The boomerang connectivity table (BCT) was introduced by C. Cid et al. Using the BCT, for SPN block cipher, the dependency between sub-ciphers in boomerang structure can be computed more precisely. However, the existing method to generate BCT is difficult to be applied to the ARX-based cipher, because of the huge domain size. In this paper, we show a method to compute the dependency between sub-ciphers in boomerang structure for modular addition. Using bit relation in modular addition, we compute the dependency sequentially in bitwise. And using this method, we find boomerang characteristics and amplified boomerang characteristics for the ARX-based ciphers LEA and SPECK. For LEA-128, we find a reduced 15-round boomerang characteristic and reduced 16-round amplified boomerang characteristic which is two rounds longer than previous boomerang characteristic. Also for SPECK64/128, we find a reduced 13-round amplified boomerang characteristic which is one round longer than previous rectangle characteristic.

The current orthogonal cooperative system (OCS) achieves diversity through the use of relays and the consumption of an additional time slot (TS). To guarantee the orthogonality of the received signal and avoid the mutual interference at the destination, the source has to be mute in the second TS. Consequently, the spectral efficiency (SE) is halved. In this paper, linear constellation precoded orthogonal frequency division multiplexing with index modulation (LCP-OFDM-IM) based OCS is proposed, where the source activates the complementary subcarriers to convey the symbols over two TSs. Hence the source can consecutively transmit information to the destination without the mutual interference. Compared with the current OFDM based OCS, the LCP-OFDM-IM based OCS can achieve a higher SE, since the subcarrier activation patterns (SAPs) can be exploited to convey additional information. Furthermore, the optimal precoder, in the sense of maximizing the minimum Euclidean distance of the symbols conveyed on each subcarrier over two TSs, is provided. Simulation results show the superiority of the LCP-OFDM-IM based OCS over the current OFDM based OCS.

Angular Momentum (AM) has been considered as a new dimension of wireless transmissions as well as the intrinsic property of Electro-Magnetic (EM) waves. So far, AM is utilized as a discrete mode not only in the quantum states, but also in the statistical beam forming. Traditionally, the continuous value of AM is ignored and only the quantized mode number is identified. However, the recent discovery on electrons in spiral motion producing twisted radiation with AM, including Spin Angular Momentum (SAM) and Orbital Angular Momentum (OAM), proves that the continuous value of AM is available in the statistical EM wave beam. This is also revealed by the so-called fractional OAM, which is reported in optical OAM beams. Then, as the new dimension with continuous real number field, AM should turn out to be a certain spectrum, similar to the frequency spectrum usually in the wireless signal processing. In this letter, we mathematically define the AM spectrum and show the applications in the information theory analysis, which is expected to be an efficient tool for the future wireless communications with AM.

This paper analyzes and compares two methods to estimate electromagnetically coupled noises introduced to an antenna due to the nearby circuits at a circuit design stage. One of them is to estimate the power spectrum, and the other one is to estimate the active S11 parameter at the victim antenna, respectively, and both of them use simulated standard S-parameters for the electromagnetic coupling in the circuit. They also need the assumed or measured excitation of noise sources. To confirm the validness of the two methods, an evaluation board consisting of an antenna and noise sources were designed and fabricated in which voltage controlled oscillator (VCO) chips are placed as noise sources. The generated electromagnetic noises are transferred to an antenna via loop-shaped transmission lines, degrading the performance of the antenna. In this paper, detailed analysis procedures are described using the evaluation board, and it is shown that the two methods are equivalent to each other in terms of the induced voltages in the antenna. Finally, a procedure to estimate antenna performance degradation at the design stage is summarized.

Here, we present a novel spectroscopic imaging method based on the boundary-extraction scheme for wide-beam terahertz (THz) three-dimensional imaging. Optical-lens-focusing systems for THz subsurface imaging generally require the depth of the object from the surface to be input beforehand to achieve the desired azimuth resolution. This limitation can be alleviated by incorporating a wide-beam THz transmitter into the synthetic aperture to automatically change the focusing depth in the post-signal processing. The range point migration (RPM) method has been demonstrated to have significant advantages in terms of imaging accuracy over the synthetic-aperture method. Moreover, in the RPM scheme, spectroscopic information can be easily associated with each scattering center. Thus, we propose an RPM-based terahertz spectroscopic imaging method. The finite-difference time-domain-based numerical analysis shows that the proposed algorithm provides accurate target boundary imaging associated with each frequency-dependent characteristic.

In this paper, we propose an image to sound mapping method. This technique treats an image as a spectrogram and maps it to a sound by taking inverse FFT of the spectrogram. Amplitude spectra of a speech signal are embedded to the spectrogram to give speech intelligibility for the mapped sound. Specifically, we hold amplitude spectra of a speech signal with strong power and embed the image brightness in other frequency bands. Holding amplitude spectra of a speech signal with strong power preserves a speech spectral envelope and improves the speech quality of the mapped sound. The amplitude spectra of the mapped sound with weak power represent the image brightness, and then the image is successfully reconstructed from the mapped sound. Simulation results show that the proposed method achieves sufficient speech quality.

This paper proposes a generative moment matching network (GMMN)-based post-filtering method for providing inter-utterance pitch variation to singing voices and discusses its application to our developed mixing method called neural double-tracking (NDT). When a human singer sings and records the same song twice, there is a difference between the two recordings. The difference, which is called inter-utterance variation, enriches the performer's musical expression and the audience's experience. For example, it makes every concert special because it never recurs in exactly the same manner. Inter-utterance variation enables a mixing method called double-tracking (DT). With DT, the same phrase is recorded twice, then the two recordings are mixed to give richness to singing voices. However, in synthesized singing voices, which are commonly used to create music, there is no inter-utterance variation because the synthesis process is deterministic. There is also no inter-utterance variation when only one voice is recorded. Although there is a signal processing-based method called artificial DT (ADT) to layer singing voices, the signal processing results in unnatural sound artifacts. To solve these problems, we propose a post-filtering method for randomly modulating synthesized or natural singing voices as if the singer sang again. The post-filter built with our method models the inter-utterance pitch variation of human singing voices using a conditional GMMN. Evaluation results indicate that 1) the proposed method provides perceptible and natural inter-utterance variation to synthesized singing voices and that 2) our NDT exhibits higher double-trackedness than ADT when applied to both synthesized and natural singing voices.

Under the framework of traditional power spectrum based feature extraction, in order to extract more discriminative information for playback attack detection, this paper proposes a feature by making use of deep neural network to describe the nonlinear relationship between power spectrum and discriminative information. Namely, constant-Q deep coefficients (CQDC). It relies on constant-Q transform, deep neural network and discrete cosine transform. In which, constant-Q transform is used to convert signal from the time domain into the frequency domain because it is a long-term transform that can provide more frequency detail, deep neural network is used to extract more discriminative information to discriminate playback speech from genuine speech and discrete cosine transform is used to decorrelate among the feature dimensions. ASVspoof 2017 corpus version 2.0 is used to evaluate the performance of CQDC. The experimental results show that CQDC outperforms the existing power spectrum obtained from constant-Q transform based features, and equal error can reduce from 19.18% to 51.56%. In addition, we found that discriminative information of CQDC hides in all frequency bins, which is different from commonly used features.

The major weakness of global navigation satellite system receivers is their vulnerability to intentional and unintentional interference. Frequency domain interference suppression (FDIS) technology is one of the most useful countermeasures. The pseudo-range measurement is unbiased after FDIS filtering given an ideal analog channel. However, with the influence of the analog modules used in RF front-end, the amplitude response and phase response of the channel equivalent filter are non-ideal, which bias the pseudo-range measurement after FDIS filtering and the bias varies along with the frequency of the interference. This paper proposes an unbiased interference suppression method based on signal estimation and spectrum compensation. The core idea is to use the parameters calculated from the tracking loop to estimate and reconstruct the desired signal. The estimated signal is filtered by the equivalent filter of actual channel, then it is used for compensating the spectrum loss caused by the FDIS method in the frequency domain. Simulations show that the proposed algorithm can reduce the pseudo-range measurement bias significantly, even for channels with asymmetrical group delay and multiple interference sources at any location.

There is currently much interest in the development of Optic Wireless and Visible Light Communication (VLC) systems in the ITS field. Research in VLC and boomerang systems in particular often remain at a theoretical or computer-simulated level. This paper reports the 3-stage development of a boomerang prototype communication and ranging system using visible light V2V communication via LEDs and photodiodes, with direct-sequence spread spectrum techniques. The system uses simple and widely available components aiming for a low-cost frugal innovation approach. Results show that while we have to improve the prototype distance measurement unit due to a margin of error, simultaneous communication and ranging is possible with our newly designed prototype. The benefits of further research and development of boomerang technology prototypes are confirmed.

In cognitive radio (CR) networks, spectrum sensing is an essential task for enabling dynamic spectrum sharing. However, the problem becomes quite challenging in wideband spectrum sensing due to high sampling pressure, limited power and computing resources, and serious channel fading. To overcome these challenges, this paper proposes a distributed collaborative spectrum sensing scheme based on 1-bit compressive sensing (CS). Each secondary user (SU) performs local 1-bit CS and obtains support estimate information from the signal reconstruction. To utilize joint sparsity and achieve spatial diversity, the support estimate information among the network is fused via the average consensus technique based on distributed computation and one-hop communications. Then the fused result on support estimate is used as priori information to guide the next local signal reconstruction, which is implemented via our proposed weighted binary iterative hard thresholding (BIHT) algorithm. The local signal reconstruction and the distributed fusion of support information are alternately carried out until reliable spectrum detection is achieved. Simulations testify the effectiveness of our proposed scheme in distributed CR networks.

The spectrum sensing of the orthogonal frequency division multiplexing (OFDM) system in cognitive radio (CR) has always been challenging, especially for user terminals that utilize the full-duplex (FD) mode. We herein propose an advanced FD spectrum-sensing scheme that can be successfully performed even when severe self-interference is encountered from the user terminal. Based on the “classification-converted sensing” framework, the cyclostationary periodogram generated by OFDM pilots is exhibited in the form of images. These images are subsequently plugged into convolutional neural networks (CNNs) for classifications owing to the CNN's strength in image recognition. More importantly, to realize spectrum sensing against residual self-interference, noise pollution, and channel fading, we used adversarial training, where a CR-specific, modified training database was proposed. We analyzed the performances exhibited by the different architectures of the CNN and the different resolutions of the input image to balance the detection performance with computing capability. We proposed a design plan of the signal structure for the CR transmitting terminal that can fit into the proposed spectrum-sensing scheme while benefiting from its own transmission. The simulation results prove that our method has excellent sensing capability for the FD system; furthermore, our method achieves a higher detection accuracy than the conventional method.

The arms race between offense and defense in the cloud impels the innovation of techniques for monitoring attacks and unauthorized activities. The promising technique of virtual machine introspection (VMI) becomes prevalent for its tamper-resistant capability. However, some elaborate exploitations are capable of invalidating VMI-based tools by breaking the assumption of a trusted guest kernel. To achieve a more reliable and robust introspection, we introduce a practical approach to monitor and detect attacks that attempt to subvert VMI in this paper. Our approach combines supervised machine learning and hardware architectural events to identify those malicious behaviors which are targeted at VMI techniques. To demonstrate the feasibility, we implement a prototype named HyperMon on the Xen hypervisor. The results of our evaluation show the effectiveness of HyperMon in detecting malicious behaviors with an average accuracy of 90.51% (AUC).

In this paper, we propose a new scheme which uses blind detection algorithm for recovering the conventional user signal in a system which the sporadic machine-to-machine (M2M) communication share the same spectrum with the conventional user. Compressive sensing techniques are used to estimate the M2M devices signals. Based on the Hopfield neural network (HNN), the blind detection algorithm is used to recover the conventional user signal. The simulation results show that the conventional user signal can be effectively restored under an unknown channel. Compared with the existing methods, such as using the training sequence to estimate the channel in advance, the blind detection algorithm used in this paper with no need for identifying the channel, and can directly detect the transmitted signal blindly.

We propose a method of non-blind speech watermarking based on direct spread spectrum (DSS) using a linear prediction scheme to solve sound distortion due to spread spectrum. Results of evaluation simulations revealed that the proposed method had much lower sound-quality distortion than the DSS method while having almost the same bit error ratios (BERs) against various attacks as the DSS method.

This letter presents ternary convolutional codes and their punctured codes with optimum distance spectrum.

In order to obtain high-performance circuits in advanced technology nodes, design methodology has to take the existence of large delay variations into account. Clock scheduling and speculative execution have overheads to realize them, but have potential to improve the performance by averaging the imbalance of maximum delay among paths and by utilizing valid data available earlier than worst-case scenarios, respectively. In this paper, we propose a high-performance digital circuit design method with speculative executions with less overhead by utilizing clock scheduling with delay insertions effectively. The necessity of speculations that cause overheads is effectively reduced by clock scheduling with delay insertion. Experiments show that a generated circuit achieves 26% performance improvement with 1.3% area overhead compared to a circuit without clock scheduling and without speculative execution.

The windowed interpolation DFT methods have been utilized to estimate the parameters of a single frequency and multi-frequency signal. Nevertheless, they do not work well for the real-valued sinusoids with closely spaced positive- and negative- frequency. In this paper, we describe a novel three-point windowed interpolation DFT method for frequency measurement of real-valued sinusoid signal. The exact representation of the windowed DFT with maximum sidelobe decay window (MSDW) is constructed. The spectral superposition of positive- and negative-frequency is considered and calculated to improve the estimation performance. The simulation results match with the theoretical values well. In addition, computer simulations demonstrate that the proposed algorithm provides high estimation accuracy and good noise suppression capability.

Conventional sequential processing on software with a general purpose CPU has become significantly insufficient for certain heavy computations due to the high demand of processing power to deliver adequate throughput and performance. Due to many reasons a high degree of interest could be noted for high performance real time video processing on embedded systems. However, embedded processing platforms with limited performance could least cater the processing demand of several such intensive computations in computer vision domain. Therefore, hardware acceleration could be noted as an ideal solution where process intensive computations could be accelerated using application specific hardware integrated with a general purpose CPU. In this research we have focused on building a parallelized high performance application specific architecture for such a hardware accelerator for HOG-SVM computation implemented on Zynq 7000 FPGA. Histogram of Oriented Gradients (HOG) technique combined with a Support Vector Machine (SVM) based classifier is versatile and extremely popular in computer vision domain in contrast to high demand for processing power. Due to the popularity and versatility, various previous research have attempted on obtaining adequate throughput on HOG-SVM. This research with a high throughput of 240FPS on single scale on VGA frames of size 640x480 out performs the best case performance on a single scale of previous research by approximately a factor of 3-4. Further it's an approximately 15x speed up over the GPU accelerated software version with the same accuracy. This research has explored the possibility of using a novel architecture based on deep pipelining, parallel processing and BRAM structures for achieving high performance on the HOG-SVM computation. Further the above developed (video processing unit) VPU which acts as a hardware accelerator will be integrated as a co-processing peripheral to a host CPU using a novel custom accelerator structure with on chip buses in a System-On-Chip (SoC) fashion. This could be used to offload the heavy video stream processing redundant computations to the VPU whereas the processing power of the CPU could be preserved for running light weight applications. This research mainly focuses on the architectural techniques used to achieve higher performance on the hardware accelerator and on the novel accelerator structure used to integrate the accelerator with the host CPU.

Hand-dorsa vein recognition is solved based on the convolutional activations of the pre-trained deep convolutional neural network (DCNN). In specific, a novel task-specific cross-convolutional-layer pooling is proposed to obtain the more representative and discriminative feature representation. Rigorous experiments on the self-established database achieves the state-of-the-art recognition result, which demonstrates the effectiveness of the proposed model.