• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.11
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• pp.617-624
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• 2000

The negative weight can be ofter seen in Hopfield neural network, which is difficult to implement negative conductance in circuits. Usually, the inverted output of amplifier is used to avoid negative resistors for expressing the negative weights in hardware implementation. However, there is some difference between using negative resistor and the inverted output of amplifier for representing the negative weight. This difference is discussed in this paper.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.10 s.101
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• pp.1010-1019
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• 2005

In this paper, we used neural network technique to model memory effects of RF power amplifier which is fed by two-tone input signals. The memory effects in power amplifier were identified by observing the unsymmetrical distribution of IMD(Inter-Modulation Distortion) measurements with the change of tone spacings and power levels. Different asymmetries of IMD were also found at different center frequencies. We applied TDNN technique to model LDMOS power amplifier based on two tone IMD data, and the accuracy was very high compared to other modeling methods such as the(memoryless) adaptive modeling method.

• BioChip Journal
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• v.12 no.4
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• pp.332-339
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• 2018

The future neuro-prosthetic devices would be required spikes data monitoring through sub-nanoscale transistors that enables to neuroscientists and clinicals for scalable, wireless and implantable applications. This research investigates the spikes monitoring through integrated CNT front-end amplifier for neuro-prosthetic diagnosis. The proposed carbon nanotube-based architecture consists of front-end amplifier (FEA), integrate fire neuron and pseudo resistor technique that observed high electrical performance through neural activity. A pseudo resistor technique ensures large input impedance for integrated FEA by compensating the input leakage current. While carbon nanotube based FEA provides low-voltage operation with directly impacts on the power consumption and also give detector size that demonstrates fidelity of the neural signals. The observed neural activity shows amplitude of spiking in terms of action potential up to $80{\mu}V$ while local field potentials up to 40 mV by using proposed architecture. This fully integrated architecture is implemented in Analog cadence virtuoso using design kit of CNT process. The fabricated chip consumes less power consumption of $2{\mu}W$ under the supply voltage of 0.7 V. The experimental and simulated results of the integrated FEA achieves $60G{\Omega}$ of input impedance and input referred noise of $8.5nv/{\sqrt{Hz}}$ over the wide bandwidth. Moreover, measured gain of the amplifier achieves 75 dB midband from range of 1 KHz to 35 KHz. The proposed research provides refreshing neural recording data through nanotube integrated circuit and which could be beneficial for the next generation neuroscientists.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.10
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• pp.1157-1162
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• 2007

In this paper, E-TDLNN(Expanded-Tapped Delay Line Neural Network) method is suggested to make the model of power amplifier effectively. This method is the one for making the model of power amplifier through the study in neural network to the target value, the measured output spectrum of power amplifier, after adding the external value factor, gate bias, as an invariant input to the TDLNN method which suggested the memory effect of power amplifier effectively. To prove the validity of suggested method, the data at 2 points, 3.45 V and 3.50 V of gate bias range $3.4{\sim}3.6V$ with the 0.01 V step change, are studied and the predicted results at the gate bias 3.40 V, 3.48 V, 3.53 V and 3.60 V shows good coincidence with the measured values.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.261-264
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• 2004

In this paper, we identify the memory effect of high power(125W) laterally diffused metal oxide-semiconductor(LDMOS) RF Power Amplifier(PA) by two tone IMD measurement. We measure two tone IMD by changing the tone spacing and the power level. Different asymmetric IMD is founded at different center frequency measurements. We propose the Tapped Delay Line-Neural Network(TDNN) technique as the modeling method of LDMOS PA based on two tone IMD data. TDNN's modeling accuracy is highly reasonable compared to the memoryless adaptive modeling method.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.10
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• pp.34-39
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• 2016

A low-voltage, low-power analog front-end IC for neural recording implant devices is presented. The proposed IC consists of a low-noise neural amplifier and a programmable active bandpass filter to process neural signals residing in the band of 1 Hz to 5 kHz. The neural amplifier is based on a source-degenerated folded-cascode operational transconductance amplifier (OTA) for good noise performance while the following bandpass filter utilizes a low-power current-mirror based OTA with programmable high-pass cutoff frequencies from 1 Hz to 300 Hz and low-pass cutoff frequencies from 300 Hz to 8 kHz. The total recording analog front-end provides 53.1 dB of voltage gain, $4.68{\mu}Vrms$ of integrated input referred noise within 1 Hz to 10 kHz, and noise efficiency factor of 3.67. The IC is designed using $18-{\mu}m$ CMOS process and consumes a total of $3.2{\mu}W$ at 1-V supply voltage. The layout area of the IC is $0.19 mm^2$.

• Proceedings of the KIEE Conference
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• 2005.07c
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• pp.2327-2329
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• 2005

In this paper, we identify the memory effects of the RF high-power base station amplifiers with Vector Signal Analyzer (VSA). It is found that the model of power- amplifier using Tapped Delayed Neural - Networks with back-propagation algorithm shows very accurate modeling performance. Based on this behavioral modeling, we conducted inverse compensation process which also uses Neural Networks.

• Journal of Biomedical Engineering Research
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• v.17 no.2
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• pp.179-188
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• 1996

A clinically oriented 32-channel electroencephalogram (EEG) and evoked potential (EP) mapping system has been developed EEG and EP signals acquired from 32-channel electrodes attached on the heroid surface are amplified by a pre-amplifier which is separated from main amplifier and is located near the patient to reduce signal attenuation and noise contamination between electrodes and the amplifier. The amplified signals are further amplified by a main amplifier where various filtering and gain contr61 are achieved An automatic artifact rejection scheme is employed using neural network-based EEG and artifact classifier, by which examination time is substantially reduce4 The continuously measured EEG sigrlals are used for spectral mapping, and auditory and visual evoked potentials measured in synchronous to the auditory and visual stimuli are used for temporal evoked potential mapping. A user-friendly graphical interface based on the Microsoft Window 3.1 is developed for the operation of the system. Statistical databases for comparisons of group and individual are included to support a statistically-based diagnosis.

• ETRI Journal
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• v.46 no.2
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• pp.227-237
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• 2024

This study proposes a novel, conditionally applied neural network technique to reduce the overall peak-to-average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) system while maintaining an acceptable bit error rate (BER) level. The main purpose of the proposed scheme is to adjust only those subcarriers whose peaks exceed a given threshold. In this respect, the developed C-ANN algorithm suppresses only the peaks of the targeted subcarriers by slightly shifting the locations of their corresponding frequency samples without affecting their phase orientations. In turn, this achieves a reasonable system performance by sustaining a tolerable BER. For practical reasons and to cover a wide range of application scenarios, the threshold for the subcarrier peaks was chosen to be proportional to the saturation level of the nonlinear power amplifier used to pass the generated OFDM blocks. Consequently, the optimal values of the factor controlling the peak threshold were obtained that satisfy both reasonable PAPR reduction and acceptable BER levels. Furthermore, the proposed system does not require a recovery process at the receiver, thus making the computational process less complex. The simulation results show that the proposed system model performed satisfactorily, attaining both low PAPR and BER for specific application settings using comparatively fewer computations.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.4
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• pp.279-285
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• 2016

Reliable recording and analysis of peripheral nerve activity is important to recognize the user's intention for controlling a neuro-prosthetic hand. In this paper, we present a peripheral nerve recording system that consisted of an intrafascicular multi-electrode array, an electrode insertion device, and a multi-channel neural amplifier. The 16 channel multi-electrode array was stably implanted into the sciatic nerve of the rat under anesthesia using the electrode insertion device. During passive movements and mechanical stimuli, muscle and cutaneous afferent signals were recorded with the multi-channel neural amplifier. Furthermore, we propose a spike sorting method to isolate individual neuronal unit. The muscle proprioceptive units were classified as muscle spindle afferents or Golgi tendon organ afferents, and the skin exteroceptive units were categorized as slow adapting afferents or fast adapting afferents. Experimental results showed that the proposed method could be applicable to record and analyze peripheral nerve activity in neuro-prosthetic systems.