This paper presents a modified orthogonal fractal super-resolution (OFSR) method to improve the visual quality of an image along sharp edges. Although the OFSR method constructs a high-quality high-resolution image from a low-resolution counterpart, there are ringing artifacts observed along sharp edges which make the visual quality relatively low with respect to the numerical quantity. These artifacts are mainly caused by unnecessarily exaggerated pixel contrast along sharp edges within a range block. We restrict each contracted pixel value in a range block to a value between the minimum and maximum of its domain block pixel values. We also extend the domain block of the contraction function and find a better domain block using the range block mean. At the final step of the iteration, we adjust each pixel in the range block so that the range block mean and the corresponding pixel value of the low-resolution image are equal. According to our experimental results, the proposed method improves the visual quality along sharp edges and shows higher levels of numerical quantity than the OFSR method.

In this letter, we present a fast image/video super resolution framework using edge and nonlocal constraint. The proposed method has three steps. First, we improve the initial estimation using content-adaptive bilateral filtering to strengthen edge. Second, the high resolution image is estimated by using classical back projection method. Third, we use joint content-adaptive nonlocal means filtering to get the final result, and self-similarity structures are obtained by the low resolution image. Furthermore, content-adaptive filtering and fast self-similarity search strategy can effectively reduce computation complexity. The experimental results show the proposed method has good performance with low complexity and can be used for real-time environment.

This study investigated the relationship between human use of automation and their sensitivity to changes in automation and manual performance. In the real world, automation and manual performance change dynamically with changes in the environment. However, a few studies investigated whether changes in automation or manual performance have more effect on whether users choose to use automation. We used two types of experimental tracking tasks in which the participants had to select whether to use automation or conduct manual operation while monitoring the variable performance of automation and manual operation. As a result, we found that there is a mutual relationship between human use of automation and their sensitivity to automation and manual performance changes. Also, users do not react equally to both automation and manual performance changes although they use automation adequately.

Super resolution (SR) reconstruction is the process of fusing a sequence of low-resolution images into one high-resolution image. Many researchers have introduced various SR reconstruction methods. However, these traditional methods are limited in the extent to which they allow recovery of high-frequency information. Moreover, due to the self-similarity of face images, most of the facial SR algorithms are machine learning based. In this paper, we introduce a facial SR algorithm that combines learning-based and regularized SR image reconstruction algorithms. Our conception involves two main ideas. First, we employ separated frequency components to reconstruct high-resolution images. In addition, we separate the region of the training face image. These approaches can help to recover high-frequency information. In our experiments, we demonstrate the effectiveness of these ideas.

High-resolution time of arrival (TOA) estimation techniques have great promise for the high range resolution required in recently developed radar systems. A widely known super-resolution TOA estimation algorithm for such applications, the multiple-signal classification (MUSIC) in the frequency domain, has been proposed, which exploits an orthogonal relationship between signal and noise eigenvectors obtained by the correlation matrix of the observed transfer function. However, this method suffers severely from a degraded resolution when a number of highly correlated interference signals are mixed in the same range gate. As a solution for this problem, this paper proposes a novel TOA estimation algorithm by introducing a maximum likelihood independent component analysis (MLICA) approach, in which multiple complex sinusoidal signals are efficiently separated by the likelihood criteria determined by the probability density function (PDF) of a complex sinusoid. This MLICA schemes can decompose highly correlated interference signals, and the proposed method then incorporates the MLICA into the MUSIC method, to enhance the range resolution in richly interfered situations. The results from numerical simulations and experimental investigation demonstrate that our proposed pre-processing method can enhance TOA estimation resolution compared with that obtained by the original MUSIC, particularly for lower signal-to-noise ratios.

We propose a compact and high-range resolution 76 GHz millimeter-wave radar system for autonomous unmanned helicopters. The purpose of the radar system is to detect and avoid obstacles that may affect the flight safety. To achieve these objectives, a high range resolution and a long detection range are required for the radar systems with small volume and weight. The radar broadband RF front-end module which employs a simple direct conversion method is proposed. The radar module enables the 6 GHz RF signal transmission as well as the output power of about 8 dBm using commercially available low-cost monolithic microwave integrated circuits. The radar system comprises the broadband RF front-end module, a Ku-band local frequency-modulated continuous wave signal synthesizer, and a very light weight carbon fiber reinforced plastic parabolic reflector antenna. The 5 cm of range resolution is experimentally obtained using the 6 GHz RF signal bandwidth. The results of the power line measurement confirm an about 23 dB signal to noise ratio, which is measured from the reflection of the high-voltage power lines about 150 m ahead. In addition, the results of the radar system on-board test using an unmanned helicopter are evaluated. The real-time radar scope, which is transferred through the wireless connection, confirms the detection of the power lines and the other surrounding objects.

This paper proposes a high frequency resolution Digitally-Controlled Oscillator (DCO) using a single-period control bit switching scheme. The proposed scheme controls the tuning word of DCO in a single period for the fine frequency tuning. The LC type DCO is implemented to realize the proposed scheme, and is fabricated using a standard 65 nm CMOS technology. The measurement results show that the implemented DCO improves the frequency resolution from 560 kHz to 180 kHz without phase noise degradation with an additional area of 200 µm2.

In this paper, an imaging components transmission scheme for the improvement of multipath delay resolution in a Fractional Sampling (FS) OFDM receiver is proposed. FS has been proposed as a diversity scheme and achieves path diversity by enlarging the bandwidth of the baseband filters in order to transmit the imaging components of the desired signal. However, FS is not able to achieve diversity with very short delay multipaths because of its low multipath delay resolution. Wider bandwidth of the transmission signal is required to improve the resolution of the delay. On the other hand, cognitive radio is an emerging technology to utilize frequency spectrum flexibly through dynamic spectrum access (DSA). To resolve the small delay multipaths and to use the spectrum flexibly with DSA, this paper investigates the FS path diversity with the imaging components on the separated frequency channel. The correlation between the 2 FS branches is analyzed theoretically on the 2 path channel under the conditions of sampling interval, delay spread, and frequency separation. Numerical results through computer simulation show that the proposed scheme improves the multipath resolution and the bit error rate (BER) performance under the existence of small delay multipaths.

Link prediction is the task of inferring the existence or absence of certain relationships among data objects such as identity, interaction, and collaboration. Link prediction is found in various applications in the fields of information integration, recommender systems, bioinformatics, and social network analysis. The increasing interest in dynamically changing networks has led to growing interest in a more general link prediction problem called temporal link prediction in the data mining and machine learning communities. However, only links among nodes at the same time point are considered in temporal link prediction. We propose a new link prediction problem called cross-temporal link prediction in which the links among nodes at different time points are inferred. A typical example of cross-temporal link prediction is cross-temporal entity resolution to determine the identity of real entities represented by data objects observed in different time periods. In dynamic environments, the features of data change over time, making it difficult to identify cross-temporal links by directly comparing observed data. Other examples of cross-temporal links are asynchronous communications in social networks such as Facebook and Twitter, where a message is posted in reply to a previous message. We adopt a dimension reduction approach to cross-temporal link prediction; that is, data objects in different time frames are mapped into a common low-dimensional latent feature space, and the links are identified on the basis of the distance between the data objects. The proposed method uses different low-dimensional feature projections in different time frames, enabling it to adapt to changes in the latent features over time. Using multi-task learning, it jointly learns a set of feature projection matrices from the training data, given the assumption of temporal smoothness of the projections. The optimal solutions are obtained by solving a single generalized eigenvalue problem. Experiments using a real-world set of bibliographic data for cross-temporal entity resolution and a real-world set of emails for unobserved asynchronous communication inference showed that introducing time-dependent feature projections improved the accuracy of link prediction.

A method for spatio-temporal resolution enhancement of video sequences based on super-resolution reconstruction is proposed. A new observation model is defined for accurate resolution enhancement, which enables subpixel motion in intermediate frames to be obtained. A modified optimization formula for obtaining a high-resolution sequence is also adopted.

In this paper, we propose a high accuracy motion estimation method based on the spatio-temporal gradient method using high frame-rate images. In the method, we adopt spatial gradients with low estimated errors by the previous motion vectors. In addition, we evaluate the proposed method and confirm the effectiveness. Finally, we apply the method to super-resolution as an application of the proposed method.

The authors have proposed Multi-Input Multi-Output (MIMO)-Constant Envelope Modulation, (MIMO-CEM), as a power and complexity efficient alternative to MIMO-OFDM, suitable for wireless backhaul networks in which relay nodes are fixed in their positions. One of the major problems hindering the real application of MIMO-CEM is to estimate MIMO channel characteristics. MIMO-CEM is based upon two contrary schemes; one is nonlinear equalization such as maximum likelihood sequence estimator, which needs accurate channel information to replicate the received signal passing through it. The other is a low resolution analog-to-digital converter (ADC), e.g., 1-bit in the default operation that removes the received signal amplitude fluctuation. In this paper, as a solution to the channel estimation problem in MIMO-CEM with low resolution ADC receiver, we propose an adaptive MIMO-CEM channel estimation scheme where iterative adaptive channel estimation is carried out to minimize the error between the received preamble signal and the replicated one. We also prove that Code Division Multiplexing (CDM) preamble transmission is effective in estimating MIMO channel parameters in the presence of large quantization noise. Computer simulation results show that MIMO-CEM with the proposed channel estimator using CDM preambles achieves identical BER performance to that with the ideal channel estimation even in presence of severe quantization noise caused by a low resolution ADC.

A hybrid buffer structured optical packet switch and its scheduling algorithm are presented for a limited number of tunable wavelength convertors (TWCs) and internal wavelengths. The hybrid buffer consists of the fiber delay line (FDL) buffer and the electronic buffer. With the proposed algorithm, it could lead realizable packet loss reduction that the LAUC-VF algorithm with only the FDL buffer does not reach. Also, we optimized the number of TWCs and internal wavelengths of the hybrid buffer structured OPS. For the fully shared TWC structure, the optimum number of TWCs and internal wavelengths to guarantee minimum packet loss is evaluated to prevent resource waste under the hybrid buffer.

Dynamic Framed Slotted ALOHA (DFSA) is one of the most popular protocols to resolve tag collisions in RFID systems. In DFSA, it is widely known that the optimal performance is achieved when the frame size is equal to the number of tags. So, a reader dynamically adjusts the next frame size according to the current number of tags. Thus it is important to estimate the number of tags exactly. In this paper, we propose a novel tag estimation and identification method using litmus (test) slots for DFSA. We compare the performance of the proposed method with those of existing methods by analysis. We conduct simulations and show that our scheme improves the speed of tag identification.

Based on Turbo-Decoding Message-Passing (TDMP) and Normalized Min-Sum (NMS) algorithm, an area efficient LDPC decoder that supports both structured and unstructured LDPC codes is proposed in this paper. We introduce a solution to solve the memory access conflict problem caused by TDMP algorithm. We also arrange the main timing schedule carefully to handle the operations of our solution while avoiding much additional hardware consumption. To reduce the memory bits needed, the extrinsic message storing strategy is also optimized. Besides the extrinsic message recover and the accumulate operation are merged together. To verify our architecture, a LDPC decoder that supports both China Multimedia Mobile Broadcasting (CMMB) and Digital Terrestrial/ Television Multimedia Broadcasting (DTMB) standards is developed using SMIC 0.13 µm standard CMOS process. The core area is 4.75 mm2 and the maximum operating clock frequency is 200 MHz. The estimated power consumption is 48.4 mW at 25 MHz for CMMB and 130.9 mW at 50 MHz for DTMB with 5 iterations and 1.2 V supply.

To reconstruct low-resolution facial photographs which are in focus and without motion blur, a novel algorithm based on local similarity preserving is proposed. It is based on the theories of local manifold learning. The innovations of the new method include mixing point-based entropy and Euclidian distance to search for the nearest points, adding point-to-patch degradation model to restrict the linear weights and compensating the fusing patch to keep energy coherence. The compensation reduces the algorithm dependence on training sets and keeps the luminance of reconstruction constant. Experiments show that our method can effectively reconstruct 1612 images with the magnification of 88 and the 3224 facial photographs in focus and without motion blur.

We give characterizations of stable scaling functions with compact band regions, which have the oversampling property.

A monopulse angle measurement method is often utilized to measure a target angle. However, this method cannot measure correct angles for multiple targets which cannot be distinguished by range, Doppler frequency and beamwidth. When the number of targets which cannot be distinguished by these parameters is restricted to two, a method which can measure two targets angles has been proposed. However, an approximation is utilized with this method, so that measured angles have errors even though the signal-to-noise ratio is infinite. Another method which can simultaneously measure azimuths and elevations for only two targets has also been proposed. However, this conventional method requires four receivers, and is therefore difficult to apply when there is a hard ware limitation. In this paper, we propose a method to measure azimuths and elevations of two targets by using two receivers and a time division system. A pairing problem has occurred due to the time division angle measurement with this method, so we also propose an algorithm to solve this pairing problem. We finally verify the proposed method by a numerical simulation and experimentation. The results show that the angles of two targets can be measured by our proposed method by using two receivers.

Concomitantly with the progress of wireless communications, cognitive radio has attracted attention as a solution for depleted frequency bands. Cognitive radio is suitable for wireless sensor networks because it reduces collisions and thereby achieves energy-efficient communication. To make cognitive radio practical, we propose a low-power multi-resolution spectrum sensing (MRSS) architecture that has flexibility in sensing frequency bands. The conventional MRSS scheme consumes much power and can be adapted only slightly to process scaling because it comprises analog circuits. In contrast, the proposed architecture carries out signal processing in a digital domain and can detect occupied frequency bands at multiple resolutions and with low power. Our digital MRSS module can be implemented in 180-nm and 65-nm CMOS processes using Verilog-HDL. We confirmed that the processes respectively dissipate 9.97 mW and 3.45 mW.

This paper presents the design and implementation of a supply and process-insensitive 12-bit Digital Pulse Width Modulator (DPWM) for digital DC-DC converters. The DPWM is realized by a ring oscillator-based segmented tapped delay line and a counter-comparator. The number of delay cells required is reduced by employing a proposed delay cell reuse technique. The ring oscillator of the tapped delay line is made insensitive to supply and process variation by biasing the differential delay cells with a supply-insensitive replica bias circuit. Simulation results show that the variation of the switching frequency of the DPWM at 1.02 MHz is 0.4% for supply voltage variation between 1.5 V and 2.5 V and 0.95% over the temperature range from -40 to 90. Monte-Carlo simulation was also performed to account for the effect of mismatch between the transistors of the ring oscillator. The worst case delay of the delay cells is 0.87% for 5% (3-σ) mismatch. The design was fabricated in CMOS 0.18 µm process and the fabricated DPWM achieved a supply sensitivity of 0.82% and a current consumption of 14 µA.