This work addresses two key topics in the field of energy harvesting and wireless power transfer. The first is the optimum signal design for improved RF-DC conversion efficiency in rectifier circuits by using time varying envelope signals. The second is the design of rectifiers that present reduced sensitivity to input power and output load variations by introducing resistance compression network (RCN) structures.

In order to efficiently drive a low-power DC motor using microwave power transfer (MPT), a compact power-receiving device is developed, which consists of a rectenna array and an improved DC-DC converter with constant input resistance characteristics. Since the conversion efficiency of the rectenna is strongly affected by the output load, it is difficult to efficiently drive a dynamic load resistance device such as DC motor. Using both continuous-wave (CW) and pulsed-wave MPT, experiments are carried out on driving the DC motor whose load resistance is varying from 36 to 140 Ω. In the CW case, the measured overall efficiency of the power-receiving device is constant over 50% for the power density of 0.25 to 2.08 mW/cm2. In particular, the overall efficiency is 62%, 70.8% for the power density of 0.25, 0.98 mW/cm2 where the received power of the single antenna is 13, 50 mW, respectively. In the pulsed-wave case, the measured overall efficiency is over 44% for a duty ratio of 0.2 to 1 for the power density of 0.98 mW/cm2.

Time redundancy is sometimes an only option for enhancing circuit reliability when the circuit area is severely restricted. In this paper, a time-redundant error-correction scheme, which is particularly suitable for coarse-grained reconfigurable arrays (CGRAs), is proposed. It judges the correctness of the executions by comparing the results of two identical runs. Once a mismatch is found, the second run is terminated immediately to start the third run, under the assumption that the errors tend to persist in many applications, for selecting the correct result in the three runs. The circuit area and reliability of the proposed method is compared with a straightforward implementation of time-redundancy and a selective triple modular redundancy (TMR). A case study on a CGRA revealed that the area of the proposed method is 1% larger than that of the implementation for the selective TMR. The study also shows the proposed scheme is up to 2.6x more reliable than the full-TMR when the persistent error is predominant.

A cascaded video denoising method based on frame averaging is proposed in this paper. A novel segmentation approach using intensity and structure tensor is used for change compensation, which can effectively suppress noise while preserving the structure of an image. The cascaded framework solves the problem of noise residual caused by single-frame averaging. The classical Wiener filter is used for spatial denoising in changing areas. Our algorithm works in real-time on an FPGA, since it does not involve future frames. Experiments on standard grayscale videos for various noise levels demonstrate that the proposed method is competitive with current state-of-the-art video denoising algorithms on both peak signal-to-noise ratio and structural similarity evaluations, particularly when dealing with large-scale noise.

We propose a fully automatic method for detecting the carotid artery from volumetric ultrasound images as a preprocessing stage for building three-dimensional images of the structure of the carotid artery. The proposed detector utilizes support vector machine classifiers to discriminate between carotid artery images and non-carotid artery images using two kinds of LBP-based features. The detector switches between these features depending on the anatomical position along the carotid artery. We evaluate our proposed method using actual clinical cases. Accuracies of detection are 100%, 87.5% and 68.8% for the common carotid artery, internal carotid artery, and external carotid artery sections, respectively.

We evaluated software maintenance of an open source cloud platform system we developed using an agile software development method. We previously reported on a rapid service launch using the agile software development method in spite of large-scale development. For this study, we analyzed inquiries and the defect removal efficiency of our recently developed software throughout one-year operation. We found that the defect removal efficiency of our recently developed software was 98%. This indicates that we could achieve sufficient quality in spite of large-scale agile development. In term of maintenance process, we could answer all enquiries within three business days and could conduct version-upgrade fast. Thus, we conclude that software maintenance of agile software development is not ineffective.

In the localization systems based on time difference of arrival (TDOA), multipath fading and the interference source will deteriorate the localization performance. In response to this situation, TDOA estimation based on blind beamforming is proposed in the frequency domain. An additional constraint condition is designed for blind beamforming based on maximum power collecting (MPC). The relationship between the weight coefficients of the beamformer and TDOA is revealed. According to this relationship, TDOA is estimated by discrete Fourier transform (DFT). The efficiency of the proposed estimator is demonstrated by simulation results.

Using traditional single-layer dictionary learning methods, it is difficult to reveal the complex structures hidden in the hyperspectral images. Motivated by deep learning technique, a deep dictionary learning approach is proposed for hyperspectral image denoising, which consists of hierarchical dictionary learning, feature denoising and fine-tuning. Hierarchical dictionary learning is helpful for uncovering the hidden factors in the spectral dimension, and fine-tuning is beneficial for preserving the spectral structure. Experiments demonstrate the effectiveness of the proposed approach.

It is unclear whether Hidden Markov Model (HMM) or Dynamic Time Warping (DTW) mapping is more appropriate for visual speech recognition when only small data samples are available. In this letter, the two approaches are compared in terms of sensitivity to the amount of training samples and computing time with the objective of determining the tipping point. The limited training data problem is addressed by exploiting a straightforward template matching via weighted-DTW. The proposed framework is a refined DTW by adjusting the warping paths with judicially injected weights to ensure a smooth diagonal path for accurate alignment without added computational load. The proposed WDTW is evaluated on three databases (two in the public domain and one developed in-house) for visual recognition performance. Subsequent experiments indicate that the proposed WDTW significantly enhances the recognition rate compared to the DTW and HMM based algorithms, especially under limited data samples.

In this paper the amplitude probability distribution (APD) measurement method is applied to evaluate noise coupling to an antenna on an evaluation board that uses mixed RF and digital signals of an IC. We analytically investigate noise coupling path to the antenna where the correlation coefficient matches the APD curve of the evaluation board. Moreover, in order to verify the analysis results, the noise coupling path in the board is evaluated by measurements involving In-phase/Quadrature (I/Q) signals as well as electromagnetic simulations. As a result, we demonstrate that APD method is effective in evaluating a degree of noise coupling from an IC to multiple antennas on the board, and confirm that the intensity of noise coupling to each antenna is affected greatly by the board layout patterns.

This paper proposes reflection and transmission control panels using artificially designed materials. As the artificially designed material, finite- and infinite-length metal wire array sheets are used here. Laminated structures consisting of the metal wire array sheets and dielectric material are proposed. Reflection and transmission characteristics of these structures can be controlled by changing the metal wire parameters such as wire length, spacing gaps between the wires, and the dielectric material's thickness and relative permittivity. The reflection and transmission characteristics of the laminated structures are evaluated by measurements in free space and by transmission line theory.

With the development of wireless technologies, wireless relay systems have become a popular topic. To design practical wireless relay systems, link adaptation is an important technique. Because there are both broadcast and multiple access channels in wireless relay systems, link adaptation is difficult to design and hence the optimal throughput is hard to achieve. In this study, a novel method is proposed to maximize the system throughput of wireless relay systems by utilizing the most popular link adaptation methods, adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ). The proposed method utilizes the characteristics and operations of AMC and HARQ to adaptively adjust the thresholds for selecting modulation and coding scheme (MCS) to be used. Thus the system can keep tracking the optimal values of the thresholds. Therefore, the system throughput can be maximized. We set up simulations for different relay environment settings, such as different relay HARQ protocols, placements, and multiplexing schemes, to verify the capability of the proposed method. The simulation results show that, compared to the existing method, the proposed method indeed improves system throughput under a variety of relay settings and can be easily applied to different system platforms.

Electromagnetic analysis (EMA) against public-key cryptographic software on an embedded OS is presented in this paper. First, we propose a method for finding an observation point for EMA, where the EM radiation caused by cryptographic operations can be observed with low noise. The basic idea is to find specific EM radiation patterns produced by cryptographic operations given specific input pattern. During the operations, we scan the surface of the target device(s) with a micro magnetic probe. The scan is optimized in advanced using another compatible device that has the same central processing unit (CPU) and OS as the target device. We demonstrate the validity of the proposed EMAs through some EMA experiments with two types of RSA software on an embedded OS platform. The two types of RSA software have different implementations for modular multiplication algorithms: one is a typical and ready-made implementation using BigInteger class on Java standard library, and another is a custom-made implementation based on the Montgomery multiplication algorithm. We conduct experiments of chosen-message EMA using our scanning method, and show such EMAs successfully reveal the secret key of RSA software even under the noisy condition of the embedded OS platform. We also discuss some countermeasures against the above EMAs.

In this paper, a new parameter estimator for coherently distributed (CD) noncircular (NC) signals is proposed, and can estimate both the central direction-of-arrivals (DOAs) and the angular spreads. It can also be considered as an extended version of the generalized Capon method by using both covariance matrix and an elliptic covariance matrix. The central DOAs and angular spreads are obtained by two-dimensional spectrum-peak searching. Numerical examples illustrate that the proposed method can estimate the central DOAs and the angular spreads when the number of signals is greater than the number of sensors. The proposed method also offers better performance than the methods against which it is compared.

The deployment of small cells is one of the most effective means to cope with the traffic explosion of cellular mobile systems. However, a small cell system increases the inter-cell interference, which limits the capacity and degrades the cell-edge user throughput. Inter-cell interference coordination (ICIC), such as fractional frequency reuse (FFR), is a well-known scheme that autonomously mitigates inter-cell interference. In the Long Term Evolution (LTE)-Advanced, the three-dimensional (3D) beamforming, which combines conventional horizontal beamforming and vertical beamforming, has been gaining increasing attention. This paper proposes a novel centralized ICIC scheme that controls the direction of narrow 3D beam for each frequency band of each base station. The centralized controller collects information from the base stations and calculates sub-optimum combinations of narrow beams so as to maximize the proportional fair (PF) utility of all users. This paper describes the throughput of the new centralized ICIC scheme as evaluated by computer simulations and shows it has a significant gain in both average user throughput and cell-edge user throughput compared with the conventional ICIC scheme. This paper also investigates the feasibility of the scheme by assessing its throughput performance in a realistic deployment scenario.

A 32bit CPU, which can operate more than 15 years with 220mAH Li battery, or eternally operate with an energy harvester of in-door light is presented. The CPU was fabricated by using 65nm SOTB CMOS technology (Silicon on Thin Buried oxide) where gate length is 60nm and BOX layer thickness is 10nm. The threshold voltage was designed to be as low as 0.19V so that the CPU operates at over threshold region, even at lower supply voltages down to 0.22V. Large reverse body bias up to -2.5V can be applied to bodies of SOTB devices without increasing gate induced drain leak current to reduce the sleep current of the CPU. It operated at 14MHz and 0.35V with the lowest energy of 13.4 pJ/cycle. The sleep current of 0.14µA at 0.35V with the body bias voltage of -2.5V was obtained. These characteristics are suitable for such new applications as energy harvesting sensor network systems, and long lasting wearable computers.

As energy consumption of cache memories increases, an energy-efficient cache management mechanism is required. While a dynamic cache resizing mechanism is one promising approach to the energy reduction of microprocessors, one problem is that its effect is limited by the existence of dead-on-fill blocks, which are not used until their evictions from the cache memory. To solve this problem, this paper proposes a cache management policy named FLEXII, which can reduce the number of dead-on-fill blocks and help dynamic cache resizing mechanisms further reduce the energy consumption of the cache memories.

In this paper, we propose a method for reconstructing 3D sequential patterns from multiple images without knowing exact image correspondences and without calibrating linear camera sensitivity parameters on intensity. The sequential pattern is defined as a series of colored 3D points. We assume that the series of the points are obtained in multiple images, but the correspondence of individual points is not known among multiple images. For reconstructing sequential patterns, we consider a camera projection model which combines geometric and photometric information of objects. Furthermore, we consider camera projections in the frequency space. By considering the multi-view relationship on the new projection model, we show that the 3D sequential patterns can be reconstructed without knowing exact correspondence of individual image points in the sequential patterns; moreover, the recovered 3D patterns do not suffer from changes in linear camera sensitivity parameters. The efficiency of the proposed method is tested using real images.

In this Letter, we investigate the outage performance of MIMO amplify-and-forward (AF) multihop relay networks with maximum ratio transmission/receiver antenna selection (MRT/RAS) over Nakagami-m fading channels in the presence of co-channel interference (CCI) or not. In particular, the lower bounds for the outage probability of MIMO AF multihop relay networks with/without CCI are derived, which provides an efficient means to evaluate the joint effects of key system parameters, such as the number of antennas, the interfering power, and the severity of channel fading. In addition, the asymptotic behavior of the outage probability is investigated, and the results reveal that the full diversity order can be achieved regardless of CCI. In addition, simulation results are provided to show the correctness of our derived analytical results.

Texture feature descriptors such as local binary patterns (LBP) have proven effective for ground-based cloud classification. Traditionally, these texture feature descriptors are predefined in a handcrafted way. In this paper, we propose a novel method which automatically learns discriminative features from labeled samples for ground-based cloud classification. Our key idea is to learn these features through mutual information maximization which learns a transformation matrix for local difference vectors of LBP. The experimental results show that our learned features greatly improves the performance of ground-based cloud classification when compared to the other state-of-the-art methods.