Distributed degree-based link scheduling for collision avoidance in wireless sensor networks.

Wireless sensor networks (WSNs) consist of multiple sensor nodes, which communicate with each other under the constrained energy resources. Retransmissions caused by collision and interference during the communication among sensor nodes increase overall network delay. Since the network delay increases as the node's waiting time increases, the network performance is reduced. Thus, the link scheduling scheme is needed to communicate without collision and interference. In the distributed WSNs environment, a sensor node has limited information about its neighboring nodes. Therefore, a comprehensive link scheduling scheme is required for distributed WSNs. Many schemes in the literature prevent collision and interference through time division multiple access (TDMA) protocol. However, considering the collision and interference in TDMA-based schedule increases the delay time and decreases the communication efficiency. This paper proposes the distributed degree-based link scheduling (DDLS) scheme, based on the TDMA. The DDLS scheme achieves the link scheduling more efficiently than the existing schemes and has the low delay and the duty cycle in the distributed environment. Communication between sensor nodes in the proposed DDLS schemes is based on collision avoidance maximal independent link set, which enables to assign collision-free timeslots to sensor nodes, and meanwhile decreases the number of timeslots needed and has low delay time and the duty cycle. Simulation results show that the proposed DDLS scheme reduces the scheduling length by average 81%, the transmission delay by 82%, and duty cycle by over 85% in comparison with distributed collision-free low-latency scheduling scheme.N/

Learning Graph Patterns of Reflection Coefficient for Non-destructive Diagnosis of Cu Interconnects

With the increasing operating frequencies and clock speeds in processors, interconnects affect both the reliability and performance of entire electronic systems. Fault detection and diagnosis of the interconnects are crucial for prognostics and health management (PHM) of electronics. However, traditional approaches using electrical signals as prognostic factors often face challenges in distinguishing defect root causes, necessitating additional destructive evaluations, and are prone to noise interference, leading to potential false alarms. To address these limitations, this paper introduces a novel approach for non-destructive detection and diagnosis of defects in Cu interconnects, offering early detection, enhanced diagnostic accuracy, and noise resilience. Our approach uniquely analyzes both the root cause and severity of interconnect defects by leveraging graph patterns of reflection coefficient, a technique distinct from traditional time series signal analysis. We experimentally demonstrate that the graph patterns possess the capability for fault diagnosis and serve as effective input data for learning algorithms. Additionally, we introduce a novel severity rating ensemble learning (SREL) approach, which significantly enhances diagnostic accuracy and noise robustness. Experimental results demonstrate that the proposed method outperforms conventional machine learning methods and multi-class convolutional neural networks (CNN), achieving a maximum accuracy of 99.3%, especially under elevated noise levels

Characterization of Etching Process and Etch Rate of Aluminum Thin Film

This thesis reports on characterization of etching process of aluminum thin film. It offers sequential steps of optimized etching process as well as the etch rate of aluminum thin film--one of the ubiquitous metals in MEMS and IC industries| for on-going RF MEMS research. To gain high device performance, an effi cient and accurate fabrication process is critical. Despite a number of previous studies on etch rate characterization, unique tuning methods and fabrication recipes are required for different instruments and laboratories. In this report, the etching characterization is performed using PlasmaLab Master/Slave Reactive Ion Etching (RIE) System with flows of boron trichloride (BCl3) and chlorine gas (Cl2). Drawbacks of the tools and chemicals as well as corresponding adjustments are also discussed. Along with aluminum etch rate, etch rate of photoresist under the same parameters can also be evaluated in the same manner. The results of both etch rates are covered in this paper.Ope

Two Cases of Lichen Planus Pigmentosus Presenting with a Linear Pattern

We report two cases of lichen planus pigmentosus (LPP) that developed in a unilateral linear pattern. The patients presented with unilateral linear brown macules on the extremities. Skin biopsy showed orthokeratosis, basal hydropic degeneration with scarce lymphohistiocytic infiltrates, and numerous melanophages in both patients. These patients, to the best of our knowledge, are the first cases of LPP presenting with a linear pattern. LPP should be considered in the differential diagnosis of linear hyperpigmented skin lesions

Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy

Due to scarcity of anomaly situations in the early manufacturing stage, an unsupervised anomaly detection (UAD) approach is widely adopted which only uses normal samples for training. This approach is based on the assumption that the trained UAD model will accurately reconstruct normal patterns but struggles with unseen anomalous patterns. To enhance the UAD performance, reconstruction-by-inpainting based methods have recently been investigated, especially on the masking strategy of suspected defective regions. However, there are still issues to overcome: 1) time-consuming inference due to multiple masking, 2) output inconsistency by random masking strategy, and 3) inaccurate reconstruction of normal patterns when the masked area is large. Motivated by this, we propose a novel reconstruction-by-inpainting method, dubbed Excision And Recovery (EAR), that features single deterministic masking based on the ImageNet pre-trained DINO-ViT and visual obfuscation for hint-providing. Experimental results on the MVTec AD dataset show that deterministic masking by pre-trained attention effectively cuts out suspected defective regions and resolve the aforementioned issues 1 and 2. Also, hint-providing by mosaicing proves to enhance the UAD performance than emptying those regions by binary masking, thereby overcomes issue 3. Our approach achieves a high UAD performance without any change of the neural network structure. Thus, we suggest that EAR be adopted in various manufacturing industries as a practically deployable solution.Comment: 10 pages, 5 figures, 5 table

New Concept of a Surgical Hospitalist: Early Experience of Managing the Admission, Critical Care, Trauma Surgery Team

The demand for hospitalists is increasing due to decreasing numbers of clinical residents and increased concerns regarding patient safety. However, several limitations in the surgical hospitalist role exist. The personnel of the surgical hospitalist, rapid response team (RRT), surgical critical care, and trauma surgery teams were unified under the admission, critical care, and trauma surgery (ACTs) team at this institution. The ACTs target are patients with severe disease not undergoing general postoperative course, or patients with the potential for severe conversion. Two surgical intensivists are on duty once a week and oversee the intensive care unit (ICU), the back-up treatment of critically ill patients, and the immediate treatment of trauma patients. ACTs also participate in the surgical RRT and select patients with a high probability of severe exacerbation. Between 2019–2021, the cardiopulmonary resuscitation incidence per 1,000 hospitalized patients in the surgical department decreased significantly from 0.81 to 0.55. From March to December 2021, the ACTs team were involved with 101 of 158 surgical patients admitted to the ICU: 62 with postoperative status, 29 with severe trauma, and 10 transferred to the ICU via RRT screening. Based on our experience, the role of the ACTs team can help improve patient safety

Characteristics and Clinical Outcomes of Elderly Patients with Trauma Treated in a Local Trauma Center

Purpose This study aimed to investigate the characteristics of elderly patients who visited a non-regional trauma center to examine the effects of old age on the clinical outcomes of patients. Methods The medical charts of 159 patients with trauma who visited the National Health Insurance Service Ilsan Hospital between March 2020 and February 2022 were retrospectively analyzed. Results Of the 159 patients, 41 were assigned to the elderly patient group (EPG) and 118 were assigned to the non-elderly patient group (NEPG). The average age of patients in each group was 75.5 and 38.2 years in the EPG and the NEPG, respectively. Comparing the injury mechanism between the two groups, pedestrian traffic accidents (TA) were the most common (24.4%), followed by slipping (19.5%), motorcycle TA, and bicycle TA (14.6%) in EPG. In the NEPG, motorcycle TA (28.0%) was the most common, followed by car TA (27.1%), and fall injury (16.9%), with a significant difference between the two groups (p < 0.001). The significant differences between the two groups were the injury severity score (ISS; p = 0.004), severe trauma (p = 0.045), intensive care unit admission (p = 0.028), emergency operation (p = 0.034), and mortality (p = 0.013). The statistically significant risk factors for mortality were old age (p = 0.024) and chest injury (p = 0.013). Conclusion Patients in the EPG compared with the NEPG group showed different injury mechanisms. The EPG has a higher severity and mortality rate than the NEPG