Biometric authentication, namely using biometric features for authentication is gaining popularity in recent years as further modalities, such as fingerprint, iris, face, voice, gait, and others are exploited. We explore the effectiveness of three simple Electroencephalography (EEG) related biometric authentication tasks, namely resting, thinking about a picture, and moving a single finger. We present details of the data processing steps we exploit for authentication, including extracting features from the frequency power spectrum and MFCC, and training a multilayer perceptron classifier for authentication. For evaluation purposes, we record an EEG dataset of 27 test subjects. We use three setups, baseline, task-agnostic, and task-specific, to investigate whether person-specific features can be detected across different tasks for authentication. We further evaluate, whether different tasks can be distinguished. Our results suggest that tasks are distinguishable, as well as that our authentication approach can work both exploiting features from a specific, fixed, task as well as using features across different tasks.

Optical I/O core based on silicon photonics technology and optical/electrical assembly was developed as a fingertip-size optical module with high bandwidth density, low power consumption, and high temperature operation. The advantages of the optical I/O core, including hybrid integration of quantum dot laser diode and optical pin, allow us to achieve 300-m transmission at 25Gbps per channel when optical I/O core is mounted around field-programmable gate array without clock data recovery.

We developed a high-frequency and integrated design based on a flip-chip interconnection technique (Hi-FIT) as a wire-free interconnection technique that provides a high modulation bandwidth. The Hi-FIT can be applied to various high-speed (>100 Gbaud) optical devices. The Hi-FIT EA-DFB laser module has a 3-dB bandwidth of 59 GHz. And with a 4-intensity-level pulse amplitude modulation (PAM) operation at 107 Gbaud, we obtained a bit-error rate (BER) of less than 3.8×10-3, which is an error-free condition, by using a 7%-overhead (OH) hard-decision forward error correction (HD-FEC) code, even after a 10-km SMF transmission. The 3-dB bandwidth of the Hi-FIT employing an InP-MZM sub-assembly was more than 67 GHz, which was the limit of our measuring instrument. We also demonstrated a 120-Gbaud rate IQ modulation.

We propose a method for extracting multi-view images from a light field (plenoptic) camera that accurately handles the physical pixel arrangement of this camera. We use a Lytro Illum camera to obtain 4D light field data (a set of multi-viewpoint images) through a micro-lens array. The light field data are multiplexed on a single image sensor, and thus, the data is first demultiplexed into a set of multi-viewpoint (sub-aperture) images. However, the demultiplexing process usually includes interpolation of the original data such as demosaicing for a color filter array and pixel resampling for the hexagonal pixel arrangement of the original sub-aperture images. If this interpolation is performed, some information is added or lost to/from the original data. In contrast, we preserve the original data as faithfully as possible, and use them directly for the super resolution reconstruction, where the super-resolved image and the corresponding depth map are alternatively refined. We experimentally demonstrate the effectiveness of our method in resolution enhancement through comparisons with Light Field Toolbox and Lytro Desktop Application. Moreover, we also mention another type of light field cameras, a Raytrix camera, and describe how it can be handled to extract high-quality multi-view images.

In recent years we have witnessed an increasing demand to process queries on large datasets in Non-ordered Discrete Data Spaces (NDDS). In particular, one type of query in an NDDS, called box queries, is used in many emerging applications including error corrections in bioinformatics and network intrusion detection in cybersecurity. Effective indexing methods are necessary for efficiently processing queries on large datasets in disk. However, most existing NDDS indexing methods were not designed for box queries. Several recent indexing methods developed for box queries on a large NDDS dataset in disk are based on the popular data-partitioning approach. Unfortunately, a space-partitioning based indexing scheme, which is more effective for box queries in an NDDS, has not been studied before. In this paper, we propose a novel indexing method based on space-partitioning, called the BINDS-tree, for supporting efficient box queries on a large NDDS dataset in disk. A number of effective strategies such as node split based on minimum span and cross optimal balance, redundancy reduction utilizing a singleton dimension inheritance property, and a space-efficient structure for the split history are incorporated in the constructing algorithm for the BINDS-tree. Experimental results demonstrate that the proposed BINDS-tree significantly improves the box query I/O performance, comparing to that of the state-of-the-artdata-partitioning based NDDS indexing method.

We propose a method to find assembly models contained in another assembly model given as a query from a set of 3D CAD assembly models. A 3D CAD assembly model consists of multiple components and is constructed using a 3D CAD software. The proposed method distinguishes assembly models which consist of a subset of components constituting the query model and also whose components have the same layout as the subset of the components. We compute difference between the shapes and the layouts of the components from the sinograms which are constructed by the Radon transform of their projections from various angles. We evaluate the proposed method experimentally using the assembly models which we prepare as a benchmark. The proposed method can also be used to find the database models which contains a query model.

As big data attracts attention in a variety of fields, research on data exploration for analyzing large-scale scientific data has gained popularity. To support exploratory analysis of scientific data, effective summarization and visualization of the target data as well as seamless cooperation with modern data management systems are in demand. In this paper, we focus on the exploration-based analysis of scientific array data, and define a spatial V-Optimal histogram to summarize it based on the notion of histograms in the database research area. We propose histogram construction approaches based on a general hierarchical partitioning as well as a more specific one, the l-grid partitioning, for effective and efficient data visualization in scientific data analysis. In addition, we implement the proposed algorithms on the state-of-the-art array DBMS, which is appropriate to process and manage scientific data. Experiments are conducted using massive evacuation simulation data in tsunami disasters, real taxi data as well as synthetic data, to verify the effectiveness and efficiency of our methods.

Given a graph G=(V,E), where V and E are vertex and edge sets of G, and a subset VNT of vertices called a non-terminal set, the minimum spanning tree with a non-terminal set VNT, denoted by MSTNT, is a connected and acyclic spanning subgraph of G that contains all vertices of V with the minimum weight where each vertex in a non-terminal set is not a leaf. On general graphs, the problem of finding an MSTNT of G is NP-hard. We show that if G is a series-parallel graph then finding an MSTNT of G is linearly solvable with respect to the number of vertices.

Most recent work used raw electroencephalograph (EEG) data to train deep learning (DL) models, with the assumption that DL models can learn discriminative features by itself. It is not yet clear what kind of RSVP specific features can be selected and combined with EEG raw data to improve the RSVP classification performance of DL models. In this paper, we tried to extract RSVP specific features and combined them with EEG raw data to capture more spatial and temporal correlations of target or non-target event and improve the EEG-based RSVP target detection performance. We tested on X2 Expertise RSVP dataset to show the experiment results. We conducted detailed performance evaluations among different features and feature combinations with traditional classification models and different CNN models for within-subject and cross-subject test. Compared with state-of-the-art traditional Bagging Tree (BT) and Bayesian Linear Discriminant Analysis (BLDA) classifiers, our proposed combined features with CNN models achieved 1.1% better performance in within-subject test and 2% better performance in cross-subject test. This shed light on the ability for the combined features to be an efficient tool in RSVP target detection with deep learning models and thus improved the performance of RSVP target detection.

This letter proposes a fast superpixel segmentation method based on boundary sampling and interpolation. The basic idea is as follow: instead of labeling local region pixels, we estimate superpixel boundary by interpolating candidate boundary pixel from a down-sampling image segmentation. On the one hand, there exists high spatial redundancy within each local region, which could be discarded. On the other hand, we estimate the labels of candidate boundary pixels via sampling superpixel boundary within corresponding neighbour. Benefiting from the reduction of candidate pixel distance calculation, the proposed method significantly accelerates superpixel segmentation. Experiments on BSD500 benchmark demonstrate that our method needs half the time compared with the state-of-the-arts while almost no accuracy reduction.

Due to the need to protect personal information and the impracticality of exhaustive data collection, there is increasing need to deal with datasets with various levels of granularity, such as user-individual data and user-group data. In this study, we propose a new method for jointly analyzing multiple datasets with different granularity. The proposed method is a probabilistic model based on nonnegative matrix factorization, which is derived by introducing latent variables that indicate the high-resolution data underlying the low-resolution data. Experiments on purchase logs show that the proposed method has a better performance than the existing methods. Furthermore, by deriving an extension of the proposed method, we show that the proposed method is a new fundamental approach for analyzing datasets with different granularity.

This paper presents a method combining array signal processing and adaptive noise cancellation to suppress unwanted ambient interferences in in situ measurement of radiated emissions of equipment. First, the signals received by the antenna array are processed to form a main data channel and an auxiliary data channel. The main channel contains the radiated emissions of the equipment under test and the attenuated ambient interferences. The auxiliary channel only contains the attenuated ambient interferences. Then, the adaptive noise cancellation technique is used to suppress the ambient interferences based on the correlation of the interferences in the main and auxiliary channels. The proposed method overcomes the problem that the ambient interferences in the two channels of the virtual chamber method are not correlated, and realizes the suppression of multi-source ambient noises in the use of fewer array elements. The results of simulation and experiment show that the proposed method can effectively extract radiated emissions of the equipment under test in complex electromagnetic environment. Finally, discussions on the effect of the beam width of the main channel and the generalization of the proposed method to three dimensionally distributed signals are addressed.

There are different types of social ties among people, and recognizing specialized types of relationship, such as family or friend, has important significance. It can be applied to personal credit, criminal investigation, anti-terrorism and many other business scenarios. So far, some machine learning algorithms have been used to establish social relationship inferencing models, such as Decision Tree, Support Vector Machine, Naive Bayesian and so on. Although these algorithms discover family members in some context, they still suffer from low accuracy, parameter sensitive, and weak robustness. In this work, we develop a Novel Family Relationship Recognition (NFRR) algorithm on telecom dataset for identifying one's family members from its contact list. In telecom dataset, all attributes are divided into three series, temporal, spatial and behavioral. First, we discover the most probable places of residence and workplace by statistical models, then we aggregate data and select the top-ranked contacts as the user's intimate contacts. Next, we establish Relational Spectrum Matrix (RSM) of each user and its intimate contacts to form communication feature. Then we search the user's nearest neighbors in labelled training set and generate its Specialized Family Spectrum (SFS). Finally, we decide family relationship by comparing the similarity between RSM of intimate contacts and the SFS. We conduct complete experiments to exhibit effectiveness of the proposed algorithm, and experimental results also show that it has a lower complexity.

In this paper, we propose a quality-level selection method for adaptive video streaming with scalable video coding (SVC). The proposed method works on the client with the dynamic adaptive streaming over HTTP (DASH) with SVC. The proposed method consists of two components: introducing segment group and a buffer-aware layer selection algorithm. In general, quality of experience (QoE) performance degrades due to stalling (playback buffer underflow), low playback quality, frequent quality-level switching, and extreme-down quality switching. The proposed algorithm focuses on reducing the frequent quality-level switching, and extreme-down quality switching without increasing stalling and degrading playback quality. In the proposed method, a SVC-DASH client selects a layer every G segments, called a segment group to prevent frequent quality-level switching. In addition, the proposed method selects the quality of a layer based on a playback buffer in a layer selection algorithm for preventing extreme-down switching. We implement the proposed method on a real SVC-DASH system and evaluate its performance by subjective evaluations of multiple users. As a result, we confirm that the proposed algorithm can obtain better mean opinion score (MOS) value than a conventional SVC-DASH, and confirm that the proposed algorithm is effective to improve QoE performance in SVC-DASH.

A novel algorithm is proposed for estimating the direction of arrival (DOA) of linear frequency modulated (LFM) signals for the uniform circular array (UCA). Firstly, the UCA is transformed into an equivalent virtual uniform linear array (ULA) using the mode-space algorithm. Then, the short time Fourier transform (STFT) of each element's output is worked out. We can obtain the spatial time-frequency distribution matrix of the virtual ULA by selecting the single-source time-frequency (t-f) points in the t-f plane and then get the signal subspace of the array. The characteristics nature of the Bessel function allow us to obtain the multiple invariance (MI) of the virtual ULA. So the multiple rotational invariant equation of the array can be obtained and its closed-form solution can be worked out using the multi-least-squares (MLS) criterion. Finally, the two dimensional (2-D) DOA estimation of LFM signals for UCA can be obtained. Numerical simulation results illustrate that the UCA-STFT-MI-ESPRIT algorithm proposed in this paper can improve the estimation precision greatly compared with the traditional ESPRIT-like algorithms and has much lower computational complexity than the MUSIC-like algorithms.

Approximate computing is a promising paradigm to realize fast, small, and low power characteristics, which are essential for modern applications, such as Internet of Things (IoT) devices. This paper proposes the Carry-Predicting Adder (CPredA), an approximate adder that is scalable relative to accuracy and power consumption. The proposed CPredA improves the accuracy of a previously studied adder by performing carry prediction. Detailed simulations reveal that, compared to the existing approximate adder, accuracy is improved by approximately 50% with comparable energy efficiency. Two application-level evaluations demonstrate that the proposed approximate adder is sufficiently accurate for practical use.

The influence of pump phase error on phase-sensitive optical amplifier (PSA) repeaters and the waveform degradation due to chromatic dispersion and fiber nonlinearities in the optical multi-relay transmission of quadrature phase-shift keying phase-conjugated twin waves are considered theoretically. First, the influence of noise from the pump phase error, optical local oscillator, receiver, and the amplified spontaneous-emission (ASE) in PSA repeaters is investigated with the assumption that transmission fibers are linear lossy channels. The bit-error rate (BER) is estimated as a function of the signal-to-noise ratio, and the relationship between the number of transmission relays and the fiber launch power is clarified. Waveform degradation due to chromatic dispersion and the optical fiber nonlinearities in transmission fibers are investigated with the noiseless condition, and the maximum repeatable number as a function of the fiber launch power is calculated. Finally, we show the relationship among the maximum repeatable number, standard deviation of pump phase error in PSA repeaters, and the fiber launch power to clarify the optimum transmission condition with consideration of the noise and the waveform degradation.

This paper presents a high power efficient pulse VCO with tail-filter for the chip-scale atomic clock (CSAC) application. The stringent power and clock stability specifications of next-generation CSAC demand a VCO with ultra-low power consumption and low phase noise. The proposed VCO architecture aims for the high power efficiency, while further reducing the phase noise using tail filtering technique. The VCO has been implemented in a standard 45nm SOI technology for validation. At an oscillation frequency of 5.0GHz, the proposed VCO achieves a phase noise of -120dBc/Hz at 1MHz offset, while consuming 1.35mW. This translates into an FoM of -191dBc/Hz.

In the current era of data science, data quality has a significant and critical impact on business operations. This is no different for the meteorological data encountered in the field of meteorology. However, the conventional methods of meteorological data quality control mainly focus on error detection and null-value detection; that is, they only consider the results of the data output but ignore the quality problems that may also arise in the workflow. To rectify this issue, this paper proposes the Total Meteorological Data Quality (TMDQ) framework based on the Total Quality Management (TQM) perspective, especially considering the systematic nature of data warehousing and process focus needs. In practical applications, this paper uses the proposed framework as the basis for the development of a system to help meteorological observers improve and maintain the quality of meteorological data in a timely and efficient manner. To verify the feasibility of the proposed framework and demonstrate its capabilities and usage, it was implemented in the Tamsui Meteorological Observatory (TMO) in Taiwan. The four quality dimension indicators established through the proposed framework will help meteorological observers grasp the various characteristics of meteorological data from different aspects. The application and research limitations of the proposed framework are discussed and possible directions for future research are presented.