This paper proposes a novel multiple access method that enables concurrent sensor data streaming from multiple batteryless, wireless sensor tags. The access method is a pseudo-FDMA scheme based on the subcarrier backscatter communication principle, which is widely employed in passive RFID and radar systems. Concurrency is realized by assigning a dedicated subcarrier to each sensor tag and letting all sensor tags backscatter simultaneously. Because of the nature of the subcarrier, which is produced by constant rate switching of antenna impedance without any channel filter in the sensor tag, the tag-to-reader link always exhibits harmonics. Thus, it is important to reject harmonics when concurrent data streaming is required. This paper proposes a harmonics rejecting receiver to allow simultaneous multiple subcarrier usage. This paper particularly focuses on analog sensor data streaming which minimizes the functional requirements on the sensor tag and frequency bandwidth. The harmonics rejection receiver is realized by carefully handling group delay and phase delay of the subcarrier envelope and the carrier signal to accurately produce replica of the harmonics by introducing Hilbert and inverse Hilbert transformations. A numerical simulator with Simulink and a hardware implementation with USRP and LabVIEW have been developed. Simulations and experiments reveal that even if the CIR before harmonics rejection is 0dB, the proposed receiver recovers the original sensor data with over 0.98 cross-correlation.

Scalable high efficiency video coding (SHVC) can provide variable video quality according to terminal devices. However, the computational complexity of SHVC is increased by introducing new techniques based on high efficiency video coding (HEVC). In this paper, a hardware oriented low complexity algorithm is proposed. The hardware oriented proposals have two key points. Firstly, the coding unit depth is determined by analyzing the boundary correlation between coding units before encoding process starts. Secondly, the redundant calculation of R-D optimization is reduced by adaptively using the information of the neighboring coding units and the co-located units in the base layer. The simulation results show that the proposed algorithm can achieve over 62% computation complexity reduction compared to the original SHM11.0. Compared with other related work, over 11% time saving have been achieved without PSNR loss. Furthermore, the proposed algorithm is hardware friendly which can be implemented in a small area.

Interconnection network is one of the inevitable components in parallel computers, since it is responsible to communication capabilities of the systems. It affects the system-level performance as well as the physical and logical structure of the systems. Although many studies are reported to enhance the interconnection network technology, we have to discuss many issues remaining. One of the most important issues is congestion management. In an interconnection network, many packets are transferred simultaneously and the packets interfere to each other in the network. Congestion arises as a result of the interferences. Its fast spreading speed seriously degrades communication performance and it continues for long time. Thus, we should appropriately control the network to suppress the congested situation for maintaining the maximum performance. Many studies address the problem and present effective methods, however, the maximal performance in an ideal situation is not sufficiently clarified. Solving the ideal performance is, in general, an NP-hard problem. This paper introduces particle swarm optimization (PSO) methodology to overcome the problem. In this paper, we first formalize the optimization problem suitable for the PSO method and present a simple PSO application as naive models. Then, we discuss reduction of the size of search space and introduce three practical variations of the PSO computation models as repetitive model, expansion model, and coding model. We furthermore introduce some non-PSO methods for comparison. Our evaluation results reveal high potentials of the PSO method. The repetitive and expansion models achieve significant acceleration of collective communication performance at most 1.72 times faster than that in the bursty communication condition.

Scaling the supply voltage (Vdd) and threshold voltage (Vth) for minimizing the energy consumption of processors dynamically is highly desired for applications such as wireless sensor network and Internet of Things (IoT). In this paper, we refer to the pair of Vdd and Vth, which minimizes the energy consumption of the processor under a given operating condition, as a minimum energy point (MEP in short). Since the MEP is heavily dependent on an operating condition determined by a chip temperature, an activity factor, a process variation, and a performance required for the processor, it is not very easy to closely track the MEP at runtime. This paper proposes a simple but effective algorithm for dynamically tracking the MEP of a processor under a wide range of operating conditions. Gate-level simulation of a 32-bit RISC processor in a 65nm process demonstrates that the proposed algorithm tracks the MEP under a situation that operating condition widely vary.

This paper deals with a broadcast network with a server and many users. The server has files of content such as music and videos, and each user requests one of these files, where each file consists of some separated layers like a file encoded by a scalable video coding. On the other hand, each user has a local memory, and a part of information of the files is cached (i.e., stored) in these memories in advance of users' requests. By using the cached information as side information, the server encodes files based on users' requests. Then, it sends a codeword through an error-free shared link for which all users can receive a common codeword from the server without error. We assume that the server transmits some layers up to a certain level of requested files at each different transmission rate (i.e., the codeword length per file size) corresponding to each level. In this paper, we focus on the region of tuples of these rates such that layers up to any level of requested files are recovered at users with an arbitrarily small error probability. Then, we give inner and outer bounds on this region.

In this work, two new proposed NMF models are developed for facial expression recognition. They are called maximum volume constrained nonnegative matrix factorization (MV_NMF) and maximum volume constrained graph nonnegative matrix factorization (MV_GNMF). They achieve sparseness from a larger simplicial cone constraint and the extracted features preserve the topological structure of the original images.

Multivariate Public Key Cryptography (MPKC) is one of the main candidates for secure communication in a post-quantum era. Recently, Yasuda and Sakurai proposed at ICICS 2015 a new multivariate encryption scheme called SRP, which offers efficient decryption, a small blow up factor between plaintext and ciphertext and resists all known attacks against multivariate schemes. However, similar to other MPKC schemes, the key sizes of SRP are quite large. In this paper we propose a technique to reduce the key size of the SRP scheme, which enables us to reduce the size of the public key by up to 54%. Furthermore, we can use the additional structure in the public key polynomials to speed up the encryption process of the scheme by up to 50%. We show by experiments that our modifications do not weaken the security of the scheme.

In this letter, we propose a novel technique to increase the visibility of the hazy image. Benefiting from the atmospheric scattering model and the invariance principle for scene structure, we formulate structure constraint equations that derive from two simulated inputs by performing gamma correction on the input image. Relying on the inherent boundary constraint of the scattering function, the expected scene albedo can be well restored via these constraint equations. Extensive experimental results verify the power of the proposed dehazing technique.

The effect of the hidden terminal (HT) over multi-hop cascaded wireless networks with the omni-directional full-duplex relays will cause data collision. We allocate the frequency band among different hops in an orthogonal way based on link grouping strategy to avoid this HT problem. In order to maximize the achievable rate, an optimal frequency allocation scheme is proposed by boundary alignment. Performance analyses are provided and further validated by the simulation results.

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 paper, the concept of gradient order is introduced and a novel gradient order curve descriptor (GOCD) for curve matching is proposed. The GOCD is constructed in the following main steps: firstly, curve support region independent of the dominant orientation is determined and then divided into several sub-regions based on gradient magnitude order; then gradient order feature (GOF) of each feature point is generated by encoding the local gradient information of the sample points; the descriptor is finally achieved by turning to the description matrix of GOF. Since both the local and the global gradient information are captured by GOCD, it is more distinctive and robust compared with the existing curve matching methods. Experiments under various changes, such as illumination, viewpoint, image rotation, JPEG compression and noise, show the great performance of GOCD. Furthermore, the application of image mosaic proves GOCD can be used successfully in actual field.

A fully integrated CMOS pulse transceiver with digital beam-formability for mm-wave active imaging is presented. The on-chip pulse transmitter of the transceiver includes an eight-element antenna array connected to eight pulse transmitters and a built-in relative pulse delay calibration system. The receiver employs a non-coherent detection method by using a FET direct-power detection circuit integrated with an antenna. The receiver dipole-patch antenna derives from the transmitter antenna but is modified with an on-chip DC-bias tail by shorting two arms of the dipole. The bandwidth of the receiver antenna with the DC-bias tail is designed to achieve 50.4-GHz in simulation and to cover the bandwidth of transmitter antennas. The output of the receiver antenna is connected to a resistive self-mixer followed by an on-chip low pass filter and then an amplifier stage. The built-in relative pulse delay calibration system is used to align the pulse delays of each transmitter array elements for the purpose of controlling the beam steering towards imaging objects. Both transmitter and receiver chips are fabricated in a 65-nm CMOS technology process. Measured pulse waveform of the receiver after relatively aligning all Tx's pulses is 0.91 mV (peak-peak) and 3-ns duration with a distance of 25mm between Rx and Tx. Beam steering angles are achieved in measurement by changing the digital delay code of antenna elements. Experimental results show that the proposed on-chip transceiver has an ability of digital transmitted-pulse calibration, controlling of beam-steeting, and pulse detection for active imaging applications.

Hotspots occur frequently in 3D multi-core processors (3D-MCPs), and they may adversely impact both the reliability and lifetime of a system. We present a new thermally constrained task scheduler based on a thermal-pattern-aware voltage assignment (TPAVA) to reduce hotspots in and optimize the performance of 3D-MCPs. By analyzing temperature profiles of different voltage assignments, TPAVA pre-emptively assigns different initial operating-voltage levels to cores for reducing temperature increase in 3D-MCPs. The proposed task scheduler consists of an on-line allocation strategy and a new voltage-scaling strategy. In particular, the proposed on-line allocation strategy uses the temperature-variation rates of the cores and takes into two important thermal behaviors of 3D-MCPs that can effectively minimize occurrences of hotspots in both thermally homogeneous and heterogeneous 3D-MCPs. Furthermore, a new vertical-grouping voltage scaling (VGVS) strategy that considers thermal correlation in 3D-MCPs is used to handle thermal emergencies. Experimental results indicate that, when compared to a previous online thermally constrained task scheduler, the proposed task scheduler can reduce hotspot occurrences by approximately 66% (71%) and improve throughput by approximately 8% (2%) in thermally homogeneous (heterogeneous) 3D-MCPs. These results indicate that the proposed task scheduler is an effective technique for suppressing hotspot occurrences and optimizing throughput for 3D-MCPs subject to thermal constraints.

In this paper, we treat (k, L, n) ramp secret sharing schemes (SSSs) that can detect impersonation attacks and/or substitution attacks. First, we derive lower bounds on the sizes of the shares and random number used in encoding for given correlation levels, which are measured by the mutual information of shares. We also derive lower bounds on the success probabilities of attacks for given correlation levels and given sizes of shares. Next we propose a strong (k, L, n) ramp SSS against substitution attacks. As far as we know, the proposed scheme is the first strong (k, L, n) ramp SSSs that can detect substitution attacks of at most k-1 shares. Our scheme can be applied to a secret SL uniformly distributed over GF(pm)L, where p is a prime number with p≥L+2. We show that for a certain type of correlation levels, the proposed scheme can achieve the lower bounds on the sizes of the shares and random number, and can reduce the success probability of substitution attacks within nearly L times the lower bound when the number of forged shares is less than k. We also evaluate the success probability of impersonation attack for our schemes. In addition, we give some examples of insecure ramp SSSs to clarify why each component of our scheme is essential to realize the required security.

Scaling supply voltage (VDD) and threshold voltage (Vth) dynamically has a strong impact on energy efficiency of CMOS LSI circuits. Techniques for optimizing VDD and Vth simultaneously under dynamic workloads are thus widely investigated over the past 15 years. In this paper, we refer to the optimum pair of VDD and Vth, which minimizes the energy consumption of a circuit under a specific performance constraint, as a minimum energy point (MEP). Based on the simple transregional models of a CMOS circuit, this paper derives a simple necessary and sufficient condition for the MEP operation. The simple condition helps find the MEP of CMOS circuits. Measurement results using standard-cell based memories (SCMs) fabricated in a 65-nm process technology also validate the condition derived in this paper.

Coordinated checkpointing is a widely-used checkpoint/restart protocol for fault-tolerance in large-scale HPC systems. However, this protocol will involve massive amounts of I/O concentration, resulting in considerably high checkpoint overhead and high energy consumption. This paper focuses on speculative checkpointing, a CPR mechanism that allows for temporal distribution of checkpointings to avoid I/O concentration. We propose execution time and energy models for speculative checkpointing, and investigate energy-performance characteristics when speculative checkpointing is adopted in exascale systems. Using these models, we study the benefit of speculative checkpointing over coordinated checkpointing under various realistic scenarios for exascale HPC systems. We show that, compared to coordinated checkpointing, speculative checkpointing can achieve up to a 11% energy reduction at the cost of a relatively-small increase in the execution time. In addition, a significant energy-performance trade-off is expected when the system scale exceeds 1.2 million nodes.

Cache affinity has been proved to have great impact on the performance of packet processing applications on multi-core platforms. Flow-based packet scheduling can make the best of data cache affinity with flow associated data and context structures. However, little work on packet scheduling algorithms has been conducted when it comes to instruction cache (I-Cache) affinity in modified pipelining (MPL) architecture for multi-core systems. In this paper, we propose a protocol-aware packet scheduling (PAPS) algorithm aiming at maximizing I-Cache affinity at protocol dependent stages in MPL architecture for multi-protocol processing (MPP) scenario. The characteristics of applications in MPL are analyzed and a mapping model is introduced to illustrate the procedure of MPP. Besides, a stage processing time model for MPL is presented based on the analysis of multi-core cache hierarchy. PAPS is a kind of flow-based packet scheduling algorithm and it schedules flows in consideration of both application-level protocol of flows and load balancing. Experiments demonstrate that PAPS outperforms the Round Robin algorithm and the HRW-based (HRW) algorithm for MPP applications. In particular, PAPS can eliminate all I-Cache misses at protocol dependent stage and reduce the average CPU cycle consumption per packet by more than 10% in comparison with HRW.

Various types of classifiers and feature extraction methods for acoustic scene classification have been recently proposed in the IEEE Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 Challenge Task 1. The results of the final evaluation, however, have shown that even top 10 ranked teams, showed extremely low accuracy performance in particular class pairs with similar sounds. Due to such sound classes being difficult to distinguish even by human ears, the conventional deep learning based feature extraction methods, as used by most DCASE participating teams, are considered facing performance limitations. To address the low performance problem in similar class pair cases, this letter proposes to employ a recurrent neural network (RNN) based source separation for each class prior to the classification step. Based on the fact that the system can effectively extract trained sound components using the RNN structure, the mid-layer of the RNN can be considered to capture discriminative information of the trained class. Therefore, this letter proposes to use this mid-layer information as novel discriminative features. The proposed feature shows an average classification rate improvement of 2.3% compared to the conventional method, which uses additional classifiers for the similar class pair issue.

This work presents a new approach which derives a learned data representation method through matrix factorization on the complex domain. In particular, we introduce an encoding matrix-a new representation of data-that satisfies the simplicial constraint of the projective basis matrix on the field of complex numbers. A complex optimization framework is provided. It employs the gradient descent method and computes the derivative of the cost function based on Wirtinger's calculus.

In this letter, we propose a novel and effective haze removal method by using the structure-aware atmospheric veil. More specifically, the initial atmospheric veil is first estimated based on dark channel prior and morphological operator. Furthermore, an energy optimization function considering the structure feature of the input image is constructed to refine the initial atmospheric veil. At last, the haze-free image can be restored by inverting the atmospheric scattering model. Additionally, brightness adjustment is also performed for preventing the dehazing result too dark. Experimental results on hazy images reveal that the proposed method can effectively remove the haze and yield dehazing results with vivid color and high scene visibility.