A Primal-Dual Based Power Control Approach for Capacitated Edge Servers

The intensity of radio waves decays rapidly with increasing propagation distance, and an edge server's antenna needs more power to form a larger signal coverage area. Therefore, the power of the edge server should be controlled to reduce energy consumption. In addition, edge servers with capacitated resources provide services for only a limited number of users to ensure the quality of service (QoS). We set the signal transmission power for the antenna of each edge server and formed a signal disk, ensuring that all users were covered by the edge server signal and minimizing the total power of the system. This scenario is a typical geometric set covering problem, and even simple cases without capacity limits are NP-hard problems. In this paper, we propose a primal-dual-based algorithm and obtain an $m$-approximation result. We compare our algorithm with two other algorithms through simulation experiments. The results show that our algorithm obtains a result close to the optimal value in polynomial time

A Local-Ratio-Based Power Control Approach for Capacitated Access Points in Mobile Edge Computing

Terminal devices (TDs) connect to networks through access points (APs) integrated into the edge server. This provides a prerequisite for TDs to upload tasks to cloud data centers or offload them to edge servers for execution. In this process, signal coverage, data transmission, and task execution consume energy, and the energy consumption of signal coverage increases sharply as the radius increases. Lower power leads to less energy consumption in a given time segment. Thus, power control for APs is essential for reducing energy consumption. Our objective is to determine the power assignment for each AP with same capacity constraints such that all TDs are covered, and the total power is minimized. We define this problem as a \emph{minimum power capacitated cover } (MPCC) problem and present a \emph{minimum local ratio} (MLR) power control approach for this problem to obtain accurate results in polynomial time. Power assignments are chosen in a sequence of rounds. In each round, we choose the power assignment that minimizes the ratio of its power to the number of currently uncovered TDs it contains. In the event of a tie, we pick an arbitrary power assignment that achieves the minimum ratio. We continue choosing power assignments until all TDs are covered. Finally, various experiments verify that this method can outperform another greedy-based way

BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading

Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is not addressed in conventional research methods of binary DR image classification. Small objects in the eye images, like lesions and microaneurysms, are essential to DR grading in medical imaging, but they could easily be influenced by other objects. To address these challenges, we propose a new deep learning architecture, called BiRA-Net, which combines the attention model for feature extraction and bilinear model for fine-grained classification. Furthermore, in considering the distance between different grades of different DR categories, we propose a new loss function, called grading loss, which leads to improved training convergence of the proposed approach. Experimental results are provided to demonstrate the superior performance of the proposed approach.Comment: Accepted at ICIP 201

A Literature Review Study: a Meta-Analysis and Investigation of the Frequency Pattern of Point Selection Based on Clinical Studies of Acupuncture for Postoperative Treatment of the Anterior Cruciate Ligament

A Literature Review Study: a Meta-Analysis and Investigation of the Frequency Pattern of Point Selection Based on Clinical Studies of Acupuncture for Postoperative Treatment of the Anterior Cruciate Ligamen

Ameliorative effects of parecoxib in combination with ultrasound-guided paravertebral block (UGPB) on stress and inflammatory responses following thoracoscopic surgery

Purpose: To investigate the ameliorative effects of parecoxib combined with ultrasound-guided paravertebral block (UGPB) on stress and inflammatory responses after thoracoscopic surgery.Methods: Forty thoracoscopic surgery patients were randomized into placebo (control) and parecoxib groups. Parecoxib was administered pre-operation, 24 h and 48 h after operation. Arterial blood was collected, and endotoxin (ET), thromboxane A2 (TXA2), interleukin 6 (IL-6) and tumor necrosis factor alpha (TNF-α) levels were measured. Opioid dosage, infusion volume, blood loss, operation time, visual analogue scale (VAS) score at 24 h and 48 h, and hospitalization period were recorded.Results: No significant differences were observed in age, sex, height, body weight, opioid dosage, surgery time, blood loss, or infusion volume between groups. VAS scores in the parecoxib group were significantly lower than the control group after 24 and 48 h. The hospitalization period of the parecoxib group was significantly shorter than the control group. Plasma levels of ET, TXA2, IL-6 and TNF-α in the parecoxib group were lower than the control group after 24 h; however, there was no significant difference after 48 h.Conclusion: Parecoxib, combined with UGPB, effectively relieves thoracoscopic pain, stress, and inflammatory responses of patients after thoracoscopic surgery. This treatment would improve the postoperative quality of life of lung cancer patients.Keywords: Parecoxib, Paravertebral block, Stress response, Inflammatory respons