Interstitial Lung Disease （ILD） segmentation labels are highly costly， leading to small sample sizes in existing datasets and resulting in poor performance of trained models. To address this issue， a segmentation algorithm for ILD based on multi-task learning was proposed. Firstly， a multi-task segmentation model was constructed based on U-Net. Then， the generated lung segmentation labels were used as auxiliary task labels for multi-task learning. Finally， a method of dynamically weighting the multi-task loss functions was used to balance the losses of the primary task and the secondary task. Experimental results on a self-built ILD dataset show that the Dice Similarity Coefficient （DSC） of the multi-task segmentation model reaches 82.61%， which is 2.26 percentage points higher than that of U-Net. The experimental results demonstrate that the proposed algorithm can improve the segmentation performance of ILD and can assist clinical doctors in ILD diagnosis.

In order to improve the total utility of task allocation in spatiotemporal crowdsourcing dynamic reality, a Dynamic Threshold algorithm based on online Random Forest (DTRF) was proposed. Firstly, the online random forest was initialized based on the historical matching data of workers and tasks on the crowdsourcing platform. Then, the online random forest was used to predict the expected task return rate of each worker as the threshold, and the candidate matching set was selected for each worker according to the threshold. Finally, the matching with the highest sum of current utility was selected from the candidate match set, and the online random forest was updated based on the allocation result. The experiments show that the algorithm can improve the average income of workers while increasing the total utility. Compared with the greedy algorithm, the proposed algorithm has the task assignment rate increased by 4.1%, the total utility increased by 18.2%, and the average worker income increased by 11.2%. Compared with the random threshold algorithm, this algorithm has a better improvement in task allocation rate, total utility, average income of workers with better stability.

Aiming at the complex communication environment and low efficiency of big data processing in agricultural Internet Of Things (IOT), a big data processing mechanism was proposed based on the adaptive collaborative opportunities. According to the requirements of agricultural application and the impact of agricultural environment on wireless data transmission, a cross-layer interaction analysis model was established, which was combined with opportunities for collaborative mechanisms and big data processing requirements. Then the design of a large data processing was proposed. Experimental analysis and testing show that the proposed big data processing scheme has better system throughput, reliability, and system processing performance than traditional coordination mechanism and data processing programs.

Making use of the flexibility of the combination resultant method, rapid elimination and the diversity of combination resultant derived polynomial, the author proposed an improved algorithm of constructing Bezout matrix, and the combination resultant method was applied in solving the nonlinear equations, inferring unknown relationship, parametric curve and surface implicitization, structural triangular column, etc. Compared with the original method to solve the problems by examples, combination resultant method is more simple and feasible.

The shortened LDPC codes based on the current IEEE802.16e Standard, has plenty of small girth. In order to resolve this problem, this paper presents a new scheme of designing the shortened LDPC codes. In the proposed scheme, the design has modified the sub-parity check matrix under the frame of the IEEE802.16e Standard. The proposed check matrix with the spreading factor (zf=48) is constructed with quasi-cyclic matrix method and finite geometry method. Moreover, parity check points was searched the node degree in the way of synchronous sequential, the proposed sub- parity check matrix has minor girth-6 and no girth-4. The results of simulation in the AWGN channels show that the improved short code with the code rate approaching to 0.5, can fast encode as excellent as the IEEE802.16e protocol contains，moreover, a good BER performance of the proposed code is 1.1dB more than the Shannon limit of this channel.

Effective and timely Link Going Down (LGD) trigger mechanisms are critical to handover performance. Firstly, this paper introduced IEEE 802.21 MIH standard, and the required handover time was estimated using the information service provided by Media Independent Handover (MIH). Then, a LGD trigger mechanism based on grey prediction was proposed. It established grey prediction model by the required handover time, and the trigger time can be dynamically determined by the predictive Received Signal Strength (RSS) of mobile terminal. Furthermore, in order to reduce the prediction costs, a grey prediction modeling method based on signal decay detection was proposed. The simulation results show that this algorithm achieves the goal of effective and timely LGD trigger, and can reduce unnecessary prediction costs and avoid the waste of terminal resources.

Comprehensive review of the research of one kind of anti-collision algorithms and analysis of their disadvantages were provided. Based on the analysis of the frame size adjusting method of the most in common use branch dynamic framed slotted Aloha algorithm, this paper brought forward an improved resolution by dynamically adjusting the frame size and group division, which can consumedly enhance the system efficiency and its stability.

Anti-collision protocol in EPC Gen2 protocol is flexible, so reasonable algorithm can evidently improve the system performance. Based on the research of its Q value adjusting method and the process of this important protocol, a new Q value adjusting method and an improved slotted random Aloha method were proposed. The simulation results show that the improved algorithms can increase the system throughput and lower the delay of tag identification, showing a good performance.