A Robust Parallel Object Tracking Method for Illumination Variations

Liu, Shuai; Liu, Gaocheng; Zhou, Huiyu

doi:10.1007/s11036-018-1134-8

Cited by 132 publications

(12 citation statements)

References 49 publications

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“…Generally, P can be selected near 200-bit according to comprehensive judgment. Encryption operation method: in ECC algorithm design, elliptic encryption is completed by point multiplication, multiple point, point addition, modular multiplication and other series of operations [24,25]. But it can be explained by three layers: interface layer, application layer, middle layer (such as complex mixed operation), and bottom layer (modular multiplication and other unitary operations).…”

Section: Ecc Algorithmmentioning

confidence: 99%

Encryption technology of voice transmission in mobile network based on 3DES-ECC algorithm

Chang

Woźniak

2020

Mobile Netw Appl

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The traditional design of voice collector has poor anti attack ability, which makes the encryption effect of voice transmission poor. Therefore, taking the mobile network voice collector as the research object, 3des-ecc algorithm is applied to the information transmission encryption of the mobile network voice collector.An improved speech signal collector is designed, which combines 3DES and ECC algorithm to realize the encryption of speech transmission information. An improved voice signal collector is designed, which combines 3DES and ECC algorithm to realize the encryption of voice transmission information. In the process of encryption, 168-bit random key is generated first, and it is grouped according to 56 bits as 3DES key, and then the plaintext is encrypted by the key to generate ciphertext; the random key is encrypted by ECC public key of the receiver. The experimental results show that the encryption time of this method is less than 1 s, the data integrity is 93%, and the data loss rate is only 0.33%. It has better anti attack ability, fast encryption speed and good encryption effect.

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Section: Ecc Algorithmmentioning

confidence: 99%

Encryption technology of voice transmission in mobile network based on 3DES-ECC algorithm

Chang

Woźniak

2020

Mobile Netw Appl

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“…The wireless network sensor node structure consists of two parts, including a wireless transceiver module designed with the chip as the core and an development board used as a real-time data processing module (Model by Xilinx) , with 500,000 system doors) [10]. The wireless radio frequency module is mainly composed of chip and serial port driver chip , which is used to complete network wireless interconnection and data transceiving work.The development board uses the embedded CLinux system to implement data exchange with the wireless transceiver module through the serial port after completing the hardware configuration and software development of the system [11].…”

Section: Analysis Of Mobile Node Structure In Wireless Sensor Netwmentioning

confidence: 99%

A Node Density Control Learning Method for the Internet of Things

Lou

Srivastava

Liu

2019

Sensors

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When examining density control learning methods for wireless sensor nodes, control time is often long and power consumption is usually very high. This paper proposes a node density control learning method for wireless sensor nodes and applies it to an environment based on Internet of Things architectures. Firstly, the characteristics of wireless sensors networks and the structure of mobile nodes are analyzed. Combined with the flexibility of wireless sensor networks and the degree of freedom of real-time processing and configuration of field programmable gate array (FPGA) data, a one-step transition probability matrix is introduced. In addition, the probability of arrival of signals between any pair of mobile nodes is also studied and calculated. Finally, the probability of signal connection between mobile nodes is close to 1, approximating the minimum node density at T. We simulate using a fully connected network identifying a worst-case test environment. Detailed experimental results show that our novel proposed method has shorter completion time and lower power consumption than previous attempts. We achieve high node density control as well at close to 90%.

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“…Traditional trackers based on Discriminative correlation filter (DCF) have recently obtained promising performance to hold robustness under illumination variation. However, features of target object will be not extracted and discriminated from the background when target object has significant appearance variation due to intense illumination variation [1].…”

Section: Editorialmentioning

confidence: 99%