A Novel Three-Point Modulation Technique for Fractional-N Frequency Synthesizer Applications

This paper presents a novel three-point modulation technique for fractional-N frequency synthesizer applications. Convention modulated fractional-N frequency synthesizers suffer from quantization noise, which degrades not only the phase noise performance but also the modulation quality. To solve this problem, this work proposes a three-point modulation technique, which not only cancels the quantization noise, but also markedly boosts the channel switching speed. Measurements reveal that the implemented 2.4 GHz fractional-N frequency synthesizer using three-point modulation can achieve a 2.5 Mbps GFSK data rate with an FSK error rate of only 1.4 %. The phase noise is approximately -98 dBc/Hz at a frequency offset of 100 kHz. The channel switching time is only 1.1 μs with a frequency step of 80 MHz. Comparing with conventional two-point modulation, the proposed three-point modulation greatly improves the FSK error rate, phase noise and channel switching time by about 10 %, 30 dB and 126 μs, respectively

Production lot sizing with rework and fixed quantity deliveries

This paper is concerned with determination of the optimal lot size for an economic production quantity (EPQ) model with the reworking of random defective items and fixed quantity multiple deliveries. Classic EPQ model assumes continuous issuing policy for satisfying product demand and perfect quality production for all items produced. However, in real life vendor-buyer integrated production-inventory system, multi-delivery policy is used practically in lieu of the continuous issuing policy and generation of defective items during production run is inevitable. In this study, all nonconforming items produced are considered to be repairable and are reworked in each cycle when regular production ends. The finished items can only be delivered to customers if the whole lot is quality assured at the end of the rework. Fixed quantity multiple installments of the finished batch are delivered to customers at a fixed interval of time. The long-run average integrated cost function per unit time is derived. A closed-form optimal batch size solution to the problem is obtained. A numerical example demonstrates its practical usage

The Trend and Intellectual Structure of Digital Archives Research

Archives are an extremely valuable part of cultural heritage since they represent the trace of the activities of a juridical person or organization in the course of their business. Through various information technology (IT), tremendous amount of digital archives (DA) are created. These archives are the basis for providing evidence and knowledge in everlasting memory of human society. The management of digital archives becomes a fast growing field throughout last decade and introduces abundant articles in academia. However, their trend and intellectual structure have remained obscure in the research community. To map the trend and intellectual structure of DA research, this study identifies the high-impact articles as well as the correlations among these scholar publications. In this study, text mining techniques, such as co-word and cluster analysis, have been deployed to investigate the intellectual pillars of the DA literature. This study exposes researchers to a new way of profiling knowledge networks and their relationships in the research area of DA, thereby helping academia and practitioners better understand up-to-date studies. The results of the mapping can help identify the research direction of DA research, provide a valuable tool for researchers to access DA literature, and act as an exemplary model for future research

Modeling Knowledge Sharing and Interemployee Helping From a Perspective of Flow Theory: A Survey of Online Knowledge Works

This study proposes a model based on flow theory by postulating key antecedents as the critical drivers of knowledge sharing and interemployee helping. In the model, knowledge sharing is influenced by flow experience directly and also indirectly via the mediation of interemployee helping. Accordingly, the flow experience is influenced simultaneously by four exogenous factors related to individuals’ perception about their work: work skills, self-fulfillment in challenges, perceived control, and vividness. The empirical findings of this study confirm the applicability of flow theory in business organizations by investigating online knowledge workers from business organizations. This study contributes to the knowledge management literature by extending flow theory to the area of knowledge sharing and interemployee helping, by validating idiosyncratic antecedent drivers of the flow theory, and by performing a practical operationalization of the flow experience. This research also provides managerial implications and limitations

Compact Dual-Band Dipole Antenna with Asymmetric Arms for WLAN Applications

A dual-band dipole antenna that consists of a horn- and a C-shaped metallic arm is presented. Depending on the asymmetric arms, the antenna provides two −10 dB impedance bandwidths of 225 MHz (about 9.2% at 2.45 GHz) and 1190 MHz (about 21.6% at 5.5 GHz), respectively. This feature enables it to cover the required bandwidths for wireless local area network (WLAN) operation at the 2.4 GHz band and 5.2/5.8 GHz bands for IEEE 802.11 a/b/g standards. More importantly, the compact size (7 mm × 24 mm) and good radiating performance of the antenna are profitable to be integrated with wireless communication devices on restricted RF-elements spaces

ANALYZING MEDICAL TRANSACTION DATA BY USING ASSOCIATION RULE MINING WITH MULTIPLE MINIMUM SUPPORTS

The quick development of IS has a huge impact on the healthcare industry. almost all the existing hospitals, clinics and other healthcare-related institutes have adopted a functionally powerful and highly integrated Hospital Information System (HIS) for management of clinic or medical-related affairs. The medical data stored in the HIS are collected from many different medical subsystems, However, problems of failed data sharing and inconsistent data content often occur among these subsystems, resulting in many hospitals collect a large amount of medical data, but not the ability to process and analyse these data properly, letting the valuable data in the HIS all go to waste. In this study, we made a practical visit to a certain hospital in Taiwan and collected radioimmunoassay (RIA) data from the Laboratory Information System (LIS) and the Departmental Registration System (DRS) of this hospital. Further, we proposed a method of the association rule mining in combination with the concept of multiple minimum supports to analyse and find valuable association rules from the RIA data. The analytical results found the method we proposed can indeed find association rules that would not be able to be found with the traditional association mining methods. It is very helpful in improving doctor-patient relationship and upgrading health care quality

A single-producer multi-retailer integrated inventory model with a rework process

This study considers a single-producer multi-retailer integrated inventory model with the reworking of random defective items produced. The objective is to find the optimal production lot size and optimal number of shipments that minimizes total expected costs for such a specific supply chains system. It is assumed that a product is manufactured by a producer. All items are screened for quality purpose and random nonconforming items will be picked up and reworked at the end of regular production in each cycle. After the entire lot is quality assured, multiple shipments will be delivered synchronously to m different retailers in each production cycle. Each retailer has its own annual product demand, unit stock holding cost, and fixed and variable delivery costs. Mathematical modeling and analysis is used to deal with the proposed model and to derive the expected system cost. Hessian matrix equations are employed to prove the convexity of the cost function. As a result, a closed-form optimal replenishment-delivery policy for such a specific single-producer multi-retailer integrated inventory model is obtained. A numerical example is provided to show the practical usage of the proposed model

THE INTELLECTUAL STRUCTURE OF ELECTRONIC RECORDS MANAGEMENT

A number of countries have launched projects with a particular emphasis on using information technologies (IT) to provide electronic information and services to citizens and businesses. Through various IT, tremendous amount of electronic records in government agencies are created. These records and archives are the basis of knowledge management. Electronic records management (ERM) is a fast growing field throughout the last decades. Theoretical foundations for ERM have remained obscure from the research community. To map the intellectual structure of ERM research, this study identifies the high-impact articles as well as the correlations among these scholar publications. In this study, co-citation, co-word, association rule and cluster analysis techniques are used to investigate the intellectual pillars of the ERM literature. This study exposes researchers to a new way of profiling knowledge networks and their relationships the area of ERM, thereby helping academia and practitioners better understand contemporary studies. The results of the mapping can help identify the research direction of ERM research, provide a valuable tool for researchers to access ERM literature, and acts as an exemplary model for future researches

MENTOR: Multilingual tExt detectioN TOward leaRning by analogy

Text detection is frequently used in vision-based mobile robots when they need to interpret texts in their surroundings to perform a given task. For instance, delivery robots in multilingual cities need to be capable of doing multilingual text detection so that the robots can read traffic signs and road markings. Moreover, the target languages change from region to region, implying the need of efficiently re-training the models to recognize the novel/new languages. However, collecting and labeling training data for novel languages are cumbersome, and the efforts to re-train an existing/trained text detector are considerable. Even worse, such a routine would repeat whenever a novel language appears. This motivates us to propose a new problem setting for tackling the aforementioned challenges in a more efficient way: "We ask for a generalizable multilingual text detection framework to detect and identify both seen and unseen language regions inside scene images without the requirement of collecting supervised training data for unseen languages as well as model re-training". To this end, we propose "MENTOR", the first work to realize a learning strategy between zero-shot learning and few-shot learning for multilingual scene text detection.Comment: 8 pages, 4 figures, published to IROS 202