In forthcoming sensor networks, a multitude of sensor nodes deployed over a large geographical area for monitoring traffic, climate, etc. are expected to become an inevitable infrastructure. Clustering algorithms play an important role in aggregating a large volume of data that are produced continuously by the huge number of sensor nodes. In such networks, equal-sized multi-hop clusters which include an equal number of nodes are useful for efficiency and resiliency. In addition, scalability is important in such large-scale networks. In this paper, we mathematically design a decentralized equal-sized clustering algorithm using a partial differential equation based on the Fourier transform technique, and then design its protocol by discretizing the equation. We evaluated through simulations the equality of cluster sizes and the resiliency against packet loss and node failure in two-dimensional perturbed grid topologies.

We investigated the effects of chemical treatments for removing native oxide layers on InAlN surfaces by X-ray photoelectron spectroscopy (XPS). The untreated surface of the air exposed InAlN layer was covered with the native oxide layer mainly composed of hydroxides. Hydrochloric acid treatment and ammonium hydroxide treatment were not efficient for removing the native oxide layer even after immersion for 15 min, while hydrofluoric acid (HF) treatment led to a removal in a short treatment time of 1 min. After the HF treatment, the surface was prevented from reoxidation in air for 1 h. We also found that the 5-min buffered HF treatment had almost the same effect as the 1-min HF treatment. Finally, an attempt was made to apply the HF-based treatment to the metal-InAlN contact to confirm the XPS results.

Automatic modulation classification (AMC) is an important function of radio surveillance systems in order to identify unknown signals. Many previous works on AMC have utilized signal cyclostationarity, particularly spectral correlation density (SCD), but many of them fail to address several implementation issues, such as the assumption of perfect knowledge of the symbol rate. In this paper, we discuss several practical issues, e.g. cyclic frequency mismatch, which may affect the SCD, and propose compensation techniques to overcome those issues. We also propose a novel feature extraction technique from the SCD, which utilizes the SCD of not only the original received signal, but also the squared received signal. A symbol rate estimation technique which complements the feature extraction is also proposed. Finally, the classification performance of the system is evaluated through Monte Carlo simulations using a wide variety of modulated signals, and simulation results show that the proposed technique can estimate the symbol rate and classify modulation with a probability of above 0.9 down to SNRs of 5 dB.

In summary, we successfully fabricated the nonvolatile hybrid polymer 4F2 memory-cell. It was based on bistable state, which was observed in PS layer that is containing a Ni nanocrystals capped with NiO tunneling barrier sandwiched by Al electrodes. The current conduction mechanism for polymer memory-cell was demonstrated by fitting the I-V curves. The electrons were charged and discharged on Ni nanocrystals by tunneling through the NiO tunneling barrier. In addition, the memory-cell showed a good and reproducible nonvolatile memory-cell characteristic. Its memory margin is about 1.410. The retention-time is more than 105 seconds and the endurance cycles of program-and-erase is more than 250 cycles. Furthermore, Thefore, polymer memory-cell would be good candidates for nonvolatile 4F2 cross-bar memory-cell.

IEEE802.11 Wireless Local Area Networks (WLANs) are becoming more and more pervasive due to their simple channel access mechanism, Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), but this mechanism provides all nodes including Access Point and other Stations with the same channel access probability. This characteristic does not suit the infrastructure mode which has so many downlink flows to be transmitted at the Access Point that congestion at the Access Point is more likely to occur. To resolve this asymmetry traffic problem, we develop an Optimal Contention Window Adjustment method assuming the condition of erroneous channels over WLANs. This method can be easily implemented and is compatible with the original CSMA/CA mechanism. It holds the ratio of downlink and uplink flows and at the same time achieves the maximum saturation throughput in the WLANs. We use the Markov Chain analytical model to analyze its performance and validate it through the simulations.

A novel and energy-efficient algorithm with Quality-of-Service (QoS) guarantee is proposed for cooperative spectrum sensing (CSS) with soft information fusion and hard information fusion. By weighting the sensing performance and the consumption of system resources in a utility function that is maximized with respect to the number of secondary users (SUs), it is shown that the optimal number of SUs is related to the price of these QoS requirements.

A novel tunneling field-effect transistor (TFET) featuring the sigma-shape embedded SiGe sources and recessed channel is proposed. The gate facing the source effectively focuses the E-field at the tip of the source and eliminates the gradual turn-on issue of planar TFETs. The fabrication scheme modified from the state-of-the-art 45 nm/32 nm CMOS technology flows provides a unique benefit in the co-integrability and the control of ID-VGS characteristics. The feasibility is verified with TCAD process simulation of the device with 14 nm of the gate dimension. The device simulation shows 5-order change in the drain current with a gate bias change less than 300 mV.

Multiple-input multiple-output (MIMO) systems with antenna selection are practical in that they can alleviate the computational complexity at the receiver and achieve good reception performance. Channel correlation, not just carrier-to-noise ratio (CNR), has a great impact on reception performance in MIMO channels. We propose a practical receive antenna subset selection algorithm with reduced complexity that uses the condition number of the partial channel matrix and a predetermined CNR threshold. This paper describes the algorithm and its performance evaluation by both computer simulation and indoor experiments using a prototype receiver and received signals obtained in an actual mobile outdoor experiment. The results confirm that our proposed method provides good bit error rate performance by setting the CNR threshold properly.

According to the process scaling, radiation-hard devices are becoming sensitive to soft errors caused by Multiple Cell Upset (MCUs). In this paper, the parasitic bipolar effects are utilized to suppress MCUs of the radiation-hard dual-modular flip-flops. Device simulations reveal that a simultaneous flip of redundant latches is suppressed by storing opposite values instead of storing the same value due to its asymmetrical structure. The state of latches becomes a specific value after a particle hit due to the bipolar effects. Spallation neutron irradiation proves that MCUs are effectively suppressed in the D-FF arrays in which adjacent two latches in different FFs store opposite values. The redundant latch structure storing the opposite values is robust to the simultaneous flip.

This paper presents an adaptive cache architecture for wide-range reliable low-voltage operations. The proposed associativity-reconfigurable cache consists of pairs of cache ways so that it can exploit the recovery feature of the novel 7T/14T SRAM cell. Each pair has two operating modes that can be selected based upon the required voltage level of current operating conditions: normal mode for high performance and dependable mode for reliable low-voltage operations. We can obtain reliable low-voltage operations by application of the dependable mode to weaker pairs that cannot operate reliably at low voltages. Meanwhile leaving stronger pairs in the normal mode, we can minimize performance losses. Our chip measurement results show that the proposed cache can trade off its associativity with the minimum operating voltage. Moreover, it can decrease the minimum operating voltage by 140 mV achieving 67.48% and 26.70% reduction of the power dissipation and energy per instruction. Processor simulation results show that designing the on-chip caches using the proposed scheme results in 2.95% maximum IPC losses, but it can be chosen various performance levels. Area estimation results show that the proposed cache adds area overhead of 1.61% and 5.49% in 32-KB and 256-KB caches, respectively.

Computer-aided diagnosis (CAD) systems on diffuse lung diseases (DLD) were required to facilitate radiologists to read high-resolution computed tomography (HRCT) scans. An important task on developing such CAD systems was to make computers automatically recognize typical pulmonary textures of DLD on HRCT. In this work, we proposed a bag-of-features based method for the classification of six kinds of DLD patterns which were consolidation (CON), ground-glass opacity (GGO), honeycombing (HCM), emphysema (EMP), nodular (NOD) and normal tissue (NOR). In order to successfully apply the bag-of-features based method on this task, we focused to design suitable local features and the classifier. Considering that the pulmonary textures were featured by not only CT values but also shapes, we proposed a set of statistical measures based local features calculated from both CT values and eigen-values of Hessian matrices. Additionally, we designed a support vector machine (SVM) classifier by optimizing parameters related to both kernels and the soft-margin penalty constant. We collected 117 HRCT scans from 117 subjects for experiments. Three experienced radiologists were asked to review the data and their agreed-regions where typical textures existed were used to generate 3009 3D volume-of-interest (VOIs) with the size of 323232. These VOIs were separated into two sets. One set was used for training and tuning parameters, and the other set was used for evaluation. The overall recognition accuracy for the proposed method was 93.18%. The precisions/sensitivities for each texture were 96.67%/95.08% (CON), 92.55%/94.02% (GGO), 97.67%/99.21% (HCM), 94.74%/93.99% (EMP), 81.48%/86.03%(NOD) and 94.33%/90.74% (NOR). Additionally, experimental results showed that the proposed method performed better than four kinds of baseline methods, including two state-of-the-art methods on classification of DLD textures.

We present a new approach for sparse Cholesky factorization on a heterogeneous platform with a graphics processing unit (GPU). The sparse Cholesky factorization is one of the core algorithms of numerous computing applications. We tuned the supernode data structure and used a parallelization method for GPU tasks to increase GPU utilization. Results show that our approach substantially reduces computational time.

Parametric images can help investigate disease mechanisms and vital functions. To estimate parametric images, it is necessary to obtain the tissue time activity curves (tTACs), which express temporal changes of tracer activity in human tissue. In general, the tTACs are calculated from each voxel's value of the time sequential PET images estimated from dynamic PET data. Recently, spatio-temporal PET reconstruction methods have been proposed in order to take into account the temporal correlation within each tTAC. Such spatio-temporal algorithms are generally quite computationally intensive. On the other hand, typical algorithms such as the preconditioned conjugate gradient (PCG) method still does not provide good accuracy in estimation. To overcome these problems, we propose a new spatio-temporal reconstruction method based on the dynamic row-action maximum-likelihood algorithm (DRAMA). As the original algorithm does, the proposed method takes into account the noise propagation, but it achieves much faster convergence. Performance of the method is evaluated with digital phantom simulations and it is shown that the proposed method requires only a few reconstruction processes, thereby remarkably reducing the computational cost required to estimate the tTACs. The results also show that the tTACs and parametric images from the proposed method have better accuracy.

Novel joint motion-compensated interpolation using eight-neighbor block motion vectors (8J-MCI) is presented. The proposed method uses bi-directional motion estimation (BME) to obtain the motion vector field of the interpolated frame and adopts motion vectors of the interpolated block and its 8-neighbor blocks to jointly predict the target block. Since the smoothness of the motion vector filed makes the motion vectors of 8-neighbor blocks quite close to the true motion vector of the interpolated block, the proposed algorithm has the better fault-tolerancy than traditional ones. Experiments show that the proposed algorithm outperforms the motion-aligned auto-regressive algorithm (MAAR, one of the state-of-the-art frame rate up-conversion (FRUC) schemes) in terms of the average PSNR for the test image sequence and offers better subjective visual quality.

This paper presents an ensemble learning algorithm for liver tumour segmentation from a CT volume in the form of U-Boost and extends the loss functions to improve performance. Five segmentation algorithms trained by the ensemble learning algorithm with different loss functions are compared in terms of error rate and Jaccard Index between the extracted regions and true ones.

The paper describes how a robust and compact on-line handwritten Japanese text recognizer was developed by compressing each component of an integrated text recognition system including a SVM classifier to evaluate segmentation points, an on-line and off-line combined character recognizer, a linguistic context processor, and a geometric context evaluation module to deploy it on hand-held devices. Selecting an elastic-matching based on-line recognizer and compressing MQDF2 via a combination of LDA, vector quantization and data type transformation, have contributed to building a remarkably small yet robust recognizer. The compact text recognizer covering 7,097 character classes just requires about 15 MB memory to keep 93.11% accuracy on horizontal text lines extracted from the TUAT Kondate database. Compared with the original full-scale Japanese text recognizer, the memory size is reduced from 64.1 MB to 14.9 MB while the accuracy loss is only 0.5% from 93.6% to 93.11%. The method is scalable so even systems of less than 11 MB or less than 6 MB still remain 92.80% or 90.02% accuracy, respectively.

Our purpose in this study is to develop a scheme to segment the rectus abdominis muscle region in X-ray CT images. We propose a new muscle recognition method based on the shape model. In this method, three steps are included in the segmentation process. The first is to generate a shape model for representing the rectus abdominis muscle. The second is to recognize anatomical feature points corresponding to the origin and insertion of the muscle, and the third is to segment the rectus abdominis muscles using the shape model. We generated the shape model from 20 CT cases and tested the model to recognize the muscle in 10 other CT cases. The average value of the Jaccard similarity coefficient (JSC) between the manually and automatically segmented regions was 0.843. The results suggest the validity of the model-based segmentation for the rectus abdominis muscle.

In this paper, we present an algorithm to segment the liver in low-contrast CT images. As the first step of our algorithm, we define a search range for the liver boundary. Then, the EM algorithm is utilized to estimate parameters of a 'Gaussian Mixture' model that conforms to the intensity distribution of the liver. Using the statistical parameters of the intensity distribution, we introduce a new thresholding technique to classify image pixels. We assign a distance feature vectors to each pixel and segment the liver by a K-means clustering scheme. This initial boundary of the liver is conditioned by the Fourier transform. Then, a Geodesic Active Contour algorithm uses the boundaries to find the final surface. The novelty in our method is the proper selection and combination of sub-algorithms so as to find the border of an object in a low-contrast image. The number of parameters in the proposed method is low and the parameters have a low range of variations. We applied our method to 30 datasets including normal and abnormal cases of low-contrast/high-contrast images and it was extensively evaluated both quantitatively and qualitatively. Minimum of Dice similarity measures of the results is 0.89. Assessment of the results proves the potential of the proposed method for segmentation in low-contrast images.

In this letter, we consider the ultimate boundedness of the singularly perturbed system with measurement noise. The composite controller is commonly used to regulate the singularly perturbed system. However, in the presence of measurement noise, the composite controller does not guarantee the ultimate boundedness of the singularly perturbed system. Thus, we propose the modified composite controller to show the ultimate boundedness of the singularly perturbed system with measurement noise.

The widespread adoption of IP-based telecommunication core networks is leading to a paradigm shift in international interconnection where the traditional Time-Division Multiplexing (TDM) interconnection between telecommunication networks is being replaced by IP interconnection. IP eXchange (IPX) is an emerging paradigm in international IP interconnection that has novel requirements, such as an end-to-end Quality of Service (QoS) guarantee across multiple carriers. IPX is a future direction for international telecommunications, but it is not easy to understand the overall concept of IPX because it is derived from a wide variety of services, technical knowledge, and telecommunication backgrounds. The confusion and complexity of the technical elements hinder the development of IPX. Thus, this paper clarifies the state-of-the-art technical elements from an IPX perspective and discusses ongoing challenges and emerging services on IPX, particularly end-to-end QoS, Voice over IP issues, IP Multimedia Subsystem (IMS) interworking, and Long Term Evolution (LTE) roaming. This paper also surveys published academic research studies that were not focused primarily on IPX but which are likely to provide potential solutions to the challenges.