• Journal of Communications and Networks
• /
• v.12 no.4
• /
• pp.317-329
• /
• 2010

In recent years, many sparse estimation methods, also known as compressed sensing, have been developed. However, most of these methods presume that the measurement matrix is completely known. We develop a new blind maximum likelihood method-the expectation-sparse-maximization (ESpaM) algorithm-for models where the measurement matrix is the product of one unknown and one known matrix. This method is a variant of the expectation-maximization algorithm to deal with the resulting problem that the maximization step is no longer unique. The ESpaM algorithm is justified theoretically. We present as well numerical results for two concrete examples of blind channel identification in digital communications, a doubly-selective channel model and linear time invariant sparse channel model.

• Journal of the Semiconductor & Display Technology
• /
• v.15 no.2
• /
• pp.55-60
• /
• 2016

This study was developed through the secondary battery module charging/discharger possible utilization in the production process equipment circuit. The developed module is ensuring construction of efficient and productive charging and discharger through this research a limit on the yield and the price of existing single -channel charge and discharger circuit as a 5V 70A grade secondary battery Formation charge and discharger for up to 1 board 4 channels. In order to improve the sensing accuracy, through a robust differential amplifier circuit described using 16bit Analog-Digital Converter and noise was secured 16bit resolution sensing. The configuration also made demands for property Rise / Fall Time. Data Acquisition, discharge efficiency and also to fit the sink circuit temperature level for mass production.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.6
• /
• pp.628-630
• /
• 2016

Compressive sensing can recover an original sparse signal from a few measurements. Its performance is affected by the number of non-zero elements in the signal. The knowledge of partial locations of non-zero elements can improve the recovery performance. In this paper, we apply the partial location knowledge to the multipath matching pursuit. The numerical results show it improves the signal recovery performance and the channel estimation performance in the ITU-VB channel.

• Korean Journal of Remote Sensing
• /
• v.9 no.2
• /
• pp.1-5
• /
• 1993

Real time transparency estimation method was studied using AVHRR Channel 1 (Visible band, 0.58 - 0.68 $\mu\textrm{m}$) digital number (DN) of NOAA Satellite. The relationshop between the measured transparency and the digital number shows following exponential equation in the southeastern Yellow Sea : Tr = 4820 $\times$ exp (-0.082 $\times$ DN), 77 $\leq$ DN < 105 where Tr is the transparency in meters and DN is the digital number of AVHRR Channel 1. From this equation, real time transparency can be simply estimated using data from the NOAA Ground Station of National Fisheries Research and Development Agency.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.8
• /
• pp.3498-3511
• /
• 2016

We investigate the channel state information (CSI) in multi-input multi-output (MIMO) cooperative networks that employ the amplify-and-forward transmission scheme. Least squares and expectation conditional maximization have been proposed in the system. However, neither of these two approaches takes advantage of channel sparsity, and they cause estimation performance loss. Unlike linear channel estimation methods, several compressed channel estimation methods are proposed in this study to exploit the sparsity of the MIMO cooperative channels based on the theory of compressed sensing. First, the channel estimation problem is formulated as a compressed sensing problem by using sparse decomposition theory. Second, the lower bound is derived for the estimation, and the MIMO relay channel is reconstructed via compressive sampling matching pursuit algorithms. Finally, based on this model, we propose a novel algorithm so called sparsity adaptive expectation maximization (SAEM) by using Kalman filter and expectation maximization algorithm so that it can exploit channel sparsity alternatively and also track the true support set of time-varying channel. Kalman filter is used to provide soft information of transmitted signals to the EM-based algorithm. Various numerical simulation results indicate that the proposed sparse channel estimation technique outperforms the previous estimation schemes.

• Journal of KIISE:Information Networking
• /
• v.35 no.2
• /
• pp.130-138
• /
• 2008

The scheme for recognizing the channel availability is one of the most important research issues in cognitive radio systems utilizing unused frequency bands. In this paper, we propose a novel scheme of selecting sensing channel in order to improve the sensing ability of frequency status in cognitive radio ad hoc networks. To fully exploit the sensing ability of each cognitive radio user, we adopt a master for a cluster which is made of several cognitive radio users. By gathering and analyzing the sensing information from cognitive radio users in the cluster, the cooperative sensing is realized. Since the transmission range of a licensed user is limited, it is possible that a master determines different sensing channels to each cognitive radio users based on their location. By making cognitive radio users sense different channels, the proposed scheme can recognize the state of wireless spectrum fast and precisely. Using the simulation, we compare the performance of the proposed scheme with those of two different compared schemes that one makes cognitive radio users recognize the frequency status based on their own sensing results and the other shares frequency status information but does not utilize the location information of licensed user. Simulation results show that the proposed scheme provides available channels as many as possible while detecting the activation of licensed user immediately.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1258-1260
• /
• 2003

Cloud's properties can be showed on different spectral channel. The 0.65${\mu}$m reflectance is mainly function of cloud optical thickness and reflectance of 1.6${\mu}$m is sensitive to cloud phase and particle size distribution. So we can use multi-spectral information to analysis cloud's microphysical properties.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.7 no.3
• /
• pp.75-83
• /
• 2011

Cognitive radio (CR) technology is to maximize the spectrum utilization by allocating the unused spectrums to the unlicensed users. This technology enables the sharing of channels among secondary (unlicensed) and primary (licensed) users on a non-interference basis after sensing the vacant channel and as a result, it is possible to harness wireless frequency more efficiently. To enhance the accuracy of sensing, RDSS was suggested. It is a fusion mechanism based on the reputation of sensing nodes and WSPRT (weighted sequential probability ratio test). However, in RDSS, the execution number of WSPRT could increase according to the order of inputted sensing values, and the fast defense against the forged values is difficult. In this paper, we propose a transaction signing-based authentication scheme for secure distributed spectrum sensing to response the forged values. The validity of proposed scheme is provided by BAN logic.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.63 no.1
• /
• pp.34-39
• /
• 2014

In cognitive radios, secondary users can use the spectrum exclusively allocated to a primary wireless system if the secondary users detect the spectrum in idle states. Because the secondary users can utilize the idle state of the spectrum, the utilization rate of the spectrum can be improved. The idle states can be detected by using secondary users' sensing schemes. However, the wireless channel environment where secondary users perform the spectrum sensing is not very friendly to secondary users because the signal-to-noise ratio of the received primary signal is very low. Hence, cooperative sensing scheme where more than one secondary user take part in the spectrum sensing is generally used in cognitive radios. In this paper, we investigate the cooperative sensing performance for machine-to-machine communication devices operated by batteries with limited energy. In general, the energy consumed for the spectrum sensing increases as the length of the sensing period and the number of cooperative sensing nodes. Accordingly, even though the total amount of the consumed energy is the same, an energy allocation methodology how to distribute the energy to the sensing period and sensing nodes can achieve the optimum sensing performance, which is numerically analyzed.

• Journal of Sensor Science and Technology
• /
• v.25 no.5
• /
• pp.344-348
• /
• 2016

We developed a 2-channel fiber-optic temperature sensor (FOTS) using a temperature sensing probe, a fiber-optic coupler, transmitting optical fiber, and an optical time domain reflectometer (OTDR). The temperature sensing probe is divided into a sensing probe and a reference probe for accurate thermometry. A sensing probe is composed of a silicon oil, a FC terminator, a brass pipe, and a singlemode optical fiber and the structure of a reference probe is identical with that of the sensing probe excluding a silicon oil. In this study, we measured the modified optical powers of the light signals reflected from the temperature sensing probe placed inside of the water with a thermal variation from 5 to $70^{\circ}C$. Although the optical power of the reference probe was constant regardless of the temperature change, the optical power of the sensing probe decreased linearly as the temperature increased. As experimental results, the FOTS using a subtraction method showed a small difference (i.e., hysteresis) in its response due to heating and cooling. The reversibility and reproducibility of the FOTS were also evaluated.