Earticle

In this study, an identification model based on computer vision and artificial neural network technologies is proposed for the identification of the ripeness of fresh corn ears. For collected images of corn ears, 2D discrete wavelet transform is performed to extract information of low-frequency sub-band as color features, and discrete Fourier transform is performed to extract energy spectrum information as texture features. Principle component analysis is employed for the fusion and dimensionality reduction of color and texture features, and the first three principle components are chosen as inputs of the network model in order to establish probabilistic neural network model for the automated ripeness identification of fresh corn ears. Simulation analysis demonstrates that the identification accuracy of this model reaches 90.67%.

Medical image edge detection is an important work for object recognition of the human organs, and it is an essential pre-processing step in medical image segmentation and 3D reconstruction. Although many edge-detection evaluation methods have been developed in the past years, however this is still a challenging and unsolved problem. Conventionally, edge is detected according to some early brought forward algorithms like Canny, LOG, Sobel, Prewitt, Roberts algorithms but in theory they belong to the high pass filtering, which are not fit for noise medical image edge detection because noise and edge belong to the scope of high frequency. In real world applications, medical images contain object boundaries and object shadows and noise. Therefore, they may be difficult to distinguish the exact edge from noise or trivial geometric features. After studying all traditional methods of edge detection, it has been analyzed that for these situations, a new algorithm is needed which is optimal. In this paper, we propose a new algorithm for edge detection of noisy medical images based on both Tsallis and Shannon entropy together. The performance of our method is compared against other methods by using blood cells image corrupted with various levels of "salt and pepper". It is observed that the proposed algorithm displayed superior noise resilience and decrease the computation time.

Bottleneck (BN) feature has attracted considerable attentions by its capacity of improving the accuracies in speech recognition tasks. Recently, researchers have proposed some modified approaches for extracting more effective BN feature, but these approaches still need further improvement. In this paper, motivated by both deep belief networks (DBN) and hierarchical Multilayer Perceptron (MLP), we propose hierarchical DBNs based BN feature and employed it for keyword spotting task. The hierarchical DBNs based BN feature is constructed with two DBNs in series which are sequentially trained. The first DBN outputs the posterior probabilities features, as well as the second DBN transforms the posterior probability features into a low dimensional representation with the information pertinent to classification through the BN layer. Experiments on hierarchical DBNs based BN feature is conducted with TIMIT dataset and using Point Process Model as the baseline system. Experimental results show that the hierarchical DBNs based BN feature is more robust and can achieve better accuracies than other features.

The goal of this paper is to suppress time-varying single tone jamming (STJ) in the CDMA system by using the frequency interference cancellation method. We consider the practical scenario where STJ signal experiences time-varying fading channel. In this approach, the parameters (i.e. frequency, amplitude and phase) of the time-varying STJ are estimated in frequency domain, and then it is reconstructed and subtracted from the raw signals. The theoretical BER (bit error rate) expression is analyzed to assess the performance of the method, and computer simulations are executed. Simulation results demonstrate the effectiveness of the method in mitigating time-varying STJ and approaching the interference-free performance limit over practical ranges of STJ power levels and mobility levels.

Cognitive radio (CR) has recently been identified as a promising technology to solve the spectrum inefficiency problem. CR users or secondary users (SUs) need to sense the presence of primary users (PUs) constantly and rapidly to utilize their unused spectrum. However, detection is compromised when a user experiences shadowing or fading effects. In such cases, the user cannot distinguish between an unused band and a deep fade. So, cooperative spectrum sensing is proposed to improve sensing performance. In this paper, we analyze the different spectrum sensing schemes with different fusion rules and their comparative behavior has also been studied. Moreover, the relationship between the throughput and sensing time in local and cooperative spectrum sensing has been investigated for PU protection and SU spectrum utilization mode. We also observe that average channel utilization depends on the number of cooperative users under PU protection scenario. The analytical results show that cooperative spectrum sensing employing OR rule has better performance than other fusion rules as well as non-cooperative scheme.

Distinguished with traditional computer realistic graphics, qualities of real time and fidelity are regarded as significant indexes of weighing system validity for real-time simulation of ocean wave simultaneously. Based on ocean waves’ fractal character and the linear superposition method of Linear Ocean Wave Theory, the paper puts forward the simulation algorithm of ocean surfaces and realizes the real-time simulation of 3D ocean waves. In the course of programming, the character of real time is researched and corresponding simplified means are presented. The approaches and key techniques of ocean wave real-time simulation put forward in the paper better satisfy the requirements of realistic rendering.