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Table Of Integrals Series And Products

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable applications can be visualized, for instance, in medical, security, entertainment, and tactical scenarios. Despite human activity recognition (HAR) being an active field for more than a decade, there are still key aspects that, if addressed, would constitute a significant turn in the way people interact with mobile devices. This paper surveys the state of the art in HAR based on wearable sensors. A general architecture is first presented along with a description of the main components of any HAR system. We also propose a two-level taxonomy in accordance to the learning approach (either supervised or semi-supervised) and the response time (either offline or online). Then, the principal issues and challenges are discussed, as well as the main solutions to each one of them. Twenty eight systems are qualitatively evaluated in terms of recognition performance, energy consumption, obtrusiveness, and flexibility, among others. Finally, we present some open problems and ideas that, due to their high relevance, should be addressed in future research.

A Survey on Human Activity Recognition using Wearable Sensors

In this paper we review the most peculiar and interesting information-theoretic and communications features of fading channels. We first describe the statistical models of fading channels which are frequently used in the analysis and design of communication systems. Next, we focus on the information theory of fading channels, by emphasizing capacity as the most important performance measure. Both single-user and multiuser transmission are examined. Further, we describe how the structure of fading channels impacts code design, and finally overview equalization of fading multipath channels.

/pdf/fading-channels-information-theoretic-and-communications-2ipqbail97.pdf

Fading channels: information-theoretic and communications aspects

Optical wireless communication (OWC) refers to transmission in unguided propagation media through the use of optical carriers, i.e., visible, infrared (IR), and ultraviolet (UV) bands. In this survey, we focus on outdoor terrestrial OWC links which operate in near IR band. These are widely referred to as free space optical (FSO) communication in the literature. FSO systems are used for high rate communication between two fixed points over distances up to several kilometers. In comparison to radio-frequency (RF) counterparts, FSO links have a very high optical bandwidth available, allowing much higher data rates. They are appealing for a wide range of applications such as metropolitan area network (MAN) extension, local area network (LAN)-to-LAN connectivity, fiber back-up, backhaul for wireless cellular networks, disaster recovery, high definition TV and medical image/video transmission, wireless video surveillance/monitoring, and quantum key distribution among others. Despite the major advantages of FSO technology and variety of its application areas, its widespread use has been hampered by its rather disappointing link reliability particularly in long ranges due to atmospheric turbulence-induced fading and sensitivity to weather conditions. In the last five years or so, there has been a surge of interest in FSO research to address these major technical challenges. Several innovative physical layer concepts, originally introduced in the context of RF systems, such as multiple-input multiple-output communication, cooperative diversity, and adaptive transmission have been recently explored for the design of next generation FSO systems. In this paper, we present an up-to-date survey on FSO communication systems. The first part describes FSO channel models and transmitter/receiver structures. In the second part, we provide details on information theoretical limits of FSO channels and algorithmic-level system design research activities to approach these limits. Specific topics include advances in modulation, channel coding, spatial/cooperative diversity techniques, adaptive transmission, and hybrid RF/FSO systems.

/pdf/survey-on-free-space-optical-communication-a-communication-239v68hfyw.pdf

Survey on Free Space Optical Communication: A Communication Theory Perspective

For more than a decade now, radio frequency identification (RFID) technology has been quite effective in providing anti-counterfeits measures in the supply chain. However, the genuineness of RFID tags cannot be guaranteed in the post supply chain, since these tags can be rather easily cloned in the public space. In this paper, we propose a novel product ownership management system (POMS) of RFID-attached products for anti-counterfeits that can be used in the post supply chain. For this purpose, we leverage the idea of Bitcoin ’s blockchain that anyone can check the proof of possession of balance. With the proposed POMS, a customer can reject the purchase of counterfeits even with genuine RFID tag information, if the seller does not possess their ownership. We have implemented a proof-of-concept experimental system employing a blockchain-based decentralized application platform, Ethereum , and evaluated its cost performance. Results have shown that, typically, the cost of managing the ownership of a product with up to six transfers is less than U.S. $1.

A Novel Blockchain-Based Product Ownership Management System (POMS) for Anti-Counterfeits in the Post Supply Chain

With the rapid growth of social networks and microblogging websites, communication between people from different cultural and psychological backgrounds has become more direct, resulting in more and more “cyber” conflicts between these people. Consequently, hate speech is used more and more, to the point where it has become a serious problem invading these open spaces. Hate speech refers to the use of aggressive, violent or offensive language, targeting a specific group of people sharing a common property, whether this property is their gender (i.e., sexism), their ethnic group or race (i.e., racism) or their believes and religion. While most of the online social networks and microblogging websites forbid the use of hate speech, the size of these networks and websites makes it almost impossible to control all of their content. Therefore, arises the necessity to detect such speech automatically and filter any content that presents hateful language or language inciting to hatred. In this paper, we propose an approach to detect hate expressions on Twitter. Our approach is based on unigrams and patterns that are automatically collected from the training set. These patterns and unigrams are later used, among others, as features to train a machine learning algorithm. Our experiments on a test set composed of 2010 tweets show that our approach reaches an accuracy equal to 87.4% on detecting whether a tweet is offensive or not (binary classification), and an accuracy equal to 78.4% on detecting whether a tweet is hateful, offensive, or clean (ternary classification).

Hate Speech on Twitter: A Pragmatic Approach to Collect Hateful and Offensive Expressions and Perform Hate Speech Detection

Sarcasm is a sophisticated form of irony widely used in social networks and microblogging websites. It is usually used to convey implicit information within the message a person transmits. Sarcasm might be used for different purposes, such as criticism or mockery. However, it is hard even for humans to recognize. Therefore, recognizing sarcastic statements can be very useful to improve automatic sentiment analysis of data collected from microblogging websites or social networks. Sentiment Analysis refers to the identification and aggregation of attitudes and opinions expressed by Internet users toward a specific topic. In this paper, we propose a pattern-based approach to detect sarcasm on Twitter. We propose four sets of features that cover the different types of sarcasm we defined. We use those to classify tweets as sarcastic and non-sarcastic. Our proposed approach reaches an accuracy of 83.1% with a precision equal to 91.1%. We also study the importance of each of the proposed sets of features and evaluate its added value to the classification. In particular, we emphasize the importance of pattern-based features for the detection of sarcastic statements.

A Pattern-Based Approach for Sarcasm Detection on Twitter

Sentiment analysis and opinion mining in social networks present nowadays a hot topic of research. However, most of the state of the art works and researches on the automatic sentiment analysis and opinion mining of texts collected from social networks and microblogging websites are oriented toward the binary classification (i.e., classification into “positive” and “negative”) or the ternary classification (i.e., classification into “positive,” “negative,” and “neutral”) of texts. In this paper, we propose a novel approach that, in addition to the aforementioned tasks of binary and ternary classifications, goes deeper in the classification of texts collected from Twitter and classifies these texts into multiple sentiment classes. While in this paper, we limit our scope to seven different sentiment classes, the proposed approach is scalable and can be run to classify texts into more classes. We first introduce SENTA, our tool built to help users select out of a wide variety of features the ones that fit the most for their application, to run the classification, through an easy-to-use graphical user interface. We then use SENTA to run our own experiments of multi-class classification. Our experiments show that the proposed approach can reach up to 60.2% accuracy on the multi-class classification. Nevertheless, the approach proves to be very accurate in binary classification and ternary classification: in the former case, we reach an accuracy of 81.3% for the same data set used after removing neutral tweets, and in the latter case, we reached an accuracy of classification equal to 70.1%.

A Pattern-Based Approach for Multi-Class Sentiment Analysis in Twitter

This article overviews multiple-subcarrier modulation techniques in optical wireless communications. The basic principles and characteristics of MSM techniques in optical wireless communications are presented. MSM optical wireless systems are explained where some block codes that convert information bits to be transmitted to the symbol amplitudes of subcarriers are used to increase the minimum value of the MS electrical waveform. MSM optical communications systems using subcarrier signal point sequences (SSPS) that can improve the power efficiency of MSM systems are also explained. The performance of MSM optical communications systems is presented in the intensity modulation with direct detection (IM/DD) channel without dispersion and in atmospheric optical communications where the effects of scintillation exist.

Tomoaki Ohtsuki

Papers

A Novel Blockchain-Based Product Ownership Management System (POMS) for Anti-Counterfeits in the Post Supply Chain

Hate Speech on Twitter: A Pragmatic Approach to Collect Hateful and Offensive Expressions and Perform Hate Speech Detection

A Pattern-Based Approach for Sarcasm Detection on Twitter

A Pattern-Based Approach for Multi-Class Sentiment Analysis in Twitter

Multiple-subcarrier modulation in optical wireless communications