This paper presents the design and experimental evaluation of 60GHz small cell radio access based on IEEE 802.11ad/WiGig. The access point (AP) prototype used combines three RF modules with beamforming technology to provide 360° area coverage. In order to compensate for limited communication distance, multiple APs are employed to achieve wide area coverage. A handover algorithm suitable for IEEE 802.11ad/WiGig is employed to achieve flexible control of the cell coverage of each AP. As a proof of concept, a prototype system is set up at Narita International Airport and the capability of multiuser Gb/s wireless access is successfully demonstrated. In addition, the system behavior under stringent conditions is evaluated by load testing and throughput degradation due to co-channel and inter-channel interference is investigated.

Object detection is at the heart of nearly all the computer vision systems. Standard off-the-shelf embedded processors are hard to meet the trade-offs among performance, power consumption and flexibility required by object detection applications. Therefore, this paper presents an Application Specific Instruction set Processor (ASIP) for object detection using AdaBoost-based learning algorithm with Haar-like features as weak classifiers. Algorithm optimizations are employed to reduce memory bandwidth requirements without losing reliability. In the proposed ASIP, Single Instruction Multiple Data (SIMD) architecture is adopted for fully exploiting data-level parallelism inherent to the target algorithm. With adding pipeline stages, application-specific hardware components and custom instructions, the AdaBoost algorithm is accelerated by a factor of 13.7x compared to the optimized pure software implementation. Compared with ARM946 and TMS320C64+, our ASIP shows 32x and 7x better throughput, 10x and 224x better area efficiency, 6.8x and 18.8x better power efficiency, respectively. Furthermore, compared to hard-wired designs, evaluation results show an advantage of the proposed architecture in terms of chip area efficiency while maintain a reliable performance and achieve real-time object detection at 32fps on VGA video.

This paper proposes a novel framework for enhancing underwater images captured by optical imaging model and non-local means denoising. The proposed approach adjusts the color balance using biasness correction and the average luminance. Scene visibility is then enhanced based on an underwater optical imaging model. The increase in noise in the enhanced images is alleviated by non-local means (NLM) denoising. The final enhanced images are characterized by improved visibility while retaining color fidelity and reducing noise. The proposed method does not require specialized hardware nor prior knowledge of the underwater environment.

An automotive control system is a typical safety-critical embedded software, which requires extensive verification and validation (V&V) activities. This article introduces a toolset for automated V&V of automotive control system, including a test generator for automotive operating systems, a task simulator for validating task design of control software, and an API-call constraint checker to check emergent properties when composing control software with its underlying operating system. To the best of our knowledge, it is the first integrated toolset that supports V&V activities for both control software and operating systems in the same framework.

In this paper, we propose a redundant task allocation method for multi-core systems based on the Duplication with Temporary Triple-Modular Redundancy and Reconfiguration (DTTR) scheme. The proposed method determines task allocation of a given task graph to a given multi-core system model from task scheduling in given fault patterns. Fault patterns defined in this paper consist of a set of faulty cores and a set of surviving cores. To optimize the average failure rate of the system, task scheduling minimizes the execution time of the task graph preserving the property of the DTTR scheme. In addition, we propose a selection method of fault patterns to be scheduled to reduce the task allocation time. In the experiments, at first, we evaluate the proposed selection method of fault patterns in terms of the task allocation time. Then, we compare the average failure rate among the proposed method, a task allocation method which packs tasks into particular cores as much as possible, a task allocation method based on Simulated Annealing (SA), a task allocation method based on Integer Linear Programming (ILP), and a task allocation method based on task scheduling without considering the property of the DTTR scheme. The experimental results show that task allocation by the proposed method results in nearly the same average failure rate by the SA based method with shorter task allocation time.

Self-Aligned Quadruple Patterning (SAQP) is an important manufacturing technique for sub 14nm technology node. Although various routing algorithms for SAQP have been proposed, it is not easy to find a dense SAQP compliant routing pattern efficiently. Even though a grid for SAQP compliant routing pattern was proposed, it is not easy to find a valid routing pattern on the grid. The routing pattern of SAQP on the grid consists of three types of routing. Among them, third type has turn prohibition constraint on the grid. Typical routing algorithms often fail to find a valid routing for third type. In this paper, a simple directed grid-graph for third type is proposed. Valid SAQP compliant two dimensional routing patterns are found effectively by utilizing the proposed directed grid-graph. Experiments show that SAQP compliant routing patterns are found efficiently by our proposed method.

Decentralized channel assignment schemes are proposed to obtain low system-wide spatial overlap regions in wireless local area networks (WLANs). The important point of channel assignment in WLANs is selecting channels with fewer contending stations rather than mitigating interference power due to its medium access control mechanism. This paper designs two potential game-based channel selection schemes, basically each access point (AP) selects a channel with smaller spatial overlaps with other APs. Owing to the property of potential games, each decentralized channel assignment is guaranteed to converge to a Nash equilibrium. In order that each AP selects a channel with smaller overlaps, two metrics are proposed: general overlap-based scheme yields the largest overlap reduction if a sufficient number of stations (STAs) to detect overlaps are available; whereas decomposed overlap-based scheme need not require such STAs, while the performance would be degraded due to the shadowing effect. In addition, the system-wide overlap area is analytically shown to be upper bounded by the negative potential functions, which derives the condition that local overlap reduction by each AP leads to system-wide overlap reduction. The simulation results confirm that the proposed schemes perform better reductions in the system-wide overlap area compared to the conventional interference power-based scheme under the spatially correlated shadowing effect. The experimental results demonstrate that the channel assignment dynamics converge to stable equilibria even in a real environment, particularly when uncontrollable APs exist.

Deploying low power nodes (LPNs), which reuse the spectrum licensed to a macrocell network, is considered to be a promising way to significantly boost network capacity. Due to the spectrum-sharing, the deployment of LPNs could trigger the severe problem of interference including intra-tier interference among dense LPNs and inter-tier interference between LPNs and the macro base station (MBS), which influences the system performance strongly. In this paper, we investigate a spectrum-sharing approach in the downlink for two-tier networks, which consists of small cells (SCs) with several LPNs and a macrocell with a MBS, aiming to mitigate the interference and improve the capacity of SCs. The spectrum-sharing approach is described as a multi-objective optimization problem. The problem is solved by the nondominated sorting genetic algorithm version II (NSGA-II), and the simulations show that the proposed spectrum-sharing approach is superior to the existing one.

Task mapping is one of the most important design processes in embedded manycore systems. This paper proposes a static task mapping technique for manycore real-time systems. The technique minimizes the number of cores while satisfying deadline constraints of individual tasks.

We propose an architecture for a 1-bit band-pass delta-sigma modulator (BP-DSM) that outputs concurrent multiband RF signals. The proposed BP-DSM consists of parallel bandpass filters (BPFs) in the feedback loop to suppress the quantization noise at each target frequency band while maintaining the stability. Each BPF is based on second-order parallel infinite impulse response (IIR) filters. This architecture can unify and reconfigure the split BPFs according to the number of bands. The architecture complexity is proportional to the bandwidth of each RF signal and is independent of the carrier spacing between the bands. The conventional architecture of a concurrent multiband digital modulator, reported previously, has multiple input ports to the dedicated BPF at each band and so it cannot be efficiently integrated. Measurements show that the proposed architecture is feasible for transmitting a concurrent dual-band and a triple-band by changing the 1-bit digital data stream while keeping a data transmission rate of 10Gb/s. We demonstrate that the proposed architecture outputs the signal with LTE intra-band and inter-band carrier aggregation on 0.8GHz, 2.1GHz and 3.5GHz, each with 40MHz bandwidth in 120MHz aggregated bandwidth, whose bandwidth surpasses the bandwidth with carrier aggregation of LTE-A up to 100MHz. Adjacent channel leakage ratios of -49dBc and -46dBc are achieved at 3.5GHz in the concurrent dual-band and triple-band, respectively.

Feature representation, as a key component of scene character recognition, has been widely studied and a number of effective methods have been proposed. In this letter, we propose the novel method named coupled spatial learning (CSL) for scene character representation. Different from the existing methods, the proposed CSL method simultaneously discover the spatial context in both the dictionary learning and coding stages. Concretely, we propose to build the spatial dictionary by preserving the corresponding positions of the codewords. Correspondingly, we introduce the spatial coding strategy which utilizes the spatiality regularization to consider the relationship among features in the Euclidean space. Based on the spatial dictionary and spatial coding, the spatial context can be effectively integrated in the visual representations. We verify our method on two widely used databases (ICDAR2003 and Chars74k), and the experimental results demonstrate that our method achieves competitive results compared with the state-of-the-art methods. In addition, we further validate the proposed CSL method on the Caltech-101 database for image classification task, and the experimental results show the good generalization ability of the proposed CSL.

Maximizing the profit of datacenter networks (DCNs) demands to satisfy more flows' requirements simultaneously, but existing schemes always allocate resource based on single flow attribute, which cannot carry out accurate resource allocation and make many flows failed. In this letter, we propose Highest Priority Flow First (HPFF) to maximize DCN profit, which allocates resource for flows according to the priority. HPFF employs a utility function that considers multiple flow attributes, including flow size, deadline and demanded bandwidth, to calculate the priority for each flow. The experiments on the testbed show that HPFF can improve the network profit by 6.75%-19.7% and decrease the number of failed flow by 26.3%-83.3% compared with existing schemes under real DCN workloads.

Small group detection is still a challenging problem in crowds. Traditional methods use the trajectory information to measure pairwise similarity which is sensitive to the variations of group density and interactive behaviors. In this paper, we propose two types of information by simultaneously incorporating trajectory and interaction information, to detect small groups in crowds. The trajectory information is used to describe the spatial proximity and motion information between trajectories. The interaction information is designed to capture the interactive behaviors from video sequence. To achieve this goal, two classifiers are exploited to discover interpersonal relations. The assumption is that interactive behaviors often occur in group members while there are no interactions between individuals in different groups. The pairwise similarity is enhanced by combining the two types of information. Finally, an efficient clustering approach is used to achieve small group detection. Experiments show that the significant improvement is gained by exploiting the interaction information and the proposed method outperforms the state-of-the-art methods.

Replacement of highly stressed logic gates with internal node control (INC) logics is known to be an effective way to alleviate timing degradation due to NBTI. We propose a path clustering approach to accelerate finding effective replacement gates. Upon the observation that there exist paths that always become timing critical after aging, critical path candidates are clustered to select representative path in each cluster. With efficient data structure to further reduce timing calculation, INC logic optimization has first became tractable in practical time. Through the experiments using a processor, 171x speedup has been demonstrated while retaining almost the same level of mitigation gain.

This paper presents a technique to reduce the quantum cost by making temporary changes to the functionality of a given Boolean function. This technique is one of the very few known methods based on manipulating Exclusive-or Sum-Of-Products (ESOP) expressions to reduce the quantum cost of the corresponding circuit. The idea involves adding Mixed Polarity Multiple-Control Toffoli (MPMCT) gates to temporarily change the functionality of the given function, so that the modified function has a smaller quantum cost. To compensate for the temporary change, additional gates are inserted into the circuit. The proposed method finds a small ESOP expression for the given function, and then finds a good pair of product terms in the ESOP expression so that the quantum cost can be reduced by applying the transformation. The proposed approach is likely to produce a better quantum cost reduction than the existing methods, and indeed experimental results confirm this expectation.

We study a reconfiguration problem for Steiner trees in an unweighted graph, which determines whether there exists a sequence of Steiner trees that transforms a given Steiner tree into another one by exchanging a single edge at a time. In this paper, we show that the problem is PSPACE-complete even for split graphs, while solvable in linear time for interval graphs and for cographs.

Verification of logic designs has been a long-standing bottleneck in the process of hardware design, where its automation and improvement of efficiency has demanding needs. Mainly simulation-based verification has been used for this purpose, and recently, coverage-driven verification has been widely used, of which target is improvement of some metric called coverage. Our target is the metric called toggle coverage. To find input patterns which cause some toggles on each signal, a SAT solver could be used, but this is computationally costly. In this paper, we study the effect of combination of random simulation and usage of a SAT solver. In particular, we use a SAT solver which can find multiple “diverse” solutions. With this solver, we can avoid generating similar patterns, which are unlikely to improve coverage. The experimental results show that, a small number of calls of a SAT solver can improve entire toggle coverage effectively, compared with simple random simulation.

During the development of a human embryo, the position of eyes moves medially and caudally in the viscerocranium. A statistical model of this process can play an important role in embryology by facilitating qualitative analyses of change. This paper proposes an algorithm to construct a spatiotemporal statistical model for the eyeballs of a human embryo. The proposed modeling algorithm builds a statistical model of the spatial coordinates of the eyeballs independently for each Carnegie stage (CS) by using principal component analysis (PCA). In the process, a q-Gaussian distribution with a model selection scheme based on the Aaike information criterion is used to handle a non-Gaussian distribution with a small sample size. Subsequently, it seamlessly interpolates the statistical models of neighboring CSs, and we present 10 interpolation methods. We also propose an estimation algorithm for the CS using our spatiotemporal statistical model. A set of images of eyeballs in human embryos from the Kyoto Collection was used to train the model and assess its performance. The modeling results suggested that information geometry-based interpolation under the assumption of a q-Gaussian distribution is the best modeling method. The average error in CS estimation was 0.409. We proposed an algorithm to construct a spatiotemporal statistical model of the eyeballs of a human embryo and tested its performance using the Kyoto Collection.

Collaborative filtering with only implicit feedbacks has become a quite common scenario (e.g. purchase history, click-through log, and page visitation). This kind of feedback data only has a small portion of positive instances reflecting the user's interaction. Such characteristics pose great challenges to dealing with implicit recommendation problems. In this letter, we take full advantage of matrix factorization and relative preference to make the recommendation model more scalable and flexible. In addition, we propose to take into consideration the concept of covisitation which captures the underlying relationships between items or users. To this end, we propose the algorithm Integrated Collaborative Filtering for Implicit Feedback incorporating Covisitation (ICFIF-C) to integrate matrix factorization and collaborative ranking incorporating the covisitation of users and items simultaneously to model recommendation with implicit feedback. The experimental results show that the proposed model outperforms state-of-the-art algorithms on three standard datasets.

This paper proposes 0-1-A-Ā LUT, a new programmable logic using atom switches, and a delay-optimal mapping algorithm for it. Atom switch is a non-volatile memory device of very small geometry which is fabricated between metal layers of a VLSI, and it can be used as a switch device of very small on-resistance and parasitic capacitance. While considerable area reduction of Look Up Tables (LUTs) used in conventional Field Programmable Gate Arrays (FPGAs) has been achieved by simply replacing each SRAM element with a memory element using a pair of atom switches, our 0-1-A-Ā LUT achieves further area and delay reduction. Unlike the conventional atom-switch-based LUT in which all k input signals are fed to a MUX, one of input signals is fed to the switch array, resulting area reduction due to the reduced number of inputs of the MUX from 2k to 2k-1, as well as delay reduction due to reduced fanout load of the input buffers. Since the fanout of this input buffers depends on the mapped logic function, this paper also proposes technology mapping algorithms to select logic function of fewer number of fanouts of input buffers to achieve further delay reduction. From our experiments, the circuit delay using our k-LUT is 0.94% smaller in the best case compared with using the conventional atom-switch-based k-LUT.