Optimization-Based Channel Constrained Data Aggregation Routing Algorithms in Multi-Radio Wireless Sensor Networks

In wireless sensor networks, data aggregation routing could reduce the number of data transmissions so as to achieve energy efficient transmission. However, data aggregation introduces data retransmission that is caused by co-channel interference from neighboring sensor nodes. This kind of co-channel interference could result in extra energy consumption and significant latency from retransmission. This will jeopardize the benefits of data aggregation. One possible solution to circumvent data retransmission caused by co-channel interference is to assign different channels to every sensor node that is within each other's interference range on the data aggregation tree. By associating each radio with a different channel, a sensor node could receive data from all the children nodes on the data aggregation tree simultaneously. This could reduce the latency from the data source nodes back to the sink so as to meet the user's delay QoS. Since the number of radios on each sensor node and the number of non-overlapping channels are all limited resources in wireless sensor networks, a challenging question here is to minimize the total transmission cost under limited number of non-overlapping channels in multi-radio wireless sensor networks. This channel constrained data aggregation routing problem in multi-radio wireless sensor networks is an NP-hard problem. I first model this problem as a mixed integer and linear programming problem where the objective is to minimize the total transmission subject to the data aggregation routing, channel and radio resources constraints. The solution approach is based on the Lagrangean relaxation technique to relax some constraints into the objective function and then to derive a set of independent subproblems. By optimally solving these subproblems, it can not only calculate the lower bound of the original primal problem but also provide useful information to get the primal feasible solutions. By incorporating these Lagrangean multipliers as the link arc weight, the optimization-based heuristics are proposed to get energy-efficient data aggregation tree with better resource (channel and radio) utilization. From the computational experiments, the proposed optimization-based approach is superior to existing heuristics under all tested cases

EFFECTS OF A FOUR-WEEK ELASTIC TUBING TRAINING ON THROWING PERFORMANCE IN YOUTH BASEBALL PLAYERS

The purpose of this study was to identify the training effect of elastic tubing exercise in youth baseball players on throwing velocity, throwing accuracy and parameters of pitching mechanics. Participants (n=24) aging from 13 to 16 years old were equally and randomly allocated to the training group and control group. A four-week elastic tubing training was conducted in the training group. After four weeks of training, throwing velocity in training group improved significantly. Kinematics changed significantly mainly in parameters of shoulder and elbow. Since the muscular strength did not improve significantly, we may attribute the improvement to the effect of neural adaptation mechanism caused by training

A Novel Energy-Efficient MAC Aware Data Aggregation Routing in Wireless Sensor Networks#

Embedding data-aggregation capabilities into sensor nodes of wireless networks could save energy by reducing redundant data flow transmissions. Existing research describes the construction of data aggregation trees to maximize data aggregation times in order to reduce data transmission of redundant data. However, aggregation of more nodes on the same node will incur significant collisions. These MAC (Media Access Control) layer collisions introduce additional data retransmissions that could jeopardize the advantages of data aggregation. This paper is the first to consider the energy consumption tradeoffs between data aggregation and retransmissions in a wireless sensor network. By using the existing CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) MAC protocol, the retransmission energy consumption function is well formulated. This paper proposes a novel non-linear mathematical formulation, whose function is to minimize the total energy consumption of data transmission subject to data aggregation trees and data retransmissions. This solution approach is based on Lagrangean relaxation, in conjunction with optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithms could construct MAC aware data aggregation trees that are up to 59% more energy efficient than existing data aggregation algorithms

Delay QoS and MAC Aware Energy-Efficient Data-Aggregation Routing in Wireless Sensor Networks

By eliminating redundant data flows, data aggregation capabilities in wireless sensor networks could transmit less data to reduce the total energy consumption. However, additional data collisions incur extra data retransmissions. These data retransmissions not only increase the system energy consumption, but also increase link transmission delays. The decision of when and where to aggregate data depends on the trade-off between data aggregation and data retransmission. The challenges of this problem need to address the routing (layer 3) and the MAC layer retransmissions (layer 2) at the same time to identify energy-efficient data-aggregation routing assignments, and in the meantime to meet the delay QoS. In this paper, for the first time, we study this cross-layer design problem by using optimization-based heuristics. We first model this problem as a non-convex mathematical programming problem where the objective is to minimize the total energy consumption subject to the data aggregation tree and the delay QoS constraints. The objective function includes the energy in the transmission mode (data transmissions and data retransmissions) and the energy in the idle mode (to wait for data from downstream nodes in the data aggregation tree). The proposed solution approach is based on Lagrangean relaxation in conjunction with a number of optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithm outperforms existing heuristics that do not take MAC layer retransmissions and the energy consumption in the idle mode into account

Decoupled Contrastive Learning

Contrastive learning (CL) is one of the most successful paradigms for self-supervised learning (SSL). In a principled way, it considers two augmented "views" of the same image as positive to be pulled closer, and all other images as negative to be pushed further apart. However, behind the impressive success of CL-based techniques, their formulation often relies on heavy-computation settings, including large sample batches, extensive training epochs, etc. We are thus motivated to tackle these issues and establish a simple, efficient, yet competitive baseline of contrastive learning. Specifically, we identify, from theoretical and empirical studies, a noticeable negative-positive-coupling (NPC) effect in the widely used InfoNCE loss, leading to unsuitable learning efficiency concerning the batch size. By removing the NPC effect, we propose decoupled contrastive learning (DCL) loss, which removes the positive term from the denominator and significantly improves the learning efficiency. DCL achieves competitive performance with less sensitivity to sub-optimal hyperparameters, requiring neither large batches in SimCLR, momentum encoding in MoCo, or large epochs. We demonstrate with various benchmarks while manifesting robustness as much less sensitive to suboptimal hyperparameters. Notably, SimCLR with DCL achieves 68.2% ImageNet-1K top-1 accuracy using batch size 256 within 200 epochs pre-training, outperforming its SimCLR baseline by 6.4%. Further, DCL can be combined with the SOTA contrastive learning method, NNCLR, to achieve 72.3% ImageNet-1K top-1 accuracy with 512 batch size in 400 epochs, which represents a new SOTA in contrastive learning. We believe DCL provides a valuable baseline for future contrastive SSL studies.Comment: Accepted by ECCV202

Assessing Computational Amino Acid β-Turn Propensities with a Phage-Displayed Combinatorial Library and Directed Evolution

SummaryStructure propensities of amino acids are important determinants in guiding proteins' local and global structure formation. We constructed a phage display library—a hexa-HIS tag upstream of a CXXC (X stands for any of the 20 natural amino acids) motif appending N-terminal to the minor capsid protein pIII of M13KE filamentous phage—and developed a novel directed-evolution procedure to select for amino acid sequences forming increasingly stable β-turns in the disulfide-bridged CXXC motif. The sequences that emerged from the directed-evolution cycles were in good agreement with type II β-turn propensities derived from surveys of known protein structures, in particular, Pro-Gly forming a type II β-turn. The agreement strongly supported the notion that β-turn formation plays an active role in initiating local structure folding in proteins

Measurements of Foot Arch in Standing, Level Walking, Vertical Jump and Sprint Start

Foot arch is important for force transfer and shock absorption in impact sports. The purposes of this study were to measure the height of foot arch in static standing and dynamic activities, and to compare the difference of foot arch between level walkin

Impact of active layer design on InGaN radiative recombination coefficient and LED performance

The relative roles of radiative and nonradiative processes and the polarization field on the light emission from blue (∼425 nm) InGaN light emitting diodes (LEDs) have been studied. Single and multiple double heterostructure (DH) designs have been investigated with multiple DH structures showing improved efficiencies. Experimental results supported by numerical simulations of injection dependent electron and hole wavefunction overlap and the corresponding radiative recombination coefficients suggest that increasing the effective active region thickness by employing multiple InGaN DH structures separated by thin and low barriers is promising for LEDs with high efficiency retention at high injection. The use of thin and low barriers is crucial to enhance carrier transport across the active region. Although increasing the single DH thickness from 3 to 6 nm improves the peak external quantum efficiency (EQE) by nearly 3.6 times due to increased density of states and increased emitting volume, the internal quantum efficiency (IQE) suffers a loss of nearly 30%. A further increase in the DH thickness to 9 and 11 nm results in a significantly slower rate of increase of EQE with current injection and lower peak EQE values presumably due to degradation of the InGaN material quality and reduced electron-hole spatial overlap. Increasing the number of 3 nm DH active regions separated by thin (3 nm) In0.06Ga0.94N barriers improves EQE, while maintaining high IQE (above 95% at a carrier concentration of 1018 cm−3) and showing negligible EQE degradation up to 550 A/cm2 in 400 × 400 μm2 devices due to increased emitting volume and high radiative recombination coefficients and high IQE. Time-resolved photoluminescence measurements revealed higher radiative recombination rates with increasing excitation due to screening of the internal field and enhanced electron and hole overlap at higher injection levels. To shed light on the experimental observations, the effect of free-carrier screening on the polarization field at different injection levels and the resulting impact on the quantum efficiency were investigated by numerical simulations