Space is becoming increasingly congested and contested, which calls for effective means to conduct effective monitoring of high-value space assets, especially in Space Situational Awareness (SSA) missions, while there are imperfections in existing methods and corresponding algorithms. To overcome such a problem, this letter proposes an algorithm for accurate Connected Element Interferometry (CEI) in SSA based on more interpolation information and iterations. Simulation results show that: (i) after iterations, the estimated asymptotic variance of the proposed method can basically achieve uniform convergence, and the ratio of it to ACRB is 1.00235 in δ0 ∈ [-0.5, 0.5], which is closer to 1 than the current best AM algorithms; (ii) In the interval of SNR ∈ [-14dB, 0dB], the estimation error of the proposed algorithm decreases significantly, which is basically comparable to CRLB (maintains at 1.236 times). The research of this letter could play a significant role in effective monitoring and high-precision tracking and measurement with significant space targets during futuristic SSA missions.

In recent years, driver's visual attention has been actively studied for driving automation technology. However, the number of models is few to perceive an insight understanding of driver's attention in various moments. All attention models process multi-level image representations by a two-stream/multi-stream network, increasing the computational cost due to an increment of model parameters. However, multi-level image representation such as optical flow plays a vital role in tasks involving videos. Therefore, to reduce the computational cost of a two-stream network and use multi-level image representation, this work proposes a single stream driver's visual attention model for a critical situation. The experiment was conducted using a publicly available critical driving dataset named BDD-A. Qualitative results confirm the effectiveness of the proposed model. Moreover, quantitative results highlight that the proposed model outperforms state-of-the-art visual attention models according to CC and SIM. Extensive ablation studies verify the presence of optical flow in the model, the position of optical flow in the spatial network, the convolution layers to process optical flow, and the computational cost compared to a two-stream model.

This paper focuses on the bandwidth allocation methods based on real user experience for web browsing applications. Because the Internet and its services are rapidly increasing, the bandwidth allocation problem has become one of the typical challenges for Internet service providers (ISPs) and network planning with respect to providing high service quality. The quality of experience (QoE) plays an important role in the success of services, and the guarantee of QoE accordingly represents an important goal in network resource control schemes. To cope with this issue, this paper proposes two user-centric bandwidth resource allocation methods for web browsing applications. The first method dynamically allocates bandwidth by considering the same user's satisfaction in terms of QoE with respect to all users in the system, whereas the second method introduces an efficient trade-off between the QoE of each user group and the average QoE of all users. The purpose of these proposals is to provide a flexible solution to reasonably allocate limited network resources to users. By considering service quality from real users' perception viewpoint, the proposed allocation methods enable us to understand actual users' experiences. Compared to previous works, the numerical results show that the proposed bandwidth allocation methods achieve the following contributions: improving the QoE level for dissatisfied users and providing a fair distribution, as well as retaining a reasonable average QoE.

This letter proposes a situation-adaptive detection algorithm for the improved efficiency of the detection performance and complexity in the MIMO-OFDM system. The proposed algorithm adaptively uses the QRD-M, DFE, and iterative detection scheme in according to the detection environment. Especially, the proposed algorithm effectively reduces the occurrence probability of error in the successive interference cancellation procedure by the unit of the spatial stream. The simulations demonstrate that the adaptive detection method using the proposed algorithm provides a better trade-off between detection performance and complexity in the MIMO-OFDM 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.

Nowadays, many interface devices or training systems have been developed with recent developments in IT technology, but only a few training systems for developmentally disabled people have been introduced. In this paper, we present a real-time, interactional and situational training system based on augmented reality in order to improve cognitive capability and adaptive ability in the daily lives of developmentally disabled people. Our system is specifically based on serving food in restaurants. It allows disabled people wearing the HMD attached with camera to conduct the training to cope with a series of situations safely while serving customers food and drinks and take the training session as much as they want. After experimenting on our presented system for 3 months, we found that they actively participated in the training and their cognitive abilities increasingly went faster through repeated training, resulting in the improvement in their cognitive ability and their ability to deal with situations.

The air traffic situational awareness is an essential factor for flight safety and efficiency. Today, pilots have only two methods for situational awareness, through visual acquisition or with traffic information via voice messages from Air Traffic Controllers. These methods have limitations in reducing aircraft separation because of their delay in acquiring traffic. To improve the acquisition of traffic information, airborne surveillance with ADS-B/TIS-B has been proposed. This paper reports on the prototype TIS-B system developed by ENRI and on the results of evaluations with flight testing.

Navigation systems providing route-guidance and traffic information are one of the most widely used driver-support systems these days. Most navigation systems are based on the map paradigm which plots the driving route in an abstracted version of a two-dimensional electronic map. Recently, a new navigation paradigm was introduced that is based on the augmented reality (AR) paradigm which displays the driving route by superimposing virtual objects on the real scene. These two paradigms have their own innate characteristics from the point of human cognition, and so complement each other rather than compete with each other. Regardless of the paradigm, the role of any navigation system is to support the driver in achieving his driving goals. The objective of this work is to investigate how these map and AR navigation paradigms impact the achievement of the driving goals: productivity and safety. We performed comparative experiments using a driving simulator and computers with 38 subjects. For the effects on productivity, driver's performance on three levels (control level, tactical level, and strategic level) of driving tasks was measured for each map and AR navigation condition. For the effects on safety, driver's situation awareness of safety-related events on the road was measured. To find how these navigation paradigms impose visual cognitive workload on driver, we tracked driver's eye movements. As a special factor of driving performance, route decision making at the complex decision points such as junction, overpass, and underpass was investigated additionally. Participant's subjective workload was assessed using the Driving Activity Load Index (DALI). Results indicated that there was little difference between the two navigation paradigms on driving performance. AR navigation attracted driver's visual attention more frequently than map navigation and then reduces awareness of and proper action for the safety-related events. AR navigation was faster and better to support route decision making at the complex decision points. According to the subjective workload assessment, AR navigation was visually and temporally more demanding.

Mobile devices, such as cellular phones and car navigation systems, are essential to daily life. People acquire necessary information and preferred content over communication networks anywhere, anytime. However, usability issues arise from the simplicity of user interfaces themselves. Thus, a recommendation of content that is adapted to a user's preference and situation will help the user select content. In this paper, we describe a method to realize such a system using Bayesian networks. This user-adapted mobile system is based on a user model that provides recommendation of content (i.e., restaurants, shops, and music that are suitable to the user and situation) and that learns incrementally based on accumulated usage history data. However, sufficient samples are not always guaranteed, since a user model would require combined dependency among users, situations, and contents. Therefore, we propose the LK method for modeling, which complements incomplete and insufficient samples using knowledge data, and CPT incremental learning for adaptation based on a small number of samples. In order to evaluate the methods proposed, we applied them to restaurant recommendations made on car navigation systems. The evaluation results confirmed that our model based on the LK method can be expected to provide better generalization performance than that of the conventional method. Furthermore, our system would require much less operation than current car navigation systems from the beginning of use. Our evaluation results also indicate that learning a user's individual preference through CPT incremental learning would be beneficial to many users, even with only a few samples. As a result, we have developed the technology of a system that becomes more adapted to a user the more it is used.

The most of conventional information services are based on the implicit premise that the users has already defined their desired information. This study proposes a mobile information service that allows the users who have not yet defined their desired information or whose desired information varies according to the situation to get appropriate information. When the user can specify their desired information to the system explicitly, the authors develop a "Pull" service. Conversely, when the user cannot verbally specify their desired information to the system, this study provides "Push" service and "Don't disturb" option for the user who does not welcome this service. This study considers the characteristics of the environment of mobile terminal to focus on "Time", "Place" and user's "Preference": long term and short term preference. This study also creates rules, algorithms and filtering to the service. Furthermore, the results of experiments have been discussed to verify the idea that different of user desired requires different information services.

This paper is concerned with an ITS designed for augmenting a student's capability in problem solving. Discussions are concentrated on helping students acquire strategic knowledge and assisting them to build it in their heads. In this paper, many kinds of strategies are treated from a unified point of view. Based on this consideration, a teaching paradigm of strategic knowledge is presented. The paradigm is realized in an ITS as a training environment for strategic knowledge. Assisting students to learn strategic knowledge, the system sets up an appropriate environment and gives them some appropriate advice in each environment. It is realized as a function of giving them appropriate problems and hints about it. In general, strategic knowledge is a kind of heuristics so that it is not easy to describe their application conditions deterministically and explicitly. For this reason, an ITS for strategic knowledge is required to be designed so as to cover not only the case where expertise is represented explicitly as an executable model but also the case where it is represented only implicitly. To realize this teaching paradigm, situation-dependent knowledge called reminding pattern is prepared in the system. It is represented by a triple of a strategy, a situation, and a key symbol in the situation. It denotes that the key usually reminds students of the strategy in the situation. The system gives students problems including positive/negative examples of applications of each strategy in its problem solving process and hints which remind them of an appropriate strategy and makes them resume the problem solving when they fall into an impasse. In this paper, the structure of the system realizing this teaching paradigm is explained in the domain of proving propositional formulas.