Today, computer vision is used in different applications for intelligent transportation systems like: traffic surveillance, driver assistance, law enforcement etc. Amongst these applications, we are concentrating on speed measurement for law enforcement. In law enforcement applications, the presence of the license plate in the scene is a presupposition and metric parameters like vehicle's speed are to be estimated with a high degree of precision. The novelty of this paper is to propose a new precise, practical and fast procedure, with hierarchical architecture, to estimate the homraphic transform of the license plate and using this transform to estimate the vehicle's speed. The proposed method uses the RANSAC algorithm to improve the robustness of the estimation. Hence, it is possible to replace the peripheral equipment with vision based systems, or in conjunction with these peripherals, it is possible to improve the accuracy and reliability of the system. Results of experiments on different datasets, with different specifications, show that the proposed method can be used in law enforcement applications to measure the vehicle's speed.

Distributed compressive video sensing (DCVS) is an emerging low-complexity video coding framework which integrates the merits of distributed video coding (DVC) and compressive sensing (CS). In this paper, we propose a novel rate-distortion optimized DCVS codec, which takes advantage of a rate-distortion optimization (RDO) model based on the estimated correlation noise (CN) between a non-key frame and its side information (SI) to determine the optimal measurements allocation for the non-key frame. Because the actual CN can be more accurately recovered by our DCVS codec, it leads to more faithful reconstruction of the non-key frames by adding the recovered CN to the SI. The experimental results reveal that our DCVS codec significantly outperforms the legacy DCVS codecs in terms of both objective and subjective performance.

In this paper a new method for noise removal from single-trial event-related potentials recorded with a multi-channel electroencephalogram is addressed. An observed signal is separated into multiple signals with a multi-channel Wiener filter whose coefficients are estimated based on parameter estimation of a probabilistic generative model that locally models the amplitude of each separated signal in the time-frequency domain. Effectiveness of using prior information about covariance matrices to estimate model parameters and frequency dependent covariance matrices were shown through an experiment with a simulated event-related potential data set.

The information maximization (Infomax) based on information entropy theory is a class of methods that can be used to blindly separate the sources. Torkkola applied the Infomax criterion to blindly separate the mixtures where the sources have been delayed with respect to each other. Compared to the frequency domain methods, this time domain method has simple adaptation rules and can be easily implemented. However, Torkkola's method works only in the real valued field. In this letter, the Infomax for blind separation of the delayed sources is extended to the complex case for processing of complex valued signals. Firstly, based on the gradient ascent the adaptation rules for the parameters of the unmixing network are derived and the steps of algorithm are given. Then, a measurement matrix is constructed to evaluate the separation performance. The results of computer experiment support the extended algorithm.

This paper proposes a new class of Hilbert pairs of almost symmetric orthogonal wavelet bases. For two wavelet bases to form a Hilbert pair, the corresponding scaling lowpass filters are required to satisfy the half-sample delay condition. In this paper, we design simultaneously two scaling lowpass filters with the arbitrarily specified flat group delay responses at ω=0, which satisfy the half-sample delay condition. In addition to specifying the number of vanishing moments, we apply the Remez exchange algorithm to minimize the difference of frequency responses between two scaling lowpass filters, in order to improve the analyticity of complex wavelets. The equiripple behavior of the error function can be obtained through a few iterations. Therefore, the resulting complex wavelets are orthogonal and almost symmetric, and have the improved analyticity. Finally, some examples are presented to demonstrate the effectiveness of the proposed design method.

To efficiently monitor the link performance in an OpenFlow network with a single measurement box (referred to a “beacon”), this paper presents a measurement scheme that calculates a set of measurement paths from the beacon to cover all links in the network based on the controllable feature of individual measurement paths in the OpenFlow network and comprehensively estimates the performance of all the physical links from round-trip active measurements. The scheme has a novel feature that minimize the maximum number of exclusive flow-entries for active measurements on OpenFlow switches by utilizing common packet header values in the probing packets to aggregate multiple entries into a single entry to save the resources in OpenFlow switches and controller. We demonstrate the effectiveness and feasibility of our solution through simulations and emulation scenarios.

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.

Blood pressure measurement by auscultatory method is a compulsory skill that is required by all healthcare practitioners. During the measurement, they must concentrate on recognizing the Korotkoff sounds, looking at the sphygmomanometer scale, and constantly deflating the cuff pressure simultaneously. This complex operation is difficult for the new learners and they need a lot of practice with the supervisor in order to guide them on their measurements. However, the supervisor is not always available and consequently, they always face the problem of lack of enough training. In order to help them mastering the skill of measuring blood pressure by auscultatory method more efficiently and effectively, we propose using a sensor device to capture the signals of Korotkoff sounds and cuff pressure during the measurement, and display the signal changes on a visualization tool through wireless connection. At the end of the measurement, the learners can verify their skill on deflation speed and recognition of Korotkoff sounds using the graphical view, and compare their measurements with the machine instantly. By using this device, the new learners do not need to wait for their supervisor for training but can practice with their colleagues more frequently. As a result, they will be able to acquire the skill in a shorter time and be more confident with their measurements.

The widespread use and increasing popularity of broadband service has prompted a focus on the measurement and analysis of its empirical performance in recent studies. The worldwide view of broadband performance has been examined over the short term with Speedtest.net, but research in this area has not yet provided a long-term evolutionary insight on how DSL, Cable, and Fiber access technologies have influenced on user experience. In this study, we present 6 years of measurement results, from 2006 to 2011, of broadband performance with fast developing broadband networks in Korea. With 57% Fiber penetration in 2011, our data consist of a total of 29M test records and 10M subscribers. Over the 6 years, we have observed a 2.9-fold improvement in download speed (57Mbps), 2.8-fold increase in upload speed (38Mbps), and 0.7-fold decrease in latency due to the high penetration rate of Fiber broadband service and the advanced Cable modem technology. In addition, we carried out longitudinal analysis of various aspects of services, providers, regions, and cost-performance. We believe that the evolutionary Korean broadband measurement results can shed light on how high-speed access technologies are substantially enhancing on end-to-end performance.

In this paper we present a novel treatment of cellular automata (CA) from an algebraic point of view. CA on monoids associated with Σ-algebras are introduced. Then an extension of Hedlund's theorem which connects CA associated with Σ-algebras and continuous functions between prodiscrete topological spaces on the set of configurations are discussed.

Patch clamp measurement technique is one of the most important techniques in the field of electrophysiology. The elucidation of the channels, nerve cells, and brain activities as well as contribution of the treatment of neurological disorders is expected from the measurement of ion current. A current-to-voltage converter, which is the front end circuit of the patch clamp measurement system is fabricated using 0.18µm CMOS technology. The current-to-voltage converter requires a resistance as high as 50MΩ as a feedback resistor in order to ensure a high signal-to-noise ratio for very small signals. However, the circuit becomes unstable due to the large parasitic capacitance between the poly layer and the substrate of the on-chip feedback resistor and the instability causes the peaking at lower frequency. The instability of a current-to-voltage converter with a high-resistance as a feedback resistor is analyzed theoretically. A compensation circuit to stabilize the amplifier by driving the N-well under poly resistor to suppress the effect of parasitic capacitance using buffer circuits is proposed. The performance of the proposed circuit is confirmed by both simulation and measurement of fabricated chip. The peaking in frequency characteristic is suppressed properly by the proposed method. Furthermore, the bandwidth of the amplifier is expanded up to 11.3kHz, which is desirable for a patch clamp measurement. In addition, the input referred rms noise with the range of 10Hz ∼ 10kHz is 2.09 Arms and is sufficiently reach the requirement for measure of both whole-cell and a part of single-channel recordings.

This paper proposes an on-chip measurement method of PLL through fully digital interface. For the measurement of the PLL transfer function, we modulated the phase of the PLL input in triangular form using Digital-to-Time Converter (DTC) and read out the response by Time-to-Digital Converter (TDC). Combination of the DTC and TDC can obtain the transfer function of the PLL both in the magnitude domain and the phase domain. Since the DTC and TDC can be controlled and observed by digital signals, the measurement can be conducted without any high speed analog signal. Moreover, since the DTC and TDC can be designed symmetrically, the measurement method is robust against Process, Voltage, and Temperature (PVT) variations. At the same time, the employment of the TDC also enables a measurement of the PLL lock range by changing the division ratio of the divider. Two time domain circuits were designed using 180nm CMOS process and the HSPICE simulation results demonstrated the measurement of the transfer function and lock range.

This paper investigates a signal area (SA) estimation method for wideband and long time duration spectrum measurements for dynamic spectrum access. SA denotes the area (in time/frequency domain) occupied by the primary user's signal. The traditional approach, which utilizes only Fourier transform (FT) and energy detector (ED) for SA estimation, can achieve low complexity, but its estimation performance is not very high. Against this issue, we apply post-processing to improve the performance of the FT-based ED. Our proposed method, simple SA (S-SA) estimation, exploits the correlation of the spectrum states among the neighboring tiles and the fact that SA typically has a rectangular shape to estimate SA with high accuracy and relatively low complexity compared to a conventional method, contour tracing SA (CT-SA) estimation. Numerical results will show that the S-SA estimation method can achieve better detection performance. The SA estimation and processing can reduce the number of bits needed to store/transmit the observed information compared to the FT-based ED. Thus, in addition to improved detection performance it also compresses the data.

This paper describes recent progress of photonically-enabled systems for millimeter-wave and terahertz measurement applications. After briefly explaining signal generation schemes as a foundation of photonics-based approach, system configurations for specific applications are discussed. Then, practical demonstrations are presented, which include frequency-domain spectroscopy, phase-sensitive measurement, electric-field measurement, and 2D/3D imaging.

RC4 stream cipher, designed by Rivest in 1987, is widely used in various standard protocols and commercial applications. After the disclosure of RC4 algorithm in 1994, many cryptanalytic results on RC4 have been reported. In 1996, Jenkins discovered correlations between a keystream byte and an internal state variable. This is known as the Glimpse theorem. In 2013, Maitra and Sen Gupta proved the Glimpse theorem and showed other correlations between two consecutive keystream bytes and an internal state variable. This is called the long-term Glimpse. These correlations provide only cases with positive biases, and hold generally on any round. In this paper, we refine known Glimpse correlations from two aspects. One is to find new positive or negative biases on all values in addition to a known value. The other is to provide precise biases on specific rounds. As a result, we can discover 6 cases with several new biases, and prove these cases theoretically. From the first refinement, combining our new biases with known one, the long-term Glimpse with positive biases is integrated into a whole. From the second refinement, we can successfully find that two correlations on specific rounds become an impossible condition.

We propose a new swept-frequency measurement method for the electromagnetic characterization of materials. The material is a multilayer cylinder that pierces a rectangular waveguide through two holes in the narrow waveguide walls. The complex permittivity and permeability of the material are calculated from measured S-parameters as an inverse problem. To this aim, the paper develops a complete electromagnetic formulation of the problem, where the effects of material insertion holes are taken into consideration. The formulation is validated through the measurement of ferrite and water samples in the S-band.

We propose an effective technique for estimation of targets by ground penetrating radar (GPR) using model-based compressive sensing (CS). We demonstrate the technique's performance by applying it to detection of buried landmines. The conventional CS algorithm enables the reconstruction of sparse subsurface images using much reduced measurement by exploiting its sparsity. However, for landmine detection purposes, CS faces some challenges because the landmine is not exactly a point target and also faces high level clutter from the propagation in the medium. By exploiting the physical characteristics of the landmine using model-based CS, the probability of landmine detection can be increased. Using a small pixel size, the landmine reflection in the image is represented by several pixels grouped in a three dimensional plane. This block structure can be used in the model based CS processing for imaging the buried landmine. The evaluation using laboratory data and datasets obtained from an actual mine field in Cambodia shows that the model-based CS gives better reconstruction of landmine images than conventional CS.

Remote Access Trojans (RAT) is a spyware which can steal the confidential information from a target organization. The detection of RATs becomes more and more difficult because of targeted attacks, since the victim usually cannot realize that he/she is being attacked. After RAT's intrusion, the attacker can monitor and control the victim's PC remotely, to wait for an opportunity to steal the confidential information. As this situation, the main issue we face now is how to prevent confidential information being leaked back to the attacker. Although there are many existing approaches about RAT detection, there still remain two challenges: to detect RAT sessions as early as possible, and to distinguish them from the normal applications with a high accuracy. In this paper, we propose a novel approach to detect RAT sessions by their network behavior during the early stage of communication. The early stage is defined as a short period of time at communication's beginning; it also can be seen as the preparation period of the communication. We extract network behavior features from this period, to differentiate RAT sessions and normal sessions. For the implementation and evaluation, we use machine learning techniques with 5 algorithms and K-Fold cross-validation. As the results, our approach could detect RAT sessions in the communication's early stage with the accuracy over 96% together with the FNR of 10% by Random Forest algorithm.

Nonnegative matrix factorization (NMF) is one of the most popular tools for speech enhancement. In this letter, we present an improved semi-supervised NMF (ISNMF)-based speech enhancement algorithm combining techniques of noise estimation and Incremental NMF (INMF). In this approach, fixed speech bases are obtained from training samples offline in advance while noise bases are trained on-the-fly whenever new noisy frame arrives. The INMF algorithm is adopted for noise bases learning because it can overcome the difficulties that conventional NMF confronts in online processing. The proposed algorithm is real-time capable in the sense that it processes the time frames of the noisy speech one by one and the computational complexity is feasible. Four different objective evaluation measures at various signal-to-noise ratio (SNR) levels demonstrate the superiority of the proposed method over traditional semi-supervised NMF (SNMF) and well-known robust principal component analysis (RPCA) algorithm.

In this paper, a method for designing of Incremental Granular Model (IGM) based on integration of Linear Regression (LR) and Linguistic Model (LM) with the aid of fuzzy granulation is proposed. Here, IGM is designed by the use of information granulation realized via Context-based Interval Type-2 Fuzzy C-Means (CIT2FCM) clustering. This clustering approach are used not only to estimate the cluster centers by preserving the homogeneity between the clustered patterns from linguistic contexts produced in the output space, but also deal with the uncertainty associated with fuzzification factor. Furthermore, IGM is developed by construction of a LR as a global model, refine it through the local fuzzy if-then rules that capture more localized nonlinearities of the system by LM. The experimental results on two examples reveal that the proposed method shows a good performance in comparison with the previous works.