diff --git a/README.md b/README.md
index 7dbd906..b36a6d7 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,7 @@
 ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/tsfel)
 ![PyPI](https://img.shields.io/pypi/v/tsfel)
 [![Downloads](https://pepy.tech/badge/tsfel)](https://pepy.tech/project/tsfel)
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/fraunhoferportugal/tsfel/blob/master/notebooks/TSFEL_HAR_Example.ipynb)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/fraunhoferportugal/tsfel/blob/development/notebooks/TSFEL_HAR_Example.ipynb)
 
 # Time Series Feature Extraction Library
 ## Intuitive time series feature extraction
diff --git a/notebooks/TSFEL_HAR_Example.ipynb b/notebooks/TSFEL_HAR_Example.ipynb
index 1f4f00b..2df19c6 100644
--- a/notebooks/TSFEL_HAR_Example.ipynb
+++ b/notebooks/TSFEL_HAR_Example.ipynb
@@ -34,7 +34,7 @@
     "#@title Import Time Series Feature Extraction Library\n",
     "import warnings\n",
     "warnings.filterwarnings('ignore')\n",
-    "!pip install https://github.com/fraunhoferportugal/tsfel/archive/v0.1.3.zip >/dev/null 2>&1\n",
+    "!pip install https://github.com/fraunhoferportugal/tsfel/archive/v0.1.4-dev.zip >/dev/null 2>&1\n",
     "from sys import platform\n",
     "if platform == \"linux\" or platform == \"linux2\":\n",
     "    !wget http://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.zip >/dev/null 2>&1\n",
diff --git a/notebooks/TSFEL_SMARTWATCH_HAR_Example.ipynb b/notebooks/TSFEL_SMARTWATCH_HAR_Example.ipynb
index 1ecc239..273e003 100644
--- a/notebooks/TSFEL_SMARTWATCH_HAR_Example.ipynb
+++ b/notebooks/TSFEL_SMARTWATCH_HAR_Example.ipynb
@@ -29,7 +29,7 @@
     "#@title Import Time Series Feature Extraction Library\n",
     "import warnings\n",
     "warnings.filterwarnings('ignore')\n",
-    "!pip install https://github.com/fraunhoferportugal/tsfel/archive/v0.1.3.zip >/dev/null 2>&1\n",
+    "!pip install https://github.com/fraunhoferportugal/tsfel/archive/v0.1.4-dev.zip >/dev/null 2>&1\n",
     "from sys import platform\n",
     "if platform == \"linux\" or platform == \"linux2\":\n",
     "    !wget http://archive.ics.uci.edu/ml/machine-learning-databases/00507/wisdm-dataset.zip >/dev/null 2>&1\n",
diff --git a/notebooks/TSFEL_predicting_NormalVsPathologicalknee.ipynb b/notebooks/TSFEL_predicting_NormalVsPathologicalknee.ipynb
index 191e053..12c6f91 100644
--- a/notebooks/TSFEL_predicting_NormalVsPathologicalknee.ipynb
+++ b/notebooks/TSFEL_predicting_NormalVsPathologicalknee.ipynb
@@ -46,7 +46,7 @@
     "#@title Import Time Series Feature Extraction Library\n",
     "import warnings\n",
     "warnings.filterwarnings('ignore')\n",
-    "!pip install https://github.com/fraunhoferportugal/tsfel/archive/v0.1.3.zip >/dev/null 2>&1\n",
+    "!pip install https://github.com/fraunhoferportugal/tsfel/archive/v0.1.4-dev.zip >/dev/null 2>&1\n",
     "!pip install pandas_profiling >/dev/null 2>&1\n",
     "!pip install patool >/dev/null 2>&1\n",
     "from sys import platform\n",
diff --git a/setup.py b/setup.py
index 6ff10c1..d5956ca 100644
--- a/setup.py
+++ b/setup.py
@@ -13,7 +13,7 @@
 
 setuptools.setup(
     name="tsfel",
-    version="0.1.3",
+    version="0.1.4",
     author="Fraunhofer Portugal",
     description="Library for time series feature extraction",
     long_description=long_description,
diff --git a/tsfel/utils/signal_processing.py b/tsfel/utils/signal_processing.py
index 8632c22..7449997 100644
--- a/tsfel/utils/signal_processing.py
+++ b/tsfel/utils/signal_processing.py
@@ -23,7 +23,7 @@ def signal_window_splitter(signal, window_size, overlap=0):
     """
 
     step = int(round(window_size)) if overlap == 0 else int(round(window_size * (1 - overlap)))
-    if len(signal) % window_size == 0:
+    if len(signal) % window_size == 0 and overlap == 0:
         return [signal[i:i + window_size] for i in range(0, len(signal), step)]
     else:
         return [signal[i:i + window_size] for i in range(0, len(signal) - window_size, step)]