Pui-In Mak

Bio: Pui-In Mak is an academic researcher from University of Macau. The author has contributed to research in topics: CMOS & Computer science. The author has an hindex of 30, co-authored 353 publications receiving 3937 citations. Previous affiliations of Pui-In Mak include University of Miami & University of Lisbon.

Transceiver architecture selection: Review, state-of-the-art survey and case study

Abstract: © P H O T O D IS C A N D C R E A TA S Feature

A 0.016-mm $^{2}$ 144- $\mu$ W Three-Stage Amplifier Capable of Driving 1-to-15 nF Capacitive Load With $> $ 0.95-MHz GBW

Abstract: High-color-depth LCD drivers require nF-range capacitors as the charge reservoirs to handle the glitch energy during the conversion of the DAC [1]. The reference buffers based on multi-stage amplifiers can enhance the precision under low-voltage supplies, but are exposed to instability when loaded by such large capacitive loads (C L ). Frequency compensation via damping-factor control [2] is capable of extending the C L -drivability up to 1nF, however, at the cost of penalizing the power (426μW) and area (0.14mm2). Although recent works [3–4] have enhanced gain-bandwidth product (GBW) and slew rate (SR) showing better FOM S (=GBW·C L /Power) and FOM L (=SR·C L /Power), the C L -drivability has not been improved (i.e., 0.8nF in [3] and 0.15nF in [4]). This paper describes a three-stage amplifier managed to afford particularly large and wide range of C L (1 to 15nF) with optimized power (144μW) and die size (0.016mm2), being very suitable for compact LCD drivers [5] with different resolution targets. The design barriers are methodically surmounted via local feedback loop (LFL) analysis expanded from [6], which is an insightful control-centric method. Measured at 15nF C L , the attained FOM S (FOM L ) is >4.48× (>2.55×) beyond that of the state-of-the-art (Fig. 21.6.1).

Implementation of SSVEP based BCI with Emotiv EPOC

Abstract: In recent years, steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has received much attentions. However, most SSVEP based BCI devices are not portable and have high price, which are not suitable to be used for clinical and commercial purpose. Thanks to the low cost and portable Emotiv EPOC, it brings BCI into daily life. In this paper, SSVEP based BCI through Emotiv EPOC is implemented. BCI 2000 is employed to connect Emotiv EPOC and Matlab to implement the online system. The online experiments have the accuracy of 95.83±3.59 %, information transfer rate (ITR) with 22.85±1.85 bits/min and detection duration of 5.25±2.14 sec.

A 0.83- $\mu {\rm W}$ QRS Detection Processor Using Quadratic Spline Wavelet Transform for Wireless ECG Acquisition in 0.35- $\mu{\rm m}$ CMOS

Abstract: Healthcare electronics count on the effectiveness of the on-patient signal preprocessing unit to moderate the wireless data transfer for better power efficiency. In order to reduce the system power in long-time ECG acquisition, this work describes an on-patient QRS detection processor for arrhythmia monitoring. It extracts the concerned ECG part, i.e., the RR-interval between the QRS complex for evaluating the heart rate variability. The processor is structured by a scale-3 quadratic spline wavelet transform followed by a maxima modulus recognition stage. The former is implemented via a symmetric FIR filter, whereas the latter includes a number of feature extraction steps: zero-crossing detection, peak (zero-derivative) detection, threshold adjustment and two finite state machines for executing the decision rules. Fabricated in 0.35-μm CMOS the 300-Hz processor draws only 0.83 μW, which is favorably comparable with the prior arts. In the system tests, the input data is placed via an on-chip 10-bit SAR analog-to-digital converter, while the output data is emitted via an off-the-shelf wireless transmitter (TI CC2500) that is configurable by the processor for different data transmission modes: 1) QRS detection result, 2) raw ECG data or 3) both. Validated with all recordings from the MIT-BIH arrhythmia database, 99.31% sensitivity and 99.70% predictivity are achieved. Mode 1 with solely the result of QRS detection exhibits 6× reduction of system power over modes 2 and 3.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Patterns of Somatic Mutation in Human Cancer Genomes

Abstract: AACR Centennial Conference: Translational Cancer Medicine-- Nov 4-8, 2007; Singapore PL02-05 All cancers are due to abnormalities in DNA. The availability of the human genome sequence has led to the proposal that resequencing of cancer genomes will reveal the full complement of somatic mutations and hence all the cancer genes. To explore the nature of the information that will be derived from cancer genome sequencing we have sequenced the coding exons of the family of 518 protein kinases, ~1.3Mb DNA per cancer sample, in 210 cancers of diverse histological types. Despite the screen being directed toward the coding regions of a gene family that has previously been strongly implicated in oncogenesis, the results indicate that the majority of somatic mutations detected are “passengers”. There is considerable variation in the number and pattern of these mutations between individual cancers, indicating substantial diversity of processes of molecular evolution between cancers. The imprints of exogenous mutagenic exposures, mutagenic treatment regimes and DNA repair defects can all be seen in the distinctive mutational signatures of individual cancers. This systematic mutation screen and others have previously yielded a number of cancer genes that are frequently mutated in one or more cancer types and which are now anticancer drug targets (for example BRAF , PIK3CA , and EGFR ). However, detailed analyses of the data from our screen additionally suggest that there exist a large number of additional “driver” mutations which are distributed across a substantial number of genes. It therefore appears that cells may be able to utilise mutations in a large repertoire of potential cancer genes to acquire the neoplastic phenotype. However, many of these genes are employed only infrequently. These findings may have implications for future anticancer drug development.

Multimodal fast optical interrogation of neural circuitry

Abstract: Our understanding of the cellular implementation of systems-level neural processes like action, thought and emotion has been limited by the availability of tools to interrogate specific classes of neural cells within intact, living brain tissue. Here we identify and develop an archaeal light-driven chloride pump (NpHR) from Natronomonas pharaonis for temporally precise optical inhibition of neural activity. NpHR allows either knockout of single action potentials, or sustained blockade of spiking. NpHR is compatible with ChR2, the previous optical excitation technology we have described, in that the two opposing probes operate at similar light powers but with well-separated action spectra. NpHR, like ChR2, functions in mammals without exogenous cofactors, and the two probes can be integrated with calcium imaging in mammalian brain tissue for bidirectional optical modulation and readout of neural activity. Likewise, NpHR and ChR2 can be targeted together to Caenorhabditis elegans muscle and cholinergic motor neurons to control locomotion bidirectionally. NpHR and ChR2 form a complete system for multimodal, high-speed, genetically targeted, all-optical interrogation of living neural circuits.

Statistical Pattern Recognition

Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Design Of Analog Cmos Integrated Circuits

Abstract: Thank you for downloading design of analog cmos integrated circuits. Maybe you have knowledge that, people have look hundreds times for their chosen books like this design of analog cmos integrated circuits, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some harmful virus inside their computer. design of analog cmos integrated circuits is available in our book collection an online access to it is set as public so you can download it instantly. Our digital library spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the design of analog cmos integrated circuits is universally compatible with any devices to read.