• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.270-274
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• 2008

This paper proposes an advanced signal processing circuit for a fingerprint sensor based on ridge resistivity. A novel fingerprint integrated sensor using ridge resistivity variation resulting from ridges and valleys on the fingertip is presented. The pixel level simple detection circuit converts from a small and variable sensing current to binary voltage out effectively. The sensor circuit blocks were designed and simulated in a standard CMOS 0.35 ${\mu}m$ process.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.857-869
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• 2016

This paper proposes an X-MAC/BEB protocol that runs a binary exponential backoff (BEB) algorithm on top of an X-MAC protocol to save more energy by reducing collision, especially in densely populated wireless sensor networks (WSNs). X-MAC, a lightweight asynchronous duty cycle medium access control (MAC) protocol, was introduced for spending less energy than its predecessor, B-MAC. One of X-MAC 's conspicuous technique is a mechanism to allow senders to promptly send their data when their receivers wake up. X-MAC, however, has no mechanism to deal with sudden traffic fluctuations that often occur whenever closely located nodes simultaneously diffuse their sense data. To precisely evaluate the impact of the BEB algorithm on X-MAC, this paper builds an analytical model of X-MAC/BEB that integrates the BEB model with the X-MAC model. The analytical and simulation results confirmed that X-MAC/BEB outperformed X-MAC in terms of throughput, delay, and energy consumption, especially in congested WSNs.

• Journal of Sensor Science and Technology
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• v.25 no.5
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• pp.326-332
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• 2016

A novel CMOS temperature sensor with binary output is implemented by using fully differential switched-capacitor circuits for resistorless implementation of the temperature sensor core. Temperature sensing is based on the temperature characteristics of the pn diodes implemented by substrate pnp transistors fabricated using standard CMOS processes. The binary outputs are generated by using the charge-balance principle that eliminates the division operation of the PTAT voltage by the bandgap reference voltage. The chip was designed in a MagnaChip $0.35-{\mu}m$ CMOS process, and the designed circuit was verified using Spectre circuit simulations. The verified circuit was laid out in an area of $950{\mu}m{\times}557 {\mu}m$ and is currently under fabrication.

• Bulletin of the Korean Chemical Society
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• v.26 no.1
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• pp.47-50
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• 2005

Solvatochromic fluorescence behavior of 8-aminoquinoline based benzothiazole derivative in varying solvent systems has been investigated. Benzothiazole appended 8-aminoquinoline 3 showed distinctive fluorescence color changes depending upon the solvent polarities and the fluorescence color changes occurred over relatively wide span in visible region from 486 nm to 598 nm which can be detected with naked eye. Compound 3 also exhibited significant spectral shifts in ${\lambda}_{em}$ as a function of water composition in binary aqueous solvent systems. The changes are due to the specific interaction of 3 by hydrogen bonding with water as well as general solvent effect. The observed solvatochromic fluorescence characteristics of 3 could be used as a new probe for the micro-environmental polarity changes as well as a sensitive sensor for the determination of water composition in binary aqueous solutions.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.10
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• pp.917-923
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• 2005

In this paper, effect of catalytic configuration on the sensing properties of $SnO_2$ nanoparticle gas sensitive thick film was investigated. Two types of catalytic configuration, mono and binary, were made on the $SnO_2$ nanoparticle. In case of mono catalytic system, $3 wt\%$ Pd or Pt catalyst was doped onto the $SnO_2$ nanoparticle, respectively. In case of binary catalytic system, Pd and Pt was doped simultaneously with concentration ratio of 1:2 to 2:1 onto the $SnO_2$ nanoparticle. After doping, gas sensitive thick film was printed on alumina substrate and heat-treated at 450 to $600^{\circ}C$. Gas sensing properties was evaluated using 500 to 10,000 ppm $CH_4$ gas. As a result, gas sensitive thick film with binary catalytic system showed unstable phenomena that the gas sensitivity was changed according to aging time. In contrary, the mono catalytic system showed relatively stable phenomena despite of aging time. Especially, gas sensitive thick film doped with $3 wt\%$ Pt catalyst and heat-treated at $500^{\circ}C$ showed good sensing properties such as 0.57 of $R_{3500}/R_{1000}$ and very small variation within $3.5\%$ after aging for 5 hours, and response time was very short less than 20 seconds.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.152-157
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• 2024

In this study, we propose a novel 8T ternary SRAM that can process three logic values (0, 1, and 2) with only two additional transistors, compared with the conventional 6T binary SRAM. The circuit structure consists of positive and negative ternary inverters (PTI and NTI, respectively) with carbon-nanotube field-effect transistors, replacing conventional cross-coupled inverters. In logic '0' or '2,' the proposed SRAM cell operates the same way as conventional binary SRAM. For logic '1,' it works differently as storage nodes on each side retain voltages of V_DD/2 and V_DD, respectively, using the subthreshold current of two additional transistors. By applying the ternary system, the data capacity increases exponentially as the number of cells increases compared with the 6T binary SRAM, and the proposed design has an 18.87% data density improvement. In addition, the Synopsys HSPICE simulation validates the reduction in static power consumption by 71.4% in the array system. In addition, the static noise margins are above 222 mV, ensuring the stability of the cell operation when V_DD is set to 0.9 V.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4754-4773
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• 2018

In sensor medium access control (SMAC) protocol, sensor nodes can only access the channel in the scheduling and listening period. However, this fixed working method may generate data latency and high conflict. To solve those problems, scheduling duty in the original SMAC protocol is divided into multiple small scheduling duties (micro duty MD). By applying different micro-dispersed contention channel, sensor nodes can reduce the collision probability of the data and thereby save energy. Based on the given micro-duty, this paper presents an adaptive duty cycle (DC) and back-off algorithm, aiming at detecting the fixed duty cycle in SMAC protocol. According to the given buffer queue length, sensor nodes dynamically change the duty cycle. In the context of low duty cycle and low flow, fair binary exponential back-off (F-BEB) algorithm is applied to reduce data latency. In the context of high duty cycle and high flow, capture avoidance binary exponential back-off (CA-BEB) algorithm is used to further reduce the conflict probability for saving energy consumption. Based on the above two contexts, we propose an improved SMAC protocol, micro duty adaptive SMAC protocol (MDA-SMAC). Comparing the performance between MDA-SMAC protocol and SMAC protocol on the NS-2 simulation platform, the results show that, MDA-SMAC protocol performs better in terms of energy consumption, latency and effective throughput than SMAC protocol, especially in the condition of more crowded network traffic and more sensor nodes.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.931-938
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• 2016

Application of vector control scheme for consumer products is enlarging to improve control performance. For the field-oriented control, accurate position detection is essential and generally requires expensive sensors. On the other hand, cost-reduction is important in home appliances, so that binary-type Hall-effect sensors are commonly used rather than using an expensive sensor such as an encoder. The control performance is directly influenced by the accuracy of the position information, and there exist non-uniformities related to Hall sensors in electrical and mechanical aspects, which result in distorted position information. Therefore, to get high-precision position information from low-resolution Hall sensors, this paper proposes a new position estimator consisting of a Kalman filter and feedforward compensation scheme, which generates a linearly changing position signal. The efficacy of the proposed scheme is verified by simulation and experimental results carried out with a 48-pole permanent magnet motor.

• Journal of Sensor Science and Technology
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• v.24 no.1
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• pp.47-53
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• 2015

Fall-related injuries in elderly people are a major health care problem. This paper introduces determination of fall direction before impact using support vector machine (SVM). Once a falling phase is detected, dynamic characteristic parameters measured by the accelerometer and gyroscope and then processed by a Kalman filter are used in the SVM to determine the fall directions, i.e., forward (F), backward (B), rightward (R), and leftward (L). This paper compares the determination sensitivities according to the selected parameters for the SVM (velocities, tilt angles, vs. accelerations) and sensor attachment locations (waist vs. chest) with regards to the binary classification (i.e., F vs. B and R vs. L) and the multi-class classification (i.e., F, B, R, vs. L). Based on the velocity of waist which was superior to other parameters, the SVM in the binary case achieved 100% sensitivities for both F vs. B and R vs. L, while the SVM in the multi-class case achieved the sensitivities of F 93.8%, B 91.3%, R 62.3%, and L 63.6%.

• Journal of IKEEE
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• v.26 no.3
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• pp.364-372
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• 2022

This paper proposes the design and implementation results for human and object classification systems utilizing frequency modulated continuous wave (FMCW) radar sensor. Such a system requires the process of radar sensor signal processing for multi-target detection and the process of deep learning for the classification of human and object. Since deep learning requires such a great amount of computation and data processing, the lightweight process is utmost essential. Therefore, binary neural network (BNN) structure was adopted, operating convolution neural network (CNN) computation in a binary condition. In addition, for the real-time operation, a hardware accelerator was implemented and verified via FPGA platform. Based on performance evaluation and verified results, it is confirmed that the accuracy for multi-target classification of 90.5%, reduced memory usage by 96.87% compared to CNN and the run time of 5ms are achieved.