Human-centred design: An emergent conceptual model

(Human-centred design: an emergent conceptual module by Zhang T and Dong H) Understanding human needs and how design responds to human needs are essential for human-centred design (HCD). By combining Maslow’s hierarchy of needs model and Küthe’s “design and society” model, this paper proposes a conceptual model of human-centred design which marries psychology and sociology in investigating the relationship between design and human needs. The study reveals a tendency that design evolution responds to the hierarchy of human needs. Nowadays design tends to care for more levels of human needs

Green's function method for single-particle resonant states in relativistic mean field theory

Relativistic mean field theory is formulated with the Green's function method in coordinate space to investigate the single-particle bound states and resonant states on the same footing. Taking the density of states for free particle as a reference, the energies and widths of single-particle resonant states are extracted from the density of states without any ambiguity. As an example, the energies and widths for single-neutron resonant states in $^{120}$Sn are compared with those obtained by the scattering phase-shift method, the analytic continuation in the coupling constant approach, the real stabilization method and the complex scaling method. Excellent agreements are found for the energies and widths of single-neutron resonant states.Comment: 20 pages, 7 figure

Sharp RIP Bound for Sparse Signal and Low-Rank Matrix Recovery

This paper establishes a sharp condition on the restricted isometry property (RIP) for both the sparse signal recovery and low-rank matrix recovery. It is shown that if the measurement matrix $A$ satisfies the RIP condition $\delta_k^A<1/3$, then all $k$-sparse signals $\beta$ can be recovered exactly via the constrained $\ell_1$ minimization based on $y=A\beta$. Similarly, if the linear map $\cal M$ satisfies the RIP condition $\delta_r^{\cal M}<1/3$, then all matrices $X$ of rank at most $r$ can be recovered exactly via the constrained nuclear norm minimization based on $b={\cal M}(X)$. Furthermore, in both cases it is not possible to do so in general when the condition does not hold. In addition, noisy cases are considered and oracle inequalities are given under the sharp RIP condition.Comment: to appear in Applied and Computational Harmonic Analysis (2012