This article investigates the limited-time bipartite synchronization (LTBS) problem of coupled T-S fuzzy memristive neural networks (CTSFMNNs) with antagonistic interactions, where LTBS covers both fixed-time bipartite synchronization (FXTBS) and preassigned-time bipartite synchronization (PATBS). To address this issue, a fuzzy exponential control strategy is developed based on an improved adaptive event-triggered mechanism (IAETM). By employing constant gains and exponential terms instead of conventional switching gains and multiple power-law structures, the controller design is simplified and its implementability is enhanced. Furthermore, an adaptive function and two dynamic regulation terms are incorporated into the ETMs to adjust the triggering threshold according to measurement-error variations, thereby reducing controller updates and conserving communication resources. Then, algebraic inequality criteria are established through Lyapunov theory and the fuzzy set method to guarantee the LTBS of CTSFMNNs. Finally, numerical simulations are conducted to verify both the effectiveness of the derived synchronization conditions and the feasibility of an image data protection framework for Pantograph-Catenary Monitoring System (PCMS) constructed from the obtained LTBS theory. The proposed framework provides controllable key-generation time, a decoupled design of the key and the synchronous sequence, and enhanced resistance to structure-inference attacks, thereby improving security and demonstrating strong engineering potential.

With the development of Internet of Things, the Healthcare Industrial IoT has become an effective way to curb the high mortality rate of heart disease. The accuracy of such system is mainly rely on the quality of ECG signals, in which noise reduction has been widely used. However, in the IoT environment, many kinds of noise which cannot be predicted in advance exist in signals, and make the signal morphology seriously damaged, which brings great challenge to the existing de-noise methods. By considering the self-adaptation and self-learning of deep neural network, we have proposed a multilayer LSTM model to the noise reduction of dynamic ECGs. Unlike other methods, our model makes both noise and ECG signals as part of time-series data, while other methods always consider them separately. Benefit from the recurrent structure of LSTM model, the most representative features will be extracted in LSTM memory units. By stacking multiple layers per time step, the useful information will be continuously refined and the noise signal will be discarded. Even if the ECG signals comprise many kinds of noise simultaneously, the model can still restore ECG signals with high quality without relying on threshold or signal quality. The experimental results show that the proposed model is insensitive to noise and the improvement of signal-to-noise ratio up to 55dB. This result is much better than the existing methods, which indicates LSTM is a new competitive method for ECG noise reduction.

With the tremendous growth of cloud computing, it is increasingly critical to provide quantifiable performance to tenants and to improve resource utilization for the cloud provider. Though many recent proposals focus on guaranteeing job performance (with a particular note on network bandwidth) in the cloud, they usually lack efficient utilization of cloud resource, or vice versa. In this paper we present DCloud, which leverages the (soft) deadlines of cloud computing jobs to enable flexible and efficient resource utilization in data centers. With the deadline requirement of a job guaranteed, DCloud employs both time sliding (postponing the launching time of a job) and bandwidth scaling (adjusting the bandwidth associated with VMs) in resource allocation, so as to better match the resource allocated to the job with the cloud's residual resource. Extensive simulations and testbed experiments show that DCloud can accept much more jobs than existing solutions, and significantly increase the cloud provider's revenue with less cost for individual tenants.

The ability to provide guaranteed network bandwidth for tenants is essential to the prosperity of cloud computing platforms, as it is a critical step for offering predictable performance to applications. Despite its importance, it is still an open problem for efficient network bandwidth sharing in a multitenant environment, especially when applications have diverse bandwidth requirements. More precisely, it is not only that different tenants have distinct demands, but also that one tenant may want to assign bandwidth differently across her virtual machines (VMs), i.e., the heterogeneous bandwidth requirements. In this paper, we tackle the problem of VM allocation with bandwidth guarantee in multitenant data center networks. We first propose an online VM allocation algorithm that improves on the accuracy of the existing work. Next, we develop a VM allocation algorithm under heterogeneous bandwidth demands. We conduct extensive simulations to demonstrate the efficiency of our method.