Abstract
In this paper, a novel Bayesian model fusion (BMF) method is proposed for test cost reduction based on wafer-level spatial variation modeling. BMF relies on the assumption that a large number of wafers of the same circuit design (e.g., all wafers from the same lot) share a similar spatial pattern. Hence, the measurement data from one wafer can be borrowed to model the spatial variation of other wafers via Bayesian inference. By applying the Sherman-Morrison-Woodbury formula, a fast numerical algorithm is derived to reduce the computational cost of BMF for practical test applications. Furthermore, a new test methodology is developed based on BMF and it closely monitors the escape rate and yield loss. As is demonstrated by the wafer probe measurement data of an industrial RF transceiver, BMF achieves 1.125x reduction in test cost and 2.6x reduction in yield loss, compared to the conventional approach based on virtual probe (VP).

Abstract
Commercial off-the-shelf devices and applications are expected to be pivotal in the coming massive deployment of pervasive computing technology in home settings. The integration of these devices and applications in the same household may result in unplanned interactions involving users and entertainment, communication, and health-related devices and applications. These unplanned interactions are a serious concern when, for example, communication or entertainment applications interfere with the behavior of health-related devices. This paper presents a novel graph-based approach for representing the expected behavior of commercial off-the-shelf devices and applications, their interactions, and for detecting interference in pervasive computing systems. A set of home care scenarios is used to assess the applicability of this approach. We then provide two setups where this approach can be applied: (i) in a pre-deployment setup, where simulation is used to detect possible instances of interference, and (ii) at run-time, collecting observations from devices and applications and detecting interference as it occurs. For pre-deployment and simulation we use Opensim to recreate a home household. For run-time, we use Simple Network Management Protocol for systems state introspection and a sliding window mechanism to process the collected data-stream. Crown Copyright

Abstract
This paper presents a planar antenna using low temperature co-fired ceramics (LTCC) substrate for extreme environment applications. An ultra wideband (UWB) elliptical patch antenna was designed and fabricated using an LTCC Ceramtec GC substrate to demonstrate the capabilities of the technology for wideband applications. The simulated results were further validated experimentally. The fabricated antenna provides a peak gain of 5dB over a bandwidth of 4 GHz (3 GHz 7 GHz) with return loss better than -10dB. The radiation pattern is omni-directional in the horizontal plane (theta=90 degrees) over the whole frequency range.

Abstract
This paper analyzes transparent two-TFT current mirrors using a-GIZO TFTs with different mirroring ratios. In order to achieve a high mirroring ratio, the output TFT in the circuit employed a fingered structure layout to minimize area and overlap capacitance. The analysis of the current mirrors is performed in three phases. In the first, a radial basis function based (RBF) model is developed using measured data from fabricated TFTs on the same chip. Then, in the second phase, the RBF model is implemented in Verilog-A that is used to simulate two-TFT current mirrors with different mirroring ratios. The simulations are carried out using Cadence spectre simulator. In the third phase, simulation results are validated with the measured response from the fabricated circuits.

Abstract
Sparse coding techniques have seen an increasing range of applications in recent years, especially in the area of image processing. In particular, sparse coding using l1-regularization has been efficiently solved with the Augmented Lagrangian (AL) applied to its dual formulation (DALM). This paper proposes the decomposition of the dictionary matrix in its Singular Value/Vector form in order to simplify and speed-up the implementation of the DALM algorithm. Furthermore, we propose an update rule for the penalty parameter used in AL methods that improves the convergence rate. The SVD of the dictionary matrix is done as a pre-processing step prior to the sparse coding, and thus the method is better suited for applications where the same dictionary is reused for several sparse recovery steps, such as block image processing. © 2014 IEEE.

Abstract
Sparse coding techniques have seen an increasing range of applications in recent years, especially in the area of image processing. In particular, sparse coding using l(1)-regularization has been efficiently solved with the Augmented Lagrangian (AL) applied to its dual formulation (DALM). This paper proposes the decomposition of the dictionary matrix in its Singular Value/Vector form in order to simplify and speed-up the implementation of the DALM algorithm. Furthermore, we propose an update rule for the penalty parameter used in AL methods that improves the convergence rate. The SVD of the dictionary matrix is done as a pre-processing step prior to the sparse coding, and thus the method is better suited for applications where the same dictionary is reused for several sparse recovery steps, such as block image processing.