In this paper, an algorithm based on Taylor series expansion is proposed to calculate the logarithm (log2x) of IEEE754 binary32 accuracy floating-point number by a multi-domain partitioning method. The general mantissa (1≤x<2) is multiplied by 2, 4, 8, … (or equivalently left-shifted by 1, 2, 3, … bits), the regions of (2≤x<4), (4≤x<8), (8≤x<16),… are considered, and Taylor-series expansion is applied. In those regions, the slope of f(x)=log2 x with respect to x is gentle compared to the region of (1≤x<2), which reduces the required number of terms. We also consider the trade-offs among the numbers of additions, subtractions, and multiplications and Look-Up Table (LUT) size in hardware to select the best algorithm for the engineer's design and build the best hardware device.

As known to us all, L-shaped co-prime array (LCA) is a recently introduced two-dimensional (2-D) sparse array structure, which is extended from linear co-prime array (CA). Such sparse array geometry can be used for 2-D parameters estimation with higher degrees-of-freedom (DOF). However, in the scenario where several narrowband transmissions spread over a wide spectrum, existing technique based on LCA with Nyquist sampling may encounter a bottleneck for both analog and digital processing. To alleviate the burden of high-rate Nyquist sampling, a method of joint wideband spectrum and direction-of-arrival (DOA) estimation with compressed sampling based on LCA, which is recognized as LCA-based modulated wideband converter (MWC), is presented in this work. First, the received signal along each antenna is mixed to basebands, low-pass filtered and down-sampled to get the compressed sampling data. Then by constructing the virtual received data of 2-D difference coarray, we estimate the wideband spectrum and DOA jointly using two recovery methods where the first is a joint ESPRIT method and the other is a joint CS method. Numerical simulations illustrate the validity of the proposed LCA based MWC system and show the superiority.

Intelligent transportation systems (ITSs) have been extensively studied in recent years to improve the safety and efficiency of transportation. The use of a radar system to enable the ITSs monitor the environment is robust to weather conditions and is less invasive to user privacy. Moreover, equipping the roadside units (RSUs) with radar modules has been deemed an economical and efficient option for ITS operators. However, because the detection and tracking parameters can significantly influence the radar system performance and the best parameters for different scenarios are different, the selection of appropriate parameters for the radar systems is critical. In this study, we investigated radar parameter selection and consequently proposes a parameter selection approach capable of automatically choosing the appropriate detection and tracking parameters for radar systems. The experimental results indicate that the proposed method realizes appropriate selection of parameters, thereby significantly improving the detection and tracking performance of radar systems.

Manufacturers are coping with increasing pressures in quality, cost and efficiency as more and more industries are moving from traditional setup to industry 4.0 based digitally transformed setup due to its numerous playbacks. Within the manufacturing domain organizational structures and processes are complex, therefore adopting industry 4.0 and finding an optimized re-engineered business process is difficult without using a systematic methodology. Authors have developed Business Process Re-engineering (BPR) and Business Process Optimization (BPO) methods but no consolidated methodology have been seen in the literature that is based on industry 4.0 and incorporates both the BPR and BPO. We have presented a consolidated and systematic re-engineering and optimization framework for a manufacturing industry setup. The proposed framework performs Evolutionary Multi-Objective Combinatorial Optimization using Multi-Objective Genetic Algorithm (MOGA). An example process from an aircraft manufacturing factory has been optimized and re-engineered with available set of technologies from industry 4.0 based on the criteria of lower cost, reduced processing time and reduced error rate. At the end to validate the proposed framework Business Process Model and Notation (BPMN) is used for simulations and perform comparison between AS-IS and TO-BE processes as it is widely used standard for business process specification. The proposed framework will be used in converting an industry from traditional setup to industry 4.0 resulting in cost reduction, increased performance and quality.

Optical phase conjugation (OPC) is an all-optical signal processing technique for mitigating fiber nonlinearity and is promising for building cost-efficient fiber networks with few optic-electric-optic conversions and long amplification spacing. In lumped amplified systems, OPC has a little nonlinearity mitigation efficiency for nonlinear distortion induced by cross-phase modulation (XPM) due to the asymmetry of power and chromatic dispersion (CD) maps during propagation in transmission fiber. In addition, the walk-off of XPM-induced noise becomes small due to the CD compensation effect of OPC, so the deterministic nonlinear distortion increases. Therefore, lumped amplified transmission systems with OPC are more sensitive to channel spacing than conventional systems. In this paper, we show the channel spacing dependence of NZ-DSF transmission using amplification repeater with OPC. Numerical simulations show comprehensive characteristics between channel spacing and CD in a 100-Gbps/λ WDM signal. An experimental verification using periodically poled LiNbO3-based OPC is also performed. These results suggest that channel spacing design is more important in OPC-assisted systems than in conventional dispersion-unmanaged systems.

At present, the application of different types of memristors in electronics is being deeply studied. Given the nonlinearity characterizing memristors, a circuit with memristors cannot be treated by classical circuit analysis. In this paper, memristor is equivalent to a nonlinear dynamic system composed of linear dynamic system and nonlinear static system by Volterra series. The nonlinear transfer function of memristor is derived. In the complex frequency domain, the n-order complex frequency response of memristor is established by multiple Laplace transform, and the response of MLC parallel circuit is taken as an example to verify. Theoretical analysis shows that the complex frequency domain analysis method of memristor transforms the problem of solving nonlinear circuit in time domain into n times complex frequency domain analysis of linear circuit, which provides an idea for nonlinear dynamic system analysis.

In this paper, we study the number of failed components in a consecutive-k-out-of-n:G system. The distributions and expected values of the number of failed components when system is failed or working at a particular time t are evaluated. We also apply them to the optimization problems concerned with the optimal number of components and the optimal replacement time. Finally, we present the illustrative examples for the expected number of failed components and give the numerical results for the optimization problems.

Side-channel analysis is one of the most investigated hardware Trojan detection approaches. However, nearly all the side-channel analysis approaches require golden chips for reference, which are hard to obtain actually. Besides, majority of existing Trojan detection algorithms focus on the data similarity and ignore the Trojan misclassification during the detection. In this paper, we propose a cost-sensitive golden chip-free hardware Trojan detection framework, which aims to minimize the probability of Trojan misclassification during the detection. The post-layout simulation data of voltage variations at different process corners is utilized as a golden reference. Further, a classification algorithm based on the combination of principal component analysis and Naïve bayes is exploited to identify the existence of hardware Trojan with a minimum misclassification risk. Experimental results on ASIC demonstrate that the proposed approach improves the detection accuracy ratio compared with the three detection algorithms and distinguishes the Trojan with only 0.27% area occupies even under ±15% process variations.

This paper proposes two accurate source-number estimation methods for array antennas and multi-input multi-output radar. Direction of arrival (DOA) estimation is important in high-speed wireless communication and radar imaging. Most representative DOA estimation methods require the source-number information in advance and often fail to estimate DOAs in severe environments such as those having low signal-to-noise ratio or large transmission-power difference. Received signals are often bandlimited or narrowband signals, so the proposed methods first involves denoising preprocessing by removing undesired components then comparing the original and denoised signal information. The performances of the proposed methods were evaluated through computer simulations.

The adiabatic quantum flux parametron (AQFP) is an energy-efficient, high-speed superconducting logic device. To observe the tiny output currents from the AQFP in experiments, high-speed voltage drivers are indispensable. In the present study, we develop a compact voltage driver for AQFP logic based on a Josephson latching driver (JLD), which has been used as a high-speed driver for rapid single-flux-quantum (RSFQ) logic. In the JLD-based voltage driver, the signal currents of AQFP gates are converted into gap-voltage-level signals via an AQFP/RSFQ interface and a four-junction logic gate. Furthermore, this voltage driver includes only 15 Josephson junctions, which is much fewer than in the case for the previously designed driver based on dc superconducting quantum interference devices (60 junctions). In measurement, we successfully operate the JLD-based voltage driver up to 4 GHz. We also evaluate the bit error rate (BER) of the driver and find that the BER is 7.92×10-10 and 2.67×10-3 at 1GHz and 4GHz, respectively.

We experimentally evaluated random number sequences generated by a superconducting hardware random number generator composed of a Josephson-junction oscillator, a rapid-single-flux-quantum (RSFQ) toggle flip-flop (TFF), and an RSFQ AND gate. Test circuits were fabricated using a 10 kA/cm2 Nb/AlOx/Nb integration process. Measurements were conducted in a liquid helium bath. The random numbers were generated for a trigger frequency of 500 kHz under the oscillating Josephson-junction at 29 GHz. 26 random number sequences of 20 kb length were evaluated for bias voltages between 2.0 and 2.7 mV. The NIST FIPS PUBS 140-2 tests were used for the evaluation. 100% pass rates were confirmed at the bias voltages of 2.5 and 2.6 mV. We found that the Monobit test limited the pass rates. As numerical simulations suggested, a detailed evaluation for the probability of obtaining “1” demonstrated the monotonical dependence on the bias voltage.

In this paper, we propose a new training framework named the INmfCA algorithm for nonparallel voice conversion (VC) systems. To train conversion models, traditional VC frameworks require parallel corpora, in which source and target speakers utter the same linguistic contents. Although the frameworks have achieved high-quality VC, they are not applicable in situations where parallel corpora are unavailable. To acquire conversion models without parallel corpora, nonparallel methods are widely studied. Although the frameworks achieve VC under nonparallel conditions, they tend to require huge background knowledge or many training utterances. This is because of difficulty in disentangling linguistic and speaker information without a large amount of data. In this work, we tackle this problem by exploiting NMF, which can factorize acoustic features into time-variant and time-invariant components in an unsupervised manner. The method acquires alignment between the acoustic features of a source speaker's utterances and a target dictionary and uses the obtained alignment as activation of NMF to train the source speaker's dictionary without parallel corpora. The acquisition method is based on the INCA algorithm, which obtains the alignment of nonparallel corpora. In contrast to the INCA algorithm, the alignment is not restricted to observed samples, and thus the proposed method can efficiently utilize small nonparallel corpora. The results of subjective experiments show that the combination of the proposed algorithm and the INCA algorithm outperformed not only an INCA-based nonparallel framework but also CycleGAN-VC, which performs nonparallel VC without any additional training data. The results also indicate that a one-shot VC framework, which does not need to train source speakers, can be constructed on the basis of the proposed method.

Website Fingerprinting (WF) enables a passive attacker to identify which website a user is visiting over an encrypted tunnel. Current WF attacks have two strong assumptions: (i) specific tunnel, i.e., the attacker can train on traffic samples collected in a simulated tunnel with the same tunnel settings as the user, and (ii) pseudo-open-world, where the attacker has access to training samples of unmonitored sites and treats them as a separate class. These assumptions, while experimentally feasible, render WF attacks less usable in practice. In this paper, we present Gene Fingerprinting (GF), a new WF attack that achieves cross-tunnel transferability by generating fingerprints that reflect the intrinsic profile of a website. The attack leverages Zero-shot Learning — a machine learning technique not requiring training samples to identify a given class — to reduce the effort to collect data from different tunnels and achieve a real open-world. We demonstrate the attack performance using three popular tunneling tools: OpenSSH, Shadowsocks, and OpenVPN. The GF attack attains over 94% accuracy on each tunnel, far better than existing CUMUL, DF, and DDTW attacks. In the more realistic open-world scenario, the attack still obtains 88% TPR and 9% FPR, outperforming the state-of-the-art attacks. These results highlight the danger of our attack in various scenarios where gathering and training on a tunnel-specific dataset would be impractical.

Platform-mounted small antennas increase dielectric loss and conductive loss and decrease the radiation efficiency. This paper proposes a novel antenna design method to improve radiation efficiency for platform-mounted small antennas by characteristic mode analysis. The proposed method uses mapping of modal weighting coefficient (MWC) and infinitesimal dipole and evaluate the metal casing with 100mm × 55mm × 23mm as a platform excited by an inverted-F antenna. The simulation and measurement results show that the radiation efficiency of 5% is improved with the whole system from 2.5% of the single antenna.

This paper proposes individual computation processes of the partial demodulation reference signal (DM-RS) sequence in a synchronization signal (SS)/physical broadcast channel (PBCH) block to be used to detect the radio frame timing based on SS/PBCH block index detection for New Radio (NR) initial access. We present the radio frame timing detection probability using the proposed partial DM-RS sequence detection method that is applied subsequent to the physical-layer cell identity (PCID) detection in five tapped delay line (TDL) models in both non-line-of-sight (NLOS) and line-of-sight (LOS) environments. Computer simulation results show that by using the proposed method, the radio frame timing detection probabilities of almost 100% and higher than 90% are achieved for the LOS and NLOS channel models, respectively, at the average received signal-to-noise power ratio (SNR) of 0dB with the frequency stability of a local oscillator in a set of user equipment (UE) of 5ppm at the carrier frequency of 4GHz.

In this paper, starting from the algorithm, a performance- and energy-efficient 3D structure or shape of the Tensor Processing Engine (TPE) for CNN acceleration is systematically searched and evaluated. An optimal accelerator's shape maximizes the number of concurrent MAC operations per clock cycle while minimizes the number of redundant operations. The proposed 3D vector-parallel TPE architecture with an optimal shape can be very efficiently used for considerable CNN acceleration. Due to implemented support of inter-block image data independency, it is possible to use multiple of such TPEs for the additional CNN acceleration. Moreover, it is shown that the proposed TPE can also be uniformly used for acceleration of the different CNN models such as VGG, ResNet, YOLO, and SSD. We also demonstrate that our theoretical efficiency analysis is matched with the result of a real implementation for an SSD model to which a state-of-the-art channel pruning technique is applied.

In this paper, we review the basic components of superconducting quantum computers. We mainly focus on the packaging and wiring technologies required to realize large-scalable superconducting quantum computers.

The objective of critical nodes problem is to minimize pair-wise connectivity as a result of removing a specific number of nodes in the residual graph. From a mathematical modeling perspective, it comes the truth that the more the number of fragmented components and the evenly distributed of disconnected sub-graphs, the better the quality of the solution. Basing on this conclusion, we proposed a new Cluster Expansion Method for Critical Node Problem (CEMCNP), which on the one hand exploits a contraction mechanism to greedy simplify the complexity of sparse graph model, and on the other hand adopts an incremental cluster expansion approach in order to maintain the size of formed component within reasonable limitation. The proposed algorithm also relies heavily on the idea of multi-start iterative local search algorithm, whereas brings in a diversified late acceptance local search strategy to keep the balance between interleaving diversification and intensification in the process of neighborhood search. Extensive evaluations show that CEMCNP running on 35 of total 42 benchmark instances are superior to the outcome of KBV, while holding 3 previous best results out of the challenging instances. In addition, CEMCNP also demonstrates equivalent performance in comparison with the existing MANCNP and VPMS algorithms over 22 of total 42 graph models with fewer number of node exchange operations.

In this study, we aim to improve the performance of audio source separation for monaural mixture signals. For monaural audio source separation, semisupervised nonnegative matrix factorization (SNMF) can achieve higher separation performance by employing small supervised signals. In particular, penalized SNMF (PSNMF) with orthogonality penalty is an effective method. PSNMF forces two basis matrices for target and nontarget sources to be orthogonal to each other and improves the separation accuracy. However, the conventional orthogonality penalty is based on an inner product and does not affect the estimation of the basis matrix properly because of the scale indeterminacy between the basis and activation matrices in NMF. To cope with this problem, a new PSNMF with cosine similarity between the basis matrices is proposed. The experimental comparison shows the efficacy of the proposed cosine similarity penalty in supervised audio source separation.

Software developers may write a number of similar source code fragments including the same mistake in software products. To remove such faulty code fragments, developers inspect code clones if they found a bug in their code. While various code clone detection methods have been proposed to identify clones of either code blocks or functions, those tools do not always fit the code inspection task because a faulty code fragment may be much smaller than code blocks, e.g. a single line of code. To enable developers to search code clones of such a small faulty code fragment in a large-scale software product, we propose a method using Lempel-Ziv Jaccard Distance, which is an approximation of Normalized Compression Distance. We conducted an experiment using an existing research dataset and a user survey in a company. The result shows our method efficiently reports cloned faulty code fragments and the performance is acceptable for software developers.