Coordinated Multi-point (CoMP) transmission has long been known for its ability to improve cell edge throughput. However, in a CoMP cellular network, fixed CoMP clustering results in cluster edges where system performance degrades due to non-coordinated clusters. To solve this problem, conventional studies proposed dynamic clustering schemes. However, such schemes require a complex backhaul topology and are infeasible with current network technologies. In this paper, small power base stations (BSs) are introduced instead of dynamic clustering to solve the cluster edge problem in CoMP cellular networks. This new cell topology is called the diamond cellular network since the resultant cell structure looks like a diamond pattern. In our novel cell topology, we derive the optimal locations of small power base stations and the optimal resource allocation between the CoMP base station and small power base stations to maximize the proportional fair utility function. By using the proposed architecture, in the case of perfect user scheduling, a more than 150% improvement in 5% outage throughput is achieved, and in the case of successive proportional fair user scheduling, nearly 100% improvement of 5% outage throughput is achieved compared with conventional single cell networks.

The threshold voltage variations for actual size MOSFETs obtained by capacitance measurement are compared with those obtained by the current measurement, and their differences are studied for the first time. It is found that the threshold voltage variations obtained by the capacitance measurement show the similar behavior to those current measurement and the absolute value is less than those obtained by the current measurement. The reason for the difference is partially explained by that the local channel dopant non-uniformity along the current path makes the threshold voltage variation obtained from current measurement larger. It is found that the flat-band voltage variations, which are obtained from the measured C-V curves, are small and not significant to the threshold voltage variation.

With the rapid merger of healthcare business and information technology, more healthcare institutions and medical practices are sharing information. Since these records often contain patients' sensitive personal information, Healthcare Information Systems (HISs) should be properly designed to manage these records in a secure manner. We propose a novel security design for the HIS complying with the security and privacy rules. The proposed system defines protocols to ensure secure delivery of medical records over insecure public networks and reliable management of medical record in the remote server without incurring excessive costs to implement services for security. We demonstrate the practicality of the proposed system through a security analysis and performance evaluation.

Mathematical formulae play an important role in many scientific domains. Regardless of the importance of mathematical formula search, conventional keyword-based retrieval methods are not sufficient for searching mathematical formulae, which are structured as trees. The increasing number as well as the structural complexity of mathematical formulae in scientific articles lead to the necessity for large-scale structure-aware formula search techniques. In this paper, we formulate three types of measures that represent distinctive features of semantic similarity of math formulae, and develop efficient hash-based algorithms for the approximate calculation. Our experiments using NTCIR-11 Math-2 Task dataset, a large-scale test collection for math information retrieval with about 60-million formulae, show that the proposed method improves the search precision while also keeps the scalability and runtime efficiency high.

We propose an impact and high-pitch noise-suppression method based on spectral entropy. Spectral entropy takes a large value for flat spectral amplitude and a small value for spectra with several lines. We model the impact noise as a flat spectral signal and its damped oscillation as a high-pitch periodic signal consisting of spectra with several lines. We discriminate between the current noise situations by using spectral entropy and adaptively change the noise-suppression parameters used in a zero phase-based impact-noise-suppression method. Simulation results show that the proposed method can improve the perceptual evaluation of the speech quality and speech-recognition rate compared to conventional methods.

This paper proposes a novel object tracking method via online boosting. The on-line boosting technique is combined with local features to treat tracking as a keypoint matching problem. First, We improve matching reliability by exploiting the statistical repeatability of local features. In addition, we propose 2D scale-rotation invariant quasi-keypoint matching to further improve matching efficiency. Benefiting from SURF feature's statistical repeatability and the complementary quasi-keypoint matching technique, we can easily find reliable matching pairs and thus perform accurate and stable tracking. Experimental results show that the proposed method achieves better performance compared with previously reported trackers.

With the development of silicon-based Nano-photonics, Optical Network on Chip (ONoC) is, due to its high bandwidth and low latency, becoming an important choice for future multi-core networks. As a key ONoC technology, the arbitration scheme should provide differential arbitration service with high throughput and low latency for various types and priorities of traffic in CMPs. In this work, we propose a fast hierarchical arbitration scheme based on multi-level priority QoS. First, given multi-priority data buffer queue, arbiters provide differential transmissions with fair service for all nodes and guarantee the max-transmit-delay and min-communication-bandwidth for all queues. Second, arbiter adopts the transmit bound resource reservation scheme to reserve time slots for all nodes fairly, thereby achieving a throughput of 100%. Third, we propose fast arbitration with a layout of fast optical arbitration channels (FOACs) to reduce the arbitration period, thereby reducing packet transmitting delay. Simulation results show that with our hierarchical arbitration scheme, all nodes are allocated almost equal service access probability under various traffic patterns; thus, the min-communication-bandwidth and max-transmit-delay is guaranteed to be 5% and 80 cycles, respectively, under the overload demands. This scheme improves throughput by 17% compared to FeatherWeight under a self-similar traffic pattern and decreases arbitration delay by 15% compare to 2-pass arbitration, incurring a total power overhead of 5%.

A low-cost prototypic Kinect-based rehabilitation system was developed for recovering balance capability of stroke patients. A total of 16 stroke patients were recruited to participate in the study. After excluding 3 patients who failed to finish all of the rehabilitation sessions, only the data of 13 patients were analyzed. The results exhibited a significant effect in recovering balance function of the patients after 3 weeks of balance training. Additionally, the questionnaire survey revealed that the designed system was perceived as effective and easy in operation.

In this letter, several new families of binary sequence pairs with period N=np, where p is a prime and gcd(n,p)=1, and optimal correlation values 1 and -3 are constructed. These classes of binary sequence pairs are based on Chinese remainder theorem. The constructed sequence pairs have optimum balance among 0's and 1's.

Latent variable models for relational data enable us to extract the co-cluster structures underlying observed relational data. The Infinite Relational Model (IRM) is a well-known relational model for discovering co-cluster structures with an unknown number of clusters. The IRM assumes that the link probability between two objects (e.g., a customer and an item) depends only on their cluster assignment. However, relational models based on this assumption often lead us to extract many non-informative and unexpected clusters. This is because the underlying co-cluster structures in real-world relationships are often destroyed by structured noise that blurs the cluster structure stochastically depending on the pair of related objects. To overcome this problem, in this paper, we propose an extended IRM that simultaneously estimates denoised clear co-cluster structure and a structured noise component. In other words, our proposed model jointly estimates cluster assignment and noise level for each object. We also present posterior probabilities for running collapsed Gibbs sampling to infer the model. Experiments on real-world datasets show that our model extracts a clear co-cluster structure. Moreover, we confirm that the estimated noise levels enable us to extract representative objects for each cluster.

Safety is the foremost requirement of avionics systems on aircraft. So far, avionics systems have evolved into an integrated system, i.e., integrated avionics system, and the derivative functions occur when the avionics systems are upgraded. However, the traditional safety analysis method is insufficient to be utilized in upgraded avionics systems due to these derivative functions. In this letter, a safety evaluation scheme is proposed to quantitatively evaluate the safety of the upgraded avionics systems. All the functions including the derivative functions can be traced and covered. Meanwhile, a set of safety issues based on different views is established to evaluate the safety capability from three layers, i.e., the mission layer, function layer and resource layer. The proposed scheme can be considered as an efficient scheme in the safety validation and verification in the upgraded avionics systems.

It is crucial to promote interdisciplinary research and recommend collaborators from different research fields via academic database analysis. This paper addresses a problem to characterize researchers' interests with a set of diverse research topics found in a large-scale academic database. Specifically, we first use latent Dirichlet allocation to extract topics as distributions over words from a training dataset. Then, we convert the textual features of a researcher's publications to topic vectors, and calculate the centroid of these vectors to summarize the researcher's interest as a single vector. In experiments conducted on CiNii Articles, which is the largest academic database in Japan, we show that the extracted topics reflect the diversity of the research fields in the database. The experiment results also indicate the applicability of the proposed topic representation to the author disambiguation problem.

This paper empirically validates battery-less sensor activation via wireless energy transmission to release sensors from wires and batteries. To seamlessly extend the coverage and activate sensor nodes distributed in any indoor environment, we proposed multi-point wireless energy transmission with carrier shift diversity. In this scheme, multiple transmitters are employed to compensate path-loss attenuation and orthogonal frequencies are allocated to the multiple transmitters to avoid the destructive interference that occurs when the same frequency is used by all transmitters. In our previous works, the effectiveness of the proposed scheme was validated theoretically and also empirically by using just a spectrum analyzer to measure the received power. In this paper, we develop low-energy battery-less sensor nodes whose consumed power and required received power for activation are respectively 142µW and 400µW. In addition, we conduct indoor experiments in which the received power and activation of battery-less sensor node are simultaneously observed by using the developed battery-less sensor node and a spectrum analyzer. The results show that the coverage of single-point and multi-point wireless energy transmission without carrier shift diversity are, respectively, 84.4% and 83.7%, while the coverage of the proposed scheme is 100%. It can be concluded that the effectiveness of the proposed scheme can be verified by our experiments using real battery-less sensor nodes.

Due to the periodic recovery of virtual machines regardless of whether malicious intrusions exist, proactive recovery-based Intrusion Tolerant Systems (ITSs) are being considered for mission-critical applications. However, the virtual replicas can easily be exposed to attacks during their working period, and additionally, proactive recovery-based ITSs are ineffective in eliminating the vulnerability of exposure time, which is closely related to service availability. To address these problems, we propose a novel hybrid recovery-based ITS in this paper. The proposed method utilizes availability-driven recovery and dynamic cluster resizing. The availability-driven recovery method operates the recovery process by both proactive and reactive ways for the system to gain shorter exposure times and higher success rates. The dynamic cluster resizing method reduces the overhead of the system that occurs from dynamic workload fluctuations. The performance of the proposed ITS with various synthetic and real workloads using CloudSim showed that it guarantees higher availability and reliability of the system, even under malicious intrusions such as DDoS attacks.

In this paper, we propose an undirected model of learning systems, named max-min-degree neural network, to realize centralized-decentralized collaborative computing. The basic idea of the proposal is a max-min-degree constraint which extends a k-degree constraint to improve the communication cost, where k is a user-defined degree of neurons. The max-min-degree constraint is defined such that the degree of each neuron lies between kmin and kmax. Accordingly, the Boltzmann machine is a special case of the proposal with kmin=kmax=n, where n is the full-connected degree of neurons. Evaluations show that the proposal is much better than a state-of-the-art model of deep learning systems with respect to the communication cost. The cost of the above improvement is slower convergent speed with respect to data size, but it does not matter in the case of big data processing.

We propose a practical method that acquires dense light transports from unknown 3D objects by employing orthogonal illumination based on a Walsh-Hadamard matrix for relighting computation. We assume the presence of color crosstalk, which represents color mixing between projector pixels and camera pixels, and then describe the light transport matrix by using sets of the orthogonal illumination and the corresponding camera response. Our method handles not only direct reflection light but also global light radiated from the entire environment. Tests of the proposed method using real images show that orthogonal illumination is an effective way of acquiring accurate light transports from various 3D objects. We demonstrate a relighting test based on acquired light transports and confirm that our method outputs excellent relighting images that compare favorably with the actual images observed by the system.

Electroencephalography (EEG) and magnetoencephalography (MEG) measure the brain signal from spatially-distributed electrodes. In order to detect event-related synchronization and desynchronization (ERS/ERD), which are utilized for brain-computer/machine interfaces (BCI/BMI), spatial filtering techniques are often used. Common spatial potential (CSP) filtering and its extensions which are the spatial filtering methods have been widely used for BCIs. CSP transforms brain signals that have a spatial and temporal index into vectors via a covariance representation. However, the variance-covariance structure is essentially different from the vector space, and not all the information can be transformed into an element of the vector structure. Grassmannian embedding methods, therefore, have been proposed to utilize the variance-covariance structure of variational patterns. In this paper, we propose a metric learning method to classify the brain signal utilizing the covariance structure. We embed the brain signal in the extended Grassmann manifold, and classify it on the manifold using the proposed metric. Due to this embedding, the pattern structure is fully utilized for the classification. We conducted an experiment using an open benchmark dataset and found that the proposed method exhibited a better performance than CSP and its extensions.

In this paper, we report the design of an organic thin-film transistor (OTFT) driver circuit for the actuator of an organic fluid pump, which can be integrated in a portable-size fully-organic artificial lung. Compared to traditional pump designs, lightness, compactness and scalability are achieved by adopting a creative pumping mechanism with a completely organic-material-based system concept. The transportable fluid volume is verified to be flexibly adjustable, enabling on-demand controllability and scalability of the pump's fluid-flow rate. The simulations, based on an accurate surface-potential OTFT compact model, demonstrate that the necessary driving waveforms can be efficiently generated and adjusted to the actuator requirements. At the actuator-driving-circuit frequency of 0.98Hz, an all-organic fluid pump with 40cm length and 0.2cm height is able to achieve a flow rate of 0.847L/min, which satisfies the requirements for artificial-lung assist systems to a weakened normal lung.

In this correspondence, a method of constructing optimal zero correlation zone (ZCZ) sequence sets over the 16-QAM+ constellation is presented. Based on 16-QAM orthogonal matrices and perfect ternary sequences, 16-QAM+ ZCZ sequence sets are obtained. The resulting ZCZ sequence sets are optimal with respect to the Tang-Fan-Matsufuji bound. Moreover, methods for transforming binary or quaternary orthogonal matrices into 16-QAM orthogonal matrices are proposed. The proposed 16-QAM+ ZCZ sequence sets can be potentially applied to communication systems using a 16-QAM constellation to remove the multiple access interference (MAI) and multi-path interference (MPI).

Despite logic families based on floating-gate MOS (FGMOS) transistors achieve significant reductions in terms of power and transistor count, these logics have had little impact on VLSI design due to their sensitivity to noise. In order to attain robustness to this phenomenon, Positive-Feedback Floating-Gate logic (PFFGL) uses a differential architecture and positive feedback; data obtained from a 0.5µm ON Semiconductors test chip and from SPICE simulations shows PFFGL to be immune to noise from parasitic couplings as well as to leakage even when minimum device size is used.