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In this paper, we studied resource allocation in a dynamic multiplexing scenario of two 5G services, namely, enhanced mobile broadband (eMBB) and ultrareliable low-latency communication (URLLC). Considering that eMBB services focus on high data rates while URLLC is very strict in terms of latency and reliability, we formulate a joint eMBB/URLLC resource allocation problem. To solve the formulated problem, in particular, we propose a joint resource allocation algorithm that can satisfy both eMBB user rate and URLLC interrupt probability requirements. We propose an RB allocation scheme that takes into account the needs of eMBB and URLLC users. The program consists of two phases as follows: (1) establish an RB allocation for eMBB users and (2) assign the minislot for URLLC users and calculate the transmission power of URLLC users that ensures the reliability constraint. We also realize and verify the algorithm through the simulation.

Vegetation segmentation from images is an essential issue in the application of computer vision in agriculture. In this paper, we present a new vegetation segmentation method based on Particle Swarm Optimisation (PSO) clustering and morphology modelling in CIE L∗a∗b∗ colour space. At the off-line learning stage, a new method is put forward to determine the clustering number. Secondly, the tools of morphological dilation and erosion are employed to establish the vegetation colour model. At the online segmentation stage, the PSO-based k-means is used to cluster the vegetation image into vegetation classes and non-vegetation classes. Afterwards, the established colour model is used to distinguish the vegetation classes and give the segmentation result. In the experiments, the proposed method was applied to segment 200 smaller regions of the full camera images of rice and 100 smaller regions of the full camera images of cotton. The means of segmentation qualities reached 88.1% and 91.7% respectively. Moreover, the proposed method was compared with three well-known vegetation segmentation methods and two skin segmentation methods. Experiments demonstrate that the proposed method yielded the highest mean of segmentation qualities and lowest standard deviations of segmentation qualities. In addition, the vegetation colour models built with different structuring element types are analysed.

Crop segmentation from the images taken in the outdoor fields is a complex task. In this paper, a new morphology modeling method is utilized to establish the crop color model in the CIE L^*a^*b^* (or Lab for simplification) color space and to realize the crop image segmentation. In the supervised learning stage, morphology modeling is applied to deal with the color characteristics of the crop with respect to the pixel lightness component and establish the crop color model. To verify the performance of the proposed method, 56 test images which in size of 601x601 and taken from April 27, 2011 to May 21, 2011 are utilized to compare the proposed method with eight other famous approaches. Experiment shows that the segmentation quality of the proposed method is approximately 87.2% for the Automatic Target Recognition Working Group (ATRWG) evaluation method and 96.0% for another evaluation method. Moreover, the segmentation performance for images taken on cloudy, overcast and sunny days is analyzed. Experiment demonstrates that our method is robust to the variation of illumination in the field and performed better than eight other approaches. Furthermore, the impact of different structuring element types to the proposed method is compared. Overall, the proposed crop segmentation method can be used to crop segmentation in the field effectively.

Growth stage information of field crops is not only an important basic data for analyzing the relationship between the crop growth process and the agrometeorological conditions, but it is also useful for various aspects of precision agriculture. Up to now, it is primarily obtained manually, which is time-consuming, labor-intensive, subjective and discontinuous. Therefore, a noninvasive method to note observations that also proves to be more efficient, continuous, and automatic is needed. At present, an alternative method based on computer vision has been widely used for monitoring crop growth status due to advantages linked to its low-cost, its intuitiveness and non-contact manner of data gathering it provides. However, little research has been done to improve close observation of different growth stages of field crops using digital cameras. To overcome the drawbacks caused by the current manual observation, a study was conducted to explore the application of computer vision technology for the automatic detection technology of two critical growth stages of maize (emergence and three-leaf stage). In order to identify the growth stages, the first task is to extract the plants from images properly. According to complex factors on farm fields, we proposed a novel crops segmentation method (AP-HI) which is robust and not sensitive to the challenging variation of outdoor luminosity and complex environmental elements. It has laid the foundation for subsequent studies. By virtue of the AP-HI, two automatic detection methods based on imaging were investigated for the two critical growth stages of maize. The former method uses the spatial distribution feature to judge accurately whether the field crop has reached the emergence stage or not. The latter uses the skeleton endpoint to characterize the leaf of seedling and transforms a matter of judgment into that of probability estimation, which leads to the final conclusion. In order to verify the feasibility and validity of our proposed methods, the comparing experiments have been carried out. Five well-established algorithms were utilized to make comparison with AP-HI and its results showed that our method outperformed the other algorithms in yielding the highest performance of 96.68% with the lowest standard deviation of 2.37%. As for the two automatic detection methods, the crops of two experimental fields located in Zhengzhou, Henan and Taian, Shandong provinces in China were observed both with a human observer and by using automated routines to process images obtained from a camera. In determining the time at which a growth stage occurred, the proposed methods produced the similar results to the manual observation method. Overall, the automated methods can meet the demand for practical observation needed for agronomic modeling and in triggering action alerts to farmers.

This paper proposes an efficient joint implementation algorithm for computing color space conversion, quantization and discrete cosine transform (DCT) in an image coder/decoder. By combining the three stages, the proposed algorithm reduces the operation amount of computing color space conversion considerably. In the case of color sampling 4:4:4, the proposed algorithm reduces the multiplication amount by 40% and the addition amount by 42% for the conversion from RGB to YCbCr in an image coder, and reduces the multiplication amount by 60% and the addition amount by 42% for the conversion from YCbCr to RGB in an image decoder. In the cases of down-sampling 4:2:2 and 4:1:1, there are the similar results. The existing fast methods in the literatures can still be applied together with this proposed algorithm into the implementation of the international image coding standards which use the transform coding technology, such as JPEG, MPEG and H.26X, and raises the image coding/decoding speed efficiently.

We consider energy-efficient Distributed Detection in wireless sensor networks. It is assumed that a group of sensors observe the same quantity in independent additive observation noises with possibly different variances. Each node computes a local statistic and communicates it to a fusion center over Rayleigh fading wireless channels.At the fusion,the linear minimum mean square error(LMMSE) is used. In this paper Two types of constraints are considered: 1) transmission power constraints at the nodes, and 2) the communication channel between the nodes and the decision center. we propose a optimal distributed estimation algorithm in energy-constrained wireless sensor networks. Finally, we demonstrate the applicability of our results through numerical examples.

The SC-FDE (single carrier-frequency domain equalizer) is a field that gathers more and more interest as a modulation scheme for the broadband wireless communications, for simple channel equalization, SC-FDE systems perform equalization in frequency domain instead of time-domain using FFT,IFFT and CP (cyclic prefix) like it user for example in OFDM (orthogonal frequency division multiplexing). In this paper, we propose a new residual timing offset tracking scheme for SC-FDE systems which based on the concept of CP. We analyze the effect of timing offset in a frequency-domain and propose a new method of timing synchronization, Based on the data after decision in SC-FDE receiver, we reconstruct the receive signal. By comparing the received signal and the reconstructed one, a new time offset estimation algorithm is developed. This algorithm has advantages as simple in algorithm, high computation efficiency, high precise in tracking, and requiring no additional pilots. Moreover, we verify the algorithm with a numerical analysis and computer simulation.