In order to verify the correctness of functional requirements, we have been developing a verification method of the correctness of functional requirements specification using the Requirements Frame model. In this paper, we propose a verification method of non-functional requirements specification in terms of time-response requirements written with a natural language. We established a verification method by extending the Requirements Frame model. We have also developed a prototype system based on the method using Java. The extended Requirements Frame model and the verification method will be illustrated with examples.

Regarding IEEE 802.11 wireless local area networks (WLANs), many researchers are focusing on signal-to-noise ratio (SNR)-based rate adaptation schemes, because these schemes have the advantage of accurately selecting transmission rates that suit the channel. However, even SNR-based rate adaptation schemes work poorly in a rapidly varying channel environment. If a transmitter cannot receive accurate rate information due to fast channel fading, it encounters continuous channel errors, because the cycle of rate adaptation and rate information feedback breaks. A well-designed request-to-send/clear-to-send (RTS/CTS) frame exchange policy that accurately reflects the network situation is an indispensable element for enhancing the performance of SNR-based rate adaptation schemes. In this paper, a novel rate adaptation scheme called adaptive RTS/CTS-exchange and rate prediction (ARRP) is proposed, which adapts the transmission rate efficiently for variable network situations, including rapidly varying channels. ARRP selects a transmission rate by predicting the SNR of the data frame to transmit when the channel condition becomes worse. Accordingly, ARRP prevents continuous channel errors through a pre-emptive transmission rate adjustment. Moreover, ARRP utilizes an efficient RTS/CTS frame exchange algorithm that considers the number of contending stations and the current transmission rate of data frames, which drastically reduces both frame collisions and RTS/CTS-exchange overhead simultaneously. Simulation results show that ARRP achieves better performance than other rate adaptation schemes.

High frame rate and ultra-low delay matching system plays an increasingly important role in human-machine interactive applications which call for higher frame rate and lower delay for a better experience. The large amount of processing data and the complex computation in a local feature based matching system, make it difficult to achieve a high process speed and ultra-low delay matching with limited resource. Aiming at a matching system with the process speed of more than 1000 fps and with the delay of less than 1 ms/frame, this paper puts forward a local binary feature based matching system with field-programmable gate array (FPGA). Pixel selection based 4-1-4 parallel matching and intensity directed symmetry are proposed for the implementation of this system. To design a basic framework with the high process speed and ultra-low delay using limited resource, pixel selection based 4-1-4 parallel matching is proposed, which makes it possible to use only one-thread resource consumption to achieve a four-thread processing. Assumes that the orientation of the keypoint will bisect the patch best and will point to the region with high intensity, intensity directed symmetry is proposed to calculate the keypoint orientation in a hardware friendly way, which is an important part for a rotation-robust matching system. Software experiment result shows that the proposed keypoint orientation calculation method achieves almost the same performance with the state-of-art intensity centroid orientation calculation method in a matching system. Hardware experiment result shows that the designed image process core supports to process VGA (640×480) videos at a process speed of 1306 fps and with a delay of 0.8083 ms/frame.

In this paper, we propose a novel self-supervised learning of video representation which is capable to anticipate the video category by only reading its short clip. The key idea is that we employ the Siamese convolutional network to model the self-supervised feature learning as two different image matching problems. By using frame encoding, the proposed video representation could be extracted from different temporal scales. We refine the training process via a motion-based temporal segmentation strategy. The learned representations for videos can be not only applied to action anticipation, but also to action recognition. We verify the effectiveness of the proposed approach on both action anticipation and action recognition using two datasets namely UCF101 and HMDB51. The experiments show that we can achieve comparable results with the state-of-the-art self-supervised learning methods on both tasks.

Creating an ontology from multidisciplinary knowledge is a challenge because it needs a number of various domain experts to collaborate in knowledge construction and verify the semantic meanings of the cross-domain concepts. Confusions and misinterpretations of concepts during knowledge creation are usually caused by having different perspectives and different business goals from different domain experts. In this paper, we propose a community-driven ontology-based application management (CD-OAM) framework that provides a collaborative environment with supporting features to enable collaborative knowledge creation. It can also reduce confusions and misinterpretations among domain stakeholders during knowledge construction process. We selected one of the multidisciplinary domains, which is Life Cycle Assessment (LCA) for our scenario-based knowledge construction. Constructing the LCA knowledge requires many concepts from various fields including environment protection, economic development, social development, etc. The output of this collaborative knowledge construction is called MLCA (multidisciplinary LCA) ontology. Based on our scenario-based experiment, it shows that CD-OAM framework can support the collaborative activities for MLCA knowledge construction and also reduce confusions and misinterpretations of cross-domain concepts that usually presents in general approach.

This paper proposes a sequentially iterative equalizer based on Kalman filtering and smoothing (SIEKFS) for multiple-input multiple-output (MIMO) systems under frequency selective fading channels. In the proposed SIEKFS, an iteration consists of sequentially executed subiterations, and each subiteration performs equalization and detection procedures of the symbols transmitted from a specific transmit antenna. During this subiteration, all available observations for the transmission block are utilized in the equalization procedures. Furthermore, the entire soft estimate of the desired symbols to be detected does not participate in the equalization procedures of the desired symbols, i.e., the proposed SIEKFS performs input-by-input equalization procedures for a priori information nulling. Therefore, compared with the original iterative equalizer based on Kalman filtering and smoothing, which performs symbol-by-symbol equalization procedures, the proposed SIEKFS can also perform iterative equalization based on the Kalman framework and turbo principle, with a significant reduction in computation complexity. Simulation results verify that the proposed SIEKFS achieves suboptimum error performance as the size of the antenna configuration and the number of iterations increase.

The Smart City paradigm has become one of the most important research topics around the globe. Particularly in Europe, it is considered as a solution for the unstoppable increase of high density urban environments and the European Commission has included the Smart City research as one of the key objectives for the FP7 (Seventh Framework Program) and H2020 (Horizon 2020) research initiatives. As a result, a considerable amount of quality research, with particular emphasis on information and communication technologies, has been produced. In this paper, we review the current efforts dedicated in Europe to this research topic. Particular attention is paid in the review to the platforms and infrastructure technologies adopted to introduce the Internet of Things into the city, taking into account the constraints and harshness of urban environments. Furthermore, this paper also considers the efforts in the experimental perspective, which includes the review of existing Smart City testbeds, part of wider European initiatives such as FIRE (Future Internet Research and Experimentation) and FIWARE. Last but not least, the main efforts in providing interoperability between the different experimental facilities are also presented.

A 10 Gbps transmitter bridge chip including four data lanes, which increases the bandwidth using an 8-to-1 serialization, is proposed for a field-programmable gate array (FPGA)-based frame generator to support the protocol of the D-PHY version 1.2 for the mobile industry processor interface (MIPI) display serial interface (DSI).

Wireless technologies that offer high data rate are generally energy-consuming ones while low-energy technologies commonly provide low data rate. Both kinds of technologies have been integrated in a single mobile device for different services. Therefore, if the service does not always require high data rate, the low energy technology, i.e., Bluetooth, can be used instead of the energy-consuming one, i.e., Wi-Fi, for saving energy. It is obvious that energy savings are maximized by turning the unused technology off. However, when active sessions of ongoing services migrate between different technologies, the network-layer connectivity must be maintained, or a vertical handover (VHO) between different networks is required. Moreover, when the networks are not interconnected, the VHO must be fully controlled by the device itself. The device typically navigates traffic through the firmware of the wireless network interface cards (WNIC) using their drivers, which are dependent on the vendors. To control the traffic navigation between WNICs without any modification of the WNICs' drivers, Software-Defined Networking (SDN) can be applied locally on the mobile device, the so called local SDN. In the local SDN architecture, a local SDN controller (SDNC) is used to control a virtual OpenFlow switch, which turns WNICs into its switch ports. Although the SDNC can navigate the traffic, it lacks the global view of the network topology. Hence, to correctly navigate traffic in a VHO process, an extended SDN controller (extSDNC) was proposed in a previous work. With the extSDNC, the SDNC can perform VHO based on a link layer trigger but with a significant packet loss rate. Therefore, in this paper, a framework named esVHO is proposed that executes VHO at the network layer to reduce the packet loss rate and reduce energy consumption. Experiments on VHO performance prove that esVHO can reduce the packet loss rate considerably. Moreover, the results of an energy saving experiment show that esVHO performs high energy saving up to 4.89 times compared to the others.

Smart education environment, that is a learning environment utilizing the Information Communication Technology (ICT), has attracted a great deal of attention. In order to expand this environment, we need a system that can establish the learning environment armed cloud systems to reduce a significant strain on teaching staff. The important issue for such system is extensibility because the system should be adapted to many kinds of original digital learning material with minimum modification. Therefore, this paper proposes “An Application Framework for Smart Education System: SES Framework”. In this Smart Education System, multi-aspect information concerning to a technical term embedded in the original digital learning material can be retrieved from different social media automatically. They can be also displayed on multi-screen devices according to user's operation. It is implemented based on “Transforming Model” which enables the migration of the original digital learning material to the smart education environment. It also has an easy operation flow for trainees named “three-step selection flow”. SES Framework derived from Model-View-Controller (MVC) pattern is based on the system architecture that enables triple mashup against the original digital learning material, external social media, and screen devices in front of users. All these functionalities have been implemented on cloud systems. We show SES Framework through the implementation example. We also demonstrate the effectiveness of SES Framework by indicating the system modification case study.

With rapid increase of the number of applications as well as the sizes of data, multi-query processing on the MapReduce framework has gained much attention. Meanwhile, there have been much interest in skyline query processing due to its power of multi-criteria decision making and analysis. Recently, there have been attempts to optimize multi-query processing in MapReduce. However, they are not appropriate to process multiple skyline queries efficiently and they also require modifications of the Hadoop internals. In this paper, we propose an efficient method for processing multi-skyline queries with MapReduce without any modification of the Hadoop internals. Through various experiments, we show that our approach outperforms previous studies by orders of magnitude.

This paper presents a novel framework called error case frames for correcting preposition errors. They are case frames specially designed for describing and correcting preposition errors. Their most distinct advantage is that they can correct errors with feedback messages explaining why the preposition is erroneous. This paper proposes a method for automatically generating them by comparing learner and native corpora. Experiments show (i) automatically generated error case frames achieve a performance comparable to previous methods; (ii) error case frames are intuitively interpretable and manually modifiable to improve them; (iii) feedback messages provided by error case frames are effective in language learning assistance. Considering these advantages and the fact that it has been difficult to provide feedback messages using automatically generated rules, error case frames will likely be one of the major approaches for preposition error correction.

Wi-Fi Direct is a promising and available technology for device-to-device (D2D) proximity communications. To improve the performances of Wi-Fi Direct communication, optimized radio resource allocations are important. This paper proposes network assisted Wi-Fi Direct (NAWD), which operates based on the media independent services framework of IEEE 802.21 standard, for optimizing radio resource allocations. The NAWD is enhanced Wi-Fi Direct with the assistance of infrastructure networks (e.g., cellular network) and allocates radio resources (e.g., frequency channels and transmit power) to reduce radio interferences among Wi-Fi Direct devices (e.g., smart phones and set-top boxes). The NAWD includes mechanisms for gathering configuration information (e.g., location information and network connection information) of Wi-Fi Direct devices and allocating optimized radio resources (e.g., frequency channels and transmit power) to reduce radio interferences among Wi-Fi Direct devices. Simulation results show that the proposed NAWD increases significantly SINR, power efficiency, and areal capacity compared to legacy Wi-Fi Direct, where areal capacity is total traffic throughput per unit area.

Video quality generally suffers from packet losses caused by an unreliable channel when video is transmitted over an error-prone wireless channel. This quality degradation is the main reason that a video compression encoder uses error-resilient coding to deal with the high packet-loss probability. The use of adequate error resilience can mitigate the effects of channel errors, but the coding efficiency for bit reduction will be decreased. On the other hand, H.264/AVC uses multiple reference frame (MRF) motion compensation for a higher coding efficiency. However, an increase in the number of reference frames in the H.264/AVC encoder has been recently observed, making the received video quality worse in the presence of transmission errors if the cyclic intra-refresh is used as the error-resilience method. This is because the reference-block selection in the MRF chooses blocks on the basis of the rate distortion optimization, irrespective of the intra-refresh coding. In this paper, a new error-resilient reference selection method is proposed to provide error resilience for MRF based motion compensation. The proposed error-resilient reference selection method achieves an average PSNR enhancement up to 0.5 to 2dB in 10% packet-loss-ratio environments. Therefore, the proposed method can be valuable in most MRF-based interactive video encoding system, which can be used for video broadcasting and mobile video conferencing over an erroneous network.

To analyze large-scale data efficiently, studies on Hadoop, one of the most popular MapReduce frameworks, have been actively done. Meanwhile, most of the large-scale data analysis applications, e.g., data clustering, are required to do the same map and reduce functions repeatedly. However, Hadoop cannot provide an optimal performance for iterative MapReduce jobs because it derives a result by doing one phase of map and reduce functions. To solve the problems, in this paper, we propose a new efficient resource management framework for iterative MapReduce processing in large-scale data analysis. For this, we first design an iterative job state-machine for managing the iterative MapReduce jobs. Secondly, we propose an invariant data caching mechanism for reducing the I/O costs of data accesses. Thirdly, we propose an iterative resource management technique for efficiently managing the resources of a Hadoop cluster. Fourthly, we devise a stop condition check mechanism for preventing unnecessary computation. Finally, we show the performance superiority of the proposed framework by comparing it with the existing frameworks.

In this paper, a Boolean algebra approach is proposed to encode various acceptability semantics for abstract argumentation frameworks, where each semantics can be equivalently encoded into several Boolean constraint models based on Boolean matrices and a family of Boolean operations between them. Then, we show that these models can be easily translated into logic programs, and can be solved by a constraint solver over Boolean variables. In addition, we propose some querying strategies to accelerate the calculation of the grounded, stable and complete extensions. Finally, we describe an experimental study on the performance of our encodings according to different semantics and querying strategies.

The Even-Goldreich-Micali framework is a generic method for constructing secure digital signature schemes from weaker signature schemes and one-time signature schemes. Several variations are known due to properties demanded on the underlying building blocks. It is in particular interesting when the underlying signature scheme is a so-called F-signature scheme that admits different message spaces for signing and verification. In this paper we overview these variations in the literature and add a new one to the bucket.

Sensor networks are often used to understand underlying phenomena that are reflected through sensing data. In real world applications, this understanding supports decision makers attempting to access a disaster area or monitor a certain event regularly and thus necessary actions can be triggered in response to the problems. Practitioners designing such systems must overcome difficulties due to the practical limitations of the data and the fidelity of a network condition. This paper explores the design of a network solution for the data acquisition domain with the goal of increasing the efficiency of data gathering efforts. An unmanned aerial vehicle (UAV) is introduced to address various real-world sensor network challenges such as limited resources, lack of real-time representative data, and mobility of a relay station. Towards this goal, we introduce a novel cooperative path selection framework to effectively collect data from multiple sensor sources. The framework consists of six main parts ranging from the system initialization to the UAV data acquisition. The UAV data acquisition is useful to increase situational awareness or used as inputs for data manipulation that support response efforts. We develop a system-based simulation that creates the representative sensor networks and uses the UAV for collecting data packets. Results using our proposed framework are analyzed and compared to existing approaches to show the efficiency of the scheme.

In general-synchronous framework, in which the clock is distributed periodically to each register but not necessarily simultaneously, circuit performance is expected to be improved compared to complete-synchronous framework, in which the clock is distributed periodically and simultaneously to each register. To improve the circuit performance more, logic synthesis for general-synchronous framework is required. In this paper, under the assumption that any clock schedule is realized by an ideal clock distribution circuit, when two or more cell libraries are available, a technology mapping method which assigns a cell to each gate in the given logic circuit by using integer linear programming is proposed. In experiments, we show the effectiveness of the proposed technology mapping method.

Key frame based video summarization has emerged as an important task for efficient video data management. This paper proposes a novel technique for key frame extraction based on chaos theory and color information. By applying chaos theory, a large content change between frames becomes more chaos-like and results in a more complex fractal trajectory in phase space. By exploiting the fractality measured in the phase space between frames, it is possible to evaluate inter-frame content changes invariant to effects of fades and illumination change. In addition to this measure, the color histogram-based measure is also used to complement the chaos-based measure which is sensitive to changes of camera /object motion. By comparing the last key frame with the current frame based on the proposed frame difference measure combining these two complementary measures, the key frames are robustly selected even under presence of video fades, changes of illumination, and camera/object motion. The experimental results demonstrate its effectiveness with significant improvement over the conventional method.