Immune system protects living body from various attacks by foreign invades. Based on the immune response principles, we propose an improved lateral interaction artificial immune system model in this paper. Considering that the different epitopes on the surface of antigen can be recognized by a set of different paratopes expressed on the surface of immune cells, we build a neighborhood set that consists of immune cells with different affinities to a certain input antigen. We update all the weights of the immune cells located in neighborhood set according to their affinities. Simulations on noisy pattern recognition illustrate that the proposed artificial immune system model has stronger noise tolerance ability and is more effective at recognizing noisy patterns than that of our previous models.

In this paper, we propose a new parallel algorithm for cellular radio channel assignment problem that can help the expanded maximum neural network escape from local minima by introducing a transient chaotic neurodynamics. The goal of the channel assignment problem, which is an NP-complete problem, is to minimize the total interference between the assigned channels needed to satisfy all of the communication needs. The expanded maximum neural model always guarantees a valid solution and greatly reduces search space without a burden on the parameter-tuning. However, the model has a tendency to converge to local minima easily because it is based on the steepest descent method. By adding a negative self-feedback to expanded maximum neural network, we proposed a new parallel algorithm that introduces richer and more flexible chaotic dynamics and can prevent the network from getting stuck at local minima. After the chaotic dynamics vanishes, the proposed algorithm then is fundamentally reined by the gradient descent dynamics and usually converges to a stable equilibrium point. The proposed algorithm has the advantages of both the expanded maximum neural network and the chaotic neurodynamics. Simulations on benchmark problems demonstrate the superior performance of the proposed algorithm over other heuristics and neural network methods.

In this paper, we propose the low voltage CMOS current mode reference circuit using self-regulator with adaptive biasing technique. It drastically reduces the line sensitivity (LS) of the output voltage and the power supply voltage dependence of the temperature coefficient (TC). The self-regulator used in the proposed circuit adaptively generates the minimum voltage required the reference core circuit following the PVT (process, voltage and temperature) conditions. It makes possible to improve circuit performances instead of slightly increasing minimum power supply voltage. This proposed circuit has been designed and evaluated by SPICE simulation using TSMC 65nm CMOS process with 3.3V (2.5V over-drive) transistor option. From simulation results, LS is reduced to 0.0065%/V under 0.8V < VDD < 3.0V. TC is 67.6ppm/°C under the condition that the temperature range is from -40°C to 125°C and VDD range is from 0.8V to 3.0V. The power supply rejection ratio (PSRR) is less than -80.4dB when VDD is higher than 0.8V and the noise frequency is 100Hz. According to the simulation results, we could confirm that the performances of the proposed circuit are improved compared with the conventional circuit.

In this paper, a low-voltage, wide-common-mode-range and high-CMRR OTA is presented. The proposed OTA consists of two circuit blocks; one is the input stage and operates as a differential level shifter, and the other is a highly linear output stage. Furthermore, the OTA can be operated in both weak and strong inversion regions. The proposed OTA is evaluated through Star-HSPICE with 0.18 µm CMOS device parameters (LEVEL53). Simulation results demonstrate a CMRR of 158 dB, a common-mode-input-range of 65 mV to 720 mV and a current consumption of 1.2 µA when VDD=0.8 V.

This paper describes the Fractional-N All Digital Frequency Locked Loop (ADFLL) with Robustness for PVT variation and its application for the microcontroller unit. The conventional FLL is difficult to achieve the required specification by using the fine CMOS process. Especially, the conventional FLL has some problems such as unexpected operation and long lock time that are caused by PVT variation. To overcome these problems, we propose a new ADFLL which uses dynamic selecting digital filter coefficients. The proposed ADFLL was evaluatied through the HSPICE simulation and fabricating chips using a 0.13 µm CMOS process. From these results, we observed the proposed ADFLL has robustness for PVT variation by using dynamic selecting digital filter coefficient, and the lock time is improved up to 57%, clock jitter is 0.85 nsec.

One critical "drawback" of the backpropagation algorithm is the local minima problem. We have noted that the local minima problem in the backpropagation algorithm is usually caused by update disharmony between weights connected to the hidden layer and the output layer. To solve this kind of local minima problem, we propose a modified error function with two terms. By adding one term to the conventional error function, the modified error function can harmonize the update of weights connected to the hidden layer and those connected to the output layer. Thus, it can avoid the local minima problem caused by such disharmony. Simulations on some benchmark problems and a real classification task have been performed to test the validity of the modified error function.

In this paper, we propose the novel low voltage CMOS current mode reference circuit. It reduces the minimum supply voltage by consisting the subthreshold two stage operational amplifier (OPAMP) which is regarded as the combination of the proportional to absolute temperature (PTAT) and the complementary to absolute temperature (CTAT) current generators. It makes possible to implement without extra OPAMP. This proposed circuit has been designed and evaluated by SPICE simulation using TSMC 65nm CMOS process with 3.3V (2.5V over-drive) transistor option. From simulation results, the line sensitivity is as good as 0.196%/V under the condition that the range of supply voltage (VDD) is wide as 0.6V to 3.0V. The temperature coefficient is 71ppm/ under the condition that the temperature range is from -40 to 125 and VDD=0.6V. The power supply rejection ratio (PSRR) is -47.7dB when VDD=0.6V and the noise frequency is 100Hz. According to comparing the proposed circuit with prior current mode circuits, we could confirm the performance of the proposed circuit is better than that of prior circuits.

An improved algorithm of Guided Local Search called objective function adjustment algorithm is proposed for combinatorial optimization problems. The performance of Guided Local Search is improved by objective function adjustment algorithm using multipliers which can be adjusted during the search process. Moreover, the idea of Tabu Search is introduced into the objective function adjustment algorithm to further improve the performance. The simulation results based on some TSPLIB benchmark problems showed that the objective function adjustment algorithm could find better solutions than Local Search, Guided Local Search and Tabu Search.

In this letter, two OTAs with MOSFETs operating in the weak inversion region are proposed. One of the OTAs uses the exponential-logarithm transformation algorithm. Furthermore, the other realizes the high-linearity characteristics due to a small fluctuation of the common-terminal voltage of differential pair. The performance of the proposed OTAs was confirmed by HSPICE simulation.

It is generally believed that one major function of the immune system is helping to protect multicellular organisms from foreign pathogens, especially replicating pathogens such as viruses, bacteria and parasites. The relevant events in the immune system are not only the molecules, but also their interactions. The immune cells can respond either positively or negatively to the recognition signal. A positive response would result in cell proliferation, activation and antibody secretion, while a negative response would lead to tolerance and suppression. Depending upon these immune mechanisms, an immune network model (here, we call it the binary immune network) based on the biological immune response network was proposed in our previous work. However, there are some problems like that input and memory were all binary and it did not consider the antigen diversity of immune system. To improve these problems, in this paper we propose a fuzzy immune network model by considering the antigen diversity of immune system that is the most important property to be exhibited in the immune system. As an application, the proposed fuzzy immune network is applied to pattern recognition problem. Computer simulations illustrate that the proposed fuzzy immune network model not only can improve the problems existing in the binary immune network but also is capable of clustering arbitrary sequences of large-scale analog input patterns into stable recognition categories.

Immune system protects living body from an extraordinarily large variety of bacteria, viruses, and other pathogenic organisms. Based on immunological principles, new computational techniques are being developed, aiming not only at a better understanding of the system, but also at solving engineering problems. Our overall goal for this paper is twofold: to understand the real immune system from the information processing perspective, and to use idea generated from the immune system to construct new engineering application. As one example of the latter, we propose an artificial immune system architecture inspired by the human immune system and apply it to pattern recognition. We test the proposed architecture by the simulations on arbitrary sequences of analog input pattern classification and binary input pattern recognition. The simulation results illustrate that the proposed architecture is effective at clustering arbitrary sequences of analog input patterns into stable categories and it can produce stronger noise immunity than the binary network .

A new multilayer artificial neural network learning algorithm based on the pattern search method is proposed. The learning algorithm is designed to provide a very simple and effective means of searching the minima of an objective function directly without any knowledge of its derivatives. We test this algorithm on benchmark problems, such as exclusive-or (XOR), parity and alphabetic character learning problems. For all problems, the systems are shown to be trained efficiently by our algorithm. As a simple direct search algorithm, it can be applied to hardware implementations easily.

In this letter, we describe a low power current to time converter for wireless sensor networks. The proposed circuit has some advantages of high linearity and wide measurement range. From the evaluation using HSPICE with 0.18 µm CMOS device parameters, the output differential error for the input current variation is approximately 0.1 µs/nA under the condition that the current is varied from 100 nA to 500 nA. The idle power consumption is approximately zero.

In this paper, optimization and verification of the current-mode multiple-valued digit ORNS arithmetic circuits are presented. The multiple-valued digit ORNS is the redundant number system using digit values in the multiple-valued logic and it realizes the full-parallel calculation without any ripple carry propagation. First, the 4-bit addition and multiplication algorithms employing the multiple-valued digit ORNS are optimized through logic-level analyses. In the multiplier, the maximum digit value and the number of modulo operations in series are successfully reduced from 49 to 29 and from 3 to 2, respectively, by the arrangement of addition lines. Next, circuit components such as a current mirror are verified using HSPICE. The proposed switched current mirror which has functions of a current mirror and an analog switch is effective to reduce the minimum operation voltage by about 0.13 volt. Besides an ordinary strong-inversion region, the circuit components operated under the weak-inversion region show good simulation results with the unit current of 10 nanoamperes, and it brings both of the lower power dissipation and the stable operation under the lower supply voltage.

In this paper, we propose a midpoint-validation method which improves the generalization of Support Vector Machine. The proposed method creates midpoint data, as well as a turning adjustment parameter of Support Vector Machine using midpoint data and previous training data. We compare its performance with the original Support Vector Machine, Multilayer Perceptron, Radial Basis Function Neural Network and also tested our proposed method on several benchmark problems. The results obtained from the simulation shows the effectiveness of the proposed method.

This paper presents a new multi-layered artificial immune system architecture using the ideas generated from the biological immune system for solving combinatorial optimization problems. The proposed methodology is composed of five layers. After expressing the problem as a suitable representation in the first layer, the search space and the features of the problem are estimated and extracted in the second and third layers, respectively. Through taking advantage of the minimized search space from estimation and the heuristic information from extraction, the antibodies (or solutions) are evolved in the fourth layer and finally the fittest antibody is exported. In order to demonstrate the efficiency of the proposed system, the graph planarization problem is tested. Simulation results based on several benchmark instances show that the proposed algorithm performs better than traditional algorithms.

The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. In this paper, we introduce the gaze estimation system of electrooculogram signals. Using this system, the electrooculogram signals can be recorded when the patients focused on each direct. All these recorded signals could be analyzed using math-method and the mathematical model will be set up. Gaze estimation can be recognized using electrooculogram signals follow these models.