With the purpose to guarantee the safety of drivers and passengers as well as lower the death rate collision, the early warning seatbelt intelligent adjustment system is designed by using big data analysis technology based on the aspects of hardware equipment, database, and software program. In the hardware system, microcontroller AT89C52 is applied as the control core. By means of the sensor detection and drive control, the early warning safety belt tightening, locking and lifting, and other functions are realized. Meanwhile, various components of the hardware system are coordinated through debugging several modules in the hardware system and using the modified circuit to connect them together. We determine the relational rules of the database and create the corresponding database table, to provide sufficient data support for the realization of software functions. Using the big data analysis method to process the real-time detection data received by the sensor, the software functions such as timely tightening of safety belt, humidity relaxation, and over-rolling prevention can be realized according to different driving conditions of drivers and vehicles, respectively. The conclusion is drawn through the system test experiment: compared with the traditional regulation system, the design system has a higher degree of regulation, and the application of the design results to the actual vehicle can reduce the crash fatality rate of about 22.4%.

Phase retrieval by Fourier measurements is a classical application in coherent diffraction imaging, and the modified Blaschke products (MBPs) are the generalization of linear Fourier atoms. Motivated by this, we investigate the phase retrieval modeled as to reconstruct scriptPfalse(ffalse)=∑k=0∞⟨f,Bfalse{a0,a1,…,akfalse}⟩Bfalse{a0,a1,…,akfalse} by the intensity measurements false{false|⟨f,Bk1⟩false|,false|⟨f,Bk2⟩false|,false|⟨f,Bk3⟩false|:k≥1false}, where f lies in Hardy space ℋ2false(scriptDfalse) such that f(a0)=0, Bfalse{a0,a1,…,akfalse} and Bki are all the finite MBPs. We establish the condition on Bki such that scriptPfalse(ffalse) can be determined, up to a unimodular scalar, by the above measurements. A byproduct of our result is that the instantaneous frequency of the target can be exactly reconstructed by the above intensity measurements. Moreover, a recursive algorithm for the phase retrieval is established. Numerical simulations are conducted to verify our result.

This paper concerns the reconstruction of a function f in the Hardy space of the unit disc D by using a sample value f (a) and certain n-intensity measurements | f, Ea 1 •••an |, where a1, • • • , an ∈ D, and Ea 1 •••an is the n-th term of the Gram-Schmidt orthogonalization of the Szegö kernels ka 1 , • • • , ka n , or their multiple forms. Three schemes are presented.The first two schemes each directly obtain all the function values f (z). In the first one we use Nevanlinna's inner and outer function factorization which merely requires the 1-intensity measurements equivalent to know the modulus |f (z)|. In the second scheme we do not use deep complex analysis, but require some 2-and 3-intensity measurements. The third scheme, as an application of AFD, gives sparse representation of f (z) converging quickly in the energy sense, depending on consecutively selected maximal n-intensity measurements | f, Ea 1 •••an |.

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