A low-latency asynchronous shift register

In this paper, we first introduce a straightforward asynchronous shift register design implemented by differential cascode voltage switch logic and micropipeline. The corresponding latency defect between data readout is then described. To solve this problem, we propose a new architecture for the design. Finally, a basic building block with respect to our architecture is proposed.     

Adoption of automation systems and strategy choices for Hong Kong apparel practitioners

Recent developments from the World Trade Organisation (WTO) and other international trade agreements have forced industries worldwide to face a new era of intense competition. The clothing industry sector, which has played a pivotal role in industrial development in Hong Kong, faces challenges from continued global economic restructuring and the accession of China to the WTO. Apparel manufacturers and trading firms need to review their competitive priorities and develop new business and manufacturing practices and strategies to compete. This paper presents the key findings of a recent study on the adoption of automation systems and strategy choices and discusses their relevance to the transformation of the apparel operations and businesses in Hong Kong. Two models namely, for integrated apparel business manufacture and for an apparel sourcing and coordination centre, are devised.     

LaboImage: a workstation environment for research in image processing and analysis

Numerous images are produced daily in biomedical research. In order to extract relevant and useful results, various processing and analysis steps are mandatory. The present paper describes a new, powerful and user-friendly image analysis system: LaboImage. In addition to standard image processing modules. LaboImage also contains various specialized tools. These multiple processing modules and tools are first introduced. A one-dimensional gel analysis method is then described. The new concept of 'normalized virtual one-dimensional gel' is introduced, making comparisons between gels particularly easy. This normalized gel is obtained by compensating for the bending of the lanes automatically; no information loss is incurred in the process. Finally, the model of interaction in a multi-window environment is discussed. LaboImage is designed to run in two ways: interactively, using menus and panels; and in batch mode by means of user-defined macros. Examples are given to illustrate the potentialities of the software.     

A 0.1 mm$^{2}$ , Wide Bandwidth Continuous-Time $SigmaDelta$ ADC Based on a Time Encoding Quantizer in 0.13 $mu$ m CMOS

The ADC shown in this paper uses an innovative sigma-delta (SigmaDelta) architecture that replaces the flash quantizer and mismatch corrected DAC of a multibit continuous time (CT) modulator by a time domain encoder similar to a PWM modulator to reduce the effective ADC area. The modulator achieves the resolution of a multibit design using single bit circuitry by concentrating most of the quantization error energy around a single frequency, which is afterwards removed, seizing the zeros of a sinc decimation filter. The non flat error spectrum is accomplished by use of two filter loops, one of which is made to operate in a self-oscillating mode. An experimental CT-SigmaDelta ADC prototype has been fabricated in 0.13 mum CMOS which implements a third order modulator with two operating modes. Measurements show an effective number of bits (ENOB) of 10 bits and 12 bits in a signal bandwidth of 17 MHz and 6.4 MHz, respectively, and a power-efficient figure of merit (FoM = Pwr/2 middot BW middot 2^ENOB) of 0.48 pJ/conversion at 1.5 V supply. The active area of the ADC is 0.105 mm².     

A Fully Differential Band-Selective Low-Noise Amplifier for MB-OFDM UWB Receivers

A band-selective low-noise amplifier (BS-LNA) for multiband orthogonal frequency-division multiplexing ultra-wide-band (UWB) receivers is presented. A switched capacitive network that controls the resonant frequency of the LC load for the band selection is used. It greatly enhances the gain and noise performance of the LNA in each frequency band without increasing power consumption. Moreover, a fully differential configuration is employed to suppress the common-mode switching noise that is generated during the band transition interval. Fabricated in a 0.18-$mu{hbox {m}}$ CMOS process, the BS-LNA achieves a peak power gain of 16 dB, a minimum noise figure of 2.74 dB, and an third-order input intercept point of $-$8.8 dBm at a current consumption of 7.95 mA from a 1.5-V supply. Little performance degradation is observed when the current consumption is reduced by half. The experimental results also show a worst-case band-switching time of less than 3.4 ns, with a peak switching noise voltage of less than 70 $mu hbox{V}$ at the output.      

Organic aerosol spatial/temporal patterns: perspectives of measurements and model

Ambient measurements from SEARCH and model results from CMAQ-MADRID are analyzed side by side for the southeastern United States to understand the strengths and weaknesses of an air quality model in reproducing key spatial and temporal patterns related to organic aerosol (OA), with inferences regarding secondary organic aerosol (SOA). The model predicts a larger difference in OA concentrations between an urban (JST) and a rural site (YRK) than indicated by measurements. Modeled OA concentrations at JST and YRK are more strongly correlated than measurements. On average, models may understate urban OA emissions, while overstating urban SOA production; measurements indicate that SOA production takes place on the regional scale. Modeled diurnal fluctuations for OA are stronger than measured, due partially to overestimations of the temperature dependence parameters (deltaH(vap)) for SOA in the model. Urban-rural differences in the composition of SOA, inferred from the variations of estimated deltaH(vap), are not properly captured by the model, which does not represent multiple generations of SOA or varied reaction pathways as a function of chemical regimes. Model results are hampered by day-of-the-week and diurnal allocation issues related to EC and OA emissions. Top quintile (20%) afternoon OA concentrations are observed in both warm and cold seasons at the urban site. The frequency of high OA in the cold season is overstated in the model. The model predicts the warm vs cold season frequency of elevated OA episodes better at YRK than at JST, suggesting that regional emissions, chemistry, and transport are better simulated than urban processes.     

Copy-move forgery detection using adaptive keypoint filtering and iterative region merging

Multimedia Tools and Applications - Copy-move forgery detection can generally be divided into two categories: block-based or keypoint-based methods. However, the existing block-based methods are...     

Weighted least squares estimation of background in EELS imaging

In quantitative Electron Energy Loss Spectrometry, a weighted least squares estimation should theoretically be used to estimate the background law below core edge energy, since the variances of the data vary. However, it is found that proper weighting makes the above edge signal-to-noise ratio decrease rather than increase. This result is discussed, and the influence of the bias introduced by the logarithmic transformation of the data is quantified.     

Automatic Medical Image Registration Based on an Integrated Method Combining Feature and Area Information

This paper proposes a solution based on Adaptive learning using the CNN model. The proposed method automatically updates the recognition model according to online training dataset accumulated directly from the system and retraining recognition model. The data updating task focuses on data samples that are less similar to previous trained ones. The purpose of this solution is to upgrade the model to a new one more adaptive, expecting to reach higher accuracy. In the adaptive learning approach, the recognition system is capable of self-learning and complementing data, without experts needed for data labeling or training. The proposed solution includes 5 main phases: (1) Detect and recognize low confident objects; (2) Track objects in n frames in future progress to make sure whether they are interesting objects or not. (3) In case of objects that are recognized with high confidence: labeling (same class of object) for the corresponding data samples to be recognized with low confidence scores which were tracked in the previous process. In case of objects determined not to be of interesting objects, the samples are labeled as Negative for all previous samples, which were tracked in n previous frames; (4) Initialize a training dataset based on a selective combination of previously trained data and the new data. (5) Retrain and update the model if it results in higher accuracy. We have conducted experiments to compare results of the proposed model—PDnet and some state of the art methods such as AlexNet and Vgg. The experimental results demonstrate that the proposed method provides the higher accuracy when the model are self-learned over time. On the other hand, our adaptive learning is applicable to the traditional recognition models such as AlexNet and Vgg model for improving accuracy.     

Vehicle power train optimization using multi-objective bird swarm algorithm

In this paper, a multi-objective bird swarm algorithm (MOBSA) is proposed to cope with multi-objective optimization problems. The algorithm is explored based on BSA which is an evolutionary algorithm suitable for single objective optimization. In this paper, non-dominated sorting approach is used to distinguish optimal solutions and parallel coordinates is applied to evaluate the distribution density of non-dominated solution and further update the external archive when it is full to overflowing, which ensure faster convergence and more widespread of Pareto front. Then, the MOBSA is adopted to optimize benchmark problems. The results demonstrate that MOBSA gets better performance compared with NSGA-II and MOPSO. Since a vehicle power train problem could be treated as a typical multi-objective optimization problem with constraints, with integration of constrained non-dominated solution, MOBSA is adopted to acquire optimal gear ratios and optimize vehicle power train. The results compared with other popular algorithm prove the proposed algorithm is more suitable for constrained multi-objective optimization problem in engineering field.