Blind detection of liquefaction by existing methods for New Zealand M_L6.3 earthquake on Feb. 22,2011

Real-time liquefaction monitoring and warning techniques are new ways to mitigate liquefaction hazard. A key point is to establish a reverse liquefaction detection method based on seismic records. However, the existing methods are quite limited and the reliability requires verification. On Feb. 22, 2011 an earthquake of magnitude 6.3 struck at New Zealand＇s South Island. Remarkable liquefaction phenomena were reported, which provide an opportunity to verify the existing liquefaction detection methods. 27 acceleration records within 50 km to the epicenter were selected to perform a blind detection by using the existing methods, including Miyajima method, Suzuki method, Kostadinov-Yamazaki method and Yuan-Sun method. The blind detection results indicate that Yuan-Sun method gives correct results for seven confirmed sites, and Suzuki method and Yuan-Sun method yield correct detection for a reported non-liquefied site. Four methods including the Yuan-Sun method give identical detection for four sites and three methods also including the Yuan-Sun method give identical detection for ten sites. Besides, there are five sites, for which the four methods give opposite detection.

Discussion on the application prospect of the transient electromagnetic method based on the dual launcher and mine advanced detection

The dual transmitter implements the equivalent anti-magnetic flux transient electromagnetic method, which can effectively reduce the scope of the transient electromagnetic detection blind area. However, this method is rarely reported in the detection of pipelines in urban geophysical exploration and the application of coal mines. Based on this, this paper realizes the equivalent anti-magnetic flux transient electromagnetic method based on the dual launcher. The suppression effect of this method on the blind area is analyzed by physical simulation. And the detection experiment of underground pipelines is carried out outdoors. The results show that the dual launcher can significantly reduce the turn-off time, thereby effectively reducing the impact of the blind area on the detection results, and the pipeline detection results verify the device’s effectiveness. Finally, based on the ground experimental results, the application prospect of mine advanced detection is discussed. Compared with other detection fields, the formation of blind areas is mainly caused by the equipment. If the dual launcher can be used to reduce the blind area, the accuracy of advanced detection can be improved more effectively. The above research results are of great significance for improving the detection accuracy of the underground transient electromagnetic method.

Sensor scheduling for ground maneuvering target tracking in presence of detection blind zone

Continuous and stable tracking of the ground maneuvering target is a challenging problem due to the complex terrain and high clutter. A collaborative tracking method of the multisensor network is presented for the ground maneuvering target in the presence of the detection blind zone(DBZ). First, the sensor scheduling process is modeled within the partially observable Markov decision process(POMDP) framework. To evaluate the target tracking accuracy of the sensor, the Fisher information is applied to constructing the reward function. The key of the proposed scheduling method is forecasting and early decisionmaking. Thus, an approximate method based on unscented sampling is presented to estimate the target state and the multi-step scheduling reward over the prediction time horizon. Moreover, the problem is converted into a nonlinear optimization problem, and a fast search algorithm is given to solve the sensor scheduling scheme quickly. Simulation results demonstrate the proposed nonmyopic scheduling method(Non-MSM) has a better target tracking accuracy compared with traditional methods.

Asynchronous QR-RLS-CMOE Blind Multiuser Detection

The QR-RLS-CMOE algorithm which was applied to synchronous DS/CDMA systems in AWGN channel, is modified and applied to asynchronous DS/CDMA systems in multi-path fading channel in this paper. Computer simulation experiences show that the asynchronous QR-RLS-CMOE (A-QR-RLS-CMOE) blind multiuser detection algorithm can well cancel multiple access interference and overcome multipath fading, and has a good anti-near-far effect in the case of τ<<bT .

In-Vehicle Stereo Vision Systems with Improved Ant Colony Optimization Based Lane Detection: A Solution to Accidents Involving Large Goods Vehicles Due to Blind Spots

This paper presents an in-vehicle stereo vision system as a solution to accidents involving large good vehicle due to blind spots using Nigeria as a case study. In this paper, a stereo-vision system was attached to the front of Large Good Vehicles (LGVs) with a view to presenting live feeds of vehicles close to the LGV vehicles and their distance away. The captured road images using the stereo vision system were optimized for effectiveness and optimal vehicle maneuvering using a modified metaheuristics algorithm called the simulated annealing Ant Colony Optimization (saACO) algorithm. The concept of simulated annealing is strategies used to automatically select the control parameters of the ACO algorithm. This helps to stabilize the performance of the ACO algorithm irrespective of the quality of the lane images captured in the in-vehicle vision system. The system is capable of notifying drivers through lane detection techniques of blind spots. This technique enables the driver to be more aware of what surrounds the vehicle and make decisions early. In order to test the system, the stereo-vision device was mounted on a Large good vehicle, driven in Zaria (a city in Kaduna state in Nigeria), and data were in the record. Out of 180 events, 42.22% of potential accident events were caused by Passenger Cars, while 27.22%, 18.33% and 12.22% were caused by two-wheelers, Large Good Vehicles and road users, respectively. In the same vein, the in-vehicle lane detection system shows a good performance of the saACO-based lane detection system and gives a better performance in comparison with the standard ACO method.

Wavelet Transform Modulus Maxima-Based Robust Digital Image Watermarking in Wavelet Domain

A new robust watermarking approach was proposed in 2D continuous wavelet domain （CWT）. The watermark is embedded into the large coefficients in the middle band of wavelet transform modulus maxima （WTMM） of the host image. After possible attacks, the watermark is then detected and extracted by correlation analysis. Compared with other wavelet domain watermarking approaches, the WTMM approach can endow the image with beth rotation and shift invariant properties. On the other hand, scale invariance is achieved with the geometric normalization during watermark detection. Case studies involve various attacks such as shifting, lossy compression, scaling, rotation and median filtering on the watermarked image, and the result shows that the approach is robust to these attacks.

The Least Square Constant Modulus Algorithm for Blind Adaptive Multiuser Detection

In this paper, we apply the Least Square Constant Modulus Algorithm( LSCMA ) to the blind Multiuser Detection( MUD ). The algorithm is shown to be globally stable and convergent for any linearly independent set of input data and converge faster than the conventional Steepest Descent CMA(SDCMA ) and Linearly Constrained CMA(LCCMA ),but more complex.

Joint spatial-frequency blind multiuser detection based on LCCMA

Multicarrier code division multiple access （MC-CDMA） has the ability to combat with frequency selective fading and antenna array can enhance the performance of system. The paper proposes a novel joint spatial-frequency blind multiuser detection for antenna array MC-CDMA based on linear constraint constant modulation algorithm （LCCMA）, which has robust performance and can ensue the weight vectors to converge to that of the desired user Simulation indicates the proposed algorithm has better bit error ratio （BER） performance than that of the traditional beamforming-based two-step algorithm.

Blind Spot Obstacle Detection from Monocular Camera Images with Depth Cues Extracted by CNN

The images from a monocular camera can be processed to detect depth information regarding obstacles in the blind spot area captured by the side-view camera of a vehicle.The depth information is given as a classification result“near”or“far”when two blocks in the image are compared with respect to their distances and the depth information can be used for the purpose of blind spot area detection.In this paper,the proposed depth information is inferred from a combination of blur cues and texture cues.The depth information is estimated by comparing the features of two image blocks selected within a single image.A preliminary experiment demonstrates that a convolutional neural network(CNN)model trained by deep learning with a set of relatively ideal images achieves good accuracy.The same CNN model is applied to distinguish near and far obstacles according to a specified threshold in the vehicle blind spot area,and the promising results are obtained.The proposed method uses a standard blind spot camera and can improve safety without other additional sensing devices.Thus,the proposed approach has the potential to be applied in vehicular applications for the detection of objects in the driver’s blind spot.

Steady-state performance analysis of HOS-based blind adaptive multiuser detection

The blind adaptive multiuser detections based on higher-order statistics （HOS） can obtain higher steady-state decorrelating performance than the conventional linear algorithm under the high SNR con- dition. However, the closed-form analysis for this steady-state performance is scarce due to the com- plication of analyzing the nonlinear updates of the adaptive algorithm. An analysis approach based on ordinary differential equation （ODE） method is proposed to get the closed-form excess mean-square error （EMSE） expression of the HOS-based multiuser detections. The simulation and the comparison verify the results of the analysis.

Cooperative blind spectrum sensing using autocorrelation matrix

To improve the spectrum sensing performance in cognitive radios, a scheme of cooperative blind spectrum sensing based on autocorrelation matrix is proposed. The test statistic is extracted from the autocorrelation matrix of the received signal samples and a bi-threshold hybrid decision scheme is designed for local spectrum sensing. The cognitive radio base station makes a credibility fusion based on the local soft decisions and then takes global fusion combining with the local hard decisions. The proposed method is blind in the sense since it requires no apriori knowledge of the signal and the noise power. Theoretical analysis and computer simulation results show that the proposed method can enhance the spectrum sensing capability.

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus.In copy-move forgery,the assailant intends to hide a portion of an image by pasting other portions of the same image.The detection of such manipulations in images has great demand in legal evidence,forensic investigation,and many other fields.The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors,such as local ternary pattern,local phase quantization,local Gabor binary pattern histogram sequence,Weber local descriptor,and local monotonic pattern,and classifiers such as optimized support vector machine and optimized NBC.The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated,even if the test image is subjected to attacks such as JPEG compression,scaling,rotation,and brightness variation.CoMoFoD,CASIA,and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms.The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.

Multiple access interference suppression for CDMA systems via l_(∞)-minimization

A key problem in code-division multiple access(CDMA)system is to mitigate the multiple access interference(MAI)from other users while detecting the desired user.The performance of the conventional minimum output energy(MOE)multiuser detector for CDMA system significantly degrades in the presence of signature waveform distortions induced by multipath propagation or timing asynchronism.In this paper,a robust linear programming(ROLP)algorithm for blind multiuser detection is proposed.Different from the existing MOE-based multiuser detection techniques,the proposed ROLP minimizes the l_∞-norm of the output to exploit the non-Gaussianity of the communication signals.To achieve robustness against signature waveform mismatch,the proposed method constrains the magnitude response of any signature vector within a specified uncertainty set to exceed unity.The uncertainty set is modeled as a rhombus,which differs from the spherical uncertainty region widely taken in the existing robust multiuser detectors.The resulting optimization problem is reformulated into a linear programming program and hence can be solved efficiently.The proposed ROLP is computationally simpler than its robust counterparts that requires solving a second-order cone programming.Simulation results demonstrate the superiority of the ROLP over several robust detectors,which indicate that its performance approaches the optimal performance bound.