statistics of sum of signal and interference is known in closed form, the statistics of interference highly depends on the location of the desired transmitter. In this paper, we show that there is a tradeoff between entropy power of interference on the one hand and signal and interference power on the other hand which have conflicting effects on the channel capacity. We obtain closed form results for the cumulants of the interference, when the desired transmitter node is an arbitrary neighbor of the receiver. We show that to find the cumulants, joint statistics of distances in the PPP will be required which we obtain in closed form. Using the cumulants, we approximate the interference entropy power and obtain bounds on the capacity of the channel between an arbitrary transmitter and the receiver. Our results provide insight and shed light on the capacity of links in a Poisson network. In particular, we show that, in a Poisson network, the closest hop is not necessarily the highest capacity link.

• ETRI Journal
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• v.30 no.1
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• pp.13-20
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• 2008

We evaluate the sink capacity of wireless code division multiple access (CDMA) sensor networks with layered architecture. We introduce a model of interference at a sink considering two kinds of interference: multiple access interference (MAI) and node interference (NI). We also investigate the activity of sensor nodes around the sink in relation to gathering data under a layered architecture. Based on the interference model and the activity of sensor nodes around the sink, we derive the failure probability of the transmission from a source node located one hop away from the sink using Gaussian approximation. Under the requirement of 1% failure probability of transmission, we determine the sink capacity, which is defined as the maximum number of concurrent sensor nodes located one hop away from the sink. We demonstrate that as the node activity of the MAI decreases, the variation of the sink capacity due to the node activity of the NI becomes more significant. The analysis results are verified through computer simulations.

• ETRI Journal
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• v.32 no.1
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• pp.11-21
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• 2010

A novel ultra-wideband (UWB) transceiver structure is presented to be used in wireless body area networks (WBANs). In the proposed structure, a data channel and a control channel are combined into a single transmission signal. In the signal, a modulation method mixing pulse position modulation and pulse amplitude modulation is proposed. A mathematical framework calculating the power spectrum density of the proposed pulse-based signal evaluates its coexistence with conventional radio systems. The transceiver structure is discussed, and the receiving performance is investigated in the additive white Gaussian noise channel. It is demonstrated that the proposed scheme is easier to match to the UWB emission mask than conventional UWB systems. The proposed scheme achieves the data rate requirement of WBAN; the logical control channel achieves better receiving performance than the logical data channel, which is useful for controlling and maintaining networks. The proposed scheme is also easy to implement.

• Journal of Korea Multimedia Society
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• v.22 no.3
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• pp.335-348
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• 2019

Cell segmentation and counting represent one of the most important tasks required in order to provide an exhaustive understanding of biological images. Conventional features suffer the lack of spatial consistency by causing the joining of the cells and, thus, complicating the cell counting task. We propose, in this work, a cascade of networks that take as inputs different versions of the original image. After constructing a Gaussian pyramid representation of the microscopy data, the inputs of different size and spatial resolution are given to a cascade of deep convolutional autoencoders whose task is to reconstruct the segmentation mask. The coarse masks obtained from the different networks are summed up in order to provide the final mask. The principal and main contribution of this work is to propose a novel method for the cell counting. Unlike the majority of the methods that use the obtained segmentation mask as the prior information for counting, we propose to utilize the hidden latent representations, often called the high-level features, as the inputs of a neural network based regressor. While the segmentation part of our method performs as good as the conventional deep learning methods, the proposed cell counting approach outperforms the state-of-the-art methods.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.90-97
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• 2023

Digital holographic microscopy (DHM) is a three-dimensional (3D) imaging technique that uses the phase information of coherent light. In the reconstruction process of DHM, a narrow region around the positive or negative sideband from the Fourier domain is windowed to avoid noise due to the DC spectrum of the hologram spectrum. However, the limited size of the window also degrades the high-frequency information of the 3D object profile. Although a large window can have more detailed information of the 3D object shape, the noise is increased. To solve this trade-off, we propose phase difference averaging (PDA). The proposed method yields high-frequency information of the specimen while reducing the DC noise. In this paper, we explain the reconstruction algorithm for this method and compare it to various conventional filtering methods including Gaussian, Wiener, average, median, and bilateral filtering methods.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.208-215
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• 2023

Deep learning techniques provide powerful solutions to several pattern-recognition problems, including Raman spectral classification. However, these networks require large amounts of labeled data to perform well. Labeled data, which are typically obtained in a laboratory, can potentially be alleviated by data augmentation. This study investigated various data augmentation techniques and applied multiple deep learning methods to Raman spectral classification. Raman spectra yield fingerprint-like information about chemical compositions, but are prone to noise when the particles of the material are small. Five augmentation models were investigated to build robust deep learning classifiers: weighted sums of spectral signals, imitated chemical backgrounds, extended multiplicative signal augmentation, and generated Gaussian and Poisson-distributed noise. We compared the performance of nine state-of-the-art convolutional neural networks with all the augmentation techniques. The LeNet5 models with background noise augmentation yielded the highest accuracy when tested on real-world Raman spectral classification at 88.33% accuracy. A class activation map of the model was generated to provide a qualitative observation of the results.