Parameter identification and design of a robust attitude controller using <Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> methodology for the raptor E620 small-scale helicopter

Rigorous control synthesis for an unmanned aerial vehicle necessitates the availability of a good, reasonable model for such a vehicle. The work reported in this paper focuses on the modeling of a rotary unmanned aerial vehicle (RUAV) and the development of a robust controller that can handle parameter uncertainties and disturbances. The parameters of the plant model are obtained through the use of the prediction error method with real flight data. The response of the identified linear model shows a good match with the measured flight data. The H _∞ control scheme is applied to obtain a robustly stable controller using the identified model. The proposed controller is analyzed using frequency-domain analysis and time-domain simulations. The performance of the proposed H _∞ controller is better than that of the conventional proportional derivative controller in that the proposed controller has a shorter settling time and less overshoot. Furthermore, the degradation of the proposed controller performance is negligible and stability is maintained when the input gains to the plant are doubled, which demonstrates the good performance and robustness of the controller.     

An analytical framework for event mining in video data

With the high availability of digital video contents on the internet, users need more assistance to access digital videos. Various researches have been done about video summarization and semantic video analysis to help to satisfy these needs. These works are developing condensed versions of a full length video stream through the identification of the most important and pertinent content within the stream. Most of the existing works in these areas are mainly focused on event mining. Event mining from video streams improves the accessibility and reusability of large media collections, and it has been an active area of research with notable recent progress. Event mining includes a wide range of multimedia domains such as surveillance, meetings, broadcast, news, sports, documentary, and films, as well as personal and online media collections. Due to the variety and plenty of Event mining techniques, in this paper we suggest an analytical framework to classify event mining techniques and to evaluate them based on important functional measures. This framework could lead to empirical and technical comparison of event mining methods and development of more efficient structures at future.     

A game attention model for efficient bit rate allocation in cloud gaming

The widespread availability of broadband internet access and the growth in server-based processing have provided an opportunity to run games away from the player into the cloud and offer a new promising service known as cloud gaming. The concept of cloud gaming is to render a game in the cloud and stream the resulting game scenes to the player as a video sequence over a broadband connection. To meet the stringent network bandwidth requirements of cloud gaming and support more players, efficient bit rate reduction techniques are needed. In this paper, we introduce the concept of game attention model (GAM), which is basically a game context-based visual attention model, as a means for reducing the bit rate of the streaming video more efficiently. GAM estimates the importance of each macro-block in a game frame from the player’s perspective and allows encoding the less important macro-blocks with lower bit rate. We have evaluated nine game video sequences, covering a wide range of game genre and a spectrum of scene content in terms of details, motion and brightness. Our subjective assessment shows that by integrating this model into the cloud gaming framework, it is possible to decrease the required bit rate by nearly 25 % on average, while maintaining a relatively high user quality of experience. This clearly enables players with limited communication resources to benefit from cloud gaming with acceptable quality.     

Stabilization of positive switched systems with time-varying delays under asynchronous switching

This paper investigates the state feedback stabilization problem for a class of positive switched systems with time-varying delays under asynchronous switching in the frameworks of continuous-time and discrete-time dynamics. The so-called asynchronous switching means that the switches between the candidate controllers and system modes are asynchronous. By constructing an appropriate co-positive type Lyapunov-Krasovskii functional and further allowing the functional to increase during the running time of active subsystems, sufficient conditions are provided to guarantee the exponential stability of the resulting closed-loop systems, and the corresponding controller gain matrices and admissible switching signals are presented. Finally, two illustrative examples are given to show the effectiveness of the proposed methods.     

Comparative evaluation of performance measures for human driving skills

In this paper, we evaluate the adequacy of several performance measures for the evaluation of driving skills between different drivers. This work was motivated by the need for a training system that captures the driving skills of an expert driver and transfers the skills to novice drivers using a haptic-enabled driving simulator. The performance measures examined include traditional task performance measures, e.g., the mean position error, and a stochastic distance between a pair of hidden Markov models (HMMs), each of which is trained for an individual driver. The emphasis of the latter is on the differences between the stochastic somatosensory processes of human driving skills. For the evaluation, we developed a driving simulator and carried out an experiment that collected the driving data of an expert driver whose data were used as a reference for comparison and of many other subjects. The performance measures were computed from the experimental data, and they were compared to each other. We also collected the subjective judgement scores of the driver’s skills made by a highly-experienced external evaluator, and these subjective scores were compared with the objective performance measures. Analysis results showed that the HMM-based distance metric had a moderately high correlation between the subjective scores and it was also consistent with the other task performance measures, indicating the adequacy of the HMM-based metric as an objective performance measure for driving skill learning. The findings of this work can contribute to developing a driving simulator for training with an objective assessment function of driving skills.     

A review of interoperability assessment models

Interoperability is the ability of systems to provide services to and accept services from other systems, and to use the services exchanged so as to operate together in a more effective manner. The fact that interoperability can be improved means that the metrics for measuring interoperability can be defined. For the purpose of measuring the interoperability between systems, an interoperability assessment model is required. This paper deals with the existing interoperability assessment models. A compara- tive analysis among these models is provided to evaluate the similarities and differences in their philosophy and implementation. The analysis yields a set of recommendations for any party that is open to the idea of creating or improving an interoperability assessment model.     

Sound source localization using compressive sensing-based feature extraction and spatial sparsity

In this paper, we propose a source localization algorithm based on a sparse Fast Fourier Transform (FFT)-based feature extraction method and spatial sparsity. We represent the sound source positions as a sparse vector by discretely segmenting the space with a circular grid. The location vector is related to microphone measurements through a linear equation, which can be estimated at each microphone. For this linear dimensionality reduction, we have utilized a Compressive Sensing (CS) and two-level FFT-based feature extraction method which combines two sets of audio signal features and covers both short-time and long-time properties of the signal. The proposed feature extraction method leads to a sparse representation of audio signals. As a result, a significant reduction in the dimensionality of the signals is achieved. In comparison to the state-of-the-art methods, the proposed method improves the accuracy while the complexity is reduced in some cases.     

Effect of kinesthetic and tactile haptic feedback on the quality of experience of edutainment applications

Haptic technologies and applications have received enormous attention in the last decade. The incorporation of haptic modality into multimedia applications adds excitement and enjoyment to an application. It also adds a more natural feel to multimedia applications, that otherwise would be limited to vision and audition, by engaging as well the user’s sense of touch, giving a more intrinsic feel essential for ambient intelligent applications. However, the improvement of an application’s Quality of Experience (QoE) by the addition of haptic feedback is still not completely understood. The research presented in this paper focuses on the effect of haptic feedback and what it potentially adds to the experience of the user as opposed to the traditional visual and auditory feedback. In essence, it investigates certain issues regarding stylus-based haptic education applications and haptic-enhanced entertainment videos. To this end, we used two haptic applications: the haptic handwriting learning tool to experiment with force feedback haptic interaction and the tactile YouTube application for tactile haptic feedback. In both applications, our analysis shows that the addition of haptic feedback will increase the QoE in the absence of fatigue or discomfort for this category of applications. This implies that the incorporation of haptic modality (both force feedback as well as tactile feedback) has positively contributed to the overall QoE for the users.     

MC-MLAS: Multi-channel Minimum Latency Aggregation Scheduling in Wireless Sensor Networks

Data aggregation is a key, yet time-consuming functionality in wireless sensor networks (WSNs). Multi-channel design is a promising technique to alleviate interference as a primary reason for long latency of TDMA aggregation scheduling. Indeed, it provides more potential of parallel transmissions over different frequency channels, thus minimizing time latency. In this paper, we focus on designing a multi-channel minimum latency aggregation scheduling protocol, named MC-MLAS, using a new joint approach for tree construction, channel assignment, and transmission scheduling. To our best knowledge, this is the first work in the literature which combines orthogonal channels and partially overlapping channels to consider the total latency involved in data aggregation. Extensive simulations verify the superiority of MC-MLAS in WSNs.