diff --git a/Deep-SAD-PyTorch/hardware_survey/main.py b/Deep-SAD-PyTorch/hardware_survey/main.py
new file mode 100644
index 0000000..5966a2a
--- /dev/null
+++ b/Deep-SAD-PyTorch/hardware_survey/main.py
@@ -0,0 +1,501 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+Generate a LaTeX longtable with system + software info for a thesis (Linux + NVIDIA).
+
+Requirements (preflight will check and error if missing):
+- Linux OS
+- lscpu (util-linux)
+- Python packages: nvidia-ml-py3 (pynvml), torch, numpy, scipy, scikit-learn
+- NVIDIA driver present and at least one GPU visible via NVML
+
+What it reports (per user’s list):
+System:
+- OS name + version + distribution (Linux) + kernel version + system arch
+- CPU model name, number of cores and threads, base frequencies (best-effort via lscpu)
+- Total RAM capacity
+- GPU(s): model name (only the newer one; prefer a name matching “4090”, else highest compute capability),
+  memory size, driver version, CUDA (driver) version, cuDNN version (if used via PyTorch)
+
+Software environment:
+- Python version
+- PyTorch version + built CUDA/cuDNN version
+- scikit-learn version
+- NumPy / SciPy version (+ NumPy build config summary: MKL/OpenBLAS/etc.)
+"""
+
+import argparse
+import os
+import platform
+import re
+import shutil
+import subprocess
+import sys
+from typing import Dict, List, Tuple
+
+# -------------------- Helper --------------------
+
+
+def _import_nvml():
+    """
+    Try to import NVML from the supported packages:
+    - 'nvidia-ml-py' (preferred, maintained): provides module 'pynvml'
+    - legacy 'pynvml' (deprecated but still widely installed)
+    Returns the imported module object (with nvml... symbols).
+    """
+    try:
+        import pynvml as _nvml  # provided by 'nvidia-ml-py' or old 'pynvml'
+
+        return _nvml
+    except Exception as e:
+        raise ImportError(
+            "NVML not importable. Please install the maintained package:\n"
+            "  pip install nvidia-ml-py\n"
+            "(and uninstall deprecated ones: pip uninstall nvidia-ml-py3 pynvml)"
+        ) from e
+
+
+def _to_text(x) -> str:
+    """Return a clean str whether NVML gives bytes or str."""
+    if isinstance(x, bytes):
+        try:
+            return x.decode(errors="ignore")
+        except Exception:
+            return x.decode("utf-8", "ignore")
+    return str(x)
+
+
+# -------------------- Utilities --------------------
+
+
+def which(cmd: str) -> str:
+    return shutil.which(cmd) or ""
+
+
+def run(cmd: List[str], timeout: int = 6) -> str:
+    try:
+        out = subprocess.check_output(cmd, stderr=subprocess.STDOUT, timeout=timeout)
+        return out.decode(errors="ignore").strip()
+    except Exception:
+        return ""
+
+
+def human_bytes(nbytes: int) -> str:
+    try:
+        n = float(nbytes)
+    except Exception:
+        return ""
+    units = ["B", "KiB", "MiB", "GiB", "TiB"]
+    i = 0
+    while n >= 1024 and i < len(units) - 1:
+        n /= 1024.0
+        i += 1
+    return f"{n:.2f} {units[i]}"
+
+
+LATEX_SPECIALS = {
+    "\\": r"\textbackslash{}",
+    "&": r"\&",
+    "%": r"\%",
+    "$": r"\$",
+    "#": r"\#",
+    "_": r"\_",
+    "{": r"\{",
+    "}": r"\}",
+    "~": r"\textasciitilde{}",
+    "^": r"\textasciicircum{}",
+}
+
+
+def tex_escape(s: str) -> str:
+    if s is None:
+        return ""
+    return "".join(LATEX_SPECIALS.get(ch, ch) for ch in str(s))
+
+
+def latex_table(sections: List[Tuple[str, Dict[str, str]]], caption: str) -> str:
+    lines = []
+    lines.append(r"\begin{table}[p]")  # float; use [p] or [tbp] as you prefer
+    lines.append(r"\centering")
+    lines.append(r"\caption{" + tex_escape(caption) + r"} \label{tab:system_setup}")
+    lines.append(r"\begin{tabular}{p{0.34\linewidth} p{0.62\linewidth}}")
+    lines.append(r"\toprule")
+    lines.append(r"\textbf{Item} & \textbf{Details} \\")
+    lines.append(r"\midrule")
+
+    for title, kv in sections:
+        if not kv:
+            continue
+        lines.append(r"\multicolumn{2}{l}{\textbf{" + tex_escape(title) + r"}} \\")
+        for k, v in kv.items():
+            val = tex_escape(v)
+            if "\n" in v or len(v) > 120:
+                val = (
+                    r"\begin{minipage}[t]{\linewidth}\ttfamily\small "
+                    + tex_escape(v)
+                    + r"\end{minipage}"
+                )
+            else:
+                val = r"\ttfamily " + val
+            lines.append(tex_escape(k) + " & " + val + r" \\")
+        lines.append(r"\addlinespace")
+
+    lines.append(r"\bottomrule")
+    lines.append(r"\end{tabular}")
+    lines.append(r"\end{table}")
+
+    preamble_hint = r"""
+% ---- Add to your LaTeX preamble ----
+% \usepackage{booktabs}
+% \usepackage{array}
+% ------------------------------------
+"""
+    return preamble_hint + "\n".join(lines)
+
+
+def latex_longtable(sections: List[Tuple[str, Dict[str, str]]], caption: str) -> str:
+    lines = []
+    lines.append(r"\begin{longtable}{p{0.34\linewidth} p{0.62\linewidth}}")
+    lines.append(r"\caption{" + tex_escape(caption) + r"} \label{tab:system_setup}\\")
+    lines.append(r"\toprule")
+    lines.append(r"\textbf{Item} & \textbf{Details} \\")
+    lines.append(r"\midrule")
+    lines.append(r"\endfirsthead")
+    lines.append(r"\toprule \textbf{Item} & \textbf{Details} \\ \midrule")
+    lines.append(r"\endhead")
+    lines.append(r"\bottomrule")
+    lines.append(r"\endfoot")
+    lines.append(r"\bottomrule")
+    lines.append(r"\endlastfoot")
+
+    for title, kv in sections:
+        if not kv:
+            continue
+        lines.append(r"\multicolumn{2}{l}{\textbf{" + tex_escape(title) + r"}} \\")
+        for k, v in kv.items():
+            val = tex_escape(v)
+            if "\n" in v or len(v) > 120:
+                val = (
+                    r"\begin{minipage}[t]{\linewidth}\ttfamily\small "
+                    + tex_escape(v)
+                    + r"\end{minipage}"
+                )
+            else:
+                val = r"\ttfamily " + val
+            lines.append(tex_escape(k) + " & " + val + r" \\")
+        lines.append(r"\addlinespace")
+    lines.append(r"\end{longtable}")
+
+    preamble_hint = r"""
+% ---- Add to your LaTeX preamble ----
+% \usepackage{booktabs}
+% \usepackage{longtable}
+% \usepackage{array}
+% ------------------------------------
+"""
+    return preamble_hint + "\n".join(lines)
+
+
+# -------------------- Preflight --------------------
+
+REQUIRED_CMDS = ["lscpu"]
+REQUIRED_MODULES = [
+    "torch",
+    "numpy",
+    "scipy",
+    "sklearn",
+    "pynvml",
+]  # provided by nvidia-ml-py
+
+
+def preflight() -> List[str]:
+    errors = []
+    if platform.system().lower() != "linux":
+        errors.append(
+            f"This script supports Linux only (detected: {platform.system()})."
+        )
+
+    for c in ["lscpu"]:
+        if not which(c):
+            errors.append(f"Missing required command: {c}")
+
+    for m in REQUIRED_MODULES:
+        try:
+            __import__(m)
+        except Exception:
+            errors.append(f"Missing required Python package: {m}")
+
+    # NVML driver availability
+    if "pynvml" not in errors:
+        try:
+            pynvml = _import_nvml()
+            pynvml.nvmlInit()
+            count = pynvml.nvmlDeviceGetCount()
+            if count < 1:
+                errors.append("No NVIDIA GPUs detected by NVML.")
+            pynvml.nvmlShutdown()
+        except Exception as e:
+            errors.append(f"NVIDIA NVML not available / driver not loaded: {e}")
+
+    return errors
+
+
+# -------------------- Collectors --------------------
+
+
+def collect_system() -> Dict[str, str]:
+    info: Dict[str, str] = {}
+
+    # OS / distro / kernel / arch
+    os_pretty = ""
+    try:
+        with open("/etc/os-release", "r") as f:
+            txt = f.read()
+        m = re.search(r'^PRETTY_NAME="?(.*?)"?$', txt, flags=re.M)
+        if m:
+            os_pretty = m.group(1)
+    except Exception:
+        pass
+    info["Operating System"] = os_pretty or f"{platform.system()} {platform.release()}"
+    info["Kernel"] = platform.release()
+    info["Architecture"] = platform.machine()
+
+    # CPU (via lscpu)
+    lscpu = run(["lscpu"])
+
+    def kvs(text: str) -> Dict[str, str]:
+        out = {}
+        for line in text.splitlines():
+            if ":" in line:
+                k, v = line.split(":", 1)
+                out[k.strip()] = v.strip()
+        return out
+
+    d = kvs(lscpu)
+    info["CPU Model"] = d.get("Model name", d.get("Model Name", ""))
+
+    # cores / threads
+    sockets = d.get("Socket(s)", "")
+    cores_per_socket = d.get("Core(s) per socket", "")
+    threads_total = d.get("CPU(s)", "")
+    if sockets and cores_per_socket:
+        info["CPU Cores (physical)"] = f"{cores_per_socket} × {sockets}"
+    else:
+        info["CPU Cores (physical)"] = cores_per_socket or ""
+    info["CPU Threads (logical)"] = threads_total or str(os.cpu_count() or "")
+
+    # base / max freq
+    # Prefer "CPU max MHz" and "CPU min MHz"; lscpu sometimes exposes "CPU MHz" (current)
+    base = d.get("CPU min MHz", "")
+    maxf = d.get("CPU max MHz", "")
+    if base:
+        info["CPU Base Frequency"] = f"{float(base):.0f} MHz"
+    elif "@" in info["CPU Model"]:
+        # fallback: parse from model string like "Intel(R) ... @ 2.30GHz"
+        m = re.search(r"@\s*([\d.]+)\s*([GM]Hz)", info["CPU Model"])
+        if m:
+            info["CPU Base Frequency"] = f"{m.group(1)} {m.group(2)}"
+    else:
+        cur = d.get("CPU MHz", "")
+        if cur:
+            info["CPU (Current) Frequency"] = f"{float(cur):.0f} MHz"
+    if maxf:
+        info["CPU Max Frequency"] = f"{float(maxf):.0f} MHz"
+
+    # RAM total (/proc/meminfo)
+    try:
+        meminfo = open("/proc/meminfo").read()
+        m = re.search(r"^MemTotal:\s+(\d+)\s+kB", meminfo, flags=re.M)
+        if m:
+            total_bytes = int(m.group(1)) * 1024
+            info["Total RAM"] = human_bytes(total_bytes)
+    except Exception:
+        pass
+
+    return info
+
+
+def collect_gpu() -> Dict[str, str]:
+    """
+    Use NVML to enumerate GPUs and select the 'newer' one:
+    1) Prefer a device whose name matches /4090/i
+    2) Else highest CUDA compute capability (major, minor), tiebreaker by total memory
+    Also reports driver version and CUDA driver version.
+    """
+    pynvml = _import_nvml()
+    pynvml.nvmlInit()
+    try:
+        count = pynvml.nvmlDeviceGetCount()
+        if count < 1:
+            return {"Error": "No NVIDIA GPUs detected by NVML."}
+
+        devices = []
+        for i in range(count):
+            h = pynvml.nvmlDeviceGetHandleByIndex(i)
+
+            # name can be bytes or str depending on wheel; normalize
+            raw_name = pynvml.nvmlDeviceGetName(h)
+            name = _to_text(raw_name)
+
+            mem_info = pynvml.nvmlDeviceGetMemoryInfo(h)
+            total_mem = getattr(mem_info, "total", 0)
+
+            # compute capability may not exist on very old drivers
+            try:
+                maj, minr = pynvml.nvmlDeviceGetCudaComputeCapability(h)
+            except Exception:
+                maj, minr = (0, 0)
+
+            devices.append(
+                {
+                    "index": i,
+                    "handle": h,
+                    "name": name,
+                    "mem": total_mem,
+                    "cc": (maj, minr),
+                }
+            )
+
+        # Prefer explicit "4090"
+        pick = next(
+            (d for d in devices if re.search(r"4090", d["name"], flags=re.I)), None
+        )
+        if pick is None:
+            # Highest compute capability, then largest memory
+            devices.sort(key=lambda x: (x["cc"][0], x["cc"][1], x["mem"]), reverse=True)
+            pick = devices[0]
+
+        # Driver version and CUDA driver version can be bytes or str
+        drv_raw = pynvml.nvmlSystemGetDriverVersion()
+        drv = _to_text(drv_raw)
+
+        # CUDA driver version (integer like 12040 -> 12.4)
+        cuda_drv_ver = ""
+        try:
+            v = pynvml.nvmlSystemGetCudaDriverVersion_v2()
+        except Exception:
+            v = pynvml.nvmlSystemGetCudaDriverVersion()
+        try:
+            major = v // 1000
+            minor = (v % 1000) // 10
+            patch = v % 10
+            cuda_drv_ver = f"{major}.{minor}.{patch}" if patch else f"{major}.{minor}"
+        except Exception:
+            cuda_drv_ver = ""
+
+        gpu_info = {
+            "Selected GPU Name": pick["name"],
+            "Selected GPU Memory": human_bytes(pick["mem"]),
+            "Selected GPU Compute Capability": f"{pick['cc'][0]}.{pick['cc'][1]}",
+            "NVIDIA Driver Version": drv,
+            "CUDA (Driver) Version": cuda_drv_ver,
+        }
+        return gpu_info
+    finally:
+        pynvml.nvmlShutdown()
+
+
+def summarize_numpy_build_config() -> str:
+    """
+    Capture numpy.__config__.show() and try to extract the BLAS/LAPACK backend line(s).
+    """
+    import numpy as np
+    from io import StringIO
+    import sys as _sys
+
+    buf = StringIO()
+    _stdout = _sys.stdout
+    try:
+        _sys.stdout = buf
+        np.__config__.show()
+    finally:
+        _sys.stdout = _stdout
+    txt = buf.getvalue()
+
+    # Heuristic: capture lines mentioning MKL, OpenBLAS, BLIS, LAPACK
+    lines = [
+        l
+        for l in txt.splitlines()
+        if re.search(r"(MKL|OpenBLAS|BLAS|LAPACK|BLIS)", l, re.I)
+    ]
+    if not lines:
+        # fall back to first ~12 lines
+        lines = txt.splitlines()[:12]
+    # Keep it compact
+    return "\n".join(lines[:20]).strip()
+
+
+def collect_software() -> Dict[str, str]:
+    info: Dict[str, str] = {}
+    import sys as _sys
+    import torch
+    import numpy as _np
+    import scipy as _sp
+    import sklearn as _sk
+
+    info["Python"] = _sys.version.split()[0]
+
+    # PyTorch + built CUDA/cuDNN + visible GPUs
+    info["PyTorch"] = torch.__version__
+    info["PyTorch Built CUDA"] = getattr(torch.version, "cuda", "") or ""
+    try:
+        cudnn_build = torch.backends.cudnn.version()  # integer
+        info["cuDNN (PyTorch build)"] = str(cudnn_build) if cudnn_build else ""
+    except Exception:
+        pass
+
+    # scikit-learn
+    info["scikit-learn"] = _sk.__version__
+
+    # NumPy / SciPy + build config
+    info["NumPy"] = _np.__version__
+    info["SciPy"] = _sp.__version__
+    info["NumPy Build Config"] = summarize_numpy_build_config()
+
+    return info
+
+
+# -------------------- Main --------------------
+
+
+def main():
+    ap = argparse.ArgumentParser(
+        description="Generate LaTeX table of system/software environment for thesis (Linux + NVIDIA)."
+    )
+    ap.add_argument(
+        "--output", "-o", type=str, help="Write LaTeX to this file instead of stdout."
+    )
+    ap.add_argument(
+        "--caption", type=str, default="Computational Environment (Hardware & Software)"
+    )
+    args = ap.parse_args()
+
+    errs = preflight()
+    if errs:
+        msg = (
+            "Preflight check failed:\n- "
+            + "\n- ".join(errs)
+            + "\n"
+            + "Please install missing components and re-run."
+        )
+        print(msg, file=sys.stderr)
+        sys.exit(1)
+
+    sections: List[Tuple[str, Dict[str, str]]] = []
+    sections.append(("System", collect_system()))
+    sections.append(("GPU (Selected Newer Device)", collect_gpu()))
+    sections.append(("Software Environment", collect_software()))
+
+    latex = latex_table(sections, caption=args.caption)
+
+    if args.output:
+        with open(args.output, "w", encoding="utf-8") as f:
+            f.write(latex)
+        print(f"Wrote LaTeX to: {args.output}")
+    else:
+        print(latex)
+
+
+if __name__ == "__main__":
+    main()