X communication model with two receivers is introduced to underwater acoustic networks, in which each transmitter sends an independent message to each receiver. Based on distance aligned structure, we propose a scheme, which can perform perfect interference alignment. The feasibility is also illustrated in three dimensional Euclidean space.

After the East Japan great earthquake on March 11, 2011, many Japanese coastal resident areas were isolated from other because of destruction of information infrastructure, disconnection of communication network and excessive traffic congestion. The undelivered disaster information influenced the speed of evacuation, rescue of injured residents, and sending life-support materials to evacuation shelters. From the experience of such disaster, more robust and resilient networks are strongly required, particularly for preparation of large scale disasters. In this paper, in order to respond to those problems, we introduce Delay Tolerant Network (DTN) for disaster information transmission application in challenged network environment. Message delivery by transport vehicles such as cars between disaster-response headquarter and evacuation shelters in challenged network environment is considered. A improved message delivery method combined with DTN protocols and cognitive wireless network is explained. The computer simulation for the actual rural area in Japan is made to evaluate the performance and effectiveness of proposed method.

Reliable wireless communication often requires accurate knowledge of the underlying multipath channels. Numerous measurement campaigns have shown that physical multipath channels tend to exhibit a sparse structure. Conventional blind channel identification (BCI) strategies such as the least squares, which are known to be optimal under the assumption of rich multipath channels, are ill-suited to exploiting the inherent sparse nature of multipath channels. Recently, l1-norm regularized least-squares-type approaches have been proposed to address this problem with a single parameter governing all coefficients, which is equivalent to maximum a posteriori probability estimation with a Laplacian prior for the channel coefficients. Since Laplace prior is not conjugate to the Gaussian likelihood, no closed form of Bayesian inference is possible. Following a different approach, this paper deals with blind channel identification of a single-input multiple-output (SIMO) system based on sparse Bayesian learning (SBL). The inherent sparse nature of wireless multipath channels is exploited by incorporating a transformative cross relation formulation into a general Bayesian framework, in which the filter coefficients are governed by independent scalar parameters. A fast iterative Bayesian inference method is then applied to the proposed model for obtaining sparse solutions, which completely eliminates the need for computationally costly parameter fine tuning, which is necessary in the l1-norm regularization method. Simulation results are provided to demonstrate the superior effectiveness of the proposed channel estimation algorithm over the conventional least squares (LS) scheme as well as the l1-norm regularization method. It is shown that the proposed algorithm exhibits superior estimation performance compared to both LS and l1-norm regularization methods.

The main contribution of this paper is a non-trivial expression, that is called dual expression, of the posterior values for non-adaptive group testing problems. The dual expression is useful for exact bitwise MAP estimation. We assume a simplest non-adaptive group testing scenario including N-objects with binary status and M-tests. If a group contains one or more positive object, the test result for the group is assumed to be one; otherwise, the test result becomes zero. Our inference problem is to evaluate the posterior probabilities of the objects from the observation of M-test results and the prior probabilities for objects. The derivation of the dual expression of posterior values can be naturally described based on a holographic transformation to the normal factor graph (NFG) representing the inference problem. In order to handle OR constraints in the NFG, we introduce a novel holographic transformation that converts an OR function to a function similar to an EQUAL function.

Excessive IR-drop in capture mode during at-speed scan testing may cause timing errors for defect-free circuits, resulting in undue test yield loss. Previous solutions for achieving capture-power-safety adjust the switching activity around logic paths, especially long sensitized paths, in order to reduce the impact of IR-drop. However, those solutions ignore the impact of IR-drop on clock paths, namely test clock stretch; as a result, they cannot accurately achieve capture-power-safety. This paper proposes a novel scheme, called LP-CP-aware ATPG, for generating high-quality capture-power-safe at-speed scan test vectors by taking into consideration the switching activity around both logic and clock paths. This scheme features (1) LP-CP-aware path classification for characterizing long sensitized paths by considering the IR-drop impact on both logic and clock paths; (2) LP-CP-aware X-restoration for obtaining more effective X-bits by backtracing from both logic and clock paths; (3) LP-CP-aware X-filling for using different strategies according to the positions of X-bits in test cubes. Experimental results on large benchmark circuits demonstrate the advantages of LP-CP-aware ATPG, which can more accurately achieve capture-power-safety without significant test vector count inflation and test quality loss.

Sound source localization is an essential technique in many applications, e.g., speech enhancement, speech capturing and human-robot interaction. However, the performance of traditional methods degrades in noisy or reverberant environments, and it is sensitive to the spatial location of sound source. To solve these problems, we propose a sound source localization framework based on bi-direction interaural matching filter (IMF) and decision weighting fusion. Firstly, bi-directional IMF is put forward to describe the difference between binaural signals in forward and backward directions, respectively. Then, a hybrid interaural matching filter (HIMF), which is obtained by the bi-direction IMF through decision weighting fusion, is used to alleviate the affection of sound locations on sound source localization. Finally, the cosine similarity between the HIMFs computed from the binaural audio and transfer functions is employed to measure the probability of the source location. Constructing the similarity for all the spatial directions as a matrix, we can determine the source location by Maximum A Posteriori (MAP) estimation. Compared with several state-of-the-art methods, experimental results indicate that HIMF is more robust in noisy environments.

We introduce a novel cache replacement policy to improve the entire network performance of video delivery over content-centric networking (CCN). In the case of the CCN structure, we argue that: 1) for video multiplexing scenario, general cache strategies that ignore the intrinsic linear time characteristic of video requests are unable to make better use of the cache resources, and 2) it is inadequate to simply extend the existing research conclusions of file-oriented popularity to chunk-by-chunk popularity, which are widely used in CCN. Unlike previous works in this field, the proposed policy in this study, named two-level popularity-oriented time-to-hold cache replacement policy (TLP-TTH), is designed on the basis of the following principles. Firstly, the proposed cache replacement strategy is customized for video delivery by carefully considering the essential auto-correlated request feature of video chunks within a video file. Furthermore, the popularity in video delivery is subdivided into two levels, namely chunk-level access probability and file-level popularity, in order to efficiently utilize cache resources. We evaluated the proposed policy in both a hierarchical topology and a real network based hybrid topology, and took viewers departure into consideration as well. The results validate that for video delivery over CCN, TLP-TTH policy improves the network performance from several aspects. In particular, we observed that the proposed policy not only increases the cache hit ratio at the edge of the network but the cache utilization at the intermediate routers is also improved markedly. Further, with respect to the video popularity variation scenario, the cache hit ratio of TLP-TTH policy responds sensitively to maintain efficient cache utilization.

Communication infrastructures under the influence of the disaster strike, e.g., earthquake, will be partitioned due to the significant damage of network components such as base stations. The communication model of the Internet bases on a location-oriented ID, i.e., IP address, and depends on the DNS (Domain Name System) for name resolution. Therefore such damage remarkably deprives the reachability to the information. To achieve robustness of information retrieval in disaster situation, we try to apply CCN/NDN (Content-Centric Networking/Named-Data Networking) to information networks fragmented by the disaster strike. However, existing retransmission control in CCN is not suitable for the fragmented networks with intermittent links due to the timer-based end-to-end behavior. Also, the intermittent links cause a problem for cache behavior. In order to resolve these technical issues, we propose a new packet forwarding scheme with the dynamic routing protocol which resolves retransmission control problem and cache control scheme suitable for the fragmented networks. Our simulation results reveal that the proposed caching scheme can stably store popular contents into cache storages of routers and improve cache hit ratio. And they also reveal that our proposed packet forwarding method significantly improves traffic load, energy consumption and content retrieval delay in fragmented networks.

We propose a framework to perform auto-radiometric calibration in photometric stereo methods to estimate surface orientations of an object from a sequence of images taken using a radiometrically uncalibrated camera under varying illumination conditions. Our proposed framework allows the simultaneous estimation of surface normals and radiometric responses, and as a result can avoid cumbersome and time-consuming radiometric calibration. The key idea of our framework is to use the consistency between the irradiance values converted from pixel values by using the inverse response function and those computed from the surface normals. Consequently, a linear optimization problem is formulated to estimate the surface normals and the response function simultaneously. Finally, experiments on both synthetic and real images demonstrate that our framework enables photometric stereo methods to accurately estimate surface normals even when the images are captured using cameras with unknown and nonlinear response functions.

Power packet is a unit of electric power transferred by a pulse with an information tag. This letter discusses up-stream dispatching of required power at loads to sources through density modulation of power packet. Here, power is adjusted at a proposed router which dispatches power packets according to the tags. It is analyzed by averaging method and numerically verified.

Inter-person occlusion handling is a critical issue in the field of tracking, and it has been extensively researched. Several state-of-the-art methods have been proposed, such as focusing on the appearance of the targets or utilizing knowledge of the scene. In contrast with the approaches proposed in the literature, we propose to address this issue using a social interaction model, which allows us to explore spatio-temporal information pertaining to the targets involved in the occlusion situation. Our experimental results show promising results compared with those obtained using other methods.

In this paper, a discrete particle swarm optimization method is proposed to solve the multi-objective task assignment problem in distributed environment. The objectives of optimization include the makespan for task execution and the budget caused by resource occupation. A two-stage approach is designed as follows. In the first stage, several artificial particles are added into the initialized swarm to guide the search direction. In the second stage, we redefine the operators of the discrete PSO to implement addition, subtraction and multiplication. Besides, a fuzzy-cost-based elite selection is used to improve the computational efficiency. Evaluation shows that the proposed algorithm achieves Pareto improvement in comparison to the state-of-the-art algorithms.

File system workloads are increasing write-heavy. The growing capacity of RAM in modern nodes allows many reads to be satisfied from memory while writes must be persisted to disk. Today's sophisticated local file systems like Ext4, XFS and Btrfs optimize for reads but suffer from workloads dominated by microdata (including metadata and tiny files). In this paper we present an LSM-tree-based file system, RFS, which aims to take advantages of the write optimization of LSM-tree to provide enhanced microdata performance, while offering matching performance for large files. RFS incrementally partitions the namespace into several metadata columns on a per-directory basis, preserving disk locality for directories and reducing the write amplification of LSM-trees. A write-ordered log-structured layout is used to store small files efficiently, rather than embedding the contents of small files into inodes. We also propose an optimization of global bloom filters for efficient point lookups. Experiments show our library version of RFS can handle microwrite-intensive workloads 2-10 times faster than existing solutions such as Ext4, Btrfs and XFS.

This paper proposes a low power single-ended successive approximation register (SAR) analog-to-digital converter (ADC) to replace the only analog active circuit, the comparator, with a digital circuit, which is an inverter-based comparator. The replacement helps possible design automation. The inverter threshold voltage variation impact is minimal because an SAR ADC has only one comparator, and many applications are either insensitive to the resulting ADC offset or easily corrected digitally. The proposed resetting approach mitigates leakage when the input is close to the threshold voltage. As an intrinsic headroom-free, and thus low-rail-voltage, friendly structure, an inverter-based comparator also occupies a small area. Furthermore, an 11-bit ADC was designed and manufactured through a 0.35-µm CMOS process by adopting a low-power switching procedure. The ADC achieves an FOM of 181fJ/Conv.-step at a 25kS/s sampling rate when the supply voltage VDD is 1.2V.

This paper presents a wide-input-voltage-range and high-efficiency boost converter that is assisted by a transformer-based oscillator. The oscillator can provide a sufficient amount of power to drive a following switched-inductor boost converter at low voltages. Moreover, it adopts a novel amplitude-regulation circuit (ARC) without using high power-consuming protective devices to suppress the expansion of the oscillation amplitude at high input voltages. Therefore, it can avoid over-voltage problems without sacrificing the power efficiency. Additionally, a power-down circuit (PDC) is implemented to turn off the oscillator, when the boost converter can be driven by its own output power, thus, eliminating the power consumption by the oscillator and improving the power efficiency. We implemented the ARC and the PDC with discrete components rather than one-chip integration for the proof of concept. The experimental results showed that the proposed circuit became possible to operate from an input voltage of 60mV to 3V while maintaining high peak efficiency up to 92%. To the best of our knowledge, this converter provides a wider input range in comparison with the previously-published converters. We are convinced that the proposed approach by inserting an appropriate start-up circuit in a commercial converter will be effective for rapid design proposals in order to respond promptly to customer needs as Internet of things (IoT) devices with energy harvester.

In this work, we propose general, practical and accurate models to analyze the performance of multi-cache systems, in which a cache forwards its miss stream (i.e., requests which have not found the target item) to other caches. We extend a miss stream modeling technique originally known as Melazzi's approximation, which provides a simple but accurate approximate analysis for caches with cascade configurations. We consider several practical replication strategies, which have been commonly adopted in the context of ICN, taking into account the effects of temporal locality. Also, we capture the existing state correlations between neighboring caches by exploiting the cache eviction time. Our proposed models to handle traffic patterns allow us to go beyond the standard Poisson approximation under Independent Reference Model. Our results, validated against simulations, provide interesting insights into the performance of multi-cache systems with different replication strategies.

In this paper, we investigate the degrees of freedom (DoF) of a MIMO cellular interfering network (CIN) with L (L≥3) cells and K users per cell. Previous works established the DoF upper bound of LK(M+N)/(LK+1) for the MIMO CIN by analyzing the interference alignment (IA) feasibility, where M and N denote the number of antennas at each base station (BS) and each user, respectively. However, there is still a gap between the DoF upper bound and the achievable DoF in existing designs. To address this problem, we propose two linear IA schemes without symbol extensions to jointly design transmit and receive beamforming matrices to align and eliminate interference. In the two schemes, the transmit beamforming vectors are allocated to different cluster structures so that the inter-cell interference (ICI) data streams from different ICI channels are aligned. The first scheme, named fixed cluster structure (FCS-IA) scheme, allocates ICI beamforming vectors to the cluster structures of fixed dimension and can achieve the DoF upper bound under some system configurations. The second scheme, named dynamic cluster structure IA (DCS-IA) scheme, allocates ICI beamforming vectors to the cluster structures of dynamic dimension and can get a tradeoff between the number of antennas at BSs and users so that ICI alignment can be applied under various system configurations. Through theoretical analysis and numerical simulations, we verify that the DoF upper bound can be achieved by using the FCS-IA scheme. Furthermore, we show that the proposed schemes can provide significant performance gain over the time division multiple access (TDMA) scheme in terms of DoF. From the perspective of DoF, it is shown that the proposed schemes are more effective than the conventional IA schemes for the MIMO CIN.

We have derived the physics-based equivalent circuit model of a semiconductor-integrated bow-tie antenna (BTA) for expressing its impedance and radiation characteristics as a terahertz transmitter. The equivalent circuit branches and components, consisting of 16 RLC parameters are determined based on electromagnetic simulations. All the values of the circuit elements are identified using the particle swarm optimization (PSO) that is one of the modern multi-purpose optimization methods. Moreover, each element value can also be explained by the structure of the semiconductor-integrated BTA, the device size, and the material parameters.

Content-centric networking (CCN) is an emerging networking architecture that is being actively investigated in both the research and industrial communities. In the latest version of CCN, a large number of interests have to be issued when large content is retrieved. Since CCN routers have to search several tables for each incoming interest, this could cause a serious problem of router workload. In order to solve this problem, this paper introduces a novel strategy of “grouping” multiple interests with common information and “packing” them to a special interest called the list interest. Our list interest is designed to co-operate with the manifest of CCN as its dual. This paper demonstrates that by skipping and terminating several search steps using the common information in the list interest, the router can search its tables for the list interest-based request with dramatically smaller complexity than the case of the standard interest-based request. Furthermore, we also consider the deployment of list interests and design a novel TCP-like congestion control method for list interests to employ them just like standard interests.

The three dimensional (3D) reconstruction of a medical image sequence can provide intuitive morphologies of a target and help doctors to make more reliable diagnosis and give a proper treatment plan. This paper aims to reconstruct the surface of a renal corpuscle from the microscope renal biopsy image sequence. First, the contours of renal corpuscle in all slices are extracted automatically by using a context-based segmentation method with a coarse registration. Then, a new coevolutionary-based strategy is proposed to realize a fine registration. Finally, a Gauss-Seidel iteration method is introduced to achieve a non-rigid registration. Benefiting from the registrations, a smooth surface of the target can be reconstructed easily. Experimental results prove that the proposed method can effectively register the contours and give an acceptable surface for medical doctors.