We propose a high-level fault model, the coupling fault (CF) model, that aims to cover both functional and timing faults in an integrated way. The basic properties of CFs and the corresponding tests are analyzed, focusing on their relationship with other fault models and their test requirements. A test generation program COTEGE for CFs is presented. Experiments with COTEGE are described which show that (reduced) coupling test sets can efficiently cover standard stuck-at-0/1 faults in a variety of different realizations. The corresponding coupling delay tests detect all robust path delay faults in any realization of a logic function.

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This paper proposes receiver-initiated X-MAC with tree topology (TRIX-MAC), an improved energy-efficient MAC protocol based on an asynchronous duty cycling for wireless sensor networks with tree topology. TRIX-MAC improves energy efficiency through utilizing short preambles and adopting the receiver-initiated approach that minimizes sender nodes’ energy consumption by enabling transmitters to predict receiver nodes’ wake-up times and reduces receiver nodes’ energy consumption by decreasing the number of control frames. In many sensor network applications, the data flow from source nodes to a sink forms a unidirectional tree. A property of tree topology, the parent-child relation, is also exploited to reduce the likelihood of collisions between frames sent by children nodes. We use the network simulator, ns-2, to evaluate TRIX-MAC’s performance. Compared to the prior asynchronous duty cycling approaches of X-MAC, RIX-MAC, and PW-MAC, the proposed protocol shows better performance in terms of throughput, energy efficiency, and end-to-end delay.

Breast ultrasound (BUS) is an effective clinical modality for diagnosing breast abnormalities in women. Deep-learning techniques based on convolutional neural networks (CNN) have been widely used to analyze BUS images. However, the low quality of B-mode images owing to speckle noise and a lack of training datasets makes BUS analysis challenging in clinical applications. In this study, we proposed an end-to-end CNN framework for BUS analysis using multiple parametric images generated from radiofrequency (RF) signals. The entropy and phase images, which represent the microstructural and anatomical information, respectively, and the traditional B-mode images were used as parametric images in the time domain. In addition, the attenuation image, estimated from the frequency domain using RF signals, was used for the spectral features. Because one set of RF signals from one patient produced multiple images as CNN inputs, the proposed framework overcame the limitation of datasets in a broad sense of data augmentation while providing complementary information to compensate for the low quality of the B-mode images. The experimental results showed that the proposed architecture improved the classification accuracy and recall by 5.5% and 11.6%, respectively, compared with the traditional approach using only B-mode images. The proposed framework can be extended to various other parametric images in both the time and frequency domains using deep neural networks to improve its performance.

Energy efficiency is a very important requirement in designing a MAC protocol for wireless sensor networks using battery-operated sensor nodes. We proposed a new energy-efficient MAC protocol, RIX-MAC, based on asynchronous duty cycling and receiver-initiated scheme. In this article, we analyze the performance such as throughput, delay, and energy consumption of RIX-MAC with modeling and simulation. For mathematical analysis, we first use the Markov chain model and determine the transmission and state probabilities and set the equations to solve throughput and delay. We also calculate the energy consumption by separating a cycle period into TX and RX durations. Our analysis results are validated by comparing with the simulation results obtained by NS-2.