• Journal of the Institute of Convergence Signal Processing
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• v.14 no.4
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• pp.249-254
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• 2013

A fast multiplier and ADC are essential to process the analog signals in real time. The double-base number system(DBNS) is known as an efficient method for this purpose. The DBNS uses the numbers 2 and 3 as the base numbers simultaneously. The system has an advantage of fast multiplication, less chip area, and low power consumption compared to the binary multiplier. However, the inherent errors of the log number's intrinsic tolerance in DBNS are accumulated in a FIR digital filter, so the signal-to-noise ratio(SNR) has a tendency to be degraded. In this paper, the nonlinear encoder of ADC is designed to compensate the accumulated errors of DBNS by analysing the error distributions of various filter coefficients. The new ADC does not sacrifice its own advantages because the encoder circuits are modified only. The experiments were done with an FIR filters those were designed to have -70dB of SNR in stop band. The proposed nonlinear ADC encoder could drop the SNR to -45dB in stop band, in contrast to -35dB with the linear encoder.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.54-61
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• 2008

This paper presents a new TIQ based CMOS flash 6-bit ADC to process digital signal in real time. In order to improve the conversion speed of ADC by designing new logic or layout of ADC circuits, a new design method is proposed in encoding logic circuits. The proposed encoding circuits convert analog input into digitally encoded double base number system(DBNS), which uses two bases unlike the normal binary representation scheme. The DBNS adopts binary and ternary radix to enhance digital arithmetic processing capability. In the DBNS, the addition and multiplication can be processed with just shift operations only. Finding near canonical representation is the most important work in general DBNS. But the main disadvantage of DBNS representation in ADC is the fan-in problem. Thus, an equal distribution algorithm is developed to solve the fan-in problem after assignment the prime numbers first. The conversion speed of simulation result was 1.6 GSPS, at 1.8V power with the Magna $0.18{\mu}m$ CMOS process, and the maximum power consumption was 38.71mW.

• Journal of Electrical Engineering and Technology
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• v.3 no.3
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• pp.430-435
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• 2008

In the paper, an algorithm called a Constraint algorithm is proposed to solve the fan-in problem occurred in ADC encoding circuits. The Flash ADC architecture uses a double-base number system (DBNS). The DBNS has known to represent the multi-dimensional logarithmic number system (MDLNS) used for implementing the multiplier accumulator architecture of FIR filter in digital signal processing (DSP) applications. The authors use the DBNS with the base 2 and 3 to represent binary output of ADC. A symmetric map is analyzed first, and then asymmetric map is followed to provide addition read DBNS to DSP circuitry. The simulation results are shown for the Double-Base Integer Encoder (DBIE) of the 6-bit ADC to demonstrate an effectiveness of the Constraint algorithm, using $0.18{\mu}\;m$ CMOS technology. The DBIE’s processing speed of the ADC is fast compared to the FAT tree encoder circuit by 0.95 GHz.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.3
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• pp.224-229
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• 2008

In the paper, a new Constraint algorithm is proposed to solve the fan-in problem occurred in the encoding circuitry of an ADC. The Flash ADC architecture uses a Double-Base Number System(DBNS). The DBNS has been known to represent the Multidimensional Logarithmic Number System (MDLNS) used for implementing the multiplier accumulator architecture of FIR filter in Digital Signal Processing (DSP) applications. The authors use the DBNS with the base 2 and 3 in designing ADC encoder circuits, which is called as Double Base Integer Encoder(DBIE). A symmetric map is analyzed first, and then asymmetric map is followed to provide addition ready DBNS for DSP circuitry. The simulation results of the DBIE circuits in 6-bit and 8-bit ADC show the effectiveness of the Constraint algorithm with $0.18{\mu}m$ CMOS technology. The DBIE yields faster processing speed compared to the speed of Fat Tree Encoder (FAT) circuits by 17% at more power consumption by 39%.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.139-144
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• 2011

The modern digital logic technology does not yet satisfy the speed requirements of real-time DSP circuits due to synchronized operation of multiplication and accumulation. This operation degrades DSP performance. Therefore, the double-base number system (DBNS) has emerged in DSP system as an alternative methodology because of fast multiplication and hardware simplicity. In this paper, authors propose a novel multiplication architecture. One operand is an output of a flash analog-to-digital converter (ADC) in DBNS format, while the other operand is a coefficient in the IEEE standard floating-point number format. The DBNS digital output from ADC is produced through a new double base number encoder (DBNE). The multiplied output is in the format of the IEEE standard floating-point number (FPNS). The proposed circuits process multiplication and conversion together. Compared to a typical multiplier that uses the FPNS, the proposed multiplier also consumes 45% less gates, and 44% faster than the FPNS multiplier on Spartan-3 FPGA board. The design is verified with FIR filter applications.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.285-294
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• 2007

In this paper, we investigate a novel online estimation algorithm for dynamic Bayesian network(DBN) parameters, given as conditional probabilities. We sequentially update the parameter adjustment rule based on observation data. We apply our algorithm to two well known representations of DBNs: to a first-order Markov Chain(MC) model and to a Hidden Markov Model(HMM). A sliding window allows efficient adaptive computation in real time. We also examine the stochastic convergence and stability of the learning algorithm.

• ETRI Journal
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• v.34 no.2
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• pp.256-259
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• 2012

The double-base number system has been used in digital signal processing systems for over a decade because of its fast inner product operation and low hardware complexity. This letter proposes an innovative multiplier architecture using hybrid operands. The multiplier can easily be linked to flash analog-to-digital converters or digital systems through a double-base number encoder (DBNE) for realtime signal processing. The design of the DBNE and the multiplier enable faster digital signal processing and require less hardware resources compared to the binary processing method.

• ETRI Journal
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• v.40 no.6
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• pp.774-787
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• 2018

Arabic handwriting segmentation and recognition is an area of research that has not yet been fully understood. Dealing with Arabic ligature segmentation, where the Arabic characters are connected and unconstrained naturally, is one of the fundamental problems when dealing with the Arabic script. Arabic character-recognition techniques consider ligatures as new classes in addition to the classes of the Arabic characters. This paper introduces an enhanced technique for Arabic handwriting recognition using the deep belief network (DBN) and a new morphological algorithm for ligature segmentation. There are two main stages for the implementation of this technique. The first stage involves an enhanced technique of the Sari segmentation algorithm, where a new ligature segmentation algorithm is developed. The second stage involves the Arabic character recognition using DBNs and support vector machines (SVMs). The two stages are tested on the IFN/ENIT and HACDB databases, and the results obtained proved the effectiveness of the proposed algorithm compared with other existing systems.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.10
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• pp.957-962
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• 2010

If we want to recognize the human's emotion via the facial image, first of all, we need to extract the emotional features from the facial image by using a feature extraction algorithm. And we need to classify the emotional status by using pattern classification method. The AAM (Active Appearance Model) is a well-known method that can represent a non-rigid object, such as face, facial expression. The Bayesian Network is a probability based classifier that can represent the probabilistic relationships between a set of facial features. In this paper, our approach to facial feature extraction lies in the proposed feature extraction method based on combining AAM with FACS (Facial Action Coding System) for automatically modeling and extracting the facial emotional features. To recognize the facial emotion, we use the DBNs (Dynamic Bayesian Networks) for modeling and understanding the temporal phases of facial expressions in image sequences. The result of emotion recognition can be used to rehabilitate based on biofeedback for emotional disabled.