Functional encryption is a new paradigm of public-key encryption that allows a user to compute f(x) on encrypted data CT(x) with a private key SKf to finely control the revealed information. Multi-input functional encryption is an important extension of (single-input) functional encryption that allows the computation f(x1,...,xn) on multiple ciphertexts CT(x1),...,CT(xn) with a private key SKf. Although multi-input functional encryption has many interesting applications like running SQL queries on encrypted database and computation on encrypted stream, current candidates are not yet practical since many of them are built on indistinguishability obfuscation. To solve this unsatisfactory situation, we show that practical two-input functional encryption schemes for inner products can be built based on bilinear maps. In this paper, we first propose a two-input functional encryption scheme for inner products in composite-order bilinear groups and prove its selective IND-security under simple assumptions. Next, we propose a two-client functional encryption scheme for inner products where each ciphertext can be associated with a time period and prove its selective IND-security. Furthermore, we show that our two-input functional encryption schemes in composite-order bilinear groups can be converted into schemes in prime-order asymmetric bilinear groups by using the asymmetric property of asymmetric bilinear groups.

Instruction set randomization (ISR) is a cost-effective obfuscation technique that modifies or enhances the relationship between instructions and machine languages. An Instruction Register File (IRF), a list of frequently used instructions, can be used for ISR by providing the way of indirect access to them. This study examines the IRF that integrates a positional register, which was proposed as a supplementary unit of the IRF, for the sake of tamper resistance. According to our evaluation, with a new design for the contents of the positional register, the measure of tamper resistance was increased by 8.2% at a maximum, which corresponds to a 32.2% increase in the size of the IRF. The number of logic elements increased by the addition of the positional register was 3.5% of its baseline processor.

This letter studies secure transmission design with finite alphabet input for cooperative jamming network under individual power constraint. By adopting the zero-force scheme, where the jamming signal is fully laid in the null space of the relay-destination channel, the problem of enhancing the achievable secrecy rate is decomposed into two independent subproblems: relay weights design and power control. We reveal that the problem of relay weights design is identical to the problem of minimizing the maximal equivalent source-eavesdropper channel gain, which can be transformed into a semi-definite programming (SDP) problem and thus is tackled using interior point method. Besides, the problem of power control is solved with the fundamental relation between mutual information and minimum mean square error (MMSE). Numerical results show that the proposed scheme achieves significant performance gains compared to the conventional Gaussian design.

In this study, the effect of atomically flat Si(100) surface on Hf-based Metal-Oxide-Nitride-Oxide-Silicon (MONOS) structure was investigated. After the atomically flat Si(100) surface formation by annealing at 1050/60min in Ar/4%H2 ambient, HfO2(O)/HfN1.0(N)/HfO2(O) structure with thickness of 10/3/2nm, respectively, was in-situ deposited by electron cyclotron resonance (ECR) plasma sputtering. The memory window (MW) of Al/HfO2/HfN1.0/HfO2/p-Si(100) diodes was increased from 1.0V to 2.5V by flattening of Si(100) surface. The program and erase (P/E) voltage/time were set as 10V/5s and -8V/5s, respectively. Furthermore, it was found that the gate current density after the 103P/E cycles was decreased one order of magnitude by flattening of Si(100) surface in Ar/4.0%H2 ambient.

In this paper, we have investigated the PdEr-silicide formation utilizing a developed PdEr-alloy target for sputtering, and evaluated the contact resistivity of PdEr-silicide layer formed on n-Si(100) by dopant segregation process for the first time. Pd2Si and ErSi2 have same hexagonal structure, while the Schottky barrier height for electron (Φbn) is different as 0.75 eV and 0.28 eV, respectively. A 20 nm-thick PdEr-alloy layer was deposited on the n-Si(100) substrates utilizing a developed PdEr-alloy target by the RF magnetron sputtering at room temperature. Then, 10 nm-thick TiN encapsulating layer was in-situ deposited at room temperature. Next, silicidation was carried out by the RTA at 500 for 5 min in N2/4.9%H2 followed by the selective etching. From the J-V characteristics of fabricated Schottky diode, qΦbn was reduced from 0.75 eV of Pd2Si to 0.43 eV of PdEr-silicide. Furthermore, 4.0x10-8Ωcm2 was extracted for the PdEr-silicide to n-Si(100) by the dopant segregation process.

Hard-type oscillators for ultrahigh frequency applications were proposed based on resonant tunneling diodes (RTDs) and a HEMT trigger circuit. The hard-type oscillators initiate oscillation only after external excitation. This is advantageous for suppressing the spurious oscillation in the bias line, which is one of the most significant problems in the RTD oscillators. We first investigated a series-connected circuit of a resistor and an RTD for constructing a hard-type oscillator. We carried out circuit simulation using the practical device parameters. It was demonstrated that the stable oscillation can be obtained for such oscillators. Next, we proposed to use series-connected RTDs for the gain block of the hard-type oscillators. The series circuits of RTDs show the negative differential resistance in very narrow regions, or no regions at all, which makes impossible to use such circuits for the conventional soft-type oscillators. However, with the trigger circuit, they can be used for hard-type oscillators. We confirmed the oscillation and the bias stability of these oscillators, and also demonstrated that the voltage swing can be easily increased by increasing the number of RTDs connected in series. This is promising method to overcome the power restriction of the RTD oscillators.

We have been investigating reversible quantum-flux-parametron (RQFP), which is a reversible logic gate using adiabatic quantum-flux-parametron (AQFP), toward realizing superconductor reversible computing. In this paper, we review the recent progress of RQFP. Followed by a brief explanation on AQFP, we first review the difference between irreversible logic gates and RQFP in light of time evolution and energy dissipation, based on our previous studies. Numerical calculation results reveal that the logic state of RQFP can be changed quasi-statically and adiabatically, or thermodynamically reversibly, and that the energy dissipation required for RQFP to perform a logic operation can be arbitrarily reduced. Lastly, we show recent experimental results of an RQFP cell, which was newly designed for the latest cell library. We observed the wide operation margins of more than 4.7dB with respect to excitation currents.

The issue of robust multi-input multi-output (MIMO) radar waveform design is investigated in the presence of imperfect clutter prior knowledge to improve the worst-case detection performance of space-time adaptive processing (STAP). Robust design is needed because waveform design is often sensitive to uncertainties in the initial parameter estimates. Following the min-max approach, a robust waveform covariance matrix (WCM) design is formulated in this work with the criterion of maximization of the worst-case output signal-interference-noise-ratio (SINR) under the constraint of the initial parameter estimation errors to ease this sensitivity systematically and thus improve the robustness of the detection performance to the uncertainties in the initial parameter estimates. To tackle the resultant complicated and nonlinear robust waveform optimization issue, a new diagonal loading (DL) based iterative approach is developed, in which the inner and outer optimization problems can be relaxed to convex problems by using DL method, and hence both of them can be solved very effectively. As compared to the non-robust method and uncorrelated waveforms, numerical simulations show that the proposed method can improve the robustness of the detection performance of STAP.

Public safety networks need to more effectively meet the increasing demands for images or videos to be shared among first responders and incident commanders. Long term evolution (LTE) networks are considered to be candidates to achieve such broadband services. Capital expenditures in deploying base stations need to be decreased to introduce LTE for public safety. However, out-of-coverage areas tend to occur in cell edge areas or inside buildings because the cell areas of base stations for public safety networks are larger than those for commercial networks. The 3rd Generation Partnership Program (3GPP) in Release 13 has investigated device-to-device (D2D) based relay communication as a means to fill out-of-coverage areas in public safety LTE (PS-LTE). This paper proposes a relay selection scheme based on effective path throughput from an out-of-coverage terminal to a base station via an in-coverage relay terminal, which enables the optimal relay terminal to be selected. System level simulation results assuming on radii of 20km or less revealed that the proposed scheme could provide better user ratios that satisfied the throughput requirements for video transmission than the scheme standardized in 3GPP. Additionally, an evaluation that replicates actual group of fire-fighters indicated that the proposed scheme enabled 90% of out-of-coverage users to achieve the required throughput, i.e., 1.0Mbps, to transmit video images.

In the era of e-commerce, purchase behavior prediction is one of the most important issues to promote both online companies' sales and the consumers' experience. The previous researches usually use the feature engineering and ensemble machine learning algorithms for the prediction. The performance really depends on designed features and the scalability of algorithms because the large-scale data and a lot of categorical features lead to huge samples and the high-dimensional feature. In this study, we explore an alternative to use tree-based Feature Transformation (FT) and simple machine learning algorithms (e.g. Logistic Regression). Random Forest (RF) and Gradient Boosting decision tree (GB) are used for FT. Then, the simple algorithm, rather than ensemble algorithms, is used to predict purchase behavior based on transformed features. Tree-based FT regards the leaves of trees as transformed features, and can learn high-order interactions among original features. Compared with RF, if GB is used for FT, simple algorithms are enough to achieve better performance.

3D ball tracking is of great significance in ping-pong game analysis, which can be utilized to applications such as TV contents and tactic analysis, with some of them requiring real-time implementation. This paper proposes a CPU-GPU platform based Particle Filter for multi-view ball tracking including 4 proposals. The multi-peak estimation and the ball-like observation model are proposed in the algorithm design. The multi-peak estimation aims at obtaining a precise ball position in case the particles' likelihood distribution has multiple peaks under complex circumstances. The ball-like observation model with 4 different likelihood evaluation, utilizes the ball's unique features to evaluate the particle's similarity with the target. In the GPU implementation, the double-queue structure and the vectorized data combination are proposed. The double-queue structure aims at achieving task parallelism between some data-independent tasks. The vectorized data combination reduces the time cost in memory access by combining 3 different image data to 1 vector data. Experiments are based on ping-pong videos recorded in an official match taken by 4 cameras located in 4 corners of the court. The tracking success rate reaches 99.59% on CPU. With the GPU acceleration, the time consumption is 8.8 ms/frame, which is sped up by a factor of 98 compared with its CPU version.

This paper aims to establish expressions for IEEE 802.11 string-topology multi-hop networks with transmission control protocol (TCP) traffic flow. The relationship between the throughput and transport-layer function in string-topology multi-hop network is investigated. From the investigations, we obtain an analysis policy that the TCP throughput under the TCP functions is obtained by deriving the throughput of the network with simplified into two asymmetric user datagram protocol flows. To express the asymmetry, analytical expressions in medium access control-, network-, and transport layers are obtained based on the airtime expression. The expressions of the network layer and those of transport layer are linked using the “delayed ACK constraint,” which is a new concept for TCP analysis. The analytical predictions agree well with the simulation results, which prove the validity of the obtained analytical expressions and the analysis policy in this paper.

CMOS microprocessors are limited in their capacity for clock speed improvement because of increasing computing power, i.e., they face a power-wall problem. Single-flux-quantum (SFQ) circuits offer a solution with their ultra-fast-speed and ultra-low-power natures. This paper introduces our contributions towards ultra-high-speed cryogenic SFQ computing. The first step is to design SFQ microprocessors. From qualitatively and quantitatively evaluating past-designed SFQ microprocessors, we have found that revisiting the architecture of SFQ microprocessors and on-chip caches is the first critical challenge. On the basis of cross-layer discussions and analysis, we came to the conclusion that a bit-parallel gate-level pipeline architecture is the best solution for SFQ designs. This paper summarizes our current research results targeting SFQ microprocessors and on-chip cache architectures.

Many of affine motion compensation techniques proposed thus far employ least-square-based techniques in estimating affine parameters, which requires a hardware structure different from conventional block-matching-based one. This paper proposes a new affine motion estimation/compensation framework friendly to block-matching-based parameter estimation, and applies it to an HEVC encoder to demonstrate its coding efficiency and computation cost. To avoid a nest of search loops, a new affine motion model is first introduced by decomposing the conventional 4-parameter affine model into two 3-parameter ones. Then, a block-matching-based fast parameter estimation technique is proposed for the models. The experimental results given in this paper show that our approach is advantageous over conventional techniques.

This paper presents a 27.5-29.6GHz fractional-N frequency synthesizer using reference and frequency doublers to achieve low in-band and out-of-band phase-noise for 5G mobile communications. A consideration of the baseband carrier recovery circuit helps estimate phase noise requirement for high modulation scheme. The push-push amplifier and 28GHz balun help achieving differential signals with low out-of-band phase noise while consuming low power. A charge pump with gated offset as well as reference doubler help reducing PD noise resulting in low in-band phase noise while sampling loop filter helps reduce spurs. The proposed synthesizer has been implemented in 65nm CMOS technology achieving an in-band and out-of-band phase noise of -78dBc/Hz and -126dBc/Hz, respectively. It consumes only a total power of 33mW. The jitter-power figure-of-merit (FOM) is -231dB which is the highest among the state of the art >20GHz fractional-N PLLs using a low reference clock (<200MHz). The measured reference spurs are less than -80dBc.

Mobile cloud computing (MCC) has been proposed as a new approach to enhance mobile device performance via computation offloading. The growth in cloud computing energy consumption is placing pressure on both the environment and cloud operators. In this paper, we focus on energy-efficient resource management in MCC and aim to reduce cloud operators' energy consumption through resource management. We establish a deterministic resource management model by solving a combinatorial optimization problem with constraints. To obtain the resource management strategy in deterministic scenarios, we propose a deterministic strategy algorithm based on the adaptive group genetic algorithm (AGGA). Wireless networks are used to connect to the cloud in MCC, which causes uncertainty in resource management in MCC. Based on the deterministic model, we establish a stochastic model that involves a stochastic optimization problem with chance constraints. To solve this problem, we propose a stochastic strategy algorithm based on Monte Carlo simulation and AGGA. Experiments show that our deterministic strategy algorithm obtains approximate optimal solutions with low algorithmic complexity with respect to the problem size, and our stochastic strategy algorithm saves more energy than other algorithms while satisfying the chance constraints.

Many life-log analysis applications, which transfer data from cameras and sensors to a Cloud and analyze them in the Cloud, have been developed as the use of various sensors and Cloud computing technologies has spread. However, difficulties arise because of the limited network bandwidth between such sensors and the Cloud. In addition, sending raw sensor data to a Cloud may introduce privacy issues. Therefore, we propose a pipelined method for distributed deep learning processing between sensors and the Cloud to reduce the amount of data sent to the Cloud and protect the privacy of users. In this study, we measured the processing times and evaluated the performance of our method using two different datasets. In addition, we performed experiments using three types of machines with different performance characteristics on the client side and compared the processing times. The experimental results show that the accuracy of deep learning with coarse-grained data is comparable to that achieved with the default parameter settings, and the proposed distributed processing method has performance advantages in cases of insufficient network bandwidth between realistic sensors and a Cloud environment. In addition, it is confirmed that the process that most affects the overall processing time varies depending on the machine performance on the client side, and the most efficient distribution method similarly differs.

This paper presents an algorithm for a physical activity (PA) classification and metabolic equivalents (METs) monitoring and its System-on-a-Chip (SoC) implementation to realize both power reduction and high estimation accuracy. Long-term PA monitoring is an effective means of preventing lifestyle-related diseases. Low power consumption and long battery life are key features supporting the wider dissemination of the monitoring system. As described herein, an adaptive sampling method is implemented for longer battery life by minimizing the active rate of acceleration without decreasing accuracy. Furthermore, advanced PA classification using both the heart rate and acceleration is introduced. The proposed algorithms are evaluated by experimentation with eight subjects in actual conditions. Evaluation results show that the root mean square error with respect to the result of processing with fixed sampling rate is less than 0.22[METs], and the mean absolute error is less than 0.06[METs]. Furthermore, to minimize the system-level power dissipation, a dedicated SoC is implemented using 130-nm CMOS process with FeRAM. A non-volatile CPU using non-volatile memory and a flip-flop is used to reduce the stand-by power. The proposed algorithm, which is implemented using dedicated hardware, reduces the active rate of the CPU and accelerometer. The current consumption of the SoC is less than 3-µA. And the evaluation system using the test chip achieves 74% system-level power reduction. The total current consumption including that of the accelerometer is 11.3-µA on average.

The Collaborative Filtering (CF) algorithms work fairly well in personalized recommendation except in sparse data environment. To deal with the sparsity problem, researchers either take into account auxiliary information extracted from additional data resources, or set the missing ratings with default values, e.g., video popularity. Nevertheless, the former often costs high and incurs difficulty in knowledge transference whereas the latter degrades the accuracy and coverage of recommendation results. To our best knowledge, few literatures take advantage of users' preference on video popularity to tackle this problem. In this paper, we intend to enhance the performance of recommendation algorithm via the inference of the users' popularity preferences (PPs), especially in a sparse data environment. We propose a scheme to aggregate users' PPs and a Collaborative Filtering based algorithm to make the inference of PP feasible and effective from a small number of watching records. We modify a k-Nearest-Neighbor recommendation algorithm and a Matrix Factorization algorithm via introducing the inferred PP. Experiments on a large-scale commercial dataset show that the modified algorithm outperforms the original CF algorithms on both the recommendation accuracy and coverage. The significance of improvement is significant especially with the data sparsity.

Efficient optic disc (OD) segmentation plays a significant role in retinal image analysis and retinal disease screening. In this paper, we present a full-automatic segmentation approach called double boundary extraction for the OD segmentation. The proposed approach consists of the following two stages: first, we utilize an unsupervised learning technology and statistical method based on OD boundary information to obtain the initial contour adaptively. Second, the final optic disc boundary is extracted using the proposed LSO model. The performance of the proposed method is tested on the public DIARETDB1 database and the experimental results demonstrate the effectiveness and advantage of the proposed method.