In this paper, we study the improvement of a storage location strategy through the use of big data technology, including data collection, cluster analysis and association analysis, to improve order picking efficiency. A clustering algorithm is used to categorize the types of goods in orders. Classification is performed based on the turnover of goods, value, sales volume, favorable commodity ratings, whether free shipping is provided and whether cash on delivery is supported. An association algorithm is used to determine the relationships among goods by studying the habits of consumers who buy them. A method for improving the class-based storage strategy is proposed. The picking distance of the improved storage strategy is compared with that of the traditional strategy via simulation experiments. The picking efficiency is shown to be enhanced by the improved strategy.

The security of network information in the Internet of Things faces enormous challenges. The traditional security defense mechanism is passive and certain loopholes. Intrusion detection can carry out network security monitoring and take corresponding measures actively. The neural network-based intrusion detection technology has specific adaptive capabilities, which can adapt to complex network environments and provide high intrusion detection rate. For the sake of solving the problem that the farmland Internet of Things is very vulnerable to invasion, we use a neural network to construct the farmland Internet of Things intrusion detection system to detect anomalous intrusion. In this study, the temperature of the IoT acquisition system is taken as the research object. It has divided which into different time granularities for feature analysis. We provide the detection standard for the data training detection module by comparing the traditional ARIMA and neural network methods. Its results show that the information on the temperature series is abundant. In addition, the neural network can predict the temperature sequence of varying time granularities better and ensure a small prediction error. It provides the testing standard for the construction of an intrusion detection system of the Internet of Things.

Polar code has been proven to achieve the symmetric capacity of memoryless channels. However, the successive cancellation decoding algorithm is inherent serial in nature, which will lead to high latency and low throughput. In order to obtain high throughput, we design a deeply pipelined polar decoder and optimize the processing elements and storage structure. We also propose an improved fixed‐point nonuniform quantization scheme, and it is close to the floating‐point performance. Two‐level control strategy is presented to simplify the controller. In addition, we adopt FIFO structure to implement the α _memory and β _memory and propose the 348‐stage pipeline decoder.

This paper aims to introduce some key parameters for the tracking application in wireless sensor networks.In this paper, the Nearest Closer protocol has been implemented in Jsim simulation platform, and consequently some useful trade-off analysis results among the density, reliability and lifetime have been obtained.Based on these results, two evaluation models are proposed.

We show that the codebook of every general transmission system can also be regarded as a classical code in a natural way. Consequently, using the Hamming metric, bounds on the cardinality of the codebooks of transmission systems can be obtained directly from classical coding theory.

The efficient management of scarce network resources, including energy and bandwidth, represents a central challenge for wireless sensor networks. The current trend in resource management relies on the introduction of control mechanisms, such as control message exchanges, node-specific addressing, and storage of partial network state information. These mechanisms typically incur communication and processing overhead that does not scale well for larger or denser networks. Instead of introducing control mechanisms for network resource management, this article proposes and evaluates a Directed Broadcast with Overhearing (DBO) approach for sensor networks that combines directed broadcast at the network layer with CSMA and packet overhearing at the MAC layer. Through avoidance of control messaging and exchange of network state information, DBO trades off limited packet duplication overhead for control messaging overhead. This article introduces an analytical model that provides the basis for DBO evaluation and for analysis of the approach's transient packet retransmissions, route convergence, and energy consumption in the average and worst cases. We also present the model implementation details and the simulation experiments that explore the suitability of DBO for networks of different sizes with three different radio models that vary the width of grey regions, and we compare DBO's energy consumption against conventional unicast beacon-based and snooping-based routing protocols. The results indicate that that DBO's route convergence requires an average of five hops for ideal radio reception, seven hops for narrow grey regions, and twelve hops for wide grey regions. These results confirm that DBO shifts energy consumption from critical nodes near the base station to nodes near the source. The overall energy consumption of limited packet duplication overhead with DBO compared to unicast routing shrinks for medium- to large-size networks, rendering it more favorable than conventional communication approaches for large and dense sensor networks.

Nice error bases have various applications in quantum computing such as in the teleportation of quantum states and in the theory of noiseless systems. Associated in a natural way to any nice error basis there is a so-called index group and also a group of central type. In this paper, we will extend the number of known index groups for nice error bases by exploiting the connections between groups of central type and their Sylow subgroups. Our methods will allow us find all index groups of degree up to 44 with two exceptions.

A decoding algorithm is presented for the ternary (11, 6, 5) Golay code. This algorithm is an algebraic decoding and relies on solving the Newton identities associated with the syndromes of the code. However, to complete the decoding some explicit values of Zech's logarithms need to be established.>