Intra-body communication will facilitate next-generation personalized medicine by enabling interconnection among implanted devices. To this purpose, energy-efficient communication technologies are required such as galvanic coupling (GC). Although some GC testbeds have been developed to implement the entire communication chain, synchronization problems have not yet been tackled exhaustively. While some papers simply assumea-prioriperfect synchronization between GC transmitter and receiver, other studies developed solutions that often are time-consuming. In this paper, an energy-efficient and fast maximum-log-likelihood (ML) synchronization method is proposed, that can operate in real time and follow channel variations. Experiments reveal that the proposed ML synchronization scheme is very effective for short-range GC communication up to 4 cm, with performance similar to the current State-of-the-Art. It shows slightly lower performance levels for higher distances, still it offers the benefit of lower computational requirements than the reference method.

Summary Smartphones are equipped with precise hardware sensors including accelerometer, gyroscope, and magnetometer. These devices provide real‐time semantic data that can be used to recognize daily life physical activities for personalized smart health assessment. Existing studies focus on the recognition of simple physical activities but they lacked in providing accurate recognition of physical activities having complex interclass variations. Therefore, this research focuses on the accurate recognition of physical activities having complex interclass variations. We propose a two‐layered approach calledPARCIVthat first clusters similar activities based on semantic data and then recognize them using a machine learning classifier. Our two‐layered approach first bounds the highly indistinguishable activities in clusters to avoid misclassification with other distinguishable activities and thereafter recognize them on a fine‐grained level within each cluster. To evaluate our approach, we make an android application that collects labeled data by using smartphone sensors from 10 participants, while performing activities.PARCIVrecognizes distinguishable as well as indistinguishable activities with high accuracy of 99% on the self‐collected dataset. Furthermore,PARCIVachieve 95% accuracy on the publicly available dataset used by state‐of‐the‐art studies.PARCIVoutperforms various state‐of‐the‐art studies by 8%‐17% for simple activities as well as complex activities.

In this manuscript, we discuss a new approach to snow pack monitoring from buried radar, using more than one receiver to solve the ambiguity given by the unknown refraction index, in the simplified hypothesis of homogeneous snow. The concept is presented in previous papers, whereas in this paper we briefly analyze the expected accuracy of snow height estimates as a function of where the two receivers are located.

In this work we assess the recently proposed hybrid Deep Neural Network/Gaussian Mixture Model (DNN/GMM) approach for speaker recognition considering the effects of the granularity of the phonetic DNN model, and of the precision of the corresponding GMM models, which will be referred to as the phonetic GMMs. The aim of this work is to better understand the contributions of the phonetic information provided by the DNN model with respect to the accuracy of the acous tic GMMs in fitting the distribution of the features associated to a given context-dependent phone state. The testbed for this work was the text-independent speaker recognition task defined by NIST for the 2012 Speaker Recognition Evaluation. Our experiment confirms that the acoustic and the phonetic GMMs are complementary. Thus, their score combination yields very good results if the DNN is trained on data collected in an environment similar to the one that is used for testing. We show, however, that using a single Gaussian per DNN state is not the best choice: the best single system has been obtained balancing the phonetic and acoustic precision of a DNN/GMM system