In 1995, Vanstone and Zuccherato proposed a novel method of generating RSA moduli having a predetermined set of bits which are the ASCII representation of user's identification information (i.e., name, email address, etc.). This could lead to a savings in bandwidth for data transmission and storage. In this paper, we apply this idea of Vanstone and Zuccherato for reducing the storage requirement of RSA public moduli to integer factoring based public-key schemes with their moduli of the form prq. More precisely, we explicitly propose two efficient methods for specifying high-order bits of prime factors of their public-keys. We also consider the security of the proposed methods.

In this paper we propose a discrete program-size dependent software reliability growth model flexibly describing the software failure-occurrence phenomenon based on a discrete Weibull distribution. We also conduct model comparisons of our discrete SRGM with existing discrete SRGMs by using actual data sets. The program size is one of the important metrics of software complexity. It is known that flexible discrete software reliability growth modeling is difficult due to the mathematical manipulation under a conventional modeling-framework in which the time-dependent behavior of the cumulative number of detected faults is formulated by a difference equation. Our discrete SRGM is developed under an existing unified modeling-framework based on the concept of general order-statistics, and can incorporate the effect of the program size into software reliability assessment. Further, we discuss the method of parameter estimation, and derive software reliability assessment measures of our discrete SRGM. Finally, we show numerical examples of discrete software reliability analysis based on our discrete SRGM by using actual data.

Sensor networks are often deployed in unattended environments, thus leaving these networks vulnerable to false data injection attacks in which an adversary injects forged reports into the network through compromised nodes, with the goal of deceiving the base station or depleting the resources of forwarding nodes. Several research solutions have been recently proposed to detect and drop such forged reports during the forwarding process. Each design can provide the equivalent resilience in terms of node compromising. However, their energy consumption characteristics differ from each other. Thus, employing only a single filtering scheme for a network is not a recommendable strategy in terms of energy saving. In this paper, we propose a fuzzy-based adaptive filtering scheme selection method for energy saving. A fuzzy rule-based system is exploited to choose one of three filtering schemes by considering the false traffic ratio, the security threshold value, distance, and the detection power of the filtering scheme. The adaptive selection of the filtering schemes can conserve energy, and guarantee sufficient resilience.

In this paper, we investigate the critical low coverage problem of position aware localized efficient broadcast in mobile ad hoc ubiquitous sensor networks and propose a generic framework for it. The framework is to determine a small subset of nodes and minimum transmission radiuses based on snapshots of network state (local views) along the broadcast process. To guarantee the accuracy of forward decisions, based on historical location information nodes will predict neighbors' positions at future actual transmission time and then construct predicted and synchronized local views rather than simply collect received "Hello" messages. Several enhancement technologies are also proposed to compensate the inaccuracy of prediction and forward decisions. To verify the effectiveness of our framework we apply existing efficient broadcast algorithms to it. Simulation results show that new algorithms, which are derived from the generic framework, can greatly increase the broadcast coverage ratio.

In this paper, we propose a sequential type-based detection scheme for wireless sensor networks in the case of spatially and temporally identically and independently distributed observations. First, we investigate the optimal sequential detection rule of the proposed scheme, and then with the motivation of reducing the computational complexity of the optimal detection rule, we consider an approximation scheme and derive a suboptimal detection rule. We also compare the performances of the type-based sequential detection scheme with those of the non-sequential type-based detection scheme in terms of both average number of observations and total energy consumption, and determine the region of individual node power where the proposed scheme outperforms the non-sequential scheme. In addition, we show that the approximated detection rule provides the similar results as the optimal detection rule with a significant reduction of the computational complexity, which makes the approximated detection rule useful for real-time applications.

In this paper, an inverse S-shaped slotted ground structure (S-SGS) is proposed and analyzed. The S-SGS generates dual attenuation poles that can be easily controlled by its structure parameters. The equivalent circuit of the S-SGS consists of lumped elements that can be extracted from the measured S parameters. Moreover, several S-SGS cells are applied to form a miniaturized lowpass filter (LPF), which has a smaller area and a wider stopband in comparison to previous works.

We previously proposed a method of sound source and microphone localization. The method estimates the locations of sound sources and microphones from only time differences of arrival between signals picked up by microphones even if all their locations are unknown. However, there is a problem that some estimation results converge to local minimum solutions because this method estimates locations iteratively and the error function has multiple minima. In this paper, we present a new iterative method to solve the local minimum problem. This method achieves accurate estimation by selecting effective initial locations from many random initial locations. The computer simulation and experimental results demonstrate that the presented method eliminates most local minimum solutions. Furthermore, the computational complexity of the presented method is similar to that of the previous method.

The time division duplex cellular system can support various downlink and uplink traffic ratios by setting the downlink and uplink transmission periods appropriately. However, it causes severe co-channel interference problem when some cells are active in the downlink while the others are in the uplink [2]. To mitigate this problem, a resource allocation scheme combined with sectorization is proposed for orthogonal frequency division multiple access. Simulations demonstrate that the proposed scheme improves both spectral efficiency and outage performance compared to the conventional allocation schemes.

Instead of pilot tones, the impulse sample is exploited for channel estimation in Impulse Postfix OFDM systems [1]. As the magnitude of impulse sample is increased, the accuracy of channel estimation can be enhanced, but it may significantly increase the PAPR of generated OFDM symbols. In this letter, based on the statistical analysis of the generated OFDM symbol, we propose a decision scheme for determining the magnitude of impulse sample. By using the proposed scheme, we can determine the magnitude of impulse sample that provides the enhancement of BER performance as well as the avoidance of PAPR increase. The validation of the proposed scheme is demonstrated by computer simulations.

In this study, a wideband 3/4 elliptical ring patch operating millimeter wave band is proposed. Using this structure, the patch antenna is designed for circular polarization and wide-band operation at about 32.1-40 GHz for millimeter wave communication. Simulated and measured results for main parameters such as voltage standing wave ratio (VSWR), impedance bandwidth, axial ratio, radiation patterns and gains are also discussed. The study shows that modeling of such antennas, with simplicity in designing and feeding, can well meet the requirements of millimeter-wave wireless communication systems.

Most learning-based super-resolution methods neglect the illumination problem. In this paper we propose a novel method to combine blind single-frame super-resolution and shadow removal into a single operation. Firstly, from the pattern recognition viewpoint, blur identification is considered as a classification problem. We describe three methods which are respectively based on Vector Quantization (VQ), Hidden Markov Model (HMM) and Support Vector Machines (SVM) to identify the blur parameter of the acquisition system from the compressed/uncompressed low-resolution image. Secondly, after blur identification, a super-resolution image is reconstructed by a learning-based method. In this method, Logarithmic-wavelet transform is defined for illumination-free feature extraction. Then an initial estimation is obtained based on the assumption that small patches in low-resolution space and patches in high-resolution space share a similar local manifold structure. The unknown high-resolution image is reconstructed by projecting the intermediate result into general reconstruction constraints. The proposed method simultaneously achieves blind single-frame super-resolution and image enhancement especially shadow removal. Experimental results demonstrate the effectiveness and robustness of our method.

In this paper, we describe a method of applying Collaborative Filtering with a Machine Learning technique to predict users' preferences for clothes on online shopping malls when user history is insufficient. In particular, we experiment with methods of predicting missing values, such as mean value, SVD, and support vector regression, to find the best method and to develop and utilize a unique feature vector model.

We describe a new ultrasonography system, which can identify an implant position in bone. Although conventional X-ray fluoroscopy can visualize implants, it has the serious disadvantage of X-ray exposure. Therefore, we developed a system for orthopedic surgery that involves no X-ray exposure. Barriers to the development of the system were overcome using an ultrasonic instrument and fuzzy logic techniques. We located distal transverse screw holes in an intramedullary nail during surgery for femur fracture. The screw hole positions are identified by calculating two fuzzy degrees of intensity and the variance. Results allow this system to identify the screw hole positions within an error of 1.43 mm, an error ratio adequate for clinical surgical practice.

A parametric linear filter for a linear observation model usually requires a parameter selection process so that the filter achieves a better filtering performance. Generally, criteria for the parameter selection need not only the filtered solution but also the filter itself with each candidate of the parameter. Obtaining the filter usually costs a large amount of calculations. Thus, an efficient algorithm for the parameter selection is required. In this paper, we propose a fast parameter selection algorithm for linear parametric filters that utilizes a joint diagonalization of two non-negative definite Hermitian matrices.

In this paper, the expectation maximization (EM) algorithm is used for unsupervised classification of polarimetric synthetic aperture radar (SAR) images. The EM algorithm provides an estimate of the parameters of the underlying probability distribution functions (pdf's) for each class. The feature vector is 9-dimensional, consisting of the six magnitudes and three angles of the elements of a coherency matrix. Each of the elements of the feature vector is assigned a specific parametric pdf. In this work, all the features are supposed to be statistically independent. Then we present a two-stage unsupervised clustering procedure. The EM algorithm is first run for a few iterations to obtain an initial partition of, for example, four clusters. A randomly selected sample of, for example, 2% pixels of the polarimetric SAR image may be used for unsupervised training. In the second stage, the EM algorithm may be run again to reclassify the first stage clusters into smaller sub-clusters. Each cluster from the first stage will be processed separately in the second stage. This approach makes further classification possible as shown in the results. The training cost is also reduced as the number of feature vector in a specific cluster is much smaller than the whole image.

Design automation of LCD driver circuits is not sophisticatedly established. Display fineness of an LCD panel depends on a performance metric, ratio of pixel voltage to video voltage (RPV). However, there are several other important metrics, such as area, and the best circuit cannot be decided uniquely. This paper proposes a design automation technique for a LCD column driver to provide several circuit design results with different performance so that designers can select an appropriate design among them. The proposed technique is evaluated with an actual design data, and experimental results show that the proposed method successfully performs technology migration by transistor sizing. Also, the proposed technique is experimentally verified from points of solution quality and computational time.

Ziv-Lempel incremental parsing [1] is a fundamental algorithm for lossless data compression. There is a simple enumerative implementation [7] which preserves a duality between the encoder and the decoder. However, due to its compactness, the implementation when combined with a complete integer code, allows only an input sequence with a length consistent with the parsing boundaries. In this letter, we propose a simple additional mechanism for post-processing a binary file of arbitrary length, provided the file punctuation is externally managed.

IEEE 802.11e Medium Access Control (MAC) is a supplement to the IEEE 802.11 Wireless Network (WLAN) standard to support Quality of Service (QoS). The 802.11e MAC defines a new coordination function, namely Hybrid Coordination Function (HCF), which takes the QoS requirements of flows into account and allocates Transmission Opportunity (TXOP) to stations. On the basis of mean sending rate, delay of Variable Bit Rate (VBR) traffic cannot be bounded with the reference HCF scheduling algorithm proposed in this supplement. In this paper, we propose a new Connection Admission Control (CAC) and a scheduling algorithm that utilize the token bucket and a modified Latency-Rate (LR) scheduling algorithm to guarantee a bounded delay for HCF Controlled Channel Access (HCCA). The new Service Interval (SI) is calculated to optimize the number of stations accommodated and takes into account delay bound and token bucket parameters. We show that it is possible to obtain worst-case performance guarantees on delay. First, we analyze the behavior of the new scheduler with a loss free wireless channel model and after this, with a burst loss model and we explain how it is possible to extend this scheduler for a multi-rate scheme. Properties of the proposal are investigated both theoretically and using ns-2 simulations. We present a set of simulations with both Constant Bit Rate (CBR) and VBR flows and performance comparisons with HCF scheduling algorithm. The results show that the delay upper bound can be achieved for a large range of networks load with bandwidth optimization.

Doppler diversity has been proven effective to combat time variation caused by Doppler spread in single carrier systems. However, it is not efficient to directly apply Doppler diversity into Multi-Carrier Code Division Multiple Access (MC-CDMA) systems because Inter-Carrier-Interference (ICI) increases with the artificial frequency shifts in diversity branches. In this paper, a novel Doppler diversity scheme in MC-CDMA with Three Zero Correlation Zones (T-ZCZ) sequences is proposed to further improve the performance of Doppler diversity. Particularly, zero correlation zones are employed in frequency domain for ICI cancelation caused by Doppler spread, which confirms the validity of the contribution to the wideband wireless communications in high speed mobile environment.

The computational complexity of the optimum maximum likelihood detector (OMLD) does not allow its utility for multi-user detection (MUD) in code division multiple access (CDMA) systems. As proposed in this letter, particle swarm optimization (PSO) with soft decision offers a much more efficient option with few parameters to be adjusted, flexibility to implement, that gives a much faster convergence compared to OMLD. It outperforms the conventional detector, the genetic algorithm approach and the standard suboptimal detectors considered in the literature.