Homemade cooking plays a key role for a healthy and cost-efficient life. Unfortunately, preparing multiple dishes is generally time-consuming. In this paper, an algorithm is proposed to minimize the cooking time by scheduling the cooking-step of multiple dishes. The cooking procedure of a dish is divided into a sequence of six types of cooking-steps to consider the constraints in cooks and cooking utensils in a kitchen. A cooking model is presented to optimize the cooking-step schedule and estimate the cooking time for a given starting order of dishes under various constraints of cooks and utensils. Then, a high-quality schedule is sought by repeating the generation of a new order and the model application based on exhaustive search and simulated annealing. Our simulation results and cooking experiments confirm the effectiveness of our proposal.

This paper deals with the small-scale fading distribution for UWB channels in the absence and presence of human bodies in indoor line-of-sight (LOS) environments and performance analysis of UWB systems considering the small-scale fading distribution. To obtain small-scale fading statistics, the channel measurements are performed in five representative environments that have different structure and size while locating the receiver (Rx) antenna on 49 (7×7 grid) local points with a fixed transmitter (Tx) antenna in each environment. The measured channel data are processed by a vector network analyzer and the target frequency bands range from 3 to 4.6GHz. From the measured data, we find the best fitted channel model among several typical theoretical distribution models such as Lognormal, Nakagami, and Weibull distributions, showing good agreement with the empirical channel data. We analyze the amplitude variation of the small-scale fading distribution in the absence and presence of human bodies. The results show that the small-scale fading statistics are best described by Weibull distribution and the two parameters of the distribution that determine the shape and the scale of the distribution depend on whether or not human bodies exist. We modeled and analyzed two parameters at different excess delays for all environments. Based on the measured small-scale fading distribution, this paper deals with the performance of UWB system using Rake receivers and also compares the performance with the existing channel model. The results suggest that the small-scale fading distribution in the absence and the presence of human bodies in indoor LOS environments should be considered when assessing the performance of UWB systems.

We demonstrated that load balancing using actual subscriber extension numbers was practical and effective against traffic congestion after a disaster based on actual data. We investigated the ratios of the same subscriber extension numbers in each prefecture and found that most of them were located almost evenly all over the country without being concentrated in a particular area. The ratio of every number except for the fourth-last digit in the last group of four numbers in a telephone number was used almost equally and located almost evenly all over the country. Tolerance against overload in the last, second-, and third-last single digits stays close to that in the ideal situation if we assume that each session initiation protocol server has a capacity in accordance with the ratio of each number on every single digit in the last group of four numbers in Japan. Although tolerance against overload in double-, triple-, and quadruple-digit numbers does not stay close to that in the ideal situation, it still remains sufficiently high in the case of double- and triple-digit numbers. Although tolerance against overload in the quadruple-digit numbers becomes low, disaster congestion is still not likely to occur in almost half of the area of Japan (23 out of 47 prefectures).

In this Letter, a new iris recognition approach based on local Gabor orientation feature is proposed. On one hand, the iris feature extraction method using the traditional Gabor filters can cause time-consuming and high-feature dimension. On the other hand, we can find that the changes of original iris texture in angle and radial directions are more obvious than the other directions by observing the iris images. These changes in the preprocessed iris images are mainly reflected in vertical and horizontal directions. Therefore, the local directional Gabor filters are constructed to extract the horizontal and vertical texture characteristics of iris. First, the iris images are preprocessed by iris and eyelash location, iris segmentation, normalization and zooming. After analyzing the variety of iris texture and 2D-Gabor filters, we construct the local directional Gabor filters to extract the local Gabor features of iris. Then, the Gabor & Fisher features are obtained by Linear Discriminant Analysis (LDA). Finally, the nearest neighbor method is used to recognize the iris. Experimental results show that the proposed method has better iris recognition performance with less feature dimension and calculation time.

Non-orthogonal multiple access (NOMA) utilizing the power domain and advanced receiver has been considered as one promising multiple access technology for further cellular enhancements toward the 5th generation (5G) mobile communications system. Most of the existing investigations into NOMA focus on the combination of NOMA with orthogonal frequency division multiple access (OFDMA) for either downlink or uplink. In this paper, we investigate NOMA for uplink with single carrier-frequency division multiple access (SC-FDMA) being used. Differently from OFDMA, SC-FDMA requires consecutive resource allocation to a user equipment (UE) in order to achieve low peak to average power ratio (PAPR) transmission by the UE. Therefore, sophisticated designs of scheduling algorithm for NOMA with SC-FDMA are needed. To this end, this paper investigates the key issues of uplink NOMA scheduling such as UE grouping method and resource widening strategy. Because the optimal schemes have high computational complexity, novel schemes with low computational complexity are proposed for practical usage for uplink resource allocation of NOMA with SC-FDMA. On the basis of the proposed scheduling schemes, the performance of NOMA is investigated by system-level simulations in order to provide insights into the suitability of using NOMA for uplink radio access. Key issues impacting NOMA performance are evaluated and analyzed, such as scheduling granularity, UE number and the combination with fractional frequency reuse (FFR). Simulation results verify the effectiveness of the proposed algorithms and show that NOMA is a promising radio access technology for 5G systems.

In this paper, a threshold-based I-Q diversity combining scheme for ultra-high frequency (UHF) radio frequency identification (RFID) readers with a quadrature receiver is proposed in the aspect of improving the tag detection performance. In addition, the performance of the proposed scheme is evaluated as the closed-form expressions. In particular, its statistical characteristics are detailed and its performance is compared to that of conventional schemes over independent and identically distributed Rician fading conditions in terms of average signal-to-noise ratio (SNR), bit error rate (BER), and the average number of required combining process. Numerical results indicate that the proposed scheme enables processing power control through threshold control while meeting the required quality of service compared to conventional schemes.

The Earth's ionosphere can hinder radio propagation with two serious problems: group delay and phase advance. Ionospheric irregularities are significantly troublesome since they make the amplitude and phase of the radio signals fluctuate rapidly, which is known as ionospheric scintillation. Severe ionospheric scintillation could cause loss of phase lock, which would degrade the positioning accuracy and affect the performance of navigation systems. Based on the phase screen model, this paper presents a novel power spectrum model of phase scintillation and a model of amplitude scintillation. Preliminary results show that, when scintillation intensity increases, the random phase and amplitude fluctuations become stronger, coinciding with the observations. Simulations of the scintillation effects on the acquisition of Beidou signals predict acquisition probability. In addition, acquisition probabilities of GPS and Beidou signals under different scintillation intensities are presented. And by the same SNR the acquisition probability decreases when the scintillation intensity increases. The simulation result shows that scintillation could cause the loss of the acquisition performance of Beidou navigation system. According to the comparison of Beidou and GPS simulations, the code length and code rate of satellite signals have an effect on the acquisition performance of navigation system.

Recently, Intelligent Transport Systems (ITS) are being researched and developed briskly. As a part of ITS, detecting vehicles from images taken by a camera loaded on a vehicle are conducted. From such backgrounds, authors have been conducting vehicle detection in nighttime. To evaluate the accuracy of this detection, gold standards of the detection are required. At present, gold standards are created manually, but manually detecting vehicles take time. Accordingly, a system which detects vehicles accurately without human help is needed to evaluate the accuracy of the vehicle detection in real time. Therefore the purpose of this study is to automatically detect vehicles in nighttime images, taken by an in-vehicle camera, with high accuracy in offline processing. To detect vehicles we focused on the brightness of the headlights and taillights, because it is difficult to detect vehicles from their shape in nighttime driving scenes. The method we propose uses Center Surround Extremas, called CenSurE for short, to detect blobs. CenSurE is a method that uses the difference in brightness between the lights and the surroundings. However, blobs obtained by CenSurE will also include objects other than headlights and taillights. For example, streetlights and delineators would be detected. To distinguish such blobs, they are tracked in inverse time and vehicles are detected using tags based on the characteristics of each object. Although every object appears from the same point in forward time process, each object appears from different places in images in inverse time processing, allowing it to track and tag blobs easily. To evaluate the effectiveness of this proposed method, experiment of detecting vehicles was conducted using nighttime driving scenes taken by a camera loaded on a vehicle. Experimental results of the proposed method were nearly equivalent to manual detection.

Multi-hop networks have been proposed to increase the data transmission rate in wireless mobile networks, and consequently improve the quality of experience of cell-edge users. A successive resource allocation scheme (SAS) has been proposed for a 2-hop virtual cellular network (VCN). In a multi-cell environment, the performance of SAS degrades because of intra-cell and inter-cell interference. In order to alleviate the effect of intra-cell and inter-cell interference and consequently increase the channel capacity of the VCN, this paper proposes the sequential iterative allocation scheme (SIS). Computer simulation results show that, compared to SAS, SIS can improve the fairness, the ergodic, and the outage channel capacity per mobile terminal (MT) of the VCN in a multi-cell environment. This paper also analyzes the performance of the VCN compared to that of the single hop network (SHN) when SIS is applied in a multi-cell environment. Using SIS, VCN can provide higher ergodic channel capacity, and better degree of fairness than SHN in a multi-cell environment. The effect of the number of wireless ports (WPs) in the VCN is also investigated, and the results suggest that adding more WPs per virtual cell in the VCN can enhance the outage channel capacity per MT and the degree of fairness of the VCN.

This paper presents a cell-based all-digital phase-locked loop (ADPLL) with hierarchical gated digitally controlled oscillator (G-DCO) for low voltage operation, wide frequency range as well as low-power consumption. In addition, a new time-domain hierarchical frequency estimation algorithm (HFEA) for frequency acquisition is proposed to estimate the output frequency in 1.5MF (MF = 3 in this paper) cycles and this fast lock-in time is suitable to the dynamic voltage frequency scaling (DVFS) systems. A hierarchical G-DCO is proposed to work at low supply voltage to reduce the power consumption and at the same time to achieve wide frequency range and precise frequency resolution. The core area of the proposed ADPLL is 0.02635 mm2. In near-threshold region (VDD = 0.36 V), the proposed ADPLL only dissipates 68.2 µW and has a rms period jitter of 1.25% UI at 60 MHz output clock frequency. Under 0.5 V VDD operation, the proposed ADPLL dissipates 404.2 µW at 400 MHz. The fast lock-in time of 4.489 µs and the low jitter performance below 0.5% UI at 400 MHz output clock frequency in the proposed ADPLL are suitable in event-driven or DVFS applications.

In Worldwide interoperability for Microwave Access (WiMAX) network, QoS(quality of service) is provided for service flows. For this, five classes of services are defined in IEEE 802.16. They are Unsolicited Grant Service (UGS), Extended Real-Time Polling Service (ertPS), Real-Time Polling Service (rtPS), Non Real-Time Polling Service (nrtPS) and Best Effort (BE). For real-time classes, the sent packet has a deadline. As the transmission delay is over the limitation of deadline, the packet becomes useless and will be discarded. Thus, they will be served earlier and have higher probability. Nevertheless, non-real-time packets need also to be served from time to time. The scheduler should assign proper bandwidth for non-real-time flows and send the real-time packets before they are discarded. To deicide the right allocated bandwidth, the arrival rate of each flow is a good parameter for assignment. The average µ and standard deviation σ of arrival rate correspond to the long term need and variation of load for one flow. Thus, we proposed a scheduling algorithm named BAcSOA in which µ+kσ is used as a reference to allocate bandwidth with weighted round robin for one flow [5]. Different classes of flows will be given different values of k which corresponds to the priorities of classes. In this algorithm, flow with higher priority should have larger value of k. The value of k will decide the performance of this class. In this paper, we revise the algorithm to EBAcSOA and propose a mathematical way to decide the value of k for a required performance. Then, a simulation platform is proposed to decide k such that a required performance can be obtained for an operating system. This approach may be different from other researches in which there is no required performance and the performance results are obtained only for several operating points. However, the approach proposed is more practical from the view of an operator and may become an attractive point for other researchers.

This paper investigates active period selection for cluster-based WSNs employing traffic adaptive IEEE 802.15.4 beacon enabled medium access control (MAC) under spatial non-uniform traffic and cluster mobility environments, and proposes a simple autonomous distributed superframe duration (SD) selection scheme to enhance network performance under such environments. The proposed scheme autonomously selects an active SD at each cluster head (CH) by using beacon reception power monitoring (conducted in distributed control manner) and also introduces a beacon status notice from sensor nodes (SNs) to their parent CHs in order to prevent unnecessary SD selection at CHs. Moreover, SD reuse mechanism and joint operation with previously proposed distributed backoff mechanism are proposed for the proposed SD selection scheme to further enhance the network performance. The results of computer simulation show that the proposed scheme can improve both the transmission and power efficiency performance of cluster-based WSNs under spatial non-uniform traffic and cluster mobility environments.

This paper proposes a Poisson denoising method with a union of directional lapped orthogonal transforms (DirLOTs). DirLOTs are 2-D non-separable lapped orthogonal transforms with directional characteristics under the fixed-critically-subsampling, overlapping, orthonormal, symmetric, real-valued and compact-support property. In this work, DirLOTs are used to generate symmetric orthogonal discrete wavelet transforms and then a redundant dictionary as a union of unitary transforms. The multiple directional property is suitable for representing natural images which contain diagonal textures and edges. Multiple DirLOTs can overcome a disadvantage of separable wavelets in representing diagonal components. In addition to this feature, multiple DirLOTs make transform-based denoising performance better through the redundant representation. Experimental results show that the combination of the variance stabilizing transformation (VST), Stein's unbiased risk estimator-linear expansion of threshold (SURE-LET) approach and multiple DirLOTs is able to significantly improve the denoising performance.

Breast cancer is a serious disease across the world, and it is one of the largest causes of cancer death for women. The traditional diagnosis is not only time consuming but also easily affected. Hence, artificial intelligence (AI), especially neural networks, has been widely used to assist to detect cancer. However, in recent years, the computational ability of a neuron has attracted more and more attention. The main computational capacity of a neuron is located in the dendrites. In this paper, a novel neuron model with dendritic nonlinearity (NMDN) is proposed to classify breast cancer in the Wisconsin Breast Cancer Database (WBCD). In NMDN, the dendrites possess nonlinearity when realizing the excitatory synapses, inhibitory synapses, constant-1 synapses and constant-0 synapses instead of being simply weighted. Furthermore, the nonlinear interaction among the synapses on a dendrite is defined as a product of the synaptic inputs. The soma adds all of the products of the branches to produce an output. A back-propagation-based learning algorithm is introduced to train the NMDN. The performance of the NMDN is compared with classic back propagation neural networks (BPNNs). Simulation results indicate that NMDN possesses superior capability in terms of the accuracy, convergence rate, stability and area under the ROC curve (AUC). Moreover, regarding ROC, for continuum values, the existing 0-connections branches after evolving can be eliminated from the dendrite morphology to release computational load, but with no influence on the performance of classification. The results disclose that the computational ability of the neuron has been undervalued, and the proposed NMDN can be an interesting choice for medical researchers in further research.

This paper shows that the induced peak voltage on the short monopole antenna by the EM field radiated from a small gap discharge when the gap width was experimentally changed from 10 to 360µm was not directly proportional to the discharge voltage between the gap. It was found that the 10mm short monopole antenna induced peak voltage had a peak value between 40 and 60µm gap width.

MapReduce and its open software implementation Hadoop are now widely deployed for big data analysis. As MapReduce runs over a cluster of massive machines, data transfer often becomes a bottleneck in job processing. In this paper, we explore the influence of data transfer to job processing performance and analyze the mechanism of job performance deterioration caused by data transfer oriented congestion at disk I/O and/or network I/O. Based on this analysis, we update Hadoop's Heartbeat messages to contain the real time system status for each machine, like disk I/O and link usage rate. This enhancement makes Hadoop's scheduler be aware of each machine's workload and make more accurate decision of scheduling. The experiment has been done to evaluate the effectiveness of enhanced scheduling methods and discussions are provided to compare the several proposed scheduling policies.

This paper provides a method based on electromagnetic (EM) analysis to predict conducted currents in the bulk current injection (BCI) test system for automotive components. The BCI test system is comprised of an injection probe, equipment under test (EUT), line impedance stabilization networks (LISNs), wires and an electric load. All components are modeled in full-wave EM analysis. The EM model of the injection probe enables us to handle multi wires. By using the transmission line theory, the BCI setup model is divided into several parts in order to reduce the calculation time. The proposed method is applied to an actual BCI setup of an automotive component and the simulated common mode currents at the input terminals of EUT have a good accuracy in the frequency range of 1-400MHz. The model separation reduces the calculation time to only several hours.

An adaptively phase-shift controlled self-injection locked VCO is described. A self-injection locking technique is effective to reduce phase noise. However, a conventional self-injection locked VCO has drawbacks of discontinuous frequency sweep which means narrow bandwidth, and large variation of phase noise. Our proposed adaptively phase-shift controlled self-injection locked VCO overcomes these drawbacks by detecting phase-shift of the self-injection feedback and controlling the phase-shift depending on sweep of the oscillation frequency. This paper describes analysis of relationships between the discontinuity and feedback phase-shift of the self-injection locked VCO. In addition, a VCO-IC which includes a Ka-band VCO and a phase detector is designed and fabricated in 0.18um SiGe BiCMOS technology. Measurement results of the proposed self-injection locked VCO using the fabricated IC show the improvement to the drawbacks. In the proposed self-injection locked VCO, the oscillation frequency sweep is continuous and the phase noise variation is less than 5 dB.

One of the most important processes in cellular-assisted device-to-device (D2D) communications is device discovery, which decides whether two devices are located close to each other. The discovery process is performed by devices periodically transmitting discovery signals so that neighbor devices can receive them to recognize their proximate physical presence. While a fixed set of discovery parameters are used regardless of devices in most of the existing works, discovery periods are not necessarily the same for all devices, as they can be set differently depending on their channel conditions and operational environments, e.g., the mobile speeds. In this paper, we present an optimization framework to determine the discovery periods for individual devices in cellular-assisted D2D communication systems. We consider two different types of optimization problems, taking the different user velocities into account: minimizing the average number of undiscovered device pairs, and minimizing the number of discovery signal transmissions while maintaining the average number of undiscovered device pairs for each device less than a pre-specified threshold. We present analytical and simulation results to demonstrate that short discovery periods can be beneficial to high-mobility devices, while longer discovery periods are allowed for devices with lower velocities.

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.