Tong Boon Tang

Toward a miniature wireless integrated multisensor microsystem for industrial and biomedical applications

This paper presents our work toward the integration of a multisensor microsystem with wireless communication, using system-on-chip (SoC) methodology. Four different forms of microelectronic sensors have been fabricated on two separate 5/spl times/5 mm/sup 2/ silicon chips measuring pH, conductivity, dissolved oxygen concentration, and temperature. The sensors are integrated with a sensor fusion chip comprising analog circuitry for sensor operation and signal amplification prior to digital decoding and transmission. The microsystem prototype will be packaged in a miniature capsule, which measures 16 mm /spl times/55 mm including batteries and dissipates 6.3 mW for a minimal life cycle of 12 h.

Vehicle Detection Techniques for Collision Avoidance Systems: A Review

Over the past decade, vision-based vehicle detection techniques for road safety improvement have gained an increasing amount of attention. Unfortunately, the techniques suffer from robustness due to huge variability in vehicle shape (particularly for motorcycles), cluttered environment, various illumination conditions, and driving behavior. In this paper, we provide a comprehensive survey in a systematic approach about the state-of-the-art on-road vision-based vehicle detection and tracking systems for collision avoidance systems (CASs). This paper is structured based on a vehicle detection processes starting from sensor selection to vehicle detection and tracking. Techniques in each process/step are reviewed and analyzed individually. Two main contributions in this paper are the following: survey on motorcycle detection techniques and the sensor comparison in terms of cost and range parameters. Finally, the survey provides an optimal choice with a low cost and reliable CAS design in vehicle industries.

Integrated micro-instrumentation for dynamic monitoring of the gastro-intestinal tract

The introduction of microsystem technology into diagnostic devices is a rapidly growing field where low form-factor can significantly improve device access or patient comfort. In this paper we present our results on a lab-in-a-pill device that uses laboratory-on-a-chip and system-on-chip technology to deliver analytical data from a range of sensors, and the methodology employed to build the device.

An integrated sensor microsystem for industrial and biomedical applications

There is considerable interest in the development of ultra-miniature and low-power sensor microsystems for use in applications such as medical diagnostics, environmental monitoring and other industrial applications. Such ultra-miniature sensor microsystems must contain a large diversity of complex electronics, including sensor interfaces, signal conditioning, a microprocessor core, digital signal processing, and wireless transmission technology. In this paper, we will describe the first steps towards the development of a System on Chip for such a sensor microsystem and the methodology employed to build such a microsystems.

Implementation of wireless power transfer and communications for an implantable ocular drug delivery system

A wireless power transfer and communication system based on near-field inductive coupling has been designed and implemented. The feasibility of using such a system to remotely control drug release from an implantable drug delivery system is addressed. The architecture of the wireless system is described and the signal attenuation over distance in both water and phosphate buffered saline is studied. Additionally, the health risk due to exposure to radio frequency (RF) radiation is examined using a biological model. The experimental results demonstrate that the system can trigger the release of drug within 5 s, and that such short exposure to RF radiation does not produce any significant (<or= 1 degrees C) heating in the biological model. The conclusion of the work is that this system could replace a chemical battery in an implantable system, eliminating the risks associated with battery failure and leakage and also allowing more compact designs for applications such as drug delivery.

A High Resolution Capacitive Sensing System for the Measurement of Water Content in Crude Oil

This paper presents the design of a non-intrusive system to measure ultra-low water content in crude oil. The system is based on a capacitance to phase angle conversion method. Water content is measured with a capacitance sensor comprising two semi-cylindrical electrodes mounted on the outer side of a glass tube. The presence of water induces a capacitance change that in turn converts into a phase angle, with respect to a main oscillator. A differential sensing technique is adopted not only to ensure high immunity against temperature variation and background noise, but also to eliminate phase jitter and amplitude variation of the main oscillator that could destabilize the output. The complete capacitive sensing system was implemented in hardware and experiment results using crude oil samples demonstrated that a resolution of ±50 ppm of water content in crude oil was achieved by the proposed design.

Methodology of Statistical RTS Noise Analysis With Charge-Carrier Trapping Models

Random telegraph signal (RTS) noise has shown an increased impact on circuit performance at advanced complementary metal-oxide-semiconductor technologies. However, there is not yet a computer-aided design tool available to analyze such noise based on the statistical distribution of traps. In this paper, a new methodology is proposed to enable integrated circuit designers to analyze their circuits in the presence of RTS noise, thus providing an opportunity to mitigate the noise effect by design. Using a 3-D atomistic simulator, compact models that could accurately describe the device structure, doping profile, and statistically representative distribution of traps were extracted for SPICE simulation. Carrier trapping/detrapping was represented by a pair of compact models at different threshold voltages, hence defining the amplitude of RTS noise. The timing parameters of RTS noise were predicted based on the Shockley-Read-Hall statistics. Simulation results reveals that, while RTS noise from a single device may have little impact (¿I out ¿ 0.0842 ¿A) on our test circuit performance, the cumulative effect from all devices (n = 30) in this relatively small circuit can be significant (¿I out ¿ 0.5766 ¿A) . This reinforces the need for an RTS noise analyzer for deep-submicrometer circuit designs.

Mental stress assessment using simultaneous measurement of EEG and fNIRS.

Previous studies reported mental stress as one of the major contributing factors leading to various diseases such as heart attack, depression and stroke. An accurate stress assessment method may thus be of importance to clinical intervention and disease prevention. We propose a joint independent component analysis (jICA) based approach to fuse simultaneous measurement of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) on the prefrontal cortex (PFC) as a means of stress assessment. For the purpose of this study, stress was induced by using an established mental arithmetic task under time pressure with negative feedback. The induction of mental stress was confirmed by salivary alpha amylase test. Experiment results showed that the proposed fusion of EEG and fNIRS measurements improves the classification accuracy of mental stress by +3.4% compared to EEG alone and +11% compared to fNIRS alone. Similar improvements were also observed in sensitivity and specificity of proposed approach over unimodal EEG/fNIRS. Our study suggests that combination of EEG (frontal alpha rhythm) and fNIRS (concentration change of oxygenated hemoglobin) could be a potential means to assess mental stress objectively.

Quantification of Parapapillary Atrophy and Optic Disc

PURPOSE A computer-aided measuring tool was devised to automatically detect and quantify both the parapapillary atrophy (PPA) and the optic disc (OD) regions in two-dimensional color fundus images of the retina. METHODS The OD region was segmented using the Chan-Vese model with a shape restraint. This region was then removed from the image (OD+PPA), which was cropped in a modified Chan-Vese approach, producing a first-order estimation of the PPA region. Its boundary is subsequently refined by using thresholding, a scanning filter, and multiseed region-growing methods. Dual channels (blue and red) in the red-green-blue space are used to minimize the interference effects of blood vessels and artifacts. RESULTS The software was tested on 94 randomly selected images of eyes with PPA from 66 subjects of a well-characterized cohort database. Our proposed algorithm achieved a mean accuracy level of 93.8% (SD 5.26) and 94.0% (SD 5.88) in estimating the size of the PPA and OD respectively, compared with the ground estimate defined by an ophthalmologist. In terms of correlation between the data of the ground estimate and our estimation, we obtained a correlation coefficient of 0.98 for both the PPA and the OD. CONCLUSIONS This software offers a means of quantifying the size of PPA on two-dimensional fundus images for the first time. The proposed algorithm is capable of detecting and quantifying PPA and OD regions repeatedly, with a mean accuracy of >93%, and could also provide additional information, such as the transverse and conjugate diameter of OD, which may be useful in eye-screening.

Mental Stress Quantification Using EEG Signals

Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack and stroke. To avoid this, stress quantification is very important for clinical intervention and disease prevention. In this study, we investigate the feasibility of exploiting Electroencephalography (EEG) signals to discriminate stress from rest state in mental arithmetic tasks. The experimental results showed that there were significant differences between the rest state and under stress at three levels of arithmetic task levels with p-values of 0.03, 0.042 and 0.05, respectively. We thus confirm the feasibility of EEG signals in detecting mental stress levels. Using support vector machine (SVM) we could detect mental stress with an accuracy of 94%, 85%, and 80% at level one, level two and level three of arithmetic problem difficulty respectively.