diff --git a/fastlib/u/niche/functional/prelim_funcica.m b/fastlib/u/niche/functional/prelim_funcica.m
index e1b44a98f0..13a2c29c0e 100644
--- a/fastlib/u/niche/functional/prelim_funcica.m
+++ b/fastlib/u/niche/functional/prelim_funcica.m
@@ -30,10 +30,15 @@ x = x - repmat(mean(x')', 1, N);
 
 
 % generate b-spline basis curves
-t = linspace(0,1,1000);
+t = linspace(0,1,3000);
 
 
 load s1s2_10;
 s = [s1(t); s2(t)]';
+z = normrnd(zeros(length(t), N), 1);
 
 data = s * x;
+
+data = data + z;
+
+%data = noisy_data;
diff --git a/fastlib/u/niche/functional/test_smooth_funcica.m b/fastlib/u/niche/functional/test_smooth_funcica.m
index 11c6fc11d8..ac8a6a7b63 100644
--- a/fastlib/u/niche/functional/test_smooth_funcica.m
+++ b/fastlib/u/niche/functional/test_smooth_funcica.m
@@ -18,7 +18,7 @@ cut_fraction = .5;
 cut = round(cut_fraction * N);
 data_coef = getcoef(myfd_data);
 
-num_tests = 50;
+num_tests = 1;
 
 
 % a simple method for smoothing
@@ -27,7 +27,7 @@ num_tests = 50;
 
 
 %lambda_set = 0;
-lambda_set = [0 1e-4 1e-3 5e-3 1e-2];
+lambda_set = [0 1e-6 1e-5 1e-4 1e-3 5e-3 1e-2];
 
 myfdPar_set = cell(1,length(lambda_set));
 for lambda_i = 1:length(lambda_set)