The amount of seasonings used during food preparation is quite important information for modern people to enable them to cook delicious dishes as well as to take care for their health. In this paper, we propose a near real-time automated system for measuring and recording the amount of seasonings used during food preparation. Our proposed system is equipped with two devices: electronic scales and a camera. Seasoning bottles are basically placed on the electronic scales in the proposed system, and the scales continually measure the total weight of the bottles placed on them. When a chef uses a certain seasoning, he/she first picks up the bottle containing it from the scales, then adds the seasoning to a dish, and then returns the bottle to the scales. In this process, the chef's picking and returning actions are monitored by the camera. The consumed amount of each seasoning is calculated as the difference in weight between before and after it is used. We evaluated the performance of the proposed system with experiments in 301 trials in actual food preparation performed by seven participants. The results revealed that our system successfully measured the consumption of seasonings in 60.1% of all the trials.

Many public cloud datacenters have adopted the Edge-Overlay model which supports virtual switch-based network virtualization using IP tunneling. However, software-implemented virtual switches can cause performance degradation because the packet processing load can concentrate on a particular CPU core. As a result, such load concentration decreases and destabilizes the performance of virtual networks. Although multi-queue functions like Receive Side Scaling (RSS) can distribute the load onto multiple CPU cores, they still have performance problems such as IRQ core collision between priority flows as well as competitive resource use between host and guest machines for received packet processing. In this paper, we propose Virtual Switch Extension (VSE) that adaptively determines CPU core assignment for SoftIRQ to prevent performance degradation. VSE supports two types of SoftIRQ core selection mechanisms, on-the-fly or predetermined. In the on-the-fly mode, VSE selects a SoftIRQ core based on current CPU load to exploit low-loaded CPU resources. In the predetermined mode, SoftIRQ cores are assigned in advance to differentiate the performance of priority flows. This paper describes a basic architecture and implementation of VSE and how VSE assigns a SoftIRQ cores. Moreover, we evaluate fundamental throughput of various CPU assignment models in the predetermined mode. Finally, we evaluate the performance of a priority VM in two VM usecases, the client-usecase which is receive-oriented and the router-usecase which performs bi-directional communications. In the client-usecase, the throughput of the priority VM was improved by 31% compared with RSS when the priority VM had one dedicated core. In the router-usecase, the throughput was improved by 29% when three dedicated cores were provided for the VM.

Crowd collectiveness, i.e., a quantitative metric for collective motion, has received increasing attention in recent years. Most of existing methods build a collective network by assuming each agent in the crowd interacts with neighbors within fixed radius r region or fixed k nearest neighbors. However, they usually use a universal r or k for different crowded scenes, which may yield inaccurate network topology and lead to lack of adaptivity to varying collective motion scenarios, thereby resulting in poor performance. To overcome these limitations, we propose a compressive sensing (CS) based method for measuring crowd collectiveness. The proposed method uncovers the connections among agents from the motion time series by solving a CS problem, which needs not specify an r or k as a priori. A descriptor based on the average velocity correlations of connected agents is then constructed to compute the collectiveness value. Experimental results demonstrate that the proposed method is effective in measuring crowd collectiveness, and performs on par with or better than the state-of-the-art methods.

The next generation high efficiency video coding (HEVC) standard achieves high performance by extending the encoding block to 64×64. There are some parallel tools to improve the efficiency for encoder and decoder. However, owing to the dependence of the current prediction block and surrounding block, parallel processing at CU level and Sub-CU level are hard to achieve. In this paper, focusing on the spatial motion vector prediction (SMVP) and temporal motion vector prediction (TMVP), parallel improvement for spatio-temporal prediction algorithms are presented, which can remove the dependency between prediction coding units and neighboring coding units. Using this proposal, it is convenient to process motion estimation in parallel, which is suitable for different parallel platforms such as multi-core platform, compute unified device architecture (CUDA) and so on. The simulation experiment results demonstrate that based on HM12.0 test model for different test sequences, the proposed algorithm can improve the advanced motion vector prediction with only 0.01% BD-rate increase that result is better than previous work, and the BDPSNR is almost the same as the HEVC reference software.

Nonnegative matrix factorization (NMF) is an unsupervised technique to represent nonnegative data as linear combinations of nonnegative bases, which has shown impressive performance for source separation. However, its source separation performance degrades when one signal can also be described well with the bases for the interfering source signals. In this paper, we propose a discriminative NMF (DNMF) algorithm which exploits the reconstruction error for the interfering signals as well as the target signal based on target bases. The objective function for training the bases is constructed so as to yield high reconstruction error for the interfering source signals while guaranteeing low reconstruction error for the target source signals. Experiments show that the proposed method outperformed the standard NMF and another DNMF method in terms of both the perceptual evaluation of speech quality score and signal-to-distortion ratio in various noisy environments.

This paper proposes a method that uses bistatic Multiple-Input Multiple-Output (MIMO) radar to locate living-bodies. In this method, directions of living-bodies are estimated by the MUltiple SIgnal Classification (MUSIC) method at the transmitter and receiver, where the Fourier transformed virtual Single-Input Multiple-Output (SIMO) channel matrix is used. Body location is taken as the intersection of the two directions. The proposal uses a single frequency and so has a great advantage over conventional methods that need a wide frequency band. Also, this method can be used in multipath-rich environments such as indoors. An experiment is performed in an indoor environment, and the MIMO channels yielded by various subject numbers and positions are measured. The result indicates that the proposed method can estimate multiple living-body locations with high accuracy, even in multipath environments.

Liquid crystal displays (LCDs) are suitable as elements underlying wearable and ubiquitous computing thanks to their low power consumption. A technique that uses less power to drive 1-pixel LCDs is proposed. It harvests the charges on the LCD and stores them in an external capacitor for reuse when the polarity changes. A simulation shows that the charge reduction depends on the ratio of the capacitance of the external capacitor to that of the LCD and can reach 50%. An experiment on a prototype demonstrates an almost 30% reduction with large 1-pixel LCDs. With a small 10 × 10mm2 LCD, the overhead of the micro-controller matches the reduction so no improvement could be measured. Though the technique requires longer time for polarity reversal, we confirm that it does not significantly degrade visual quality.

Although sparse coding has emerged as an extremely powerful tool for texture and image classification, it neglects the relationship of coding coefficients from the same class in the training stage, which may cause a decline in the classification performance. In this paper, we propose a novel coding strategy named compact sparse coding for ground-based cloud classification. We add a constraint on coding coefficients into the objective function of traditional sparse coding. In this way, coding coefficients from the same class can be forced to their mean vector, making them more compact and discriminative. Experiments demonstrate that our method achieves better performance than the state-of-the-art methods.

This paper presents an Adapting Block-Propagative Background Subtraction (ABPBGS) designed for Ultra High Definition Television (UHDTV) foreground detection. The main idea is to detect block after block along the objects in order to skip all areas of the image in which there is no moving object. This is particularly interesting for UHDTV when the objects of interest could represent not even 0.1% of the total area. From a seed block which is determined in a previous iteration, the detection will spread along an object as long as it detects a part of that object. A block history map guaranties that each block is processed only once. Moreover, only small blocks are loaded and processed, thus saving computational time and memory usage. The process of each block is independent enough to be easily parallelized. Compared to 9 state-of-the-art works, the ABPBGS achieved the best results with an average global quality score of 0.57 (1 being the maximum) on a dataset of 4K and 8K UHDTV sequences developed for this work. None of the state-of-the-art methods could process 4K videos in reasonable time while the ABPBGS has shown an average speed of 5.18fps. In comparison, 5 of the 9 state-of-the-art methods performed slower on 270p down-scale version of the same videos. The experiments have also shown that for the process an 8K UHDTV video the ABPBGS can divide the memory required by about 24 for a total of 450MB.

In a previous study, we proposed a technique to recommend candidate verbs for a method name so that developers can consistently use various verbs. In this study, we improve the rule extraction technique proposed in this previous study. Moreover, we confirm that the rank of each correct verb recommended by the new technique is higher than that by the previous technique.

Heterogeneous hetworks (HetNets) have been introduced as an emerging technology in order to meet the increasing demand for mobile data. HetNets are a combination of multi-layer networks such as macrocells and small cells. In such networks, users may suffer significant cross-layer interference. To manage this interference, the 3rd Generation Partnership Project (3GPP) has introduced enhanced Inter-Cell Interference Coordination (eICIC) techniques. Almost Blank SubFrame (ABSF) is one of the time-domain techniques used in eICIC solutions. We propose a dynamically optimal Signal-to-Interference-and-Noise Ratio (SINR)-based ABSF framework to ensure macro user performance while maintaining small user performance. We also study cooperative mechanisms to help small cells collaborate efficiently in order to reduce mutual interference. Simulations show that our proposed scheme achieves good performance and outperforms the existing ABSF frameworks.

In this paper, we apply the concept of software-defined data plane to defining new services for Mobile Virtual Network Operators (MVNOs). Although there are a large number of MVNOs proliferating all over the world and most of them provide low bandwidth at low price, we propose a new business model for MVNOs and empower them with capability of tailoring fine-grained subscription plans that can meet users' demands. For example, abundant bandwidth can be allocated for some specific applications, while the rest of the applications are limited to low bandwidth. For this purpose, we have recently proposed the concept of application and/or device specific slicing that classifies application and/or device specific traffic into slices and applies fine-grained quality of services (QoS), introducing various applications of our proposed system [9]. This paper reports the prototype implementation of such proposal in the real MVNO connecting customized smartphones so that we can identify applications from the given traffic with 100% accuracy. In addition, we propose a new method of identifying applications from the traffic of unmodified smartphones by machine learning using the training data collected from the customized smartphones. We show that a simple machine learning technique such as random forest achives about 80% of accuracy in applicaton identification.

In this paper, we first propose a notion of multiple authorities attribute-based designated confirmer signature scheme with unified verification. In a multiple authorities attribute-based designated confirmer signature scheme with unified verification, both the signer and the designated confirmer can run the same protocols to confirm a valid signature or disavow an invalid signature. Then, we construct a multiple authorities attribute-based designated confirmer signature scheme with unified verification. Finally, we prove the correctness and security of the proposed scheme.

In this letter, we propose a novel kind of uncertain query, top (k1,k2) query. The x-tuple model and the possible world semantics are used to describe data objects in uncertain datasets. The top (k1,k2) query is going to find k2 x-tuples with largest probabilities to be the result of top k1 query in a possible world. Firstly, we design a basic algorithm for top (k1,k2) query based on dynamic programming. And then some pruning strategies are designed to improve its efficiency. An improved initialization method is proposed for further acceleration. Experiments in real and synthetic datasets prove the performance of our methods.

Orthogonal frequency division multiplexing (OFDM) channel estimation is the key technique used in broadband wireless networks. The Doppler frequency caused by fast mobility environments will cause inter-carrier interference (ICI) and degrade the performance of OFDM systems. Due to the severe ICI, channel estimation becomes a difficult task in higher mobility scenarios. Our aim is to propose a pilot-aided channel estimation method that is robust to high Doppler frequency with low computational complexity and pilot overheads. In this paper, the time duration of each estimate covers multiple consecutive OFDM symbols, named a “window”. A close-form of polynomial channel modeling is derived. The proposed method is initialized to the least squares (LS) estimates of the channels corresponding to the time interval of the pilot symbols within the window. Then, the channel interpolation is performed in the entire window. The results of computer simulations and computation complexity evaluations show that the proposed technique is robust to high Doppler frequency with low computation complexity and low pilot overheads. Compared with the state-of-the-art method and some conventional methods, the new technique proposed here has much lower computational complexity while offering comparable performance.

To develop a flexible liquid crystal display (LCD) with a wide viewing angle range and high contrast ratio, we have proposed a flexible blue-phase LC device sustained by polymer walls inside the LC cell. We clarified that the polymer walls can maintain a constant cell gap and suppress the generation of alignment defects of the blue-phase LC in a bending state.

We investigated a control of the crystalline orientation of soluble organic semiconductor single crystals using liquid crystal solvents aligned by the electric field to improve the performance of organic thin-film transistors. We clarified that the semiconductor single crystal grows to the direction parallel to the liquid crystal alignment oriented by the lateral electric field.

A Bayer-like White-RGB (W-RGB) color filter array (CFA) was invented for overcoming the weaknesses of commonly used RGB based Bayer CFA. In order to reproduce full-color images from the Bayer-like W-RGB CFA, a demosaicing or a CFA interpolation process which estimates missing color channels of raw mosaiced images from CFA is an essential process for single sensor digital cameras having CFA. In the case of Bayer CFA, numerous demosaicing methods which have remarkable performance were already proposed. In order to take advantage of both remarkable performance of demosaicing method for Bayer CFA and the characteristic of high-sensitive Bayer-like W-RGB CFA, a new method of transforming Bayer-like W-RGB to Bayer pattern is required. Therefore, in this letter, we present a new method of transforming Bayer-like W-RGB pattern to Bayer pattern. The proposed method mainly uses the color difference assumption between different channels which can be applied to practical consumer digital cameras.

In OpenFlow, data and control plane are decoupled from switches or routers. While the data plane resides in the switches or routers, the control plane might be moved into one or more external servers (controllers). In this article, we propose verification mechanisms for the data plane functionality of switches. The latter consists of two parts: (1) Flow-Match Header part (to match a flow of incoming packets) and (2) action part (e.g., to forward incoming packets to an outgoing port). We propose a mechanism to verify the Flow-Match Header part of the data plane. The mechanism can be executed at the controller, or on an additional device or server (or virtual machines) attached to the network. Deploying a virtual machine (VM) or server for verification may decrease the load of the controller and/or consumed bandwidth between the controller and a switch. We propose a heuristic to place external verification devices or VMs in a network such that the verification time can be minimized. Verification time with respect to consumed resources are evaluated through emulation experiments. Results confirm that the verification time using the proposed heuristic is indeed shortened significantly, while requiring low bandwidth resources.