Optimal Scheduling and Power Allocation for Two-Hop Energy Harvesting Communication Systems

Energy harvesting (EH) has recently emerged as a promising technique for green communications. To realize its potential, communication protocols need to be redesigned to combat the randomness of the harvested energy. In this paper, we investigate how to apply relaying to improve the short-term performance of EH communication systems. With an EH source and a non-EH half-duplex relay, we consider two different design objectives: 1) short-term throughput maximization; and 2) transmission completion time minimization. Both problems are joint scheduling and power allocation problems, rendered quite challenging by the half-duplex constraint at the relay. A key finding is that directional water-filling (DWF), which is the optimal power allocation algorithm for the single-hop EH system, can serve as guideline for the design of two-hop communication systems, as it not only determines the value of the optimal performance, but also forms the basis to derive optimal solutions for both design problems. Based on a relaxed energy profile along with the DWF algorithm, we derive key properties of the optimal solutions for both problems and thereafter propose efficient algorithms. Simulation results will show that both scheduling and power allocation optimizations are necessary in two-hop EH communication systems.Comment: Submitted to IEEE Transaction on Wireless Communicatio

Training Optimization for Energy Harvesting Communication Systems

Energy harvesting (EH) has recently emerged as an effective way to solve the lifetime challenge of wireless sensor networks, as it can continuously harvest energy from the environment. Unfortunately, it is challenging to guarantee a satisfactory short-term performance in EH communication systems because the harvested energy is sporadic. In this paper, we consider the channel training optimization problem in EH communication systems, i.e., how to obtain accurate channel state information to improve the communication performance. In contrast to conventional communication systems, the optimization of the training power and training period in EH communication systems is a coupled problem, which makes such optimization very challenging. We shall formulate the optimal training design problem for EH communication systems, and propose two solutions that adaptively adjust the training period and power based on either the instantaneous energy profile or the average energy harvesting rate. Numerical and simulation results will show that training optimization is important in EH communication systems. In particular, it will be shown that for short block lengths, training optimization is critical. In contrast, for long block lengths, the optimal training period is not too sensitive to the value of the block length nor to the energy profile. Therefore, a properly selected fixed training period value can be used.Comment: 6 pages, 5 figures, Globecom 201

Multi-frame Image Super-resolution Reconstruction Using Multi-grained Cascade Forest

Super-resolution image reconstruction utilizes two algorithms, where one is for single-frame image reconstruction, and the other is for multi-frame image reconstruction. Single-frame image reconstruction generally takes the first degradation and is followed by reconstruction, which essentially creates a problem of insufficient characterization. Multi-frame images provide additional information for image reconstruction relative to single frame images due to the slight differences between sequential frames. However, the existing super-resolution algorithm for multi-frame images do not take advantage of this key factor, either because of loose structure and complexity, or because the individual frames are restored poorly. This paper proposes a new SR reconstruction algorithm for images using Multi-grained Cascade Forest. Multi-frame image reconstruction is processed sequentially. Firstly, the image registration algorithm uses a convolutional neural network to register low-resolution image sequences, and then the images are reconstructed after registration by the Multi-grained Cascade Forest reconstruction algorithm. Finally, the reconstructed images are fused. The optimal algorithm is selected for each step  to get the most out of the details and tightly connect the internal logic of each sequential step.This novel approach proposed in this paper, in which the depth of the cascade forest is procedurally generated for recovered images, rather than being a constant. After training each layer, the recovered image is automatically evaluated, and new layers are constructed for training until an optimal restored image is obtained. Experiments show that this method improves the quality of image reconstruction while preserving the details of the image

Binary Classification with Positive Labeling Sources

To create a large amount of training labels for machine learning models effectively and efficiently, researchers have turned to Weak Supervision (WS), which uses programmatic labeling sources rather than manual annotation. Existing works of WS for binary classification typically assume the presence of labeling sources that are able to assign both positive and negative labels to data in roughly balanced proportions. However, for many tasks of interest where there is a minority positive class, negative examples could be too diverse for developers to generate indicative labeling sources. Thus, in this work, we study the application of WS on binary classification tasks with positive labeling sources only. We propose WEAPO, a simple yet competitive WS method for producing training labels without negative labeling sources. On 10 benchmark datasets, we show WEAPO achieves the highest averaged performance in terms of both the quality of synthesized labels and the performance of the final classifier supervised with these labels. We incorporated the implementation of \method into WRENCH, an existing benchmarking platform.Comment: CIKM 2022 (short

Pattern formation of a pathway-based diffusion model: linear stability analysis and an asymptotic preserving method

We investigate the linear stability analysis of a pathway-based diffusion model (PBDM), which characterizes the dynamics of the engineered Escherichia coli populations [X. Xue and C. Xue and M. Tang, P LoS Computational Biology, 14 (2018), pp. e1006178]. This stability analysis considers small perturbations of the density and chemical concentration around two non-trivial steady states, and the linearized equations are transformed into a generalized eigenvalue problem. By formal analysis, when the internal variable responds to the outside signal fast enough, the PBDM converges to an anisotropic diffusion model, for which the probability density distribution in the internal variable becomes a delta function. We introduce an asymptotic preserving (AP) scheme for the PBDM that converges to a stable limit scheme consistent with the anisotropic diffusion model. Further numerical simulations demonstrate the theoretical results of linear stability analysis, i.e., the pattern formation, and the convergence of the AP scheme

DNN-Based ADNMPC of an Industrial Pickling Cold-Rolled Titanium Process via Field Enhancement Heat Exchange

The dynamic neural network based adaptive direct nonlinear model predictive control is designed to control an industrial microwave heating pickling cold-rolled titanium process. The identifier of the direct adaptive nonlinear model identification and the controller of the adaptive nonlinear model predictive control are designed based on series-parallel dynamic neural network training by RLS algorithm with variable incremental factor, gain, and forgetting factor. These identifier and controller are used to constitute intelligent controller for adjusting the temperature of microwave heating acid. The correctness of the controller structure, the convergence, and feasibility of the control algorithms is tested by system simulation. For a given point tracking, model mismatch simulation results show that the controller can be implemented on the system to track and overcome the mismatch system model. The control model can be achieved to track on pickling solution concentration and temperature of a given reference and overcome the disturbance

Pharmacokinetics and Pharmacodynamics of Once-Daily versus Twice-Daily Raltegravir in Treatment-Naïve HIV-Infected Patients

ABSTRACT QDMRK was a phase III clinical trial of raltegravir given once daily (QD) (800-mg dose) versus twice daily (BID) (400 mg per dose), each in combination with once-daily coformulated tenofovir-emtricitabine, in treatment-naive HIV-infected patients. Pharmacokinetic (PK) and pharmacokinetic/pharmacodynamic (PK/PD) analyses were conducted using a 2-step approach: individual non-model-based PK parameters from observed sparse concentration data were determined, followed by statistical analysis of potential relationships between PK and efficacy response parameters after 48 weeks of treatment. Sparse PK sampling was performed for all patients (QD, n = 380; BID, n = 384); selected sites performed an intensive PK evaluation at week 4 (QD, n = 22; BID, n = 20). In the intensive PK subgroup, daily exposures (area under the concentration-time curve from 0 to 24 h [AUC 0–24 ]) were similar between the two regimens, but patients on 800 mg QD experienced ∼4-fold-higher maximum drug concentration in plasma ( C max ) values and ∼6-fold-lower trough drug concentration ( C trough ) values than those on 400 mg BID. Geometric mean (GM) C trough values were similarly lower in the sparse PK analysis. With BID dosing, there was no indication of any significant PK/PD association over the range of tested PK parameters. With QD dosing, C trough values correlated with the likelihood of virologic response. Failure to achieve an HIV RNA level of <50 copies/ml appeared predominantly at high baseline HIV RNA levels in both treatment arms and was associated with lower values of GM C trough in the 800-mg-QD arm, though other possible drivers of efficacy, such as time above a threshold concentration, could not be evaluated due to the sparse sampling scheme. Together, these findings emphasize the importance of the shape of the plasma concentration-versus-time curve for long-term efficacy