Xiaohong Zhang

13 papers Journal 12Unranked 1
YearRankTypeTitle / Venue / Authors
2024 J jnl
BMC Medical Informatics Decis. Mak.
Zhidong Zhao, Jiawei Zhu, Pengfei Jiao, Jinpeng Wang, Xiaohong Zhang, Xinmiao Lu, Yefei Zhang
2023 J jnl
Biomed. Signal Process. Control.
Yefei Zhang, Yanjun Deng, Xianfei Zhang, Pengfei Jiao, Xiaohong Zhang, Zhidong Zhao
2023 J jnl
Comput. Biol. Medicine
Zhixin Zhou, Zhidong Zhao, Xianfei Zhang, Xiaohong Zhang, Pengfei Jiao, Xuanyu Ye
2023 J jnl
Biomed. Signal Process. Control.
Zhixin Zhou, Zhidong Zhao, Xiaohong Zhang, Xianfei Zhang, Pengfei Jiao
2022 J jnl
Inf. Sci.
Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang
2022 J jnl
IEEE J. Biomed. Health Informatics
Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang
2021 J jnl
Frontiers Inf. Technol. Electron. Eng.
Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang
2021 J jnl
Multim. Tools Appl.
Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang
2021 J jnl
Biomed. Signal Process. Control.
Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang
2019 J jnl
IEEE Access
Zhidong Zhao, Duoshui Shi, Guohua Hui, Xiaohong Zhang
2019 J jnl
BMC Medical Informatics Decis. Mak.
Zhidong Zhao, Yanjun Deng, Yang Zhang, Yefei Zhang, Xiaohong Zhang, Lihuan Shao
2018 J jnl
Comput. Biol. Medicine
Zhidong Zhao, Yefei Zhang, Yanjun Deng, Xiaohong Zhang
2014 conf
BMEI
Xiao Wei, Zhidong Zhao, Xiaohong Zhang
redb/extractors/decompiler/bninja/analysis/medium_level_normalization.py
← Index redb/extractors/decompiler/bninja/analysis/medium_level_normalization.py python
from binaryninja import (
    MediumLevelILInstruction,
    Variable, SSAVariable,
    ILIntrinsic,
)


class MediumLevelNormalization:
    def __init__(self):
        return

    def _collect_ops(self, il, ops):
        if il is None:
            return
        ops.append(int(il.operation))
        operands = getattr(il, "operands", None)
        if not operands:
            return
        for op in operands:
            if isinstance(op, MediumLevelILInstruction):
                self._collect_ops(op, ops)
            elif isinstance(op, (list, tuple)):
                for sub in op:
                    if isinstance(sub, MediumLevelILInstruction):
                        self._collect_ops(sub, ops)

    def normalize_instruction_all_levels(self, instr_il):
        ops = []
        self._collect_ops(instr_il, ops)
        return ops

    def _leaf_type(self, val):
        if isinstance(val, SSAVariable):
            return "SSA_VAR"
        if isinstance(val, Variable):
            return "VAR"
        if isinstance(val, ILIntrinsic):
            return "INTRINSIC"
        if isinstance(val, bool):
            return "BOOL"
        if isinstance(val, float):
            return "FLOAT_CONST"
        if isinstance(val, int):
            return "CONST"
        if isinstance(val, str):
            return "STR"
        return type(val).__name__.upper()

    def _collect(self, il, out_op):
        if il is None:
            return
        out_op.append(int(il.operation))
        operands = getattr(il, "operands", None)
        if not operands:
            return
        for op in operands:
            if isinstance(op, MediumLevelILInstruction):
                self._collect(op, out_op)
            elif isinstance(op, (list, tuple)):
                for sub in op:
                    if isinstance(sub, MediumLevelILInstruction):
                        self._collect(sub, out_op)
                    else:
                        out_op.append(self._leaf_type(sub))
            else:
                out_op.append(self._leaf_type(op))

    def normalize_instr_with_operands(self, instr_il):
        ops = []
        self._collect(instr_il, ops)
        return ops