Double-rotor drone shows totally different flight performance. Extracting and analyzing its motion track is very helpful to improve its control approaches to achieve a robust and flight attitude. A novel EMD of endpoint effect suppression is proposed in this paper to accurately extract the DR drone's motion track. The proposed algorithm can effectively suppress the endpoint effect with a complex matching of both position and slope of the record of flight data from sensors. The computer simulation and experiment results both have demonstrated the proposed method's effectiveness and the feasibility of the designed DR drone.

Magnetic Anomaly Detection (MAD) is a passive method for the detection of ferromagnetic objects. Currently, the performance of a MAD system is limited by the magnetic background noise that is non-stationary and shows self-similarity and long-range correlation. In this paper, we propose an empirical mode decomposition (EMD) trend filtering based energy detector for adaptively detecting the magnetic anomaly signal from the background noise. The input data is first detrended adaptively with the energy-ratio trend filtering approach. Then, the magnetic anomaly signal is detected using an energy detector. The proposed detector does not need any a priori knowledge about the target or assumptions regarding the background noise. Experiments also prove that the proposed detector shows a more stable performance than the existing undecimated discrete wavelet transform (UDWT) based energy detector.

A bi-dimensional empirical mode decomposition (BEMD) is one of the powerful methods for decomposing non-linear and non-stationary signals without a prior function. It can be applied in many applications such as feature extraction, image compression, and image filtering. Although modified BEMDs are proposed in several approaches, computational cost and quality of their bi-dimensional intrinsic mode function (BIMF) still require an improvement. In this paper, an iteration-free computation method for bi-dimensional empirical mode decomposition, called iBEMD, is proposed. The locally partial correlation for principal component analysis (LPC-PCA) is a novel technique to extract BIMFs from an original signal without using extrema detection. This dramatically reduces the computation time. The LPC-PCA technique also enhances the quality of BIMFs by reducing artifacts. The experimental results, when compared with state-of-the-art methods, show that the proposed iBEMD method can achieve the faster computation of BIMF extraction and the higher quality of BIMF image. Furthermore, the iBEMD method can clearly remove an illumination component of nature scene images under illumination change, thereby improving the performance of text localization and recognition.

This paper presents a novel MEMD interval thresholding denoising, where relevant modes are selected by the similarity measure between the probability density functions of the input and that of each mode. Simulation and measured EEG data processing results show that the proposed scheme achieves better performance than other traditional denoisings.

This paper presents differential-based distinguishers against double-branch compression functions and applies them to ISO standard hash functions RIPEMD-128 and RIPEMD-160. A double-branch compression function computes two branch functions to update a chaining variable and then merges their outputs. For such a compression function, we observe that second-order differential paths will be constructed by finding a sub-path in each branch independently. This leads to 4-sum attacks on 47 steps (out of 64 steps) of RIPEMD-128 and 40 steps (out of 80 steps) of RIPEMD-160. Then new properties called a (partial) 2-dimension sum and a q-multi-second-order collision are considered. The partial 2-dimension sum is generated on 48 steps of RIPEMD-128 and 42 steps of RIPEMD-160, with complexities of 235 and 236, respectively. Theoretically, the 2-dimension sum is generated faster than the brute force attack up to 52 steps of RIPEMD-128 and 51 steps of RIPEMD-160, with complexities of 2101 and 2158, respectively. The results on RIPEMD-128 can also be viewed as q-multi-second-order collision attacks. The practical attacks have been implemented and examples are presented. We stress that our results do not impact to the security of full RIPEMD-128 and RIPEMD-160 hash functions.

Automatic modulation classification (AMC) involves extracting a set of unique features from the received signal. Accuracy and uniqueness of the features along with the appropriate classification algorithm determine the overall performance of AMC systems. Accuracy of any modulation feature is usually limited by the blindness of the signal information such as carrier frequency, symbol rate etc. Most papers do not sufficiently consider these impairments and so do not directly target practical applications. The AMC system proposed herein is trained with probable input signals, and the appropriate decision tree should be chosen to achieve robust classification. Six unique features are used to classify eight analog and digital modulation schemes which are widely used by low frequency mobile emergency radios around the globe. The Proposed algorithm improves the classification performance of AMC especially for the low SNR regime.

In this paper, we propose Local Curvelet Binary Patterns (LCBP) and Learned Local Curvelet Patterns (LLCP) for presenting the local features of facial images. The proposed methods are based on Curvelet transform which can overcome the weakness of traditional Gabor wavelets in higher dimensions, and better capture the curve singularities and hyperplane singularities of facial images. LCBP can be regarded as a combination of Curvelet features and LBP operator while LLCP designs several learned codebooks from patch sets, which are constructed by sampling patches from Curvelet filtered facial images. Each facial image can be encoded into multiple pattern maps and block-based histograms of these patterns are concatenated into an histogram sequence to be used as a face descriptor. During the face representation phase, one input patch is encoded by one pattern in LCBP while multi-patterns in LLCP. Finally, an effective classifier called Weighted Histogram Spatially constrained Earth Mover's Distance (WHSEMD) which utilizes the discriminative powers of different facial parts, the different patterns and the spatial information of face is proposed. Performance assessment in face recognition and gender estimation under different challenges shows that the proposed approaches are superior than traditional ones.

In this paper, we present the first results on the preimage resistance against step-reduced versions of ISO standard hash functions RIPEMD-128 and RIPEMD-160, which were designed as strengthened versions of RIPEMD. While preimage attacks on the first 33 steps and intermediate 35 steps of RIPEMD (48 steps in total) are known, no preimage attack exists on RIPEMD-128 (64 steps) or RIPEMD-160 (80 steps). This paper shows three variations of preimage attacks of RIPEMD-128; the first 33 steps, intermediate 35 steps, and the last 32 steps. Because of the large security margin, full RIPEMD-128 is still enough secure, however, it is interesting that the number of attacked steps for RIPEMD-128 reaches the same level as for RIPEMD. We also show that our approach can be applied to RIPEMD-160, and present preimage attacks on the first 30 steps and the last 31 steps.

Even though meet-in-the-middle preimage attack framework has been successfully applied to attack most of narrow-pipe hash functions, it seems difficult to apply this framework to attack double-branch hash functions. Only few results have been published on this research. This paper proposes a refined strategy of applying meet-in-the-middle attack framework to double-branch hash functions. The main novelty is a new local-collision approach named one-message-word local collision. We have applied our strategy to two double-branch hash functions RIPEMD and RIPEMD-128, and obtain the following results.·On RIPEMD. We find a pseudo-preimage attack on 47-step compression function, where the full version has 48 steps, with a complexity of 2119. It can be converted to a second preimage attack on 47-step hash function with a complexity of 2124.5. Moreover, we also improve previous preimage attacks on (intermediate) 35-step RIPEMD, and reduce the complexity from 2113 to 296. ·On RIPEMD-128. We find a pseudo-preimage on (intermediate) 36-step compression function, where the full version has 64 steps, with a complexity of 2123. It canl be converted to a preimage attack on (intermediate) 36-step hash function with a complexity of 2126.5. Both RIPEMD and RIPEMD-128 produce 128-bit digests. Therefore our attacks are faster than the brute-force attack, which means that our attacks break the theoretical security bound of the above step-reduced variants of those two hash functions in the sense of (second) preimage resistance. The maximum number of the attacked steps on both those two hash functions is 35 among previous works based to our best knowledge. Therefore we have successfully increased the number of the attacked steps. We stress that our attacks does not break the security of full-version RIPEMD and RIPEMD-128. But the security mergin of RIPEMD becomes very narrow. On the other hand, RIPEMD-128 still has enough security margin.

In off-line analysis, the demand for high precision signal processing has introduced a new method called Empirical Mode Decomposition (EMD), which is used for analyzing a complex set of data. Unfortunately, EMD is highly compute-intensive. In this paper, we show parallel implementation of Empirical Mode Decomposition on a GPU. We propose the use of “partial+total” switching method to increase performance while keeping the precision. We also focused on reducing the computation complexity in the above method from O(N) on a single CPU to O(N/P log (N)) on a GPU. Evaluation results show our single GPU implementation using Tesla C2050 (Fermi architecture) achieves a 29.9x speedup partially, and a 11.8x speedup totally when compared to a single Intel dual core CPU.

The Hilbert transformation together with empirical mode decomposition (EMD) produces Hilbert spectrum (HS) which is a fine-resolution time-frequency representation of any nonlinear and non-stationary signal. The EMD decomposes the mixture signal into some oscillatory components each one is called intrinsic mode function (IMF). Some modification of the conventional EMD is proposed here. The instantaneous frequency of every real valued IMF component is computed with Hilbert transformation. The HS is constructed by arranging the instantaneous frequency spectra of IMF components. The HS of the mixture signal is decomposed into subspaces corresponding to the component sources. The decomposition is performed by applying independent component analysis (ICA) and Kulback-Leibler divergence based K-means clustering on the selected number of bases derived from HS of the mixture. The time domain source signals are assembled by applying some post processing on the subspaces. We have produced experimental results using the proposed separation technique.