Earticle

Secure problem has become a major concern in spectrum sensing. PUE attack is a common attack in spectrum sensing. To defend PUE attack, an anti-PUE attack method in spectrum sensing based on fractal dimension is proposed. It detects PUE attack by identifying the modulation type of the received signal using SVM classifier. Sevcik fractal dimension in frequency domain (SFDF) and Higuchi fractal dimension (HFD) of the received signal are adopted as the characteristics for classification by SVM classifier. So the task of anti-PUE attack can be carried out in spectrum sensing. Besides the parameters for spectrum sensing, i.e. SFDF and HFD, no other parameter is required to be calculated, which will decrease the calculation amount and calculation time. The Numerical results show that, the proposed method can effectively detect the PUE attack. When SNR is larger than 10 dB, its PUE detection probability can reach 1. Even when SNR is low to -10 dB, the PUE detection probability is larger than 0.97.

Temperature modulation of metal oxide semiconductor (MOS) gas sensors has been widely used due to its higher discriminating power. The temperature modulation alters the kinetics of the gas-sensor interaction leading to characteristic response patterns. However, the selection of frequencies and duty cycles is based on trial and error method. In this paper, we have introduced a method to systematically determine the optimal set of modulation frequencies and duty cycles using system identification theory for sensor modeling. Pulse modulation being a popular method of feature extraction of MOS sensors, optimization of parameters of pulse modulation becomes very significant. In our work, system identification has been applied to select the sensor model that provides the most stable and desired sensor response, hence solving problem of choosing the best frequency and duty cycle of the temperature modulating signal of the MOS sensor. The estimation of model parameters is done using iterative prediction-error minimization (PEM) method. The best suited transfer function was chosen for the MOS gas sensors based on the sensor stability and then the sensors were operated at the respective best frequencies and duty cycles. Principal Component Analysis (PCA) was used to visualize the different sample gas patterns. Data classification was performed using supervised neural network classifiers; namely the Multi-Layer Perceptron (MLP) network and Radial Basis Function (RBF) network and the classification percentage before and after optimization were compared henceforth.

Kainic acid (KA) causes neurodegeneration, but no consensus has been reached concerning its mechanism. Nitric oxide may be a regulator of the mechanism. We identified differentially expressed genes in the hippocampus of mice treated with kainic acid, together with or without L-NAME, a nonselective nitric oxide synthase inhibitor, using a new differential display PCR method based on annealing control primers. Eight genes were identified, including clathrin light polypeptide, TATA element modulatory factor 1, neurexin III, ND4, ATPase, $H^+$ transporting, V1 subunit E isoform 1, and N-myc downstream regulated gene 2. Although the functions of these genes and their products remain to be determined, their identification provides insight into the molecular mechanism(s) involved in KA-induced neuronal cell death in the hippocampal CA3 area.

In this paper, we propose a robust music audio fingerprinting system for automatic music retrieval. The fingerprint feature is extracted from the long-term dynamic modulation spectrum (LDMS) estimation in the perceptual compressed domain. The major advantage of this feature is its significant robustness against severe background noise from the street and cars. Further the fast searching is performed by looking up hash table with 32-bit hash values. The hash value bits are quantized from the logarithmic scale modulation frequency coefficients. Experiments illustrate that the LDMS fingerprint has advantages of high scalability, robustness and small fingerprint size. Moreover, the performance is improved remarkably under the severe recording-noise conditions compared with other power spectrum-based robust fingerprints.

Red ginseng extract powder (RGEP) and red ginseng dietary fiber (RGDF) contain bioactive components with potential prebiotic effects. As gut microbiota plays a critical role in obesity and is influenced by prebiotics, we investigated the effects of RGEP and RGDF supplementation on gut microbiota diversity, composition, and metabolic functions in diet-induced obese mice. RGEP and RGDF supplementation altered gut microbiota composition, increasing beneficial bacteria such as Lactobacillus, Roseburia, and Akkermansia. Alpha diversity analysis showed an increase in microbial richness, particularly in the high-dose RGDF group, whereas beta diversity analysis confirmed a distinct separation between red ginseng-fed groups and obesity models. Functional pathway analysis revealed that supplementation with RGEP and RGDF enhanced short-chain fatty acid (SCFA) metabolism, lipid metabolism, and anti-inflammatory metabolism, suggesting modulation of gut microbial functional profiles. These findings suggest that RGEP and RGDF contribute to gut microbiota modulation by enhancing microbial diversity, promoting SCFA metabolism, and suppressing pro-inflammatory bacterial taxa. While only gut microbiota profiles were analyzed, the observed restoration of microbial balance suggests a potential contribution of red ginseng components to host metabolic health, which warrants further investigation. Further studies are needed to validate these findings in human trials and elucidate the underlying molecular mechanisms.

본 논문에서는 미지의 디지털 변조신호가 입력되는 경우에 변조방식을 식별하는 방법을 제안한다. 제안하는 식별 방법은 입력신호의 순시진폭, 순시주파수, 순시 위상을 구하고, 이를 바탕으로 특징벡터를 추출하여 신경망을 이용하여 식별하도록 구성하였다. 식별방법의 타당성을 검증하기 위해 백색 가우스성 잡음환경 하에서 8가지 입력신호를 사용하여 SNR을 변화시켜가며 모의실험을 수행하였다. 모의실험 결과 신경망을 이용하여 SNR 10 [㏈]까지 모든 입력 신호들을 식별해 낼 수 있었다.

In this Paper, a new method is proposed to identify a modulation method in the case of unknown digitally modulated input signals. The proposed identification method is implemented with an artificial neural network which is based on characteristic features extracted from the instantaneous amplitude, the instantaneous phase and the instantaneous frequency of the input signals. The proposed method was simulated with 8 type signals in a noisy communication environment. The results show that the artificial neural network can accurately recognize all kinds of patterns.

This paper presents a novel method for identification of FSK modulated radar signal. Three features which measure the number of frequency tones, the regularity of the frequency shifting, and the diversity of power spectrum of detected radar signal, are introduced. A Two-step combined maximum likelihood classifier was used to identify the details of the detected FSK signal; the modulation order and the use of Costas code. We attempted to divide FSK signal into binary FSK, ternary FSK, 8-ary FSK, and FSK with Costas code of length 7. The simulation results indicated that the proposed methods achieves an averaged identification accuracy was 99.93% at a signal-to-noise of 0 dB.

뉴스와 취재 프로그램 같은 방송에서는 제보자의 신원 보호를 위해 음성을 변조한다. 음성 변조 방법으로 피치(pitch)를 조절하는 방법이 가장 많이 사용되는데, 이 방법은 피치를 재조절하는 방식으로 쉽게 원본 음성과 유사하게 음성 복원이 가능하다. 따라서 방송 음성 변조 방법은 화자의 신원 보호를 제대로 해줄 수 없고 보안상 취약하기 때문에 이를 대체하기 위한 새로운 음성 변조 방법이 필요하다. 본 논문에서는 Voice Privacy Challenge에서 비식별화 성능이 검증된 Lightweight 음성 비식별화 모델을 성능 비교 모델로 사용하여 피치 조절을 사용한 방송 음성변조 방법과 음성 비식별화 성능 비교 실험 및 평가를 진행한다. Lightweight 음성 비식별화 모델의 6가지 변조 방법 중 비식별화 성능이 좋은 3가지 변조 방법 McAdams, Resampling, Vocal Tract Length Normalization(VTLN)을 사용하였으며 한국어 음성에 대한 비식별화 성능을 비교하기 위해 휴먼 테스트와 EER(Equal Error Rate) 테스트를 진행하였다. 실험 결과로 휴먼 테스트와 EER 테스트 모두 VTLN 변조 방법이 방송 변조보다 더 높은 비식별화 성능을 보였다. 결과적으로 한국어 음성에 대해 Lightweight 모델의 변조 방법은 충분한 비식별화 성능을 가지고 있으며 보안상 취약한 방송 음성 변조를 대체할 수 있을 것이다.

In broadcasts such as news and coverage programs, voice is modulated to protect the identity of the informant. Adjusting the pitch is commonly used voice modulation method, which allows easy voice restoration to the original voice by adjusting the pitch. Therefore, since broadcast voice modulation methods cannot properly protect the identity of the speaker and are vulnerable to security, a new voice modulation method is needed to replace them. In this paper, using the Lightweight speech de-identification model as the evaluation target model, we compare speech de-identification performance with broadcast voice modulation method using pitch modulation. Among the six modulation methods in the Lightweight speech de-identification model, we experimented on the de-identification performance of Korean speech as a human test and EER(Equal Error Rate) test compared with broadcast voice modulation using three modulation methods: McAdams, Resampling, and Vocal Tract Length Normalization(VTLN). Experimental results show VTLN modulation methods performed higher de-identification performance in both human tests and EER tests. As a result, the modulation methods of the Lightweight model for Korean speech has sufficient de-identification performance and will be able to replace the security-weak broadcast voice modulation.