Further applications of impulse radio ultra-wideband radar in mobile health are hindered by the difficulty in extracting such vital signals as heartbeats from moving targets. Although the empirical mode decomposition based method is applied in recovering waveforms of heartbeats and estimating heart rates, the instantaneous heart rate is not achievable. This paper proposes a Heartbeat Estimation And Recovery (HEAR) approach to expand the application to mobile scenarios and extract instantaneous heartbeats. Firstly, the HEAR approach acquires vital signals by mapping maximum echo amplitudes to the fast time delay and compensating large body movements. Secondly, HEAR adopts the variational nonlinear chirp mode decomposition in extracting instantaneous frequencies of heartbeats. Thirdly, HEAR extends the clutter removal method based on the wavelet decomposition with a two-parameter exponential threshold. Compared to heart rates simultaneously collected by electrocardiograms (ECG), HEAR achieves a minimum error rate 4.6% in moving state and 2.25% in resting state. The Bland–Altman analysis verifies the consistency of beat-to-beat intervals in ECG and extracted heartbeat signals with the mean deviation smaller than 0.1 s. It indicates that HEAR is practical in offering clinical diagnoses such as the heart rate variability analysis in mobile monitoring.

To enhance electrocardiogram (ECG) monitoring systems in personalized detections, deep neural networks (DNNs) are applied to overcome individual differences by periodical retraining. As introduced previously [4], DNNs relieve individual differences by fusing ECG with impulse radio ultra-wide band (IR-UWB) radar. However, such DNN-based ECG monitoring system tends to overfit into personal small datasets and is difficult to generalize to newly collected unlabeled data. This paper proposes a self-adjustable domain adaptation (SADA) strategy to prevent from overfitting and exploit unlabeled data. Firstly, this paper enlarges the database of ECG and radar data with actual records acquired from 28 testers and expanded by the data augmentation. Secondly, to utilize unlabeled data, SADA combines self organizing maps with the transfer learning in predicting labels. Thirdly, SADA integrates the one-class classification with domain adaptation algorithms to reduce overfitting. Based on our enlarged database and standard databases, a large dataset of 73200 records and a small one of 1849 records are built up to verify our proposal. Results show SADA's effectiveness in predicting labels and increments in the sensitivity of DNNs by 14.4% compared with existing domain adaptation algorithms.

Recent advances in physical layer optics have made slicing of optical bands into multiple subbands with different bandwidths possible. Due to this development, spectrum allocation has become easier in elastic optical networks (EONs). More specifically, owing to the slicing technology, the defragmentation problem in EONs can be addressed easily, i.e., more demands can be fit into empty spectrum slots by breaking the demands as needed using the slicing technology. This paper proposes a spectrum allocation scheme considering the slicing process at any nodes, e.g., source node and intermediate nodes, in EONs. Slicing-and-stitching technology is applied to break the contiguous-spectrum constraint in an EON so that spectrum fragmentation is reduced. While slicing a demand from one node to another, the following questions must be answered: (1) Which parts of the spectrum band should be sliced? (2) In which node(s) along the path of a demand should the slicings be done to reduce the bandwidth blocking rate? To answer the above questions, we formulate a mixed-integer linear programming (MILP) model that jointly addresses the above questions as well as minimizes the total number of slicers in a network. To measure the performance of the MILP, we used the bandwidth blocking ratio (BBR) of the network as a performance metric. Our results from the MILP show that introducing slicing at every node in the network improves the BBR by as much as 68% compared to a conventional case where slicing a demand is allowed only at the source node.

This paper proposes a scheme to reduce the number of flow entries permanently stored in an OpenFlow switch and the number of configuration messages from a controller in a software-defined network (SDN). In an SDN, a flow table in an OpenFlow switch is used to instruct packets. The flow table consists of flow entries decided by the controller. A flow request is sent from the OpenFlow switch to the controller if the incoming packet does not match any flow entry in the flow table. The controller’s central processing unit may be overloaded to handle user requests, since the user requests for different data types have been rapidly increasing. As a result, flow configuration in switches is delayed. Moreover, the control plane may be flooded by configuration messages of those requests. A scheme to permanently keep the flow entries in the switch can reduce the number of requests. However, a large number of permanent flow entries is required. Other switch features may be degraded, since there is not enough memory in the flow table to implement those features. In the proposed scheme, switches in the network are divided into multiple regions. In order to guide packets from sources to destinations, the flow table incorporating the concept of two multiprotocol label switching tags is re-designed. One tag directs a packet from a source switch to an edge switch in the destination region. The other tag directs the packet from that edge switch to another switch in the same region. A mathematical model for the proposed scheme is formulated as an integer linear programming to determine a set of switches in each region so that the total number of permanent flow entries in the network can be minimized. The performance of the proposed scheme is analyzed. Moreover, the proposed scheme is implemented and demonstrated via Japanese Science Information Network 5.

Internet users consider content information to be useful, but the current Internet approach treats location information as more important as so ties the former to the latter. A Content-Centric Network (CCN) allows the user to obtain content without regard to its location. CCN caches the contents information at its intermediate nodes. The content is searched for along the shortest path between the user and the node that has the original content. If any cache node is located on the shortest path, the content can be obtained from the nearest cache node, so far fewer hops are needed compared to the network without any cache node. However, this efficiency is not achieved if no cache node is located on the shortest path. One proposal sets cache nodes that broadcast their contents to surrounding nodes; the user is able to obtain the content from the cache node, rather than the node that has the original content, if the cache node is closer to the user. The location of the cache node affects the number of hops. We formulate an optimization problem that determines the locations of cache nodes to minimize the hop count as an Integer Linear Programming (ILP) problem. Since large ILPs cannot be solved in a practical time, we introduce a Betweenness Centrality (BC) approach that determines the location of cache nodes by computing the BC value of each node and ranks the nodes in descending BC order. The BC value denotes the ratio of the number of shortest paths between source-receiver pairs passing through the node to the total number of shortest paths between source-receiver pairs. Simulations show that the BC approach offers drastically reduced computation time, while the average number of hops is just 5.8% higher than that determined with the ILP approach.