diff --git a/View/signal_processing_tab.py b/View/signal_processing_tab.py
index f845f0b..5de04b6 100644
--- a/View/signal_processing_tab.py
+++ b/View/signal_processing_tab.py
@@ -14,7 +14,7 @@
     #                                                                        #
     # You should have received a copy of the GNU General Public License      #
     # along with this program.  If not, see <https://www.gnu.org/licenses/>. #
-
+import math
 #                                                      by Brahim MOUDJED #
 # ============================================================================== #
 
@@ -47,6 +47,8 @@ from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as Navigatio
 from matplotlib.colors import LogNorm, BoundaryNorm
 from os import path
 
+from numba.np.arraymath import np_average
+
 from View.show_popup_combobox import ComboBoxShowPopUpWindow
 from View.plot_noise_window import PlotNoiseWindow
 
@@ -1441,8 +1443,7 @@ class SignalProcessingTab(QWidget):
                 val_max = np.nanmax(
                     BS_data[data_id][f, :, :]
                 )
-
-                if val_min == 0:
+                if val_min == 0 or math.isinf(val_min):
                     val_min = 1e-5
 
                 pcm = self.axis_BS[f].pcolormesh(
@@ -1478,10 +1479,10 @@ class SignalProcessingTab(QWidget):
 
             self.fig_BS.supxlabel('Time (sec)', fontsize=10)
             self.fig_BS.supylabel('Depth (m)', fontsize=10)
-            cbar = self.fig_BS.colorbar(pcm, ax=self.axis_BS[:],
-                                        shrink=1, location='right')
-            cbar.set_label(label='Acoustic backscatter signal (V)',
-                           rotation=270, labelpad=10)
+            # cbar = self.fig_BS.colorbar(pcm, ax=self.axis_BS[:],
+            #                             shrink=1, location='right')
+            # cbar.set_label(label='Acoustic backscatter signal (V)',
+            #                rotation=270, labelpad=10)
             self.fig_BS.canvas.draw_idle()
         else:
             self.verticalLayout_groupbox_plot_pre_processed_data_2D_field\
@@ -1527,15 +1528,8 @@ class SignalProcessingTab(QWidget):
             msgBox.exec()
         else:
             data_id = self.combobox_acoustic_data_choice.currentIndex()
-            kernel_avg = np.ones(
-                2 * int(
-                    float(
-                        self.lineEdit_horizontal_average\
-                            .text()\
-                            .replace(",", ".")
-                    )
-                ) + 1
-            )
+            n_average = 2 * int(float(self.lineEdit_horizontal_average.text().replace(",", "."))) + 1
+            kernel_avg = np.ones(n_average)
             logger.debug(f"kernel_avg: {kernel_avg}")
 
             stg.Nb_cells_to_average_BS_signal[data_id] = (
@@ -1599,16 +1593,34 @@ class SignalProcessingTab(QWidget):
 
             logger.debug(f"BS_data: {BS_data[data_id].shape}")
 
-            BS_data_ppa[data_id] = deepcopy(BS_data[data_id])
+            # BS_data_ppa[data_id] = deepcopy(BS_data[data_id])
+            #
+            # for f, _ in enumerate(stg.freq[data_id]):
+            #     for i in range(y_depth.shape[1]):
+            #         BS_data_ppa[data_id][f, i, :] = (
+            #             convolve(
+            #                 BS_data[data_id][f, i, :],
+            #                 kernel_avg
+            #             )
+            #         )
 
+            temp_list = []
             for f, _ in enumerate(stg.freq[data_id]):
+                temp0 = np.array([])
                 for i in range(y_depth.shape[1]):
-                    BS_data_ppa[data_id][f, i, :] = (
-                        convolve(
-                            BS_data[data_id][f, i, :],
-                            kernel_avg
-                        )
-                    )
+                    # temp = convolve(BS_data[data_id][f, i, :], kernel_avg, "same") / n_average
+                    temp = convolve(array=BS_data[data_id][f, i, :],
+                                    kernel=kernel_avg,
+                                    nan_treatment='interpolate')
+                    if temp0.shape == (0,):
+                        temp0 = np.array([temp])
+                    else:
+                        temp0 = np.append(temp0, np.array([temp]), axis=0)
+                temp_list.append(temp0)
+
+            BS_data_ppa[data_id] = np.array([temp_list[0]])
+            for j in range(stg.freq[data_id].shape[0]-1):
+                BS_data_ppa[data_id] = np.append(BS_data_ppa[data_id], np.array([temp_list[j+1]]), axis=0)
 
             logger.debug(
                 f"BS_data_ppa: {BS_data_ppa[data_id].shape}"