This paper proposes 1-bit feedforward distortion compensation for digital radio frequency conversion (DRFC) with 1-bit bandpass delta-sigma modulation (BP-DSM). The 1-bit BP-DSM allows direct RF signal transmission from a digitally modulated signal. However, it has been previously reported that 1-bit digital pulse trains with non-ideal rectangle waveform cause spectrum regrowth. The proposed architecture adds a feedforward path with another 1-bit BP-DSM and so can cancel out the distortion components at any target carrier frequency. Both the main signal and the distortion compensation signal are 1-bit digital pulse trains and so no additional analog RF circuit is required for distortion compensation. Simulation results show that the proposed method holds the adjacent channel leakage ratio to 60dB for LTE signal transmission. A prototype of the proposed 1-bit DRFC with an additional 1-bit BP-DSM in the feedforward path shows an ACLR of 50dB, 4dB higher than that of the conventional 1-bit DRFC.

GitHub is a developers' social networking service that hosts a great number of open source software (OSS) projects. Although some of the hosted projects are growing and have many developers, most projects are organized by a few developers and face difficulties in terms of sustainability. OSS projects depend mainly on volunteer developers, and attracting and retaining these volunteers are major concerns of the project stakeholders. To investigate the population structures of OSS development communities in detail and conduct software analytics to obtain actionable information, we apply a demographic approach. Demography is the scientific study of population and seeks to identify the levels and trends in the size and components of a population. This paper presents a case study, investigating the characteristics of the population structures of OSS projects on GitHub, and shows population projections generated with the well-known cohort component method. We found that there are four types of population structures in OSS development communities in terms of experiences and contributions. In addition, we projected the future population accurately using a cohort component population projection method. This method predicts a population of the next period using a survival rate calculated from past population. To the best of our knowledge, this is the first study that applied demography to the field of OSS research. Our approach addressing OSS-related problems based on demography will bring new insights, since studying population is novel in OSS research. Understanding current and future structures of OSS projects can help practitioners to monitor a project, gain awareness of what is happening, manage risks, and evaluate past decisions.

In this paper, we present a faster (wall-clock time) sorting method for numerical data subjected to fully homomorphic encryption (FHE). Owing to circuit-based construction and the FHE security property, most existing sorting methods cannot be applied to encrypted data without significantly compromising efficiency. The proposed algorithm utilizes the cryptographic single-instruction multiple-data (SIMD) operation, which is supported by most existing FHE algorithms, to reduce the computational overhead. We conducted a careful analysis of the number of required recryption operations, which are the computationally dominant operations in FHE. Accordingly, we verified that the proposed SIMD-based sorting algorithm completes the given task more quickly than existing sorting methods if the number of data items and (or) the maximum bit length of each data item exceed specific thresholds.

There is a strong demand to enjoy broadband and stable Internet connectivity not only in office and the home but also in high-speed train. Several systems are providing high-speed train with Internet connectivity using various technologies such as leaky coaxial cable (LCX), Wi-Fi, and WiMAX. However, their actual throughputs are less than 2Mbps. We developed a free-space optical (FSO) communication transceiver called LaserTrainComm2014 that achieves the throughput of 1 Gbps between the ground and a train. LaserTrainComm2014 employs a high-speed image sensor for coarse tracking and a quadrant photo-diode (QPD) for accurate tracking. Since the image captured by the high-speed image sensor has several types of noise, image processing is necessary to detect the beacon light of the other LaserTrainComm2014. As a result of field experiments in a vehicle test course, LaserTrainComm2014 achieves handover time of 21 milliseconds (ms) in the link layer at the speed of 60km/h. Even if the network layer signaling takes time of 10 milliseconds, the total communication disruption time due to handover is short enough to provide passengers with Internet connectivity for live streaming Internet applications such as YouTube, Internet Radio, and Skype.

An application of laser annealing process, which is used to form the shallow P-type Base junction for 20-V planar power MOSFETs (Metal Oxide Semiconductor Field Effect Transistors) is proposed. We demonstrated that the fabricated devices integrated with laser annealing process have superior electrical characteristics than those fabricated according to the standard process. Moreover, the threshold voltage variation of the devices applied by the new annealing process is effectively suppressed. This is due to that a uniform impurity distribution at the channel region is achieved by adopting laser annealing. Laser annealing technology can be applied as a reliable, effective, and advantageous process for the low-voltage power MOSFETs.

Flow classification is of great significance for network management. Machine-learning-based flow classification is widely used nowadays, but features which depict the non-Gaussian characteristics of network flows are still absent. In this paper, we propose the Windowed Higher-order Statistical Analysis (WHOSA) for machine-learning-based flow classification. In our methodology, a network flow is modeled as three different time series: the flow rate sequence, the packet length sequence and the inter-arrival time sequence. For each sequence, both the higher-order moments and the largest singular values of the Bispectrum are computed as features. Some lower-order statistics are also computed from the distribution to build up the feature set for contrast, and C4.5 decision tree is chosen as the classifier. The results of the experiment reveals the capability of WHOSA in flow classification. Besides, when the classifier gets fully learned, the WHOSA feature set exhibit stronger discriminative power than the lower-order statistical feature set does.

To find the optimal design in alleviating the effect of random variations on a SRAM cell, a worst-case sampling method is used. From the quantitative analysis using this method, the optimal designs for a process-variation-tolerant 22-nm FinFET-based 6-T SRAM cell are proposed and implemented through cell layouts and a dual-threshold-voltage designs.

Recovery of low-rank matrices has seen significant activity in many areas of science and engineering, motivated by theoretical results for exact reconstruction guarantees and interesting practical applications. Recently, numerous methods incorporated the nuclear norm to pursue the convexity of the optimization. However, this greatly restricts its capability and flexibility in dealing with many practical problems, where the singular values have clear physical meanings. This paper studies a generalized non-convex low-rank approximation, where the singular values are in lp-heuristic. Then specific results are derived for image restoration, including denoising and deblurring. Extensive experimental results on natural images demonstrate the improvement of the proposed method over the recent image restoration methods.

In recent years, multiple-input multiple-output (MIMO) channel models for crowded areas, such as indoor offices, shops, and outdoor hotspot environments, have become a topic of significant interest. In such crowded environments, propagation paths are frequently shadowed by moving objects, such as pedestrians or vehicles. These shadowing effects can cause time variations in the delay and angle-of-arrival (AoA) characteristics of a channel. In this paper, we propose a method for modeling the shadowing effects of pedestrians in a cluster-based channel model. The proposed method uses cluster power variations to model the time-varying channel properties. We also propose a novel method for estimating the cluster power variation properties from measured data. In order to validate our proposed method, channel sounding in the 3GHz band is conducted in a cafeteria during lunchtime. The results for the K parameter, delay spreads, and AoA azimuth spreads are compared for the measured data and the channel data generated using the proposed method. The results indicate that the time-varying delay-AoA characteristics can be effectively modeled using our proposed method.

In quasi-synchronous frequency-hopping multiple access (QS-FHMA) systems, relative delays are allowed to vary in a domain around the origin. Under such condition, the low hit zone (LHZ) frequency-hopping sequence (FHS) set is more propitious than the conventional FHS set to be applied by the systems. In this paper, a construction based on the interleaving techniques of FHS set with LHZ is proposed. Besides the requirement for this constructed LHZ FHS set to get the optimality or the near optimality with respect to the Peng-Fan-Lee bound is also given. It turns out that the constructed LHZ FHS set has new parameters not covered in the literature, thus it does have great significance in practice.

Single-carrier (SC) transmission with space-time block coded (STBC) transmit diversity can achieve good bit error rate (BER) performance. However, in a high mobility environment, the STBC codeword orthogonality is distorted and as consequence, the BER performance is degraded by the interference caused by the orthogonality distortion of STBC codeword. In this paper, we proposed a novel frequency-domain equalization (FDE) for SC-STBC transmit diversity in doubly selective fading channel. Multiple FDE weight matrices, each associated with a different code block, are jointly optimized based on the minimum mean square error (MMSE) criterion taking into account not only channel frequency variation but also channel time variation over the STBC codeword. Computer simulations confirm that the proposed robust FDE achieves BER performance superior to conventional FDE, which was designed based on the assumption of a quasi-static fading.

In this study, we investigated Si(100), Si(110) and Si(111) surface flattening process utilizing sacrificial oxidation method, and its effect on Metal-Insulator-Semiconductor (MIS) diode characteristics. By the etching of the 100 nm-thick sacrificial oxide formed by thermal oxidation at 1100°C, the surface roughness of Si(100), Si(110) and Si(111) substrates were reduced. The obtained Root-Mean-Square (RMS) roughness of Si(100) was reduced from 0.22 nm (as-cleaned) to 0.07 nm (after etching), while it was reduced from 0.23 nm to 0.12 nm in the case of Si(110), and from 0.23 nm to 0.11 nm in the case of Si(111), respectively. Furthermore, it was found that time-dependent dielectric breakdown (TDDB) characteristics of MIS diodes for p-Si(100), p-Si(110) and p-Si(111) were improved with the reduction of Si surface RMS roughness.

In this paper, a self optimization beamforming null control (SOBNC) scheme is proposed. There is a need of maintaining signal to interference plus noise ratio (SINR) threshold to control modulation and coding schemes (MCS) in recent technologies like Wi-Fi, Long Term Evolution (LTE) and Long Term Evolution Advanced (LTE-A). Selection of MCS depends on the SINR threshold that allows maintaining key performance index (KPI) like block error rate (BLER), bit error rate (BER) and throughput at certain level. The SOBNC is used to control the antenna pattern for SINR estimation and improve the SINR performance of the wireless communication systems. The nulling comes with a price; if wider nulls are introduced, i.e. more number of nulls are used, the 3dB beam-width and peak side lobe level (SLL) in antenna pattern changes critically. This paper proposes a method which automatically controls the number of nulls in the antenna pattern as per the changing environment based on adaptive-network based fuzzy interference system (ANFIS) to maintain output SINR level higher or equal to the required threshold. Finally, simulation results show a performance superiority of the proposed SOBNC compared with minimum mean square error (MMSE) based adaptive nulling control algorithm and conventional fixed null scheme.

Because accurate position information plays an important role in wireless sensor networks (WSNs), target localization has attracted considerable attention in recent years. In this paper, based on target spatial domain discretion, the target localization problem is formulated as a sparsity-seeking problem that can be solved by the compressed sensing (CS) technique. To satisfy the robust recovery condition called restricted isometry property (RIP) for CS theory requirement, an orthogonalization preprocessing method named LU (lower triangular matrix, unitary matrix) decomposition is utilized to ensure the observation matrix obeys the RIP. In addition, from the viewpoint of the positioning systems, taking advantage of the joint posterior distribution of model parameters that approximate the sparse prior knowledge of target, the sparse Bayesian learning (SBL) approach is utilized to improve the positioning performance. Simulation results illustrate that the proposed algorithm has higher positioning accuracy in multi-target scenarios than existing algorithms.

Human body segmentation has many applications in a wide variety of image processing tasks, from intelligent vehicles to entertainment. A substantial amount of research has been done in the field of segmentation and it is still one of the active research areas, resulting in introduction of many innovative methods in literature. Still, until today, a method that can overcome the human segmentation problems and adapt itself to different kinds of situations, has not been introduced. Many of methods today try to use the graph-cut framework to solve the segmentation problem. Although powerful, these methods rely on a distance penalty term (intensity difference or RGB color distance). This term does not always lead to a good separation between two regions. For example, if two regions are close in color, even if they belong to two different objects, they will be grouped together, which is not acceptable. Also, if one object has multiple parts with different colors, e.g. humans wear various clothes with different colors and patterns, each part will be segmented separately. Although this can be overcome by multiple inputs from user, the inherent problem would not be solved. In this paper, we have considered solving the problem by making use of a human probability map, super-pixels and Grab-cut framework. Using this map relives us from the need for matching the model to the actual body, thus helps to improve the segmentation accuracy. As a result, not only the accuracy has improved, but also it also became comparable to the state-of-the-art interactive methods.

Layout strategies including source edge to substrate space (SESS) and inserted substrate-pick stripes of gate-grounded NMOS(ggNMOS) are optimized in this work for on-chip electrostatic discharge (ESD) protection. In order to fully investigate influences of substrate resistors on triggering and conduction behaviors of ggNMOS, various devices are designed and fabricated in a 65-nm CMOS process. Direct current (DC), transmission-line-pulsing (TLP), human body model (HBM) and very-fast TLP (VF-TLP) tests are executed to fully characterize performance of fabricated ggNMOS. Test results reveal that an enlarged SESS parameter results in an earlier triggering behavior of ggNMOS, which presents a layout option for subtle adjustable triggering behaviors. Besides, inserted substrate-pick stripes are proved to have a bell-shape influence on the ESD robustness of ggNMOS and this bell-shape influence is valid in TLP, HBM and VF-TLP tests. Moreover, the most ESD-robust ggNMOS optimized under different inserted substrate-pick stripes always achieves a higher HBM level over the traditional ggNMOS at each concerned total device-width. Physical mechanisms of test results will be deeply discussed in this work.

The likelihood-ratio based score level fusion (LR-based fusion) scheme has attracted much attention, since it maximizes accuracy if a log-likelihood ratio (LLR) is accurately estimated. In reality, it can happen that a user cannot input some query samples due to temporary physical conditions such as injuries and illness. It can also happen that some modalities tend to cause false rejection (i.e. the user is a “goat” for these modalities). The LR-based fusion scheme can handle these situations by setting LLRs corresponding to missing query samples to 0. In this paper, we refer to such a mode as a “modality selection mode”, and address an issue of accuracy in this mode. Specifically, we provide the following contributions: (1) We firstly propose a “modality selection attack”, in which an impostor inputs only query samples whose LLRs are more than 0 (i.e. takes an optimal strategy) to impersonate others. We also show that the impostor can perform this attack against the SPRT (Sequential Probability Ratio Test)-based fusion scheme, which is an extension of the LR-based fusion scheme to a sequential fusion scenario. (2) We secondly consider the case when both genuine users and impostors take this optimal strategy, and show that the overall accuracy in this case is “worse” than the case when they input all query samples. More specifically, we prove that the KL (Kullback-Leibler) divergence between a genuine distribution of integrated scores and an impostor's one, which can be compared with password entropy, is smaller in the former case. We also show to what extent the KL divergence losses for each modality. (3) We finally evaluate to what extent the overall accuracy becomes worse using the NIST BSSR1 Set 2 and Set 3 datasets, and discuss directions of multibiometric applications based on the experimental results.

Named Data Networking (NDN) is a proposed future Internet architecture that shifts the fundamental abstraction of the network from host-to-host communication to request-response for named, signed data-an information dissemination focused approach. This paper describes a general design for receiver-driven, real-time streaming data (RTSD) applications over the current NDN implementation that aims to take advantage of the architecture's unique affordances. It is based on experimental development and testing of running code for real-time video conferencing, a positional tracking system for interactive multimedia, and a distributed control system for live performance. The design includes initial approaches to minimizing latency, managing buffer size and Interest retransmission, and adapting retrieval to maximize bandwidth and control congestion. Initial implementations of these approaches are evaluated for functionality and performance results, and the potential for future research in this area, and improved performance as new features of the architecture become available, is discussed.

In this paper, we propose a channel-unaware algorithm to suppress the narrowband interference (NBI) for the time synchronization, where multiple antennas are equipped at the receiver. Based on the fact that the characteristics of synchronization signal are different from those of NBI in both the time and spatial domain, the proposed algorithm suppresses the NBI by utilizing the multiple receive antennas in the eigen domain of NBI, where the eigen domain is obtained from the time domain statistical information of NBI. Because time synchronization involves incoherent detection, the proposed algorithm does not use the desired channel information, which is different from the eigen domain interference rejection combining (E-IRC). Simulation results show, compared with the traditional frequency domain NBI suppression technique, the proposed algorithm has about a 2 dB gain under the same probability of detection.

In order to minimize packet error rate in extremely dynamic vehicular networks, a novel vehicle to vehicle (V2V) mobile content transmission scheme that jointly employs random network coding and shuffling/scattering techniques is proposed in this paper. The proposed scheme consists of 3 steps: Step 1-The original mobile content data consisting of several packets is encoded to generate encoded blocks using random network coding for efficient error recovery. Step 2-The encoded blocks are shuffled for averaging the error rate among the encoded blocks. Step 3-The shuffled blocks are scattered at different vehicle locations to overcome the estimation error of optimum transmission location. Applying the proposed scheme in vehicular networks can yield error free transmission with high efficiency. Our simulation results corroborate that the proposed scheme significantly improves the packet error rate performance in high mobility environments. Thanks to the flexibility of network coding, the proposed scheme can be designed as a separate module in the physical layer of various wireless access technologies.