Based upon the Kerckhoffs' principle, illegal users can get to know the embedding and detection algorithms except for a secret key. Then, it is possible to access to a host signal which may be selected from frequency components of a digital content for embedding watermark signal. Especially for a fingerprinting scheme which embeds user's information as a watermark, the selected components can be easily found by the observation of differently watermarked copies of a same content. In this scenario, it is reported that some non-linear collusion attacks will be able to remove/modify the embedded signal. In this paper, we study the security analysis of our previously proposed spread-spectrum (SS) fingerprinting scheme[1], [2] under the Kerckhoffs' principle, and reveal its drawback when an SS sequence is embedded in a color image. If non-linear collusion attacks are performed only to the components selected for embedding, the traceability is greatly degraded while the pirated copy keeps high quality after the attacks. We also propose a simple countermeasure to enhance the robustness against non-linear collusion attacks as well as possible signal processing attacks for the underlying watermarking method.

A major challenge in wideband spectrum sensing, in cognitive radio system for example, is the requirement of a high sampling rate which may exceed today's best analog-to-digital converters (ADCs) front-end bandwidths. Compressive sampling is an attractive way to reduce the sampling rate. The modulated wideband converter (MWC) proposed recently is one of the most successful compressive sampling hardware architectures, but it has high hardware complexity owing to its parallel channels structure. In this paper, we design a single channel sub-Nyquist sampling scheme to bring substantial savings in terms of not only sampling rate but also hardware complexity, and we also present a wideband power spectrum sensing and reconstruction method for bandlimited wide-sense stationary (WSS) signals. The total sampling rate is only one channel rate of the MWC's. We evaluate the performance of the sensing model by computing the probability of detecting signal occupancy in terms of the signal-to-noise ratio (SNR) and other practical parameters. Simulation results underline the promising performance of proposed approach.

This paper shows accuracy of using azimuth-variable path-loss fitting in white-space (WS) boundary-estimation. We perform experiments to evaluate this method, and demonstrate that the required number of sensors can be significantly reduced. We have proposed a WS boundary-estimation framework that utilizes sensors to not only obtain spectrum sensing data, but also to estimate the boundaries of the incumbent radio system (IRS) coverage. The framework utilizes the transmitter position information and pathloss fitting. The pathloss fitting describes the IRS coverage by approximating the well-known pathloss prediction formula from the received signal power data, which is measured using sensors, and sensor-transmitter separation distances. To enhance its accuracy, we have further proposed a pathloss-fitting method that employs azimuth variables to reflect the azimuth dependency of the IRS coverage, including the antenna directivity of the transmitter and propagation characteristics.

In this article, we investigate the depth distribution and the depth spectra of linear codes over the ring R=F2+uF2+u2F2, where u3=1. By using homomorphism of abelian groups from R to F2 and the generator matrices of linear codes over R, the depth spectra of linear codes of type 8k14k22k3 are obtained. We also give the depth distribution of a linear code C over R. Finally, some examples are presented to illustrate our obtained results.

This letter investigates the circularity of fractional Fourier transform (FRFT) coefficients containing noise only, and proves that all coefficients coming from white Gaussian noise are circular via the discrete FRFT. In order to use the spectrum kurtosis (SK) as a Gaussian test to check if linear frequency modulation (LFM) signals are present in a set of FRFT points, the effect of the noncircularity of Gaussian variables upon the SK of FRFT coefficients is studied. The SK of the α th-order FRFT coefficients for LFM signals embedded in a white Gaussian noise is also derived in this letter. Finally the signal detection algorithm based on FRFT and SK is proposed. The effectiveness and robustness of this algorithm are evaluated via simulations under lower SNR and weaker components.

A wide-bandwidth fundamental mixer operating at a frequency above 110GHz for precise spectrum analysis was developed using the InP HEMT technology. A single-ended resistive mixer was adopted for the mixer circuit. An IF amplifier and LO buffer amplifier were also developed and integrated into the mixer chip. As for packaging into a metal block module, a flip-chip bonding technique was introduced. Compared to face-up mounting with wire connections, flip-chip bonding exhibited good frequency flatness in signal loss. The mixer module with a built-in IF amplifier achieved a conversion gain of 5dB at an RF frequency of 135GHz and a 3-dB bandwidth of 35GHz. The mixer module with an LO buffer amplifier operated well even at an LO power of -20dBm.

In the study of the capacity problem for multiple access channels (MACs), a lower bound on the error probability obtained by Han plays a crucial role in the converse parts of several kinds of channel coding theorems in the information-spectrum framework. Recently, Yagi and Oohama showed a tighter bound than the Han bound by means of Polyanskiy's converse. In this paper, we give a new bound which generalizes and strengthens the Yagi-Oohama bound, and demonstrate that the bound plays a fundamental role in deriving extensions of several known bounds. In particular, the Yagi-Oohama bound is generalized to two different directions; i.e, to general input distributions and to general encoders. In addition we extend these bounds to the quantum MACs and apply them to the converse problems for several information-spectrum settings.

In this paper, we propose the novel concept of a spectrum database for improving the efficiency of spectrum utilization. In the current design of TV white space spectrum databases, a propagation model is utilized to determine the spectrum availability. However, this propagation model has poor accuracy for radio environment estimation because it requires a large interference margin for the PU coverage area to ensure protection of primary users (PUs); thus, it decreases the spectrum sharing efficiency. The proposed spectrum database consists of radio environment measurement results from sensors on mobile terminals such as vehicles and smart phones. In the proposed database, actual measurements of radio signals are used to estimate radio information regarding PUs. Because the sensors on mobile terminals can gather a large amount of data, accurate propagation information can be obtained, including information regarding propagation loss and shadowing. In this paper, we first introduce the architecture of the proposed spectrum database. Then, we present experimental results for the database construction using actual TV broadcast signals. Additionally, from the evaluation results, we discuss the extent to which the proposed database can mitigate the excess interference margin.

We present a method for recognition of acoustic events in conversation scenarios where speech usually overlaps with other acoustic events. While speech is usually considered the most informative acoustic event in a conversation scene, it does not always contain all the information. Non-speech events, such as a door knock, steps, or a keyboard typing can reveal aspects of the scene that speakers miss or avoid to mention. Moreover, being able to robustly detect these events could further support speech enhancement and recognition systems by providing useful information cues about the surrounding scenarios and noise. In acoustic event detection, state-of-the-art techniques are typically based on derived features (e.g. MFCC, or Mel-filter-banks) which have successfully parameterized the spectrogram of speech but reduce resolution and detail when we are targeting other kinds of events. In this paper, we propose a method that learns features in an unsupervised manner from high-resolution spectrogram patches (considering a patch as a certain number of consecutive frame features stacked together), and integrates within the deep neural network framework to detect and classify acoustic events. Superiority over both previous works in the field, and similar approaches based on derived features, has been assessed by statical measures and evaluation with CHIL2007 corpus, an annotated database of seminar recordings.

Combining heterogeneous wireless networks that cross licensed and unlicensed spectra is a promising way of supporting the surge in mobile traffic. The unlicensed band is mostly used by wireless LAN (WLAN) nodes which employ carrier sense multiple access/collision avoidance (CSMA/CA). Since the number of WLAN devices and their traffic are increasing, the wireless resource of the unlicensed band is expected be more depleted in 2020s. In such a wireless environment, the throughput could be extremely low and unstable due to the hidden terminal problem and exposed terminal problem despite of the large resources of the allocated frequency band and high peak PHY rate. In this paper, we propose user equipment (UE) centric access in the unlicensed band, with support by licensed band access in the mobile network. The proposed access enables robust downlink transmission from the access point (AP) to the UEs by mitigating the hidden terminal problem. The licensed spectrum access passes information on the user data waiting at the AP to the UEs and triggers UE reception opportunity (RXOP) acquisition. Furthermore, the adaptive use of UE centric downlink access is presented by using the channel utilization measured at the AP. Computer simulations confirm that licensed access assistance enhances the robustness of the unlicensed band access against the hidden terminal problem.

This paper proposes a voice activity detection (VAD) algorithm based on an energy related feature of the frequency modulation of harmonics. A multi-resolution spectro-temporal analysis framework, which was developed to extract texture features of the audio signal from its Fourier spectrogram, is used to extract frequency modulation features of the speech signal. The proposed algorithm labels the voice active segments of the speech signal by comparing the energy related feature of the frequency modulation of harmonics with a threshold. Then, the proposed VAD is implemented on one of Texas Instruments (TI) digital signal processor (DSP) platforms for real-time operation. Simulations conducted on the DSP platform demonstrate the proposed VAD performs significantly better than three standard VADs, ITU-T G.729B, ETSI AMR1 and AMR2, in non-stationary noise in terms of the receiver operating characteristic (ROC) curves and the recognition rates from a practical distributed speech recognition (DSR) system.

A salient feature extraction algorithm is proposed to improve the recognition rate of the speech emotion. Firstly, the spectrogram of the emotional speech is calculated. Secondly, imitating the selective attention mechanism, the color, direction and brightness map of the spectrogram is computed. Each map is normalized and down-sampled to form the low resolution feature matrix. Then, each feature matrix is converted to the row vector and the principal component analysis (PCA) is used to reduce features redundancy to make the subsequent classification algorithm more practical. Finally, the speech emotion is classified with the support vector machine. Compared with the tradition features, the improved recognition rate reaches 15%.

This paper gives an outline of key technologies necessary for science-based agriculture. In order to design future agriculture, present agriculture should be redesigned based on the context of smart agriculture that indicates the overall form of agriculture including a social system while the present precision agriculture shows a technical form of agriculture only. Wireless Sensor Network (WSN) and the various type of optical sensors are assumed to be a basic technology of smart agriculture which intends the harmony with the economic development and sustainable agro-ecosystem. In this paper, the current state and development for the optical sensing for environment and plant are introduced.

A new algorithm for separating mass spectra into individual substances is proposed for explosives detection. The conventional algorithm based on probabilistic latent component analysis (PLCA) is effective in many cases because it makes use of the fact that non-negativity and sparsity hold for mass spectra in explosives detection. The algorithm, however, fails to separate mass spectra in some cases because uncertainty can not be resolved only by non-negativity and sparsity constraints. To resolve the uncertainty, an algorithm based on shift-invariant PLCA (SIPLCA) utilizing temporal correlation of mass spectra is proposed in this paper. In addition, to prevent overfitting, the temporal correlation is modeled with a function representing attenuation by focusing on the fact that the amount of a substance is attenuated continuously and slowly with time. Results of an experimental evaluation of the algorithm with data obtained in a real railway station demonstrate that the proposed algorithm outperforms the PLCA-based conventional algorithm and the simple SIPLCA-based one. The main novelty of this paper is that an evaluation of the detection performance of explosives detection is demonstrated. Results of the evaluation indicate that the proposed separation algorithm can improve the detection performance.

A new algorithm for separating mass spectra into individual substances for explosives detection is proposed. In the field of mass spectrometry, separation methods, such as principal-component analysis (PCA) and independent-component analysis (ICA), are widely used. All components, however, have no negative values, and the orthogonality condition imposed on components also does not necessarily hold in the case of mass spectra. Because these methods allow negative values and PCA imposes an orthogonality condition, they are not suitable for separation of mass spectra. The proposed algorithm is based on probabilistic latent-component analysis (PLCA). PLCA is a statistical formulation of non-negative matrix factorization (NMF) using KL divergence. Because PLCA imposes the constraint of non-negativity but not orthogonality, the algorithm is effective for separating components of mass spectra. In addition, to estimate the components more accurately, a sparsity constraint is applied to PLCA for explosives detection. The main contribution is industrial application of the algorithm into an explosives-detection system. Results of an experimental evaluation of the algorithm with data obtained in a real railway station demonstrate that the proposed algorithm outperforms PCA and ICA. Also, results of calculation time demonstrate that the algorithm can work in real time.

Passive SONAR receives a mixed form of signal that is a combination of continuous and discrete line-component spectrum signals. The conventional algorithms, DEMON and LOFAR, respectively target each type of signal, but do not consider the other type of signal also present in the practical environment. Thus when features from two types of signals are presented at the same time, analysis results may cause confusion. In this paper, we propose an integrated analysis algorithm for underwater signals using the modulation spectrogram domain. The proposed domain presents the visual difference between the different types of signal, and therefore can prevent confusion that would otherwise be feasible. Moreover, the proposed algorithm is more efficient than multiband DEMON in terms of computation complexity, while in colored ambient noise environment, it has similar performance to conventional DEMON and LOFAR. We prove the validity of the proposed algorithm through the relevant experiments with synthesized signal and actual underwater recordings.

This paper describes an evaluation of a temporally stable spectral envelope estimator proposed in our past research. The past research demonstrated that the proposed algorithm can synthesize speech that is as natural as the input speech. This paper focuses on an objective comparison, in which the proposed algorithm is compared with two modern estimation algorithms in terms of estimation performance and temporal stability. The results show that the proposed algorithm is superior to the others in both aspects.

This paper considers a method for constructing good high-rate punctured convolutional codes through dual codes. A low-rate R=1/n convolutional code has a dual code identical to a punctured convolutional code with rate R=(n-1)/n. This implies that a low-rate R=1/n convolutional code encoder can help the search of punctured convolutional code encoders. This paper provides the procedures that obtain all the useful dual code encoders to a given CC with rate R=1/n easily, and the best PCC encoder with rate R=(n-1)/n among the encoders we derive from all the obtained dual code encoders. This paper also shows an example of the PCC the procedures obtain from some CC.

We propose a method of spread spectrum digital watermarking with quantization index modulation (QIM) and evaluate the method on the basis of IHC evaluation criteria. The spread spectrum technique can make watermarks robust by using spread codes. Since watermarks can have redundancy, messages can be decoded from a degraded stego-image. Under IHC evaluation criteria, it is necessary to decode the messages without the original image. To do so, we propose a method in which watermarks are generated by using the spread spectrum technique and are embedded by QIM. QIM is an embedding method that can decode without an original image. The IHC evaluation criteria include JPEG compression and cropping as attacks. JPEG compression is lossy compression. Therefore, errors occur in watermarks. Since watermarks in stego-images are out of synchronization due to cropping, the position of embedded watermarks may be unclear. Detecting this position is needed while decoding. Therefore, both error correction and synchronization are required for digital watermarking methods. As countermeasures against cropping, the original image is divided into segments to embed watermarks. Moreover, each segment is divided into 8×8 pixel blocks. A watermark is embedded into a DCT coefficient in a block by QIM. To synchronize in decoding, the proposed method uses the correlation between watermarks and spread codes. After synchronization, watermarks are extracted by QIM, and then, messages are estimated from the watermarks. The proposed method was evaluated on the basis of the IHC evaluation criteria. The PSNR had to be higher than 30 dB. Ten 1920×1080 rectangular regions were cropped from each stego-image, and 200-bit messages were decoded from these regions. Their BERs were calculated to assess the tolerance. As a result, the BERs were less than 1.0%, and the average PSNR was 46.70 dB. Therefore, our method achieved a high image quality when using the IHC evaluation criteria. In addition, the proposed method was also evaluated by using StirMark 4.0. As a result, we found that our method has robustness for not only JPEG compression and cropping but also additional noise and Gaussian filtering. Moreover, the method has an advantage in that detection time is small since the synchronization is processed in 8×8 pixel blocks.

Delayed correlation has been used to detect orthogonal frequency division multiplexing symbols with cyclic prefix in spectrum sensing. Because of the frequency offset, the outputs of the delayed correlation do not lie only on the real axis of a complex plane. Therefore, the absolute value of the outputs of the delayed correlation is employed. Furthermore, with the use of a filter bank, the number of the outputs of the delayed correlators increases and the averaging over the outputs decreases the noise variance. This paper proposes a new delayed correlation scheme that uses a filter bank and employs the absolute of the outputs of delayed correlation. The proposed scheme improves the probability of detection as the number of the branches of the delayed correlators increases. In the case of 6 branches, the proposed scheme reduces the required sample energy by 1dB the probability of detection of 0.9.