use wavelet transform to extract frequencies from given signal let us suppose that we have following signal  values,which consists  by deterministic  components and random noise(white noise)

56.69
75.24
13.77
8.56
-12.88
-65.34
-45.33
-48.78
-22.23
54.12
83.77
11.84
2.31
39.59
-32.09
-88.86
5.45
50.24
-37.39
-35.69
38.62
7.06
-30.01
22.36
60.71
30.96
5.90
-38.91
-58.15
-40.87
-13.18
-14.77
35.36
103.24
39.04
-50.76
6.98
1.23
-87.46
-60.86
65.08
23.93
-28.36
2.42
31.67
-5.22
4.02
37.50
12.36
5.37
-18.83
-68.70
-38.11
35.47
7.47
11.64
103.89
88.26
-62.41
-62.44
10.09
-33.92
-72.48
17.83
74.35
7.56
-5.35
22.56
-2.11
-2.82
2.59
-21.33
-0.52
-4.11
-50.68
-66.68
28.14
62.10
6.73
32.39
93.62
-18.56
-112.15
-38.18
3.33
-44.78
13.79
76.52
39.92
9.55
-7.09
-28.72
-1.59
19.01
-26.25
-22.61
32.54
0.06
-80.17
-2.02
103.68
31.50
-9.29
44.79
6.10
-93.30
-87.67
2.06
5.53
20.94
67.28
48.24
16.43
4.28
-49.05
-43.92
14.48
2.02
-56.29
16.29
54.09
-42.38
-33.91
67.16
64.26
-34.38
1.31
17.57
-69.49
-86.43
-14.43
20.61
38.08
66.31
38.91
9.62
-4.38
-45.47
-91.23
-9.17
52.92
-21.17
-31.51
69.74
22.55
-62.65
22.34
72.59
-6.34
-49.96
0.06
-38.30
-55.57
-22.15
28.14
61.12
81.82
37.94
-32.41
-14.05
-16.05
-90.12
-46.52
75.39
50.48
-36.08
21.54
64.57
-32.83
-47.71
47.82
35.80
-31.32
-37.71
-30.45
-36.51
6.90
30.17
25.81
69.67
50.19
-55.42
-76.26
-11.97
-47.71
-80.69
52.76
91.17
-5.08
-12.75
49.10
5.44
-46.98
-0.40
22.20
-12.89
-17.56
-24.62
-18.95
22.94
53.27
8.67
35.09
67.76
-27.38
-111.75
-37.70
14.80
-56.16
2.87
105.52
59.46
-42.36
-7.17
12.65
-32.87
-38.61
-0.33
1.07
0.12
6.79
-41.04
3.19
76.28
18.78
-26.27
47.46
16.52
-116.17
-82.44
25.21
8.68
2.55
70.61
86.29
-10.63
-44.12
-19.48
-34.54
-31.95
8.52
-10.48
11.89
40.45
-10.66
-34.27
63.24
42.83
-32.87
-18.44
37.47
-64.48
-101.98
7.93
59.48
22.63
43.46
74.91
2.31
-49.18
-56.15
-55.96
-11.68
37.66
3.36
13.40
45.50
14.21
-57.98
6.29
74.98
-7.25
-70.83
12.29
7.90
-76.29
-24.42
69.80
60.11
28.97
39.65
-11.23
-59.30
-60.80
-63.71
-16.72
66.48
59.98
-17.27
36.01
55.04
-38.70
-59.95
48.63


i know that sampling frequency=100;i am interested what is a  basic steps  using wavelet to extract  frequencies and phases?i know that there is function of  cwt for compute continuous wavelet transform and from coefficients it tries to determine  frequencies,now if i know  sampling frequency and dont know  frequency components but suppose that it must be less then  sampling frequency/2 or fs/2,because of Nyquist criteria to held,how can i take  scales?i have tried following example

&gt;&gt; B=xlsread('data_generations1','A1','g8:g301');
&gt;&gt; scales=1:100;
&gt;&gt; coeff=cwt(B,1:100,'db2','plot');


and got following picture



now how can i determine  which frequency and phases are in signal?or which wavelet basis should i choose?please help me in this problem
A simple (hanning-windowed) FFT reveals four peaks that stand out far beyond the noise. This appears to be what you are looking for.