Chromosome karyotype analysis is of great significance in prenatal screening and genetic disease diagnosis. However， existing chromosome classification models are generally limited by insufficient feature extraction capability， high sensitivity to image quality， and inadequate attention to local details， leading to low overall classification accuracy， particularly the frequent misidentification of short chromosomes. Therefore， a coarse-to-fine chromosome cascaded classification framework integrating image texture enhancement and super-resolution techniques was proposed. Firstly， chromosomes were coarsely classified based on the International System for human Cytogenetic Nomenclature （ISCN）， and they were divided into long chromosomes and short chromosomes groups to mitigate class imbalance and feature confusion. Secondly， for the long chromosome classification task， a feature enhancement module was added to optimize the classification model's ability to perceive details of long chromosomes. Thirdly， considering the characteristics of short chromosome images， the super-resolution technique was introduced to improve image quality and the model's perceptual capability. Experimental results on a private dataset showed that the proposed framework achieved an overall chromosome classification accuracy of 98.91% and an overall chromosome F1-score of 98.77%， with 99.01% for long chromosomes and 98.31% for short chromosomes. By adopting differentiated classification strategies and task-specific models， this cascaded chromosome classification framework significantly enhances both classification accuracy and model robustness.

Accurate extraction of a target’s edge in a log-polar space is a precondition and key point to successfully apply the visual invariance of the log-polar transformation. Since it is impossible for traditional algorithms to extract the single-pixel edge in an intensively noisy environment, a unique edge extraction algorithm on the basis of active contour model and level set method was designed. After noise removal on the whole via Canny operator based level set method, the energy-driving active contour model was used to iteratively approach the potential edges. By clearing out false edges with an improved tracing way, the true target’s edge was extracted finally. The experimental results demonstrate the effective performance of the proposed algorithm with the edge feature similarity up to 96%.

The sphere decoding algorithm has the optimal Bit Error Ratio (BER) performance that approximates to Maxmun Liklihood (ML) in Long Term Evolution (LTE) system. Concerning the computational complexity and required hardware resources of this algorithm increase significantly for detection of 16-QAM and 64-QAM modulated signal streams, an improved sphere decoding algorithm, which changed symbol search strategy, was proposed. A given symbol search scheme at different detection layer, and combined with a new definition for sphere radius of dynamic modifications was adopted in this algorithm. Both of the traditional and improved algorithms were simulated on the condition of Rayleigh fading channel. The simulation results show that the improved algorithm has a small BER degradation, and it also effectively reduces both computational complexity and required hardware resources compared to the traditional sphere decoding algorithm.