Control Parameter Setting of IEEE 802.11e for Proportional Loss Rate Differentiation

AbstractAdvances in free flap reconstruction of complex head and neck defects have allowed for improved outcomes in the management of head and neck cancer. Technical refinements have decreased flap loss rate to less than 4%. However, the potential for flap failure exists at multiple levels, ranging from flap harvest and inset to pedicle lay and postoperative patient and positioning factors. While conventional methods of free flap monitoring (reliant on physical examination) remain the most frequently used, additional adjunctive methods have been developed. Herein we describe the various modalities of both invasive and noninvasive free flap monitoring available to date. Still, further prospective studies are needed to compare the various invasive and noninvasive technologies and to propel innovations to support the early recognition of vascular compromise with the goal of even greater rates of flap salvage.

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Interference mitigation in point to point wireless sensor networks using LSP protocol and time division multiplexing approach

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189028 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5449-5458

Author(s):

A. Arokiaraj Jovith ◽

S.V. Kasmir Raja ◽

A. Razia Sulthana

Keyword(s):

Energy Consumption ◽

Large Scale ◽

Control Parameter ◽

Interference Mitigation ◽

Wireless Sensor ◽

Point To Point ◽

A Minor ◽

Two Stages ◽

Time Division ◽

And Control

Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission.

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Effects of Process Parameters on Surface Roughness and MRR in the hard turning of EN 36 steel

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset11841126 ◽

2018 ◽

pp. 102-110

Author(s):

Amritpal Singh ◽

Rakesh Kumar

Keyword(s):

Surface Roughness ◽

Material Removal Rate ◽

Material Removal ◽

Hard Turning ◽

Control Parameter ◽

Removal Rate ◽

Cutting Speed ◽

Depth Of Cut ◽

Cutting Condition ◽

Dry Cutting

In the present study, Experimental investigation of the effects of various cutting parameters on the response parameters in the hard turning of EN36 steel under the dry cutting condition is done. The input control parameters selected for the present work was the cutting speed, feed and depth of cut. The objective of the present work is to minimize the surface roughness to obtain better surface finish and maximization of material removal rate for better productivity. The design of experiments was done with the help of Taguchi L9 orthogonal array. Analysis of variance (ANOVA) was used to find out the significance of the input parameters on the response parameters. Percentage contribution for each control parameter was calculated using ANOVA with 95 % confidence value. From results, it was observed that feed is the most significant factor for surface roughness and the depth of cut is the most significant control parameter for Material removal rate.

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