A directional array radiator is presented, the aim of which is to enhance the sound of the television in a particular direction and hence provide a volume boost to improve speech intelligibility for the hard of hearing. The sound radiated by the array in other directions is kept low, so as not to increase the reverberant level of sound in the listening room. The array uses 32 loudspeakers, each of which are in phase-shift enclosures to generate hypercardioid directivity, which reduces the radiation from the back of the array. The loudspeakers are arranged in 8 sets of 4 loudspeakers, each set being driven by the same signal and stacked vertically, to improve the directivity in this plane. This creates a 3D beamformer that only needs 8 digital filters to be made superdirective. The performance is assessed by means of simulations and measurements in anechoic and reverberant environments. The results show how the array obtains a high directivity in a reverberant environment.

The last decade has witnessed an explosion of interest in research on human emotion modeling for generating intelligent virtual agents. This paper proposes a novel personality model based on the Revised NEO Personality Inventory (NEO PI-R). Compared to the popular Big-Five-Personality Factors (Big5) model, our proposed model is more capable than Big5 on describing a variety of personalities. Combining with emotion models it helps to produce more reasonable emotional reactions to external stimuli. A novel Resistant formulation is also proposed to effectively simulate the complicated negative emotions. Emotional reactions towards multiple stimuli are also effectively simulated with the proposed personality model.

Software engineering education at universities plays an increasingly important role as software quality is becoming essential in realizing a safe and dependable society. This paper proposes a practical state transition model (Practical-STM) based on the Organizational Expectancy Model for the improvement of software process education based on the Personal Software Process (PSP) from a motivation point of view. The Practical-STM treats an individual trainee of the PSP course as a state machine, and formalizes a motivation process of a trainee using a set of states represented by factors regarding motivation and a set of operations carried out by course instructors. The state transition function of this model represents the features or characteristics of a trainee in terms of motivation. The model allows a formal description of the states of a trainee in terms of motivation and the educational actions of the instructors in the PSP course. The instructors are able to decide effective and efficient actions to take toward the trainees objectively by presuming a state and a state transition function of the trainees formally. Typical patterns of state transitions from an initial state to a final state, which is called a scenario, are useful for inferring possible transitions of a trainee and taking proactive operations from a motivation point of view. Therefore, the model is useful not only for improving the educational effect of the PSP course, but also for the standardization of the course management and the quality management of the instructors.

Although many companies have developed robots that assist humans in the activities of daily living, safety requirements and test methods for such robots have not been established. Given the risk associated with a robot malfunctioning in the human living space, from the viewpoints of safety and EMC, it is necessary that the robot does not create a hazardous situation even when exposed to possibly severe electromagnetic disturbances in the operating environment. Thus, in immunity tests for personal care robots, the safety functions should be more rigorously tested than the other functions, and be repeatedly activated in order to ascertain that the safety functions are not lost in the presence of electromagnetic disturbances. In this paper, immunity test procedures for personal care robots are proposed that take into account functional safety requirements. A variety of test apparatuses are presented, which were built for activating the safety functions of robots, and detecting whether they were in a safe state. The practicality of the developed immunity test system is demonstrated using actual robots.

Pieces of personal information, such as personal names and relationships, are crucial in text mining applications. Obituaries are good sources for this kind of information. This study proposes an effective method for extracting various facts about people from obituary Web pages. Experiments show that the proposed method achieves high performance in terms of recall and precision.

To facilitate the discovery of mobile apps in personal devices, we present the personalized live homescreen system. The system mines the usage patterns of mobile apps, generates personalized predictions, and then makes apps available at users' hands whenever they want them. Evaluations have verified the promising effectiveness of our system.

To understand human emotion, it is necessary to be aware of the surrounding situation and individual personalities. In most previous studies, however, these important aspects were not considered. Emotion recognition has been considered as a classification problem. In this paper, we attempt new approaches to utilize a person's situational information and personality for use in understanding emotion. We propose a method of extracting situational information and building a personalized emotion model for reflecting the personality of each character in the text. To extract and utilize situational information, we propose a situation model using lexical and syntactic information. In addition, to reflect the personality of an individual, we propose a personalized emotion model using KBANN (Knowledge-based Artificial Neural Network). Our proposed system has the advantage of using a traditional keyword-spotting algorithm. In addition, we also reflect the fact that the strength of emotion decreases over time. Experimental results show that the proposed system can more accurately and intelligently recognize a person's emotion than previous methods.

Anomaly-based worm detection is a complement to existing signature-based worm detectors. It detects unknown worms and fills the gap between when a worm is propagated and when a signature is generated and downloaded to a signature-based worm detector. A major obstacle for its deployment to personal computers (PCs) is its high false positive alarms since a typical PC user lacks the skill to handle exceptions flagged by a detector without much knowledge of computers. In this paper, we exploit the feature of personal computers in which the user interacts with many running programs and the features combining various network characteristics. The model of a program's network behaviors is conditioned on the human interactions with the program. Our scheme automates detection of unknown worms with dramatically reduced false positive alarms while not compromising low false negatives, as proved by our experimental results from an implementation on Windows-based PCs to detect real world worms.

Broadcast encryption scheme with personalized messages (BEPM) is a new primitive that allows a broadcaster to encrypt both a common message and individual messages. BEPM is necessary in applications where individual messages include information related to user's privacy. Recently, Fujii et al. suggested a BEPM that is extended from a public key broadcast encryption (PKBE) scheme by Boneh, Gentry, and Waters. In this paper, we point out that 1) Conditional Access System using Fujii et al.'s BEPM should be revised in a way that decryption algorithm takes as input public key as well, and 2) performance analysis of Fujii et al.'s BEPM should be done depending on whether the public key is transmitted along with ciphertext or stored into user's device. Finally, we propose a new BEPM that is transmission-efficient, while preserving O(1) user storage cost. Our construction is based on a PKBE scheme suggested by Park, Kim, Sung, and Lee, which is also considered as being one of the best PKBE schemes.

In this paper, we propose a high-speed full-duplex optical wireless communication system using a single channel imaging receiver for personal area network applications. This receiver is composed of an imaging lens, a small sensitive-area photodiode, and a 2-aixs actuator and it can reject most of the background light. Compared with the previously proposed system with single wide field-of-view (FOV) non-imaging receiver, the coverage area at 12.5 Gb/s is extended by > 20%. Furthermore, since the rough location information of the user is available in our proposed system, instead of searching for the focused light spot over a large area on the focal plane of the lens, only a small possible area needs to be scanned. In addition, by pre-setting a proper comparison threshold when searching for the focused light spot, the time needed for searching can be further reduced. Proof-of-concept experiments have been carried out and the results show that with this partial searching algorithm and pre-set threshold, better performance is achieved.

The IEEE has recently released IEEE 802.15.5 standard [3] to provide multi-hop mesh functions for low-rate wireless personal area networks (WPANs). In this paper, we extensively describe a link-layer reliable broadcast protocol referred to as timer-based reliable broadcast (TRB) [3] in the IEEE 802.15.5 standard. The TRB scheme exploits (1) bitmap based implicit ACK to effectively reduce the unnecessary error control messages and (2) randomized timer for ACK transmission to substantially reduce the possibility of contentions. Performance evaluation shows that the TRB scheme achieves 100% reliability compared with other schemes with expense of slightly increased energy consumption.

For personalized search, a user must provide her personal information. However, this sometimes includes the user's sensitive information about individuals such as health condition and private lifestyle. It is not sufficient just to protect the communication channel between user and service provider. Unfortunately, the collected personal data can potentially be misused for the service providers' commercial advantage (e.g. for advertising methods to target potential consumers). Our aim here is to protect user privacy by filtering out the sensitive information exposed from a user's query input at the system level. We propose a framework by introducing the concept of query generalizer. Query generalizer is a middleware that takes a query for personalized search, modifies the query to hide user's sensitive personal information adaptively depending on the user's privacy policy, and then forwards the modified query to the service provider. Our experimental results show that the best-performing query generalization method is capable of achieving a low traffic overhead within a reasonable range of user privacy. The increased traffic overhead varied from 1.0 to 3.3 times compared to the original query.

This paper proposes a proactive management system for the events that occur across multiple personal user devices, including desktop PCs, laptops, and smart phones. We implemented the Personal Event Management Service using Dynamic Bayesian Networks (PEMS-DBN) system that proactively executes appropriate tasks across multiple devices without explicit user requests by recognizing the user's device reuse intention, based on the observed actions of the user for specific devices. The client module of PEMS-DBN installed on each device monitors the user actions and recognizes user intention by using dynamic Bayesian networks. The server provides data sharing and maintenance for the clients. A series of experiments were performed to evaluate user satisfaction and system accuracy, and also the amounts of resource consumption during intention recognition and proactive execution are measured to ensure the system efficiency. The experimental results showed that the PEMS-DBN system can proactively provide appropriate, personalized services with a high degree of satisfaction to the user in an effective and efficient manner.

With the emergence of Web 2.0, social tagging systems become highly popular in recent years and thus form the so-called folksonomies. Personalized tag recommendation in social tagging systems is to provide a user with a ranked list of tags for a specific resource that best serves the user's needs. Many existing tag recommendation approaches assume that users are independent and identically distributed. This assumption ignores the social relations between users, which are increasingly popular nowadays. In this paper, we investigate the role of social relations in the task of tag recommendation and propose a personalized collaborative filtering algorithm. In addition to the social annotations made by collaborative users, we inject the social relations between users and the content similarities between resources into a graph representation of folksonomies. To fully explore the structure of this graph, instead of computing similarities between objects using feature vectors, we exploit the method of random-walk computation of similarities, which furthermore enable us to model a user's tag preferences with the similarities between the user and all the tags. We combine both the collaborative information and the tag preferences to recommend personalized tags to users. We conduct experiments on a dataset collected from a real-world system. The results of comparative experiments show that the proposed algorithm outperforms state-of-the-art tag recommendation algorithms in terms of prediction quality measured by precision, recall and NDCG.

Despite the recent advances in personal communication devices and access network technology, users still face problems such as high device maintenance costs, complication of inter-device cooperation, illegal access to devices, and leakage of personal information. Consequently, it is difficult for users to construct a secure network with local as well as remote personal devices. We propose a User-driven Service Creation Platform (USCP), which enables users to construct a secure private network using a simple and intuitive approach that leverages the authentication mechanism in mobile phone networks. USCP separates signaling and data paths in a flat, virtual network topology. In this paper, we describe the basic design of USCP, the current implementation, and system evaluations.

Blood pressure is the measurement of the force exerted by blood against the walls of the arteries. Hypertension is a major risk factor of cardiovascular diseases. The systolic and diastolic blood pressures obtained from the oscillometric method could carry clues about hypertension. However, blood pressure is influenced by individual traits such as physiology, the geometry of the heart, body figure, gender and age. Therefore, consideration of individual traits is a requisite for reliable hypertension monitoring. The oscillation waveforms extracted from the cuff pressure reflect individual traits in terms of oscillation patterns that vary in size and amplitude over time. Thus, uniform features for individual traits from the oscillation patterns were extracted, and they were applied to evaluate systolic and diastolic blood pressures using two feedforward neural networks. The measurements of systolic and diastolic blood pressures from two neural networks were compared with the average values of systolic and diastolic blood pressures obtained by two nurses using the auscultatory method. The recognition performance was based on the difference between the blood pressures measured by the auscultation method and the proposed method with two neural networks. The recognition performance for systolic blood pressure was found to be 98.2% for 20 mmHg, 93.5% for 15 mmHg, and 82.3% for 10 mmHg, based on maximum negative amplitude. The recognition performance for diastolic blood pressure was found to be 100% for 20 mmHg, 98.8% for 15 mmHg, and 88.2% for 10 mmHg based on maximum positive amplitude. In our results, systolic blood pressure showed more fluctuation than diastolic blood pressure in terms of individual traits, and subjects with prehypertension or hypertension (systolic blood pressure) showed a stronger steep-slope pattern in 1/3 section of the feature windows than normal subjects. The other side, subjects with prehypertension or hypertension (diastolic blood pressure) showed a steep-slope pattern in front of the feature windows (2/3 section) than normal subjects. This paper presented a novel blood pressure measurement system that can monitor hypertension using personalized traits. Our study can serve as a foundation for reliable hypertension diagnosis and management based on consideration of individual traits.

Provider authentication is necessary in bidirectional broadcasting services, and a digital signature scheme is often used to prevent an adversary from attempting impersonation. The cost of secure signing key management is extremely high. In addition, the key has to be updated very often, since it is frequently used. The result is that the verification key also has to be updated very often, and its redistribution cost is huge. These costs are real and substantive problems, especially when the number of users is large. In this paper, we propose a system that dramatically reduces these costs. In the system, the signing key is updated, but the corresponding verification key does not have to be updated. This means that the signing key can be updated without any cost for redistributing the verification key and that the system is secure against the threat of signing key leakage, since the key can be frequently updated. Moreover, we propose a new key management method that divides a conventional key management server's role into two. The use of a key-insulated signature (KIS) scheme enables low-cost and more secure key management with two servers. Finally, to make a bidirectional broadcasting service more secure even if the signing key is leaked, we developed a new strong KIS scheme. We performed an experiment that assessed the cost of our strong KIS scheme and found that it is sufficiently low. Accordingly, a provider authentication system employing this scheme would be more efficient and would have lower key redistribution and network costs in comparison with conventional authentication systems.

This paper proposes an ultra-wideband double-directional spatio-temporal channel sounding technique using transformation between frequency- and time-domain (FD and TD) signals. Virtual antenna arrays, composed of omnidirectional antennas and scanners, are used for transmission and reception in the FD. After Fourier transforming the received FD signals to TD ones, time of arrival (TOA) is estimated using a peak search over the TD signals, and then angle of arrivals (AOA) and angle of departure (AOD) are estimated using a weighted angle histogram with a multiple signal classification (MUSIC) algorithm applied to the FD signals, inverse-Fourier transformed from the TD signals divided into subregions. Indoor channel sounding results validated that an appropriate weighting reduced a spurious level in the angle histogram by a factor of 0.1 to 0.2 in comparison with that of non-weighting. The proposed technique successfully resolved dominant multipath components, including a direct path, a single reflection, and a single diffraction, in line-of-sight (LOS) and non-LOS environments. Joint TOA and AOA/AOD spectra were also derived from the sounding signals. The spectra illustrated the dominant multipath components (agreed with the prediction by ray tracing) as clusters.

Although a lot of works for location management in wireless networks have been reported in the literature, most of the works have been focused on designing per-user-based strategies. This means that they can achieve the performance enhancement only for a certain class of mobile users with a specific range of CMR (call-to-mobility ratio). However, these per-user-based strategies can actually degrade the performance if a user's CMR changes significantly. Therefore, an efficient uniform location management strategy, which can be commonly applied to all mobile users regardless of their CMR, is proposed and analyzed in this paper. The motivation behind the proposed strategy is to exploit the concepts of the two well-known existing strategies: the location caching strategy and the local anchor strategy. That is, the location caching strategy exploits locality in a user's calling pattern, whereas the local anchor strategy exploits locality in a user's mobility pattern. By exploiting these characteristics of both strategies together with the profile management at the HLR (home location register), the proposed strategy can reduce the frequent access to the HLR, and thus effectively results in significant reduction in terms of the total location management cost. The analytical results also demonstrate that the proposed strategy can be uniformly applied to all mobile users, while always maintaining the performance gain over the IS-41 standard regardless of a user's CMR and the network traffic conditions.

The concept of personal networks is very user-centric and representative for the next generation networks. However, the present security mechanism does not consider at all what happens whenever a mobile node (device) is compromised, lost or stolen. Of course, a compromised, lost or stolen mobile node (device) is a main factor to leak stored secrets. This kind of leakage of stored secrets remains a great danger in the field of communication security since it can lead to the complete breakdown of the intended security level. In order to solve this problem, we propose a 3-way Leakage-Resilient and Forward-Secure Authenticated Key Exchange (3LRFS-AKE) protocol and its security architecture suitable for personal networks. The 3LRFS-AKE protocol guarantees not only forward secrecy of the shared key between device and its server as well as providing a new additional layer of security against the leakage of stored secrets. The proposed security architecture includes two different types of communications: PN wide communication and communication between P-PANs of two different users. In addition, we give a performance evaluation and numerical results of the delay generated by the proposed security architecture.