To solve the problem of low estimation accuracy of spatial smoothing algorithm in scenarios of low signal-to-noise ratio (SNR) and small number of snapshots, a high-accuracy direction of arrival (DOA) estimation algorithm of coherent signals based on the time-space mutual correlation smoothing (TSMCS) is proposed in this letter. First, according to the strong correlation of the signal and the weak correlation of the noise in time and space domains, the time-space mutual correlation matrix of different sub-arrays is constructed to suppress the noise. Secondly, a set of high-order covariance matrices is obtained by multiplying all time-space mutual correlation matrices by their respective conjugate transpose, followed by spatial smoothing. Subsequently, TSMCS covariance matrices are meticulously decomposed and reconstructed, resulting in a more comprehensive matrix. Then, the particle swarm optimization (PSO) algorithm is exploited to optimize the signal subspace by adjusting the delay value. Finally, the traditional TLS-estimation signal parameters via the rotational invariance technique (ESPRIT) algorithm are employed to estimate the DOA of coherent signals. Simulation results demonstrate that the proposed algorithm has higher estimation accuracy and resolution.

OBJECTIVE: Intracranial electroencephalogram (iEEG) signals are generally recorded using multiple channels, and channel selection is therefore a significant means in studying iEEG-based seizure prediction. For n channels, [Formula: see text] channel cases can be generated for selection. However, by this means, an increase in n can cause an exponential increase in computational consumption, which may result in a failure of channel selection when n is too large. Hence, it is necessary to explore reasonable channel selection strategies under the premise of controlling computational consumption and ensuring high classification accuracy. Given this, we propose a novel method of channel reordering strategy combined with dual CNN-LSTM for effectively predicting seizures. METHOD: First, for each patient with n channels, interictal and preictal iEEG samples from each single channel are input into the CNN-LSTM model for classification. Then, the F1-score of each single channel is calculated, and the channels are reordered in descending order according to the size of F1-scores (channel reordering strategy). Next, iEEG signals with an increasing number of channels are successively fed into the CNN-LSTM model for classification again. Finally, according to the classification results from n channel cases, the channel case with the highest classification rate is selected. RESULTS: Our method is evaluated on the three iEEG datasets: the Freiburg, the SWEC-ETHZ and the American Epilepsy Society Seizure Prediction Challenge (AES-SPC). At the event-based level, the sensitivities of 100%, 100% and 90.5%, and the false prediction rates (FPRs) of 0.10/h, 0/h and 0.47/h, are achieved for the three datasets, respectively. Moreover, compared to an unspecific random predictor, our method also shows a better performance for all patients and dogs from the three datasets. At the segment-based level, the sensitivities-specificities-accuracies-AUCs of 88.1%-94.0%-93.5%-0.9101, 99.1%-99.7%-99.6%-0.9935, and 69.2%-79.9%-78.2%-0.7373, are attained for the three datasets, respectively. CONCLUSION: Our method can effectively predict seizures and address the challenge of an excessive number of channels during channel selection.

Seizure prediction using intracranial electroencephalogram (iEEG) has attracted an increasing attention during recent years. iEEG signals are commonly recorded in the form of multiple channels. Many previous studies generally used the iEEG signals of all channels to predict seizures, ignoring the consideration of channel selection. In this study, a method of one-dimensional convolutional neural networks (1D-CNN) combined with channel selection strategy was proposed for seizure prediction. First, we used 30-s sliding windows to segment the raw iEEG signals. Then, the 30-s iEEG segments, which were in three channel forms (single channel, channels only from seizure onset or free zone and all channels from seizure onset and free zones), were used as the inputs of 1D-CNN for classification, and the patient-specific model was trained. Finally, the channel form with the best classification was selected for each patient. The proposed method was evaluated on the Freiburg Hospital iEEG dataset. In the situation of seizure occurrence period (SOP) of 30[Formula: see text]min and seizure prediction horizon (SPH) of 5[Formula: see text]min, 98.60[Formula: see text] accuracy, 98.85[Formula: see text] sensitivity and 0.01/h false prediction rate (FPR) were achieved. In the situation of SOP of 60[Formula: see text]min and SPH of 5[Formula: see text]min, 98.32[Formula: see text] accuracy, 98.48[Formula: see text] sensitivity and 0.01/h FPR were attained. Compared with the many existing methods using the same iEEG dataset, our method showed a better performance.

Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial electroencephalogram (iEEG) has attracted widespread attention in recent two decades. The accurate and rapid detection of seizures not only reflects the efficiency of the algorithm, but also greatly reduces the burden of manual detection during long-term electroencephalogram (EEG) recording. In this work, a stacked one-dimensional convolutional neural network (1D-CNN) model combined with a random selection and data augmentation (RS-DA) strategy is proposed for seizure onset detection. Firstly, we segmented the long-term EEG signals using 2-s sliding windows. Then, the 2-s interictal and ictal segments were classified by the stacked 1D-CNN model. During model training, a RS-DA strategy was applied to solve the problem of sample imbalance, and the patient-specific model was trained with event-based K-fold (K is the number of seizures per patient) cross validation for detecting all seizures of each patient. Finally, we evaluated the performances of the proposed approach in the two levels: the segment-based level and the event-based level. The proposed method was tested on two long-term EEG datasets: the CHB-MIT sEEG dataset and the SWEC-ETHZ iEEG dataset. For the CHB-MIT sEEG dataset, we achieved 88.14% sensitivity, 99.62% specificity and 99.54% accuracy in the segment-based level. From the perspective of the event-based level, 99.31% sensitivity, 0.2/h false detection rate (FDR) and mean 8.1-s latency were achieved. For the SWEC-ETHZ iEEG dataset, in the segment-based level, 90.09% sensitivity, 99.81% specificity and 99.73% accuracy were obtained. In the event-based level, 97.52% sensitivity, 0.07/h FDR and mean 13.2-s latency were attained. From these results, we can see that our method can effectively use both sEEG and iEEG data to detect epileptic seizures, and this may provide a reference for the clinical application of seizure onset detection.

This paper mainly investigates event-triggered tracking control problem for second-order discrete-time multi-agent systems with directed fixed and switching topologies. Event-triggered sampling control strategies are designed to reduce the frequency of actuator updating for agents with limited energies when their measurement error exceeds a threshold. For the case of fixed topology, a necessary and sufficient condition is proposed based on Hurwitz stability theory to solve the tracking control bounded consensus problem under the assumption that the topology has a directed spanning tree rooted at the leader. For the case of switching topologies, a sufficient condition on asymptotic stability of the multi-agent system is presented by constructing a Lyapunov function under the assumption that each possible topology contains a directed spanning tree. Finally, numeral examples are given to illustrate the effectiveness of these results.

Network protocol developers typically go through a tedious and error-prone process of testing and debugging their protocol implementation for various settings. They perform a number of tasks manually such as configuration of numerous network settings, controlled reproduction of unexpected protocol behavior, and traffic capture and analysis. We present a Protocol Testing and Evaluation System (PTES) to assist developers in transparently testing their protocol implementations. PTES enables a protocol developer to construct and execute various controlled and repeatable testing scenarios. The developer can use simple iptables- like rules to specify various local and distributed network events and actions. During protocol execution, PTES triggers these events and actions in a coordinated manner and records the protocol responses to these events which can later be examined by the developer. We present the design and implementation of three variants of PTES for native, simulated, and emulated platforms for both wired and wireless networks. We demonstrate the utility of PTES by automating the testing of TCP/IP and Optimized Link State Routing (OLSR) protocols.

This paper addresses the problem of simultaneously migrating a group of co-located and live virtual machines (VMs), i.e, VMs executing on the same physical machine. We refer to such a mass simultaneous migration of active VMs as live gang migration. Cluster administrators may often need to perform live gang migration for load balancing, system maintenance, or power savings. Application performance requirements may dictate that the total migration time, network traffic overhead, and service downtime, be kept minimal when migrating multiple VMs. State-of-the-art live migration techniques optimize the migration of a single VM. In this paper, we optimize the simultaneous live migration of multiple co-located VMs. We present the design, implementation, and evaluation of a de-duplication based approach to perform concurrent live migration of co-located VMs. Our approach transmits memory content that is identical across VMs only once during migration to significantly reduce both the total migration time and network traffic. Using the QEMU/KVM platform, we detail a proof-of-concept prototype implementation of two types of de-duplication strategies (at page level and sub-page level) and a differential compression approach to exploit content similarity across VMs. Evaluations over Gigabit Ethernet with various types of VM workloads demonstrate that our prototype for live gang migration can achieve significant reductions in both network traffic and total migration time. Categories andSubjectDescriptors

The proliferation of wireless-enabled portable computing devices has spurred a growing need for efficient and powerful networking protocols. The key challenge in the development of robust wireless networking protocols is an ability to conduct effective and efficient evaluation of the protocol in order to ensure its successful working in real-world settings. We proposed MiNT-2, a fresh re-design of the original MiNT framework developed at Stony Brook University. One of the fundamental requirements of MiNT-2 is to provide location awareness of all the nodes within the network. In this paper, we demonstrate the use of radio-frequency identification (RFID) technology in order to carry out localization of the mobile nodes within the system. We also demonstrate the application of the localization system of constructing different scenarios for wireless protocols evaluation.