Byoungho Kim

Neuromorphic Hardware System for Visual Pattern Recognition With Memristor Array and CMOS Neuron

This paper presents a neuromorphic system for visual pattern recognition realized in hardware. A new learning rule based on modified spike-timing-dependent plasticity is also presented and implemented with passive synaptic devices. The system includes an artificial photoreceptor, a Pr0.7Ca0.3MnO3-based memristor array, and CMOS neurons. The artificial photoreceptor consisting of a CMOS image sensor and a field-programmable gate array converts an image into spike signals, and the memristor array is used to adjust the synaptic weights between the input and output neurons according to the learning rule. A leaky integrate-and-fire model is used for the output neuron that is built together with the image sensor on a single chip. The system has 30 input neurons that are interconnected to 10 output neurons through 300 memristors. Each input neuron corresponding to a pixel in a 5 × 6 pixel image generates voltage pulses according to the pixel value. The voltage pulses are then weighted and integrated by the memristors and the output neurons, respectively, to be compared with a certain threshold voltage above which an output neuron fires. The system has been successfully demonstrated by training and recognizing number images from 0 to 9.

Non-dimensionalized performance indices based optimal grasping for multi-fingered hands

Abstract When a multi-fingered hand grasps an object, the ways to grasp it stably are infinite, and thus an optimal grasp planning is necessary to find the relatively optimized grasp points on object for achieving the objective of the given grasping and manipulating task. For this, we first define several grasp indices to evaluate the quality of each feasible grasp. Since the physical meanings of the defined grasp indices are different from each other, it is not easy to combine those indices to identify the optimal grasping. Thus, we propose a new generalized grasping performance index to represent all of the grasp indices as one measure based on a non-dimensionalizing technique. Next, by using the proposed grasping performance index, we try to determine the optimal grasp points for multi-fingered hands performing contact tasks. Through task-based simulation studies, we discuss the feasibility of each grasp index as the grasp polygons and then, we show that the trend of the proposed optimal grasp planning is coincident to the physical sense of human grasping. Furthermore, some experimental results showing the task specific performances are incorporated to corroborate the effectiveness of the proposed optimal grasp planning algorithms.

Optimal grasping based on non-dimensionalized performance indices

For a multi-fingered hand to grasp an object, there are numerous ways to grasp it stably, and thus an optimal grasp planning is necessary to find the optimal grasp point for achieving the objective of the given task. First, we define several grasp indices to evaluate the quality of each feasible grasp. Since the physical meanings of the defined grasp induces are different from each other, it is not easy to combine those indices to identify the optimal grasping. In this paper, we propose a new generalized grasping performance index to represent all of the grasp indices as one measure based on a non-dimensional technique. Through simulations, we show that the proposed optimal grasp planning is resemblant to the physical sense of human grasping.

Compressive sensing based robust multispectral double-image encryption

We demonstrate a multispectral double-image-based cryptosystem that exploits only a tiny number of random white noise samples for proper decryption. Primarily, one of the two downsampled images is converted into the phase function after being shuffled by Arnold transform (AT), while the other image is modulated as an amplitude-based image after AT. Consecutively, a full double-image encryption can be achieved by employing classical double random phase encryption (DRPE) technique in the fractional Fourier transform domain with corresponding fractional orders. In this study, the encrypted complex data is randomly sampled via compressive sensing (CS) framework by which only 25% of the sparse white noise samples are being reserved to realize decryption with zero or small errors. As a consequence, together with correct phase keys, fractional orders and proper inverse AT operators, lpminimization must be utilized to decrypt the original information. Thus, in addition to the perfect image reconstruction, the proposed cryptosystem provides an additional layer of security to the conventional DRPE system. Both the mathematical and numerical simulations were carried out to verify the feasibility as well as the robustness of the proposed system. The simulation results are presented in order to demonstrate the effectiveness of the proposed system. To the best of our knowledge, this is the first report on integrating CS with encrypted complex samples for information security.

Independent finger and independent joint-based compliance control of multifingered robot hands

In this paper, a modified two-step compliance control method for robot hands is proposed: resolved interfinger decoupling solver (RIFDS) and resolved interjoint decoupling solver (RIJDS). For this, we first investigate how many fingers are necessary to successfully implement stiffness characteristics in the operational space. RIFDS is then proposed to decompose the desired compliance characteristic specified in the operational space into the compliance characteristic in the fingertip space without interfinger coupling, and RIJDS is also proposed to decompose the compliance characteristic in the fingertip space without interjoint coupling. It is found in the process of RIFDS that some nondiagonal stiffness elements specified in the operational space cannot be planned arbitrarily, due to grasping geometry. Similar to independent finger control, RIJDS aims at independent joint control. This scheme facilitates the joint servo control. To show the effectiveness of the proposed compliance control method, some experimental results are illustrated for a compliant task by using two- and three-fingered hands, which consist of five-bar finger mechanisms. It is concluded that grasping geometry and finger structure are crucial to successfully performing multifingered hands operations.

Spectral prediction for specification-based loopback test of embedded mixed-signal circuits

Loopback testing of mixed-signal SOCs provides a low-cost test solution, but suffers from fault masking, resulting in serious yield loss and low test accuracy. This paper presents an efficient loopback test methodology which enables individual characterization of dynamic performance of devices under test (DUTs) in loopback mode. DUTs are loop-backed externally on a loadboard (DUT board), and a simple filter and an analog adder on the loadboard produce a composite loopback response. Characteristic parameters are extracted from these loopback responses, and a non-linear regression technique based on spectral predictors is used to predict various performance parameters such as Gain, SNR, THD and SINAD. The spectral predictor provides more accurate and reliable prediction compared to a time-domain approach. Both simulation and hardware measurements are presented to validate the proposed technique

A biomimetic compliance control of robot hand by considering structures of human finger

For an object grasped by a robot hand to work in the compliance control domain, we first analyze a necessary condition for successful stiffness modulation in the operational space. Next, we propose a compliance control method for robot hands which consist of two steps: the RIFDS (resolved inter-finger decoupling solver) decomposes the desired compliance characteristic specified in the operational space into the compliance characteristic in the fingertip space without inter-finger coupling; and the RIJDS (resolved inter-joint decoupling solver) decomposes the compliance characteristic in the fingertip space into the compliance characteristic in the joint space without inter-joint coupling. According to the analysis results, the finger structure should be biomimetic in the sense that either kinematic redundancy or force redundancy are required to implement the proposed compliance control scheme. Five-bar fingered robot hands are treated as illustrative examples to implement the proposed compliance control method. To show the effectiveness of the proposed compliance control method, simulations are performed for two-fingered and three-fingered robot hands.

Implementation of a portable device for real-time ECG signal analysis

BackgroundCardiac disease is one of the main causes of catastrophic mortality. Therefore, detecting the symptoms of cardiac disease as early as possible is important for increasing the patient’s survival. In this study, a compact and effective architecture for detecting atrial fibrillation (AFib) and myocardial ischemia is proposed. We developed a portable device using this architecture, which allows real-time electrocardiogram (ECG) signal acquisition and analysis for cardiac diseases.MethodsA noisy ECG signal was preprocessed by an analog front-end consisting of analog filters and amplifiers before it was converted into digital data. The analog front-end was minimized to reduce the size of the device and power consumption by implementing some of its functions with digital filters realized in software. With the ECG data, we detected QRS complexes based on wavelet analysis and feature extraction for morphological shape and regularity using an ARM processor. A classifier for cardiac disease was constructed based on features extracted from a training dataset using support vector machines. The classifier then categorized the ECG data into normal beats, AFib, and myocardial ischemia.ResultsA portable ECG device was implemented, and successfully acquired and processed ECG signals. The performance of this device was also verified by comparing the processed ECG data with high-quality ECG data from a public cardiac database. Because of reduced computational complexity, the ARM processor was able to process up to a thousand samples per second, and this allowed real-time acquisition and diagnosis of heart disease. Experimental results for detection of heart disease showed that the device classified AFib and ischemia with a sensitivity of 95.1% and a specificity of 95.9%.ConclusionsCurrent home care and telemedicine systems have a separate device and diagnostic service system, which results in additional time and cost. Our proposed portable ECG device provides captured ECG data and suspected waveform to identify sporadic and chronic events of heart diseases. This device has been built and evaluated for high quality of signals, low computational complexity, and accurate detection.

A compliance control strategy for robot manipulators under unknown environment

In this paper, a compliance control strategy for robot manipulators that employs a self-adjusting stiffness function is proposed. Based on the contact force, each entry of the diagonal stiffness matrix corresponding to a task coordinate in the operational space is adaptively adjusted during contact along the corresponding axis. The proposed method can be used for both the unconstrained and constrained motions without any switching mechanism which often causes undesirable instability and/or vibrational motion of the end-effector. The experimental results involving a two-link direct drive manipulator interacting with an unknown environment demonstrates the effectiveness of the proposed method.

Transformer-Coupled Loopback Test for Differential Mixed-Signal Specifications

Loopback tests for a differential mixed-signal device under test (DUT) have rarely been attempted, since any imbalance introduced by a design for test (DfT) circuitry on differential signaling delivers an imperfect sinusoidal wave to the DUT input, thereby degrading the DUT performance. In addition, this methodology inherently suffers from fault masking. These problems result in low test accuracy and serious yield loss. This paper presents a novel methodology for efficient prediction of individual DUT dynamic performance parameters with a radio-frequency (RF) transformer in loopback mode to overcome the imbalance problem of DfT circuitry. Cascaded RF transformer in loopback mode produces differently weighted loopback responses, which are used to characterize the DUT dynamic performance. Hardware measurement results show that this approach can be effectively used to predict the specifications of a DUT.