System level performance and yield optimisation for analogue integrated circuits

Advances in silicon technology over the last decade have led to increased integration of analogue and digital functional blocks onto the same single chip. In such a mixed signal environment, the analogue circuits must use the same process technology as their digital neighbours. With reducing transistor sizes, the impact of process variations on analogue design has become prominent and can lead to circuit performance falling below specification and hence reducing the yield.This thesis explores the methodology and algorithms for an analogue integrated circuit automation tool that optimizes performance and yield. The trade-offs between performance and yield are analysed using a combination of an evolutionary algorithm and Monte Carlo simulation. Through the integration of yield parameter into the optimisation process, the trade off between the performance functions can be better treated that able to produce a higher yield. The results obtained from the performance and variation exploration are modelled behaviourally using a Verilog-A language. The model has been verified with transistor level simulation and a silicon prototype.For a large analogue system, the circuit is commonly broken down into its constituent sub-blocks, a process known as hierarchical design. The use of hierarchical-based design and optimisation simplifies the design task and accelerates the design flow by encouraging design reuse.A new approach for system level yield optimisation using a hierarchical-based design is proposed and developed. The approach combines Multi-Objective Bottom Up (MUBU) modelling technique to model the circuit performance and variation and Top Down Constraint Design (TDCD) technique for the complete system level design. The proposed method has been used to design a 7th order low pass filter and a charge pump phase locked loop system. The results have been verified with transistor level simulations and suggest that an accurate system level performance and yield prediction can be achieved with the proposed methodology

A Novel Sample Based Quadrature Phase Shift Keying Demodulator

This paper presents a new practical QPSK receiver that uses digitized samples of incoming QPSK analog signal to determine the phase of the QPSK symbol. The proposed technique is more robust to phase noise and consumes up to 89.6% less power for signal detection in demodulation operation. On the contrary, the conventional QPSK demodulation process where it uses coherent detection technique requires the exact incoming signal frequency; thus, any variation in the frequency of the local oscillator or incoming signal will cause phase noise. A software simulation of the proposed design was successfully carried out using MATLAB Simulink software platform. In the conventional system, at least 10 dB signal to noise ratio (SNR) is required to achieve the bit error rate (BER) of 10−6, whereas, in the proposed technique, the same BER value can be achieved with only 5 dB SNR. Since some of the power consuming elements such as voltage control oscillator (VCO), mixer, and low pass filter (LPF) are no longer needed, the proposed QPSK demodulator will consume almost 68.8% to 99.6% less operational power compared to conventional QPSK demodulator

Comparison of the effectiveness of AICA-WT technique in discriminating vascular dementia EEGs

The aim of the present study was to select the optimal denoising technique that helps in discriminating dementia in the early stages and illustrating its degree of severity. In this paper, a comparative analysis of three denoising techniques, which are wavelet (WT), automatic independent component analysis (AICA) rejection, and automatic hybrid technique using independent component analysis and wavelet (AICA-WT), has been conducted to select the optimal denoising technique. Two approaches have been used to extract meaningful features these are Permutation entropy (PEn) and Higuchi's fractal dimension (FD) from Electroencephalography (EEG) dataset of 5 vascular dementia (VD) patients, 15 stroke-related patients with mild cognitive impairment (MCI) and 15 healthy subjects during working memory task (WMT). k-nearest neighbors (kNN) classifier has been used. The results show that the AICA-WT denoising technique improved the kNN classification accuracy from 88.15% for WT and 89.26% for AICA rejection to 90.37%for AICA-WT denoising technique. These results suggest AICA-WT consistently improves the discrimination of VD, MCI patients and control normal subjects which are useful for dementia early detection

Review on support vector machine (SVM) classifier for human emotion pattern recognition from EEG signals

This study reviewed the strategy in pattern classification for human emotion recognition system based on Support Vector Machine (SVM) classifier on Electroencephalography (EEG) signal. SVM has been widely used as a classifier and has been reported as having minimum error and produce accurate classification. However, the accuracy is influenced by many factors such as the electrode placement, equipment used, preprocessing techniques and selection of feature extraction methods. There are many types of SVM classifier such as SVM via Radial Basis Function (RBF), Linear Support Vector Machine (LSVM) and Multiclass Least Squares Support Vector Machine (MC-LS-SVM). SVM via RBF states the average accuracy rate of 92.73, 85.41, 93.80 and 67.40% using different features extraction method, respectively. The accuracy using LSVM and MC-LS-SVM classifier are 91.04 and 77.15%, respectively. Although, the accuracy rate influenced by many factors in the experimental works, SVM always shows their function as a great classifier. This study will discuss and summarize a few related works of EEG signals in classifying human emotion using SVM classifier

Hardware Implementation of 32-Bit High-Speed Direct Digital Frequency Synthesizer

The design and implementation of a high-speed direct digital frequency synthesizer are presented. A modified Brent-Kung parallel adder is combined with pipelining technique to improve the speed of the system. A gated clock technique is proposed to reduce the number of registers in the phase accumulator design. The quarter wave symmetry technique is used to store only one quarter of the sine wave. The ROM lookup table (LUT) is partitioned into three 4-bit sub-ROMs based on angular decomposition technique and trigonometric identity. Exploiting the advantages of sine-cosine symmetrical attributes together with XOR logic gates, one sub-ROM block can be removed from the design. These techniques, compressed the ROM into 368 bits. The ROM compressed ratio is 534.2 : 1, with only two adders, two multipliers, and XOR-gates with high frequency resolution of 0.029 Hz. These techniques make the direct digital frequency synthesizer an attractive candidate for wireless communication applications

Feature Selection Analysis of Chewing Activity Based on Contactless Food Intake Detection

This paper presents the feature selection methods for chewing activity detection. Chewing detection typically used for food intake monitoring applications. The work aims to analyze the effect of implementing optimum feature selection that can improve the accuracy of the chewing detection.  The raw chewing data is collected using a proximity sensor. Pre-process procedures are implemented on the data using normalization and bandpass filters. The searching of a suitable combination of bandpass filter parameters such as lower cut-off frequency (Fc1) and steepness targeted for best accuracy was also included. The Fc1 was 0,5Hz, 1.0Hz and 1.2H, while the steepness varied from 0.75 to 0.9 with an interval of 0.5. By using the bandpass filter with the value of [1Hz, 5Hz] with a steepness of 0.8, the system’s accuracy improves by 1.2% compared to the previous work, which uses [0.5Hz, 5Hz] with a steepness of 0.85. The accuracy of using all 40 extracted features is 98.5%. Two feature selection methods based on feature domain and feature ranking are analyzed. The features domain gives an accuracy of 95.8% using 10 features of the time domain, while the combination of time domain and frequency domain gives an accuracy of 98% with 13 features. Three feature ranking methods were used in this paper: minimum redundancy maximum relevance (MRMR), t-Test, and receiver operating characteristic (ROC). The analysis of the feature ranking method has the accuracy of 98.2%, 85.8%, and 98% for MRMR, t-Test, and ROC with 10 features, respectively. While the accuracy of using 20 features is 98.3%, 97.9%, and 98.3% for MRMR, t-Test, and ROC, respectively. It can be concluded that the feature selection method helps to reduce the number of features while giving a good accuracy

Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition

The aim of the present study was to discriminate the electroencephalogram (EEG) of 5 patients with vascular dementia (VaD), 15 patients with stroke-related mild cognitive impairment (MCI), and 15 control normal subjects during a working memory (WM) task. We used independent component analysis (ICA) and wavelet transform (WT) as a hybrid preprocessing approach for EEG artifact removal. Three different features were extracted from the cleaned EEG signals: spectral entropy (SpecEn), permutation entropy (PerEn) and Tsallis entropy (TsEn). Two classification schemes were applied - support vector machine (SVM) and k-nearest neighbors (kNN) - with fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) as a dimensionality reduction technique. The FNPAQR dimensionality reduction technique increased the SVM classification accuracy from 82.22% to 90.37% and from 82.6% to 86.67% for kNN. These results suggest that FNPAQR consistently improves the discrimination of VaD, MCI patients and control normal subjects and it could be a useful feature selection to help the identification of patients with VaD and MCI

Human emotion classifications for automotive driver using skin conductance response signal

Risky driving, speeding, and fatigue are the main causes of traffic accidents in Malaysia. Risky driving is an attitude associated with human states of emotion. Emotions detected using facial and body movements, sounds and physiological changes which required multiple and bulky instruments such as camera, voice recorder and sensors. In this study, skin conductance response (SCR) was investigated to overcome these drawback. The main goal of this study is to recognize human emotions by using a nonintrusive sensor and low-design-complexity protocols. Five emotions, namely, happiness, sadness, disgust, fear, and anger, were identified to have a close relationship with risky driving. The emotions were elicited by using image, video-audio, and video stimulus techniques and 960 Hz raw signal sampling rate was recorded. The video clip stimulus method showed 95.7% efficacy in detecting happiness and anger. The affective assessment classification rate obtained from SCR processing was more than 70% accuracy based on the off-line support vector machine classifier-processing algorithm. Overall results confirmed that the SCR signal should be considered in the future as one of the physiological signals in automated real-time emotions recognition systems

Optical Interconnect Waveguide in Electronic Circuit

The increasing demand in silicon nano-photonics has encouraged many researchers to put more efforts to explore the feasibility of using optics in the communication medium in order to replace the conventional electrical interconnects (EIs). In this paper, we proposed a SOI- based waveguide in the optical interconnect (OI) link at an operating frequency of 1550nm to work as an interconnection path in a circuit. The performance capability of the OI link was tested using a two-stage CE amplifier to work as the interconnection path from the 1st stage to the 2nd stage amplifier. In term of optical performances, the optical waveguide interconnect managed to achieve a single mode condition for a TE mode and fulfill the receiver sensitivity of a photodiode. While, in term of electrical performance, a two-stage CE amplifier is able to produce a high gain, a wide bandwidth and high slew rate. The proposed implementation of the OIs waveguide is succesfully enhance the performance of the two-stage CE amplifier as well as the analog electronic circuit applications

Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing

The aim of the present study was to reveal markers from the electroencephalography (EEG) using approximation entropy (ApEn) and permutation entropy (PerEn). EEGs' of 15 stroke-related patients with mild cognitive impairment (MCI) and 15 control healthy subjects during a working memory (WM) task have EEG artifacts were removed using a wavelet (WT) based method. A t-test (p <; 0.05) was used to test the hypothesis that the irregularity (ApEn and PerEn) in MCIs was reduced in comparison with control subjects. ApEn and PerEn showed reduced irregularity in the EEGs of MCI patients. Therefore, ApEn and PerEn could be used as markers associated with MCI detection and identification and the EEG could be a valuable tool for inspecting the background activity in the identification of patients with MCI