Energy-efficient resource allocation is considered in sensing-based spectrum sharing for cooperative cognitive radio networks (CCRNs). The secondary user first listens to the spectrum allocated to the primary user (PU) to detect the PU state and then initiates data transmission with two power levels based on the sensing decision (e.g., idle or busy). Under this model, the optimization problem of maximizing energy efficiency (EE) is formulated over the transmission power and sensing time subject to some practical limitations, such as the individual power constraint for secondary source and relay, the quality of service (QoS) for the secondary system, and effective protection for the PU. Given the complexity of this problem, two simplified versions (i.e., perfect and imperfect sensing cases) are studied in this paper. We transform the considered problem in fractional form into an equivalent optimization problem in subtractive form. Then, for perfect sensing, the Lagrange dual decomposition and iterative algorithm are applied to acquire the optimal power allocation policy; for imperfect sensing, an exhaustive search and iterative algorithm are proposed to obtain the optimal sensing time and corresponding power allocation strategy. Finally, numerical results show that the energy-efficient design greatly improves EE compared with the conventional spectrum-efficient design.

In the classical computation theory, the language of a system features the computational behavior of the system but it does not distinguish the determinism and nondeterminism of actions. However, Milner found that the determinism and nondeterminism affect the interactional behavior of interactive systems and thus the notion of language does not features the interactional behavior. Therefore, Milner proposed the notion of (weak) bisimulation to solve this problem. With the development of internet, more and more interactive systems occur in the world, such as electronic trading system. Security is one of the most important topics for these systems. We find that different security policies can also affect the interactional behavior of a system, which exactly is the reason why a good policy can strengthen the security. In other words, two interactive systems with different security policies are not of an equivalent behavior although their functions (or business processes) are identical. However, the classic (weak) bisimulation theory draws an opposite conclusion that their behaviors are equivalent. The notion of (weak) bisimulation is not suitable for these security-oriented interactive systems since it does not consider a security policy. This paper proposes the concept of secure bisimulation in order to solve the above problem.

This paper proposes an opamp-free solution to implement single-phase-clock controlled noise shaping in a SAR ADC. Unlike a conventional noise shaping SAR ADC, the proposal realizes noise shaping by charge redistribution, which is a passive technique. The passive implementation has high power efficiency. Meanwhile, since the proposal maintains the basic architecture and operation method of a traditional SAR ADC, it retains all the advantages of a SAR ADC. Furthermore, noise shaping helps to improve the performance of SAR ADC and relaxes its non-ideal effects. Designed in a 65-nm CMOS technology, the prototype realizes 58-dB SNDR based on an 8-bit C-DAC at 50-MS/s sampling frequency. It consumes 120.7-µW power from a 0.8-V supply and achieves a FoM of 14.8-fJ per conversion step.

We have developed software that uses the R statistics software environment to automatically generate tactile graphs — i.e. graphs that can be read by blind people using their sense of touch. We released this software as a Web application to make it available to anyone, from anywhere. This Web application can automatically generate images for tactile graphs from numerical data in a CSV file. It is currently able to generate four types of graph — scatter plots, line graphs, bar charts and pie charts. This paper describes the Web application's functions, operating procedures and the results of evaluation experiments.

In the current IoT (Internet of Things) environment, more and more Things: devices, objects, sensors, and everyday items not usually considered computers, are connected to the Internet, and these Things affect and change our social life and economic activities. By using IoTs, service providers can collect and store personal information in the real world, and such providers can gain access to detailed behaviors of the user. Although service providers offer users new services and numerous benefits using their detailed information, most users have concerns about the privacy and security of their personal data. Thus, service providers need to take countermeasures to eliminate those concerns. To help eliminate those concerns, first we conduct a survey regarding users' privacy and security concerns about IoT services, and then we analyze data collected from the survey using structural equation modeling (SEM). Analysis of the results provide answers to issues of privacy and security concerns to service providers and their users. And we also analyze the effectiveness and effects of personal information management and protection functions in IoT services.

This paper presents an improved Mobile WiMAX handover (HO) algorithm for real-time application by using a Link_Going_Down (LGD) trigger technique. Mobile WiMAX is a wireless networking system based on the IEEE 802.16e standard. In order to support phone mobility, a HO scheme of some kind must be adopted, and in this standard hard handover (HHO) is defined as mandatory. Since, the fact that there will be a pause in data transmission during the HO process, delay in communication will occur. Thus, the HO time (>50ms) can degrade system performance when implemented in real-time applications such as Video Streaming or Internet Protocol Television (IPTV). Additionally, the HHO takes approximately 300ms because the HO process doesn't start at the best point. The HHO standard considers only the received signal strength (RSS) to decide initiation. The mobile station velocity is also an important factor in HO initiation that should not be neglected. To deal with the problems of handover delay, this paper proposes a new HO scheme. This scheme adopts the dynamic HO threshold that used LGD technique to define the starting HO process. This technique is based on the RSSD (measured by the Doppler Effect technique), mobile velocities and handover time. Consequently, the HO process starts at the right time and HO time is reduced (<50 ms) and the network resource utilization is enhanced to be more efficient.

An online version of convolutive non-negative sparse coding (CNSC) with the generalized Kullback-Leibler (K-L) divergence is proposed to adaptively learn spectral-temporal bases from speech streams. The proposed scheme processes training data piece-by-piece and incrementally updates learned bases with accumulated statistics to overcome the inefficiency of its offline counterpart in processing large scale or streaming data. Compared to conventional non-negative sparse coding, we utilize the convolutive model within bases, so that each basis is capable of describing a relatively long temporal span of signals, which helps to improve the representation power of the model. Moreover, by incorporating a voice activity detector (VAD), we propose an unsupervised enhancement algorithm that updates the noise dictionary adaptively from non-speech intervals. Meanwhile, for the speech intervals, one can adaptively learn the speech bases by keeping the noise ones fixed. Experimental results show that the proposed algorithm outperforms the competing algorithms substantially, especially when the background noise is highly non-stationary.

In the health IoT (Internet of Things), the specialized sensor devices can be used to monitor remote health and notify the emergency information, e.g., blood pressure, heart rate, etc. These data can help the doctors to rescue the patients. In cloud-based health IoT, patients' medical/health data is managed by the cloud service providers. Secure storage and privacy preservation are indispensable for the outsourced medical/health data in cloud computing. In this paper, we study the integrity checking and sharing of outsourced private medical/health records for critical patients in public clouds (ICS). The patient can check his own medical/health data integrity and retrieve them. When a patient is in coma, some authorized entities and hospital can cooperate to share the patient's necessary medical/health data in order to rescue the patient. The paper studies the system model, security model and concrete scheme for ICS in public clouds. Based on the bilinear pairing technique, we design an efficient ICS protocol. Through security analysis and performance analysis, the proposed protocol is provably secure and efficient.

In heterogeneous networks, network selection is an important task for reconfigurable mobile devices (MDs). In the reconfigurable MD architecture that has been standardized by the European Telecommunications Standards Institute (ETSI), the network selection functionality is handled by a software component called Mobility Policy Manager (MPM). In this paper, we present an implementation of the MPM whereby a reconfigurable MD conforming to the ETSI standard can select the most appropriate radio access network (RAN) to use. We implemented a reconfigurable MD test-bed compliant with the ETSI standard, and show that the network selection driven by the MPM enhances the throughput of the receiving MD by about 26% compared to the arbitrary network selection provided by a conventional reconfigurable MD without the functionality of MPM, verifying the functionality of the MPM.

This paper proposes an interference suppression scheme based on linear combining for multiple relay systems. Interference from base stations and relays in neighboring cells degrades the bit error rate (BER) performance of mobile stations (MSs) near cell boundaries. To suppress such interference for half-duplex relay systems, the proposed scheme linearly combines received signals of the first and second phases at MS. Without channel state information (CSI) feedback, weight coefficients for the linear combining are estimated by the recursive least-squares (RLS) algorithm, which requires only information on preamble symbols of the target MS. Computer simulations of orthogonal frequency-division multiplexing (OFDM) transmission under two-cell and frequency selective fading conditions are conducted. It is demonstrated that the RLS-based linear combining with decision directed estimation is superior to the RLS-based linear combining using only the preamble and can outperform the minimum mean-squared error (MMSE) combining with estimated CSI when the number of preamble symbols is two and four that correspond to the minimum requirements for MMSE and RLS, respectively.

Since many cyber-physical systems (CPSs) manipulate security-sensitive data, enhancing the quality of security in a CPS is a critical and challenging issue in CPS design. Although there has been a large body of research on securing general purpose PCs, directly applying such techniques to a CPS can compromise the real-time property of CPSs since the timely execution of tasks in a CPS typically relies on real-time scheduling. Recognizing this property, previous works have proposed approaches to add a security constraint to the real-time properties to cope with the information leakage problem that can arise between real-time tasks with different security levels. However, conventional works have mainly focused on non-preemptive scheduling and have suggested a very naive approach for preemptive scheduling, which shows limited analytical capability. In this paper, we present a new preemptive fixed-priority scheduling algorithm incorporating a security constraint, called lowest security-level first (LSF) and its strong schedulability analysis to reduce the potential of information leakage. Our simulation results show that LSF schedulability analysis outperforms state-of-the-art FP analysis when the security constraint has reasonable timing penalties.

In this paper, we propose a single-channel speech enhancement method for a push-to-talk enabled wireless communication device. The proposed method is based on adaptive weighted β-order spectral amplitude estimation under speech presence uncertainty and enhanced instantaneous phase estimation in order to achieve flexible and effective noise reduction while limiting the speech distortion due to different noise conditions. Experimental results confirm that the proposed method delivers higher voice quality and intelligibility than the reference methods in various noise environments.

In the European satellite broadcasting specifications, the symbol rate and the carrier frequency are not regulated. Furthermore, the first generation format DVB-S does not have any control signals. In a practical environment, the received signal condition is not stable due to the imperfect reception environment, i.e., unterminated receiver ports, cheap indoor wiring cables etc. These issues prevent correct detection of the satellite signals. For this reason, the conventional signal detection method uses brute force search for detecting the received signal's cyclostationarity, which is an extremely time-consuming approach. A coarse estimation method of the carrier frequency and the bandwidth was proposed by us based on the power spectrum. We extend this method to create a new method for detecting satellite broadcasting signals, which can significantly reduce the search range. In other words, the proposed method can detect the signals in a relatively short time. In this paper, the proposed method is applied to signals received in an actual environment. Our analysis shows that the proposed method can effectively reduce the detection time at almost a same detection performance.

Massive Open Online Courses (MOOC) have been invented to support Virtual Learning Environment (VLE) for higher education. While numerous learning courses and contents were authored, most of the existing resources are now hard to reuse/redistribute among instructors due to the privacy of the contents. Therefore, Open Educational Resources (OER) and the Creative Commons license (CC) are interesting solutions available to alleviate such problems of MOOC. This research presents a new framework that effectively connects OER and MOOC for a life-long e-Learning platform for Thai people. We utilize the Fedora Commons repository for an OER back-end, and develop a new front-end to manage OER resources. In addition, we introduce a “FedX API” - including a packet encapsulation and a data transmission module - that organizes educational resources between both systems. We also proposed the CC declaring function to help participants on-the-fly declare their content license; therefore, any resources must be granted as an open licensing. Another important function is a Central Authorized System (CAS) which is applied to develop single signing-on to facilitate the OER-MOOC connection. Since the framework is designed to support the massive demand, the concurrent access capability is also evaluated to measure the performance of the proposed framework. The results show that the proposed framework can provide up to 750 concurrencies without any defects. The FedX API does not produce bottleneck trouble on the interoperability framework in any cases. In addition, resources can be exchanged among the third-party OER repositories by an OAI-PMH harvesting tool.

It is important to collect and spread accurate information quickly during disasters. Therefore, utilizing Twitter at the time of accidents has been gaining attention in recent year. In this paper, we propose a real-time information sharing system during disaster based on the utilization of Twitter. The proposed system consists of two sub-systems, a disaster information tweeting system that automatically attaches user's current geo-location information (address) and the hashtag of the form “#(municipality name) disaster,” and a disaster information mapping system that displays neighboring disaster-related tweets on a map.

Analyzing the schedulability of hierarchical real-time systems is difficult because of the systems' complex behavior. It gets more complicated when shared resources or dependencies among tasks are included. This paper introduces a framework based on UPPAAL that can analyze the schedulability of hierarchical real-time systems.

When a massive network disruption occurs, repair of the damaged network takes time, and the recovery process involves multiple stages. We propose a fast and flow-controlled multi-stage network recovery method for determining the pareto-optimal recovery order of failed physical components reflecting the balance requirement between maximizing the total amount of traffic on all logical paths, called total network flow, and providing adequate logical path flows. The pareto-optimal problem is formulated by mixed integer linear programming (MILP). A heuristic algorithm, called the grouped-stage recovery (GSR), is also introduced to solve the problem when the problem formulated by MILP is computationally intractable in a large-scale failure. The effectiveness of the proposed method was numerically evaluated. The results show that the pareto-optimal recovery order can be determined from the balance between total network flow and adequate logical path flows, the allocated minimum bandwidth of the logical path can be drastically improved while maximizing total network flow, and the proposed method with GSR is applicable to large-scale failures because a nearly optimal recovery order with less than 10% difference rate can be determined within practical computation time.

Depth-based action recognition has been attracting the attention of researchers because of the advantages of depth cameras over standard RGB cameras. One of these advantages is that depth data can provide richer information from multiple projections. In particular, multiple projections can be used to extract discriminative motion patterns that would not be discernible from one fixed projection. However, high computational costs have meant that recent studies have exploited only a small number of projections, such as front, side, and top. Thus, a large number of projections, which may be useful for discriminating actions, are discarded. In this paper, we propose an efficient method to exploit pools of multiple projections for recognizing actions in depth videos. First, we project 3D data onto multiple 2D-planes from different viewpoints sampled on a geodesic dome to obtain a large number of projections. Then, we train and test action classifiers independently for each projection. To reduce the computational cost, we propose a greedy method to select a small yet robust combination of projections. The idea is that best complementary projections will be considered first when searching for optimal combination. We conducted extensive experiments to verify the effectiveness of our method on three challenging benchmarks: MSR Action 3D, MSR Gesture 3D, and 3D Action Pairs. The experimental results show that our method outperforms other state-of-the-art methods while using a small number of projections.

This paper presents a low-power and low-voltage 64-kb 8T three-port image memory using 28-nm FD-SOI process technology. Our proposed SRAM accommodates eight-transistor bit cells comprising one-write/two-read ports and a majority logic circuit to save active energy. The test chip operates at a supply voltage of 0.46V and access time of 140ns. The minimum energy point is a supply voltage of 0.54V and an access time of 55ns (= 18.2MHz), at which 484fJ/cycle in a write operation and 650fJ/cycle in a read operation are achieved assisted by majority logic. These factors are 69% and 47% smaller than those in a conventional 6T SRAM using the 28-nm FD-SOI process technology.