Abstract
In this paper, we adopt a novel numerical algorithm, referred to as dual augmented Lagrangian method (DALM), for efficient test cost reduction based on spatial variation modeling. The key idea of DALM is to derive the dual formulation of the L1-regularized least-squares problem posed by Virtual Probe (VP), which can be efficiently solved with substantially lower computational cost than its primal formulation. In addition, a number of unique properties associated with discrete cosine transform (DCT) are exploited to further reduce the computational cost of DALM. Our experimental results of an industrial RF transceiver demonstrate that the proposed DALM solver achieves up to 38× runtime speed-up over the conventional interior-point solver without sacrificing any performance on escape rate and yield loss for test applications.

Abstract
The existing technologies using electromagnetic waves or lasers are not very efficient due to the large attenuation in the aquatic environment. Ultrasound reveals a lower attenuation, and thus has been used in underwater long-distance communications. For high data-rates and real-time applications it is necessary to use frequencies in the MHz range, allowing communication distances of hundreds of meters with a delay of milliseconds. To achieve this goal, it is necessary to develop ultrasound transducers able to work at high frequencies and wideband, with suitable responses to digital modulations. This work shows how the acoustic impedance influences the performance of an ultrasonic emitter transducer when digital modulations are used and operating at frequencies between 100 kHz and 1 MHz. The study includes a Finite Element Method and a MATLAB/Simulink simulation with an experimental validation to evaluate two types of piezoelectric materials: one based on ceramics (high acoustic impedance) with a resonance design and the other based in polymer (low acoustic impedance) designed to optimize the performance when digital modulations are used. The transducers performance for Binary Amplitude Shift Keying, On-Off Keying, Binary Phase Shift Keying and Binary Frequency Shift Keying modulations with a 1 MHz carrier at 125 kbps baud rate are compared.

Abstract
In this paper, smart objects embedded production and quality management functions are proposed, to promote accurately support decision-making processes, from the shop floor level up to higher decision-making levels. The proposed functions contribute for different kind of problems solving in production and quality management, such as production planning and control, scheduling, factory supervision, real-time data acquisition and processing, and real-time decision making. The web access at different middleware devices and tools, at different decision levels, along with the use of integrated algorithms and tools, embedded in smart objects, promotes conditions for better decision-making for optimized use of knowledge and resources in production systems. The relevance of the proposed smart objects embedded production and quality management functions has been validated positively in a manufacturing company.

Abstract
Electroencephalographic (EEG) signals present a myriad of challenges to analysis, beginning with the detection of artifacts. Prior approaches to noise detection have utilized multiple techniques, including visual methods, independent component analysis and wavelets. However, no single method is broadly accepted, inviting alternative ways to address this problem. Here, we introduce a novel approach based on a statistical physics method, multiscale entropy (MSE) analysis, which quantifies the complexity of a signal. We postulate that noise corrupted EEG signals have lower information content, and, therefore, reduced complexity compared with their noise free counterparts. We test the new method on an open-access database of EEG signals with and without added artifacts due to electrode motion. © 2015 IEEE.

Abstract
This paper presents a new stochastic multi-layer agent-based decision making model to study the behavior of market participants in the future smart grid. In the agent-based model proposed, wholesale market players are modeled in the first layer. The players include renewable/sustainable power producers, optimizing the bidding/offering strategies to participate in the electricity markets. In the second layer, responsive customers include electric vehicle owners and consumers who participate in demand response programs, being modeled as independent agents. The objective of the responsive customers is to increase their benefit while retaining welfare. The interaction between market players in day-ahead and real-time markets is modeled using an incomplete information game theory algorithm. According to the high uncertainty of resources and customers' behavior, the model is developed using a stochastic framework. A case study containing wind power producers, aggregators and retailers providing demand response is considered to confirm the usefulness of the proposed multi-layer model.

Abstract
In some Wireless Sensor Network applications the sensor nodes share the same sensing activity, which means that for a considerable number of applications, not all nodes are required to perform sensing tasks during the network lifetime. Sleep-scheduling approaches can be applied in this scenario, enabling that some nodes turn off their radios, saving energy and bandwidth, as long as there are enough nodes to ensure the required Quality of Service (QoS) of the network. This paper presents a new adaptive approach for QoS and energy management in IEEE 802.15.4 networks, entitled Skip Game. This approach targets a trade-off between increasing the network lifetime and maintaining the QoS of the network, aiming a greater number of nodes to participate in the monitoring application. In order to evaluate the proposed approach, we performed some experiments using the OMNeT++ simulator tool under the MiXiM framework. The results show that the Skip Game outperforms both the traditional Gur Game and Gureen Game approaches in terms of QoS provision and network lifetime.