NTRU is a lattice-based public key cryptosystem featuring reasonably short, easily created keys, high speed, and low memory requirements, seems viable for wireless network. This paper presents two optimized designs based on the enhanced NTRU algorithm. One is a lightweight and fast NTRU core, it performs encryption only. This work has a gate-count of 1175 gates and a power consumption of 1.51 μW. It can finish the whole encryption process in 1498 μs at 500 kHz. As such, it is perfect for wireless sensor network. Another high-speed NTRU core is capable of both encryption and decryption, with delays of 16,064 μs and 128,010 μs in encryption and decryption respectively. Moreover, it consists of 25,758 equivalent gates and has a total power consumption of 59.2 μW (it will be reduced greatly if low power methods were adopted). This core is recommended to be used in base stations or servers in wireless network.
The development of cloud computing and data science result in rapid increases of number and scale of data centers. Because of cost and sustainability concerns, energy efficiency has been a major goal for data center architects. Focusing on reducing the cooling power and making full use of available computing power, power budgeting is an increasingly important requirement for data center operations. In this paper, we present a framework of power budgeting, considering both computing power and cooling power, in data centers to maximize the system normalized performance (SNP) of the entire center under a total power budget. Maximizing the SNP for a given power budget is equivalent to maximizing the energy efficiency. We propose a method to partition the total power budget among the cooling and computing infrastructure in a self-consistent way, where the cooling power is sufficient to extract the heat of the computing power. Intertwinedly, we devise an optimal computing power budgeting technique based on dynamic programming algorithm to determine the optimal power caps for the individual servers such that the available power could be efficiently translated to performance improvements. The optimal computing budgeting technique leverages a proposed online throughput predictor based on performance counter measurements to estimate the change in throughput of heterogeneous workloads as a function of allocated server power caps. We demonstrate that our proposed power budgeting method outperforms previous methods by 3-4 percent in terms of SNP using our data center simulation environment. While maintaining the improvement of SNP, our method improve fairness at best by 57 percent. We also evaluate the performance of our method in power saving scenario and dynamic power budgeting case.
Lightning disturbance may be misjudged as dc fault by the primary protection in the flexible high voltage dc (HVDC) grid. To solve this problem, an auxiliary fault identification strategy based on convolutional neural network with branch structures (BR-CNN) is proposed in this paper. In the proposed scheme, the voltage and current characteristic matrix is constructed as the input matrix of BR-CNN model and the output categories include positive pole-to-ground (PTG) fault and lightning disturbance. Voltage and current branches are constructed to extract high-level local features of input data layer by layer, and main branch is designed to realize the comprehensive utilization of voltage and current information. Through autonomous learning of the model, the nonlinear mapping relationship between input and output is constructed. The method only uses the single-terminal quantities, and can be used as an auxiliary criterion to improve the reliability of the primary protection. The test results verify the effectiveness of the method, and the recognition accuracy is better than the traditional classification models.
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