Haifeng Tian

12 papers C 1Journal 11
YearRankTypeTitle / Venue / Authors
2024 J jnl
Geo spatial Inf. Sci.
Haifeng Tian, Mengdan Yang, Fangli Wu, Yaochen Qin, Xiwang Zhang, Jiayi Liu, Weiyang Yan
2022 J jnl
Remote. Sens.
Haifeng Tian, Ting Chen, Qiangzi Li, Qiuyi Mei, Shuai Wang, Mengdan Yang, Yongjiu Wang, Yaochen Qin
2022 J jnl
Remote. Sens.
Pengyu Liu, Jie Pei, Han Guo, Haifeng Tian, Huajun Fang, Li Wang
2022 J jnl
Axioms
Rongrong Guo, Qingdao Huang, Haifeng Tian
2021 J jnl
Remote. Sens.
Haifeng Tian, Yongjiu Wang, Ting Chen, Lijun Zhang, Yaochen Qin
2020 J jnl
Remote. Sens.
Haifeng Tian, Jie Pei, Jianxi Huang, Xuecao Li, Jian Wang, Boyan Zhou, Yaochen Qin, Li Wang
2020 J jnl
Sensors
Haifeng Tian, Jian Wang, Jie Pei, Yaochen Qin, Lijun Zhang, Yongjiu Wang
2019 J jnl
Remote. Sens.
Haifeng Tian, Ni Huang, Zheng Niu, Yuchu Qin, Jie Pei, Jian Wang
2019 J jnl
Remote. Sens.
Jie Pei, Li Wang, Xiaoyue Wang, Zheng Niu, Maggi Kelly, Xiao-Peng Song, Ni Huang, Jing Geng, Haifeng Tian, Yang Yu, Shiguang Xu, Lei Wang, Qing Ying, Jianhua Cao
2018 J jnl
Sensors
Haifeng Tian, Mingquan Wu, Li Wang, Zheng Niu
2017 C conf
IGARSS
Ying Zhan, Haifeng Tian, Wei Liu, Zhaoying Yang, Kang Wu, Guian Wang, Ping Chen, Xianchuan Yu
2017 J jnl
Remote. Sens.
Haifeng Tian, Wang Li, Mingquan Wu, Ni Huang, Guodong Li, Xiang Li, Zheng Niu
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