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Medical devices are essential for the practice of modern medicine, and the standard open-source integrated clinical environment (OpenICE) has been well designed and widely adopted to improve their interoperability. With OpenICE, it is easy to connect individual devices into the integrated clinical system to provide a coherent patient care. In this paper, we present ICERV, the first online verification...
This paper presents a multi-PLL clock architecture used in a 4-lane multi-protocol serial link applications. The clock architecture consists of one public LC PLL and four standalone ring PLLs placed within each lane. A swtich capacitor array based LC VCO is used in the LC PLL to enlarge the frequency tuning range and decrease the VCO gain. A two stage pseudodifferential inverter based ring VCO with...
Building language models for source code enables a large set of improvements on traditional software engineering tasks. One promising application is automatic code completion. State-of-the-art techniques capture code regularities at token level with lexical information. Such language models are more suitable for predicting short token sequences, but become less effective with respect to long statement...
This paper presents a wideband LC PLL designed for multi-protocol serial link applications. Dual LC voltage controlled oscillator (VCO) cores are used to cover a wide frequency range while keeping a high Q factor of the LC tank, and multi-ratio dividers are used to satisfy the multi-protocol requirements. Each LC VCO adopts a 4-bit switch capacitor to increase the frequency tuning range and decrease...
A 16-modulo fractional-N bang-bang digital PLL (BB-DPLL) is implemented in 65nm CMOS. It is firstly shown that a hybrid FIR filtering method not only improves spur performance but also reduces in-band phase noise for finite-modulo fractional-N bang-bang PLLs. A 4-bit digital-to-time converter (DTC) with auto-tuned delay cells is also employed to further enhance the fractional-N BB-DPLL performance...
Least squares support vector machines (LSSVM) has a good performance in small data samples, but can't solve the large-scale sample problems. In this paper, large data set sparse least squares support vector machines model based on stochastic entropy is proposed, and it can be applied to large-scale data samples. Firstly, the large-scale data set is divided into several subsets. Then the entropy method...
In the big data era, machine learning has become an increasingly popular approach for data processing. Data could be in various forms, such as text, images, audios, videos and signals. The essence of machine learning is to learn any patterns from features of data. In the above types of data, the number of features is massively high, which could result in the presence of a large number of irrelevant...
Software Defined Networking is a new networking paradigm that has emerged recently as a promising solution for tackling the inflexibility of the classical IP networks. The centralized approach of SDN yields a broad area for intelligence to optimise the network at various levels. Fault tolerance is considered one of the most current research challenges that facing the SDN, hence, in this paper we introduce...
Due to the vast amount of data, searching and obtaining relevant information on the web is a challenging task. Despite that a broad range of classification techniques have been proposed to improve the information retrieval methods, many difficulties are still present because of the continuous increase in the amount of web contents, as well as its diversity. In this paper, we propose a method that...
Convolutional neural networks (CNNs) have shown great potential for remote sensing image classification. As the features obtained from a deep CNN generally exhibit high generalization capacity, the subsequent classifier is normally able to provide good results without the need for careful optimization. However it is well-known that, in the pursuit of high classification results, it is generally difficult...
Sentiment analysis aims to identify the polarity of a document through natural language processing, text analysis and computational linguistics. Over the last decade, there has been much focus on sentiment analysis as the data available on-line has grown exponentially to include many sentiment based documents (reviews, feedback, articles). Many approaches consider machine learning techniques or statistical...
The traditional Phase-Shift Full-Bridge Converter (PSFBC) cannot achieve Zero-Voltage Switching (ZVS) at light loads due to small resonant current resulting in low conversion efficiency at light loads. Therefore, a light-load conversion efficiency improvement strategy for PSFBC is emerging and needs to be realized. A novel dead-time adjustment strategy for light-load conversion efficiency improvement...
Program obfuscation is a common practice in software development to obscure source code or binary code, in order to prevent humans from understanding the purpose or logic of software. It protects intellectual property and deters malicious attacks. While tremendous efforts have been devoted to the development of various obfuscation techniques, we have relatively little knowledge on how to most effectively...
Clinical guidelines and decision support systems (DSS) play an important role in daily practices of medicine. Many textbased guidelines have been encoded for work- ow simulation of DSS to automate health care. During the collaboration with Carle hospital to develop a DSS, we identify that, for some complex and life-critical diseases, it is highly desirable to automatically rigorously verify some complex...
As a common practice in software development, program obfuscation aims at deterring reverse engineering and malicious attacks on released source or binary code. Owning ample obfuscation techniques, we have relatively little knowledge on how to most effectively use them. The biggest challenge lies in identifying the most useful combination of these techniques. We propose a unified framework to automatically...
With the fast development of Location Based Service (LBS) applications in recent years, the demands for accurate indoor localization techniques have attracted significant attention and risen rapidly. Among all the techniques, due to its stable performance without the need for additional hardware, the RSS-based fingerprinting localization is the most viable method. However, the traditional methods...
Classification is one of the most popular tasks of machine learning, which has been involved in broad applications in practice, such as decision making, sentiment analysis and pattern recognition. It involves the assignment of a class/label to an instance and is based on the assumption that each instance can only belong to one class. This assumption does not hold, especially for indexing problems...
Sentiment analysis, which is also known as opinion mining, aims to recognise the attitude or emotion of people through natural language processing, text analysis and computational linguistics. In recent years, many studies have focused on sentiment classification in the context of machine learning, e.g. to identify that a sentiment is positive or negative. In particular, the bag-of-words method has...
Advanced Metering Infrastructure (AMI) plays an important role in smart grids. Data Concentrator Unit (DCU), used to collect data from smart meters and transmit them to Meter Database Management System (MDMS) regularly, is the core component in AMI. If DCU can provide value-added functionalities, more efficient AMI management can be achieved. Therefore, this paper designs and develops IEC61850 as...
In this paper, we will propose a novel distributable clustering algorithm, called Distributed-GridMST (D–GridMST for short), which deals with large distributed spatial databases. D–GridMST employs the notion of a grid to partition the data space involved and uses density criteria to extract representative points from spatial databases, on which a global MST of representatives is constructed...
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