Non-linear precoding (NLP) scheme for downlink multi-user multiple-input multiple-output (DL-MU-MIMO) transmission has received much attention as a promising technology to achieve high capacity within the limited bandwidths available to radio access systems. In order to minimize the required transmission power for DL-MU-MIMO and achieve high spectrum efficiency, Vector Perturbation (VP) was proposed as an optimal NLP scheme. Unfortunately, the original VP suffers from significant computation complexity in detecting the optimal perturbation vector from an infinite number of the candidates. To reduce the complexity with near transmission performance of VP, several recent studies investigated various efficient NLP schemes based on the concept of Tomlinson-Harashima precoding (THP) that applies successive pre-cancellation of inter-user interference (IUI) and offsets the transmission vector based on a modulo operation. In order to attain transmission performance improvement over the original THP, a previous work proposed Minimum Mean Square Error based THP (MMSE-THP) employing IUI successive pre-cancellation based on MMSE criteria. On the other hand, to improve the transmission performance of MMSE-THP, other previous works proposed Ordered MMSE-THP and Lattice-Reduction-Aided MMSE-THP (LRA MMSE-THP). This paper investigates the further transmission performance improvement of Ordered MMSE-THP and LRA MMSE-THP. This paper starts by proposing an extension of MMSE-THP employing a perturbation vector search (PVS), called PVS MMSE-THP as a novel NLP scheme, where the modulo operation is substituted by PVS and a subtraction operation from the transmit signal vector. Then, it introduces an efficient search algorithm of appropriate perturbation vector based on a depth-first branch-and-bound search for PVS MMSE-THP. Next, it also evaluates the transmission performance of PVS MMSE-THP with the appropriate perturbation vector detected by the efficient search algorithm. Computer simulations quantitatively clarify that PVS MMSE-THP achieves better transmission performance than the conventional NLP schemes. Moreover, it also clarifies that PVS MMSE-THP increases the effect of required transmission power reduction with the number of transmit antennas compared to the conventional NLP schemes.

This paper proposes a painterly morphing algorithm for mobile smart devices, where each frame in the morphing sequence looks like an oil-painted picture with brush strokes. It can be presented, for example, during the transition between the main screen and a specific application screen. For this, a novel dissimilarity function and acceleration data structures are developed. The experimental results show that the algorithm produces visually stunning effects at an interactive time.

This paper proposes a fast safe-region generation method for several kinds of vicinity queries including set k nearest neighbor (NN) queries, ordered kNN queries, reverse kNN queries, and distance range queries. When a user is driving a car on a road network, he/she wants to know about objects located in the vicinity of the car. However, the result changes according to the movement of the car, and therefore, the user needs to request up-to-date result to the server. On the other hand, frequent requests for up-to-date results cause heavy loadings on the server. To cope with this problem efficiently, the idea of the safe-region has been proposed, however, it takes long processing time in existing works. This paper proposes a fast generation method of the safe-region applicable to several types of vicinity queries. Through experimental evaluations, we demonstrate that the proposed method outperforms the existing algorithms in the processing time by one or two orders of magnitude.

This paper proposes a novel method of reducing channel state information (CSI) feedback by using transmit antenna selection for downlink multiuser multiple input multiple output (DL-MU-MIMO) transmission in dense distributed antenna systems. It is widely known that DL-MU-MIMO transmission achieves higher total bit-rate by mitigating inter-user interference based on pre-coding techniques. The pre-coding techniques require CSI between access point (AP) and multiple users. However, overhead for CSI acquisition degrades the transmission efficiency of DL-MU-MIMO transmission. In the proposed CSI feedback reduction method, AP first selects the antenna set that maximizes the received power at each user, second it skips the sequence of CSI feedback for users whose signal to interference power ratio is larger than a threshold, and finally it performs DL-MU-MIMO transmission to multiple users by using the selected antenna set. To clarify the proposed method, we evaluate it by computer simulations in an indoor scenario. The results show that the proposed method can offer higher transmission efficiency than the conventional DL-MU-MIMO transmission with the usual CSI feedback method.

Today's enterprise, data-center, and internet-service-provider networks deploy different types of network devices, including switches, routers, and middleboxes such as network address translation and firewalls. These devices are vertically integrated monolithic systems. Software-defined networking (SDN) and network function virtualization (NFV) are promising technologies for dis-aggregating vertically integrated systems into components by using “softwarization”. Software-defined networking separates the control plane from the data plane of switch and router, while NFV decouples high-layer service functions (SFs) or Network Functions (NFs) implemented in the data plane of a middlebox and enables the innovation of policy implementation by using SF chaining. Even though there have been several survey studies in this area, this area is continuing to grow rapidly. In this paper, we present a recent survey of this area. In particular, we survey research activities in the areas of re-architecting middleboxes, state management, high-performance platforms, service chaining, resource management, and trouble shooting. Efforts in these research areas will enable the development of future virtual-network-function platforms and innovation in service management while maintaining acceptable capital and operational expenditure.

The demand for wireless traffic is increasing rapidly, which has posed huge challenges to mobile network operators (MNOs). A heterogeneous network (HetNet) framework, composed of a marcocell and femtocells, has been proved to be an effective way to cope with the fast-growing traffic demand. In this paper, we assume that both the macrocell and femtocells are owned by the same MNO, with revenue optimization as its ultimate goal. We aim to propose a pricing strategy for macro-femto HetNets with a user centric vision, namely, mobile users would have their own interest to make rational decisions on selecting between the macrocell and femtocells to maximize their individual benefit. We formulate a Stackelberg game to analyze the interactions between the MNO and users, and obtain the equilibrium solution for the Stackelberg game. Via extensive simulations, we evaluate the proposed pricing strategy in terms of its efficiency with respect to the revenue optimization.

In order to cope with severe interference in heterogeneous networks, this paper proposes a semi-blind interference cancellation scheme, which does not require multiple receive antennas or knowledge about training sequences of the interfering signals. The proposed scheme performs joint channel estimation and signal detection (JCESD) during the training period in order to blindly estimate channels of the interfering signals. On the other hand, maximum likelihood detection (MLD), which can be considered the optimum JCESD, must perform channel estimation for all transmitted signal candidates of the interfering signals and must search for the most likely signal candidate. Therefore, MLD incurs a prohibitive amount of computational complexity. To reduce such complexity drastically, the proposed scheme enhances the quantized channel approach, and applies the enhanced version to JCESD. In addition, a recalculation scheme is introduced to avoid inaccurate channel estimates due to local minima. Using the estimated channels, the proposed scheme performs multiuser detection (MUD) of the data sequences in order to cancel the interference. Computer simulations show that the proposed scheme outperforms a conventional scheme based on the Viterbi algorithm, and can achieve almost the same average bit error rate performance as the MUD with channels estimated from sufficiently long training sequences of both the desired signal and the interfering signals, while reducing the computational complexity significantly compared with full search involving all interfering signal candidates during the training period.

A fisheye map lets users view both detailed and wide areas. The Focus+Glue+Context map is a fisheye map suited for Web map systems; it consists of a detailed map (i.e., Focus), wide-area map (i.e., Context), and an area to absorb the difference in scales between Focus and Context (i.e., Glue). Because Glue is compressed, the road density is too high to draw all of the roads in this area. Although existing methods can filter roads to draw, they have problems with rendering the road density and connectivity in Glue. This paper proposes an improved method to filter roads in Glue by applying a generalization method based on weighted strokes. In addition, a technique to speed up the proposed method by using a weighted stroke database is described. A prototype Web map system with a high level of response was developed and evaluated in terms of its connectivity, road density, and response.

This paper proposes a novel secure biometric authentication scheme. The scheme deals with fingerprint minutiae as the biometric feature and the matching is checked by a widely used technique. To discuss security, we formalize the model of secure biometric authentication scheme by abstracting the related and proposed schemes. The schemes which satisfy all the proposed security requirements are guaranteed to prevent leakage of biometric information and impersonation. In particular, the definition captures well-known and practical attacks including replay attacks and hill-climbing attacks. We prove that the proposed scheme achieves all the requirements if the additive homomorphic encryption scheme used in the scheme satisfies some additional properties. As far as we know, the proposed scheme is the first one that satisfies all the requirements. Also, we show that modified Elgamal cryptosystem satisfies all the properties under the decisional Diffie-Hellman assumption.

In cellular network environments, where users are not evenly distributed across cells, overloaded base stations handling many users have difficulties in providing effective and fair services with their limited resources. Additionally, users at the cell edge may suffer from the potential problems resulting from low signal-to-interference ratio owing to the incessant interference from adjacent cells. In this paper, we propose a relay-assisted load balancing scheme to resolve these traffic imbalance. The proposed scheme can improve the performance of the overall network by utilizing relay stations to divert heavy traffic to other cells, and by adopting a partial frequency-reuse scheme to mitigate inter-cell interference. Each user and relay station calculates its own utility influence in the neighboring candidates for reassociation and decides whether to stay or move to another cell presenting the maximum total network utility increment. Simulation results show that the proposed scheme improves the overall network fairness to users by improving the performance of cell boundary users without degrading the total network throughput. We achieve a system performance gain of 16 ∼ 35% when compared with conventional schemes, while ensuring fairness among users.

Multi-camera videos with abundant information and high flexibility are useful in a wide range of applications, such as surveillance systems, web lectures, news broadcasting, concerts and sports viewing. Viewers can enjoy an enhanced viewing experience by choosing their own viewpoint through viewing interfaces. However, some viewers may feel annoyed by the need for continual manual viewpoint selection, especially when the number of selectable viewpoints is relatively large. In order to solve this issue, we propose an automatic viewpoint navigation method designed especially for sports. This method focuses on a viewer's personal preference for viewpoint selection, instead of common and professional editing rules. We assume that different trajectory distributions of viewing objects cause a difference in the viewpoint selection according to personal preference. We learn the relationship between the viewer's personal viewpoint-selection tendency and the spatio-temporal game context represented by the objects trajectories. We compare three methods based on Gaussian mixture model, SVM with a general histogram and SVM with a bag-of-words to seek the best learning scheme for this relationship. The performance of the proposed methods are evaluated by assessing the degree of similarity between the selected viewpoints and the viewers' edited records.

The purpose of this study is to investigate the effects of device size on non-visual icon search using a touch interface with voice output. We conducted an experiment in which twelve participants searched for the target icons with four different-sized touchscreen devices. We analyzed the search time, search strategies and subjective evaluations. As a result, mobile devices with a screen size of 4.7 inches had the shortest search time and obtained the highest subjective evaluation among the four devices.

In this letter, we investigate the computation offloading problem in cloud based multi-robot systems, in which user weights, communication interference and cloud resource limitation are jointly considered. To minimize the system cost, two offloading selection and resource allocation algorithms are proposed. Numerical results show that the proposed algorithms both can greatly reduce the overall system cost, and the greedy selection based algorithm even achieves near-optimal performance.

Multiport SRAMs are frequently installed in network and/or telecommunication VLSIs to implement smart functions. This paper presents a high speed and low-power dual-port (i.e., 1W+1R two-port) SRAM macro customized for serial access operations. To reduce the wasted power dissipation due to subthreshold leakage currents, the supply voltage for 10T memory cells is lowered to 1 V and a power switch is prepared for every 64 word drivers. The switch is activated with look-ahead decoder-segment activation logic, so there is no penalty when selecting a wordline. The data I/O circuitry with a new column-based configuration makes it possible to hide the bitline precharge operation with the sensing operation in the read cycle ahead of it; that is, we have successfully reduced the read latency by a half clock cycle, resulting in a pure two-stage pipeline. The SRAM macro installed in a 4K-entry × 33-bit FIFO memory, fabricated with a 0.3-µm fully-depleted-SOI CMOS process, achieved a 500-MHz operation in the typical conditions of 2- and 1-V power supplies, and 25°C. The power consumption during the standby time was less than 1.0 mW, and that at a practical operating frequency of 400 MHz was in a range of 47-57 mW, depending on the bit-stream data pattern.

Preserving hue is an important issue for color image processing. In order to preserve hue, color image processing is often carried out in HSI or HSV color space which is translated from RGB color space. Transforming from RGB color space to another color space and processing in this space usually generate gamut problem. We propose image enhancement methods which conserve hue and preserve the range (gamut) of the R, G, B channels in this paper. First we show an intensity processing method while preserving hue and saturation. In this method, arbitrary gray-scale transformation functions can be applied to the intensity component. Next, a saturation processing method while preserving hue and intensity is proposed. Arbitrary gray-scale transform methods can be also applied to the saturation component. Two processing methods are completely independent. Therefore, two methods are easily combined by applying two processing methods in succession. The combination method realizes the hue-preserving color image processing with a high arbitrariness without gamut problem. Furthermore, the concrete enhancement algorithm based on the proposed processing methods is proposed. Numerical results confirm our theoretical results and show that our processing algorithm performs much better than the conventional hue-preserving methods.

High-resolution image and video communication in home networks is highly expected to proliferate with the spread of Wi-Fi devices and the introduction of multiple-input multiple-output (MIMO) systems. This paper proposes a joint transmission and coding scheme for broadcasting high-resolution video streams over multiuser MIMO systems with an eigenbeam-space division multiplexing (E-SDM) technique. Scalable video coding makes it possible to produce the code stream comprised of multiple layers having unequal contribution to image quality. The proposed scheme jointly assigns the data of scalable code streams to subcarriers and spatial streams based on their signal-to-noise ratio (SNR) values in order to transmit visually important data with high reliability. Simulation results show that the proposed scheme surpasses the conventional unequal power allocation (UPA) approach in terms of both peak signal-to-noise ratio (PSNR) of received images and correct decoding probability. PSNR performance of the proposed scheme exceeds 35dB with the probability of over 95% when received SNR is higher than 6dB. The improvement in average PSNR by the proposed scheme compared to the conventional UPA comes up to approx. 20dB at received SNR of 6dB. Furthermore, correct decoding probability reaches 95% when received SNR is greater than 4dB.

Internet of Things (IoT) devices deployed in urban areas are seen as data sources for urban sensing IoT applications. Since installing cellular interfaces on a huge number of IoT devices is expensive, we propose to use a user equipment (UE) device with a local wireless interface as a mobile IoT gateway for fixed IoT devices. In this paper, we design a new mobile architecture based on cellular networks to accommodate non-cellular fixed IoT devices by UE devices working as IoT gateways. One key feature is that our architecture leverages proximity services (ProSe) to discover relay UE devices with low overhead in terms of discovery messages. Through simulation studies, we clarify the feasibility of our architecture including the relay UE discovery mechanism in urban areas.

The heart of Network Functions Virtualization (NFV) is both the softwarization of existing network middleboxes as Virtual Network Functions (VNFs) and the Service Function Chaining (SFC), also known as Service Chaining of them. Most existing VNFs are realized as VM-based general purpose appliances and shared by multiple user VMs. However, the cover range of VNF can be extended to directly reinforce network functionality of user VMs by introducing VM-specific VNFs. In this study, we propose micro-VNFs (µVNFs) and a VM-specific service chaining framework (vNFChain). Micro-VNFs are VM-specific lightweight VNFs that directly attach to a user VM, and can support not only traditional L2-L4 protocols but also stateful custom L7 protocols. The vNFChain framework constructs local service chains of µVNFs and transparently attaches the chain to the VM. Importantly, our framework achieves zero touch configuration for user VMs as well as no modification for existing system environments, such as virtual switch, hypervisor, and OS. In this paper, we describe architectural design and implementation of the framework. In addition, we evaluate the proposed approach in terms of throughput and CPU usage by comparing it with a DPDK-enabled VM-based µVNF model.

In recent years, spectrum sharing has received much attention as a technique for more efficient spectrum use. In the case in which all providers are cooperative, spectrum sensing can easily be realized and can improve user throughput (on average). If that is not the case, providers are not cooperative, i.e., spectrum trading, spectrum bands are rented to promote spectrum sharing. To ensure more profit, however, non-cooperative providers must correctly estimate the fluctuation of the number of connected users to be able to determine the offered channel price. In this paper, we propose a spectrum sharing method to achieve both higher throughput and provider profit via appropriate pricing using a disaggregate behavioral model. Finally, we confirm the effectiveness of the proposed method using simulation experiments.

With the rapid spread of smart devices, such as smartphones and tablet PCs, user authentication is becoming increasingly important because various kinds of data concerning user privacy are processed within them. At present, in the case of smart devices, password-based authentication is frequently used; however, biometric authentication has attracted more attention as a user authentication technology. A smart device is equipped with various sensors, such as cameras, microphones, and touch panels, many of which enable biometric information to be obtained. While the function of biometric authentication is available in many smart devices, there remain some problems to be addressed for more secure and convenient user authentication. In this paper, we summarize the current problems with user authentication on smart devices and propose a novel user authentication system based on the concept of context awareness to resolve these problems. We also present our evaluation of the performance of the system by using biometric information that was acquired from smart devices. The evaluation demonstrates the effectiveness of our system.