Wireless medical telemetry service (WMTS) is an important wireless communication system in healthcare facilities. Recently, the potential for electromagnetic interference by noise emitted by switching regulators installed in light-emitting diode (LED) lamps has been a serious problem. In this study, we evaluated the characteristics of the electromagnetic noise emitted from LED lamps and its effect on WMTS. Switching regulators generally emit wide band impulsive noise whose bandwidth reaches 400MHz in some instances owing to the switching operation, but this impulsive nature is difficult to identify in the reception of WMTS because the bandwidth of WMTS is much narrower than that of electromagnetic noise. Gaussian approximation (GA) can be adopted for band-limited electromagnetic noise whose characteristics have no repetitive variation. On the other hand, GA with the impulsive correction factor (ICF) can be adopted for band-limited electromagnetic noise that has repetitive variation. We investigate the minimum receiver sensitivity of WMTS for it to be affected by electromagnetic noise emitted from LED lamps. The required carrier-to-noise power ratio (CNR) of Gaussian noise and electromagnetic noise for which GA can be adopted was approximately 15dB, but the electromagnetic noise for which GA with the ICF can be adopted was 3 to 4dB worse. Moreover, the spatial distribution of electromagnetic noise surrounding an LED lamp installation was measured. Finally, we roughly estimated the offset distance between the receiving antenna of WMTS and LED lamps when a WMTS signal of a certain level was added in a clinical setting using our experimental result for the required CNR.

(m+k,m)-functions with good cryptographic properties when 1≤k

Rapid development of modern communications has initiated essential requirements for providing efficient algorithms that can solve the routing and wavelength assignment (RWA) problem in satellite optical networks. In this paper, the bee colony algorithm optimization based on link cost for RWA (BCO-LCRWA) is tailored for satellite networks composed of intersatellite laser links. In BCO-LCRWA, a cost model of intersatellite laser links is established based on metrics of network transmission performance namely delay and wavelengths utilization, with constraints of Doppler wavelength drift, transmission delay, wavelength consistency and continuity. Specifically, the fitness function of bee colony exploited in the proposed algorithm takes wavelength resources utilization and communication hops into account to implement effective utilization of wavelengths, to avoid unnecessary over-detouring and ensure bit error rate (BER) performance of the system. The simulation results corroborate the improved performance of the proposed algorithm compared with the existing alternatives.

Metric learning aims to generate similarity-preserved low dimensional feature vectors from input images. Most existing supervised deep metric learning methods usually define a carefully-designed loss function to make a constraint on relative position between samples in projected lower dimensional space. In this paper, we propose a novel architecture called Naive Similarity Discriminator (NSD) to learn the distribution of easy samples and predict their probability of being similar. Our purpose lies on encouraging generator network to generate vectors in fitting positions whose similarity can be distinguished by our discriminator. Adequate comparison experiments was performed to demonstrate the ability of our proposed model on retrieval and clustering tasks, with precision within specific radius, normalized mutual information and F1 score as evaluation metrics.

A unified decision scheme for the classification and localization of cable faults is proposed based on a two-step procedure. Having basis in the time domain reflectometry (TDR), the proposed scheme is capable of determining not only the locations but also types of faults in a cable without an excessive additional computational burden compared to other TDR-based schemes. Results from simulation and experiments with measured real data demonstrate that the proposed scheme exhibits a higher rate of correct decision than the conventional schemes in localizing and classifying faults over a wide range of the location of faults.

Simultaneous multithreading (SMT) technology improves CPU throughput, but also causes unpredictable performance fluctuations for co-running workloads. Although recent major SMT processors have adopted some techniques to promote hardware support for quality-of-service (QoS), achieving both precise performance guarantees and high throughput on SMT architectures is still a challenging open problem. In this paper, we demonstrate through some comprehensive investigations on a cycle-accurate simulator that not only almost all in-core resources suffer from severe contention as workloads vary but also there is a non-linear relationship between performance and available quotas of resources. We consider these observations as the fundamental reason leading to the challenging problem above. Thus, we introduce QoSMT, a novel hardware scheme that leverages a closed-loop controlling mechanism consisting of detection, prediction and adjustment to enforce precise performance guarantees for specific targets, e.g. achieving 85%, 90% or 95% of the performance of a workload running alone respectively. We implement a prototype on GEM5 simulator. Experimental results show that the average control error is only 1.4%, 0.5% and 3.6%.

This paper proposes two variants of improved Compression by Substring Enumeration (CSE) with a finite alphabet. In previous studies on CSE, an encoder utilizes inequalities which evaluate the number of occurrences of a substring or a minimal forbidden word (MFW) to be encoded. The inequalities are derived from a contingency table including the number of occurrences of a substring or an MFW. Moreover, codeword length of a substring and an MFW grows with the difference between the upper and lower bounds deduced from the inequalities, however the lower bound is not tight. Therefore, we derive a new tight lower bound based on the contingency table and consequently propose a new CSE algorithm using the new inequality. We also propose a new encoding order of substrings and MFWs based on a sorted contingency table such that both its row and column marginal total are sorted in descending order instead of a lexicographical order used in previous studies. We then propose a new CSE algorithm which is the first proposed CSE algorithm using the new encoding order. Experimental results show that compression ratios of all files of the Calgary corpus in the proposed algorithms are better than those of a previous study on CSE with a finite alphabet. Moreover, compression ratios under the second proposed CSE get better than or equal to that under a well-known compressor for 11 files amongst 14 files in the corpus.

In this letter, an efficient hybrid direction-of-arrival (DOA) estimation scheme is devised for massive uniform rectangular array. In this scheme, the DOA estimator based on a two-dimensional (2D) discrete Fourier transform is first applied to acquire coarse initial DOA estimates for single data snapshot. Then, the fine DOA is accurately estimated through using the iterative search estimator within a very small region. Meanwhile, a Nyström-based method is utilized to correctly compute the required noise-subspace projection matrix, avoiding the direct computation of full-dimensional sample correlation matrix and its eigenvalue decomposition. Therefore, the proposed scheme not only can estimate DOA, but also save computational cost, especially in massive antenna arrays scenarios. Simulation results are included to demonstrate the effectiveness of the proposed hybrid estimate scheme.

Homotopy algorithm provides a very powerful approach to select the best regularization term for the l1-norm minimization problem, but it is lack of provision for handling singularities. The singularity problem might be frequently encountered in practical implementations if the measurement matrix contains duplicate columns, approximate columns or columns with linear dependent in kernel space. The existing method for handling Homotopy singularities introduces a high-dimensional random ridge term into the measurement matrix, which has at least two shortcomings: 1) it is very difficult to choose a proper ridge term that applies to several different measurement matrices; and 2) the high-dimensional ridge term may accumulatively degrade the recovery performance for large-scale applications. To get around these shortcomings, a modified ridge-adding method is proposed to deal with the singularity problem, which introduces a low-dimensional random ridge vector into the l1-norm minimization problem directly. Our method provides a much simpler implementation, and it can alleviate the degradation caused by the ridge term because the dimension of ridge term in the proposed method is much smaller than the original one. Moreover, the proposed method can be further extended to handle the SVMpath initialization singularities. Theoretical analysis and experimental results validate the performance of the proposed method.

In this paper, the influence of high-temperature sputtering on the nitrogen-doped (N-doped) LaB6 thin film formation utilizing RF sputtering was investigated. The N-doped LaB6/SiO2/p-Si(100) MOS diode and N-doped LaB6/p-Si(100) of Schottky diode were fabricated. A 30 nm thick N-doped LaB6 thin film was deposited from room temperature (RT) to 150°C. It was found that the resistivity was decreased from 1.5 mΩcm to 0.8 mΩcm by increasing deposition temperature from RT to 150°C. The variation of work function was significantly decreased in case that N-doped LaB6 thin film deposited at 150°C. Furthermore, Schottky characteristic was observed by increasing deposition temperature to 150°C. In addition, the crystallinity of N-doped LaB6 thin film was improved by increasing deposition temperature.

Goal modeling is a method that describes requirements structurally. Goal modeling mainly consists of two tasks: extraction of goals and organization of the extracted goals. Generally, the process of the goal modeling requires intensive manual intervention and higher modeling skills than the process of the usual requirements description. In order to mitigate this problem, we propose a method that provides systematic supports for constructing goal models. In the method, the requirement analyst answers questions and a goal model is semi-automatically constructed based on the answers made. We develop a prototype tool that implements the proposed method and apply it to two systems. The results demonstrate the feasibility of the method.

Embedded SQL inserts SQL statements into the host programming language and executes them at program run time. SQL injection is a known attack technique; however, detection techniques are not introduced in embedded SQL. This paper introduces a technique based on candidate code generation that can detect SQL injection vulnerability in the C/C++ host programming language.

To improve the likability of speech, we propose a voice conversion algorithm by controlling the fundamental frequency (F0) and the spectral envelope and carry out a subjective evaluation. The subjects can manipulate these two speech parameters. From the result, the subjects preferred speech with a parameter related to higher brightness.

In this paper, we present the effectiveness of image compression based on a convolutional auto encoder (CAE) with region of interest (ROI) for quality control. We propose a method that adapts image quality for prioritized parts and non-prioritized parts for CAE-based compression. The proposed method uses annotation information for the distortion weights of the MS-SSIM-based loss function. We show experimental results using a road damage image dataset that is used to check damaged parts and an image dataset with segmentation data (ADE20K). The experimental results reveals that the proposed weighted loss function with CAE-based compression from F. Mentzer et al. learns some characteristics and preferred bit allocations of the prioritized parts by end-to-end training. In the case of using road damage image dataset, our method reduces bpp by 31% compared to the original method while meeting quality requirements that an average weighted MS-SSIM for the road damaged parts be larger than 0.97 and an average weighted MS-SSIM for the other parts be larger than 0.95.

This work reports a new technique to suppress the undesirable multiple-triggering effect in the typical diode triggered silicon controlled rectifier (DTSCR), which is frequently used as an ESD protection element in the advanced CMOS technologies. The technique is featured by inserting additional N-Well areas under the N+ region of intrinsic SCR, which helps to improve the substrate resistance. As a consequence, the delay of intrinsic SCR is reduced as the required triggering current is largely decreased and multiple-triggering related higher trigger voltage is removed. The novel DTSCR structures can alter the stacked diodes to achieve the precise trigger voltage to meet different ESD protection requirements. All explored DTSCR structures are fabricated in a 65-nm CMOS process. Transmission-line-pulsing (TLP) and Very-Fast-Transmission-line-pulsing (VF-TLP) test systems are adopted to confirm the validity of this technique and the test results accord well with our analysis.

When people learn a handicraft with instructional contents such as books, videos, and web pages, many of them often give up halfway because the contents do not always assure how to make it. This study aims to provide origami learners, especially beginners, with feedbacks on their folding operations. An approach for recognizing the state of the learner by using a single top-view camera, and pointing out the mistakes made during the origami folding operation is proposed. First, an instruction model that stores easy-to-follow folding operations is defined. Second, a method for recognizing the state of the learner's origami paper sheet is proposed. Third, a method for detecting mistakes made by the learner by means of anomaly detection using a one-class support vector machine (one-class SVM) classifier (using the folding progress and the difference between the learner's origami shape and the correct shape) is proposed. Because noises exist in the camera images due to shadows and occlusions caused by the learner's hands, the shapes of the origami sheet are not always extracted accurately. To train the one-class SVM classifier with high accuracy, a data cleansing method that automatically sifts out video frames with noises is proposed. Moreover, using the statistics of features extracted from the frames in a sliding window makes it possible to reduce the influence by the noises. The proposed method was experimentally demonstrated to be sufficiently accurate and robust against noises, and its false alarm rate (false positive rate) can be reduced to zero. Requiring only a single camera and common origami paper, the proposed method makes it possible to monitor mistakes made by origami learners and support their self-learning.

The aim of this paper is to show an upper bound for finding defective samples in a group testing framework. To this end, we exploit minimization of Hamming weights in coding theory and define probability of error for our decoding scheme. We derive a new upper bound on the probability of error. We show that both upper and lower bounds coincide with each other at an optimal density ratio of a group matrix. We conclude that as defective rate increases, a group matrix should be sparser to find defective samples with only a small number of tests.

This paper presents non-destructive millimeter-wave (MMW) imaging of sub-millimeter-wide cracks on a concrete surface covered with paper. We measured the near-field scattering of 76.5 GHz-MMW signals at concrete surface cracks for detection of the sub-millimeter-wide cracks. A decrease in the received signal magnitude by near-field scattering at the fine concrete surface crack was slight, which yielded an unclear MMW image contrast of fine cracks at the concrete surface. We have found that the received signal magnitude at concrete surface crack is larger than that at the surface without a crack, when the paper thickness is almost equal to n/4 of the effective wavelength of the MMW signal in the paper (n=1, 3, 5 ...), thus, making MMW image contrast at the surface crack reversed. By calculating the difference of two MMW images obtained from different paper thickness, we were able to improve the MMW image contrast at the surface crack by up to 3.3 dB.

Superconducting detectors have been shown to be superior to other techniques in some applications. However, superconducting devices have not been used for detecting neutrons often in the past decades. We have been developing various superconducting neutron detectors. In this paper, we review our attempts to measure neutrons using superconducting stripline detectors with DC bias currents. These include attempts with a MgB2-based detector and a Nb-based detector with a 10B converter.

Mobile wireless sensor networks (WSNs) are facing threats from malicious nodes that disturb packet transmissions, leading to poor mobile WSN performance. Existing studies have proposed a number of methods, such as decision tree-based classification methods and reputation based methods, to detect these malicious nodes. These methods assume that the malicious nodes follow only pre-defined attack models and have no learning ability. However, this underestimation of the capability of malicious node is inappropriate due to recent rapid progresses in machine learning technologies. In this study, we design reinforcement learning-based malicious nodes, and define a novel observation space and sparse reward function for the reinforcement learning. We also design an adaptive learning method to detect these smart malicious nodes. We construct a robust classifier, which is frequently updated, to detect these smart malicious nodes. Extensive experiments show that, in contrast to existing attack models, the developed malicious nodes can degrade network performance without being detected. We also investigate the performance of our detection method, and confirm that the method significantly outperforms the state-of-the-art methods in terms of detection accuracy and false detection rate.