In this paper, A CMOS voltage reference (CVR) with two-stage self-biased stacked diode connected MOS transistors (SDMTs) which actively compensates for process, voltage, and temperature (PVT) variations via a tandem differential structure (TDS) is proposed. The SDMTs core biased by new pseudo cascode current mirror guarantees better suppression against supply change and generates two reference voltages. The TDS, composed of two tandem NMOS transistors, serves as an output stage, differentially processing these voltages and compensating the final reference against PVT variations. Thus, the deviation of final reference voltage from PVT change is largely reduced. The proposed CVR is fabricated in a 0.18-$\mu $m CMOS process occupying a total area of$0.0048~\mathbf {mm^{2} } $. Measurement results from 7 chips demonstrate that the design can achieve an average temperature coefficient of 67 ppm/° C from −40° C to 140° C without trimming networks. Line sensitivity and power supply rejection ratio are 0.009 %/V with a supply range of 1.3V to 2.5 V and -83 dB at 100 Hz. 1% settling time only takes 0.28ms. The average reference voltage is 294 mV.

The vertically transverse isotropic (VTI) reservoirs subjected to horizontal in situ stress are frequently encountered in the subsurface, which can be approximately treated as an orthorhombic medium in the framework of acoustoelasticity. However, the seismic estimation for anisotropy parameters in such stress-induced reservoirs is still poorly studied. To address this issue, we derive a linearized reflection coefficient equation in stress-induced orthorhombic media by means of the theories of acoustoelasticity and elastic inverse scattering. The acoustoelasticity theory is utilized to characterize the effective elastic stiffness tensor in a stress-induced orthorhombic medium. The introduction of two dimensionless stress-induced anisotropy (SIA) parameters eliminates the need for third-order elastic constants (3oECs). Then, the stiffness perturbation is presented under the weak-anisotropy hypothesis and is substituted into the scattering function to derive the linearized reflection coefficient equation in stress-induced orthorhombic media. The feasibility of our reflection coefficient equation within the range of moderate stress (or moderate SIA) is confirmed by comparing it to the exact solution. Incorporating the wavelet effect, our reflection coefficient equation as a forward operator is utilized to establish a step-wise Bayesian inversion approach to estimate the anisotropy parameters. Specifically, the SIA parameters are inverted from the amplitude differences in seismic data at different azimuths in the first step. Next, the obtained parameters as the prior dataset are input into the second-step procedure to predict the VTI parameters with partial angle-stacked seismic data. Synthetic and field tests illustrate the robustness and effectiveness of our approach.

With the continuous development of oil and gas exploration, anisotropic medium has become an important target of oil and gas exploration. Both anisotropy and wave-induced fluid flow have significant influence on fluid identification, but the existing fluid identification methods cannot concurrently consider the impacts of medium anisotropy and wave-induced fluid flow. Therefore, to improve the fluid detection accuracy in anisotropic medium, an anisotropic media solid-liquid decoupling fluid factor with squirt flow effect suitable for the transversely isotropic media with a horizontal symmetry axis (HTI) is constructed based on the theory of rock physics. The fluid sensitivity analysis indicates that the new fluid factor exhibits the highest sensitivity for fluid indication. By introducing the nearly constant Q model to account for viscoelasticity, a reflectivity equation in terms of the new anisotropic media solid-liquid decoupling fluid factor incorporating the squirt flow is derived. Subsequently, a prestack seismic frequency-dependent amplitude variation with angle and azimuth (AVAZ) inversion method is developed. The inversion method takes advantage of the information of offset, azimuth, and frequency contained in seismic data. Synthetic and field examples illustrate the reliability and stability of the proposed prestack seismic frequency-dependent AVAZ inversion method in estimating the new anisotropic media solid-liquid decoupling fluid factor. Our method can serve as a complementary approach to enhance the accuracy of fluid detection in anisotropic reservoirs.

In wireless visual sensor networks, the generation and transmission of huge amounts of image data consume much energy of sensor nodes (SNs), and their routing and processing take quite a long time. It is of great importance to shorten event reporting delay (ERD) and prolong network lifetime, which can be achieved by the appropriate deployment of edge nodes (ENs) that can not only collect but also process data. This work investigates how to jointly optimize SN deployment, EN deployment, data routing, and data offloading to minimize the number of deployed SNs, the number of deployed ENs, and ERD and maximize network lifetime. We formulate this problem as a mixed-integer nonlinear program and propose a multiobjective differential evolution algorithm to solve it. A large number of simulation results demonstrate that it can deliver a more accurate Pareto set than the nondominated sorting genetic algorithm III.

Due to the generation and transmission of video data, the high energy consumption rates of wireless nodes result in short lifetime of a visual sensor network, which has been commonly deployed in 3D space. In many applications, lots of targets need to be covered, but the budgets of both deployment and maintenance are limited. Therefore, how to maximize total weighted target coverage while prolonging network lifetime is of great significance. This work investigates the maximum weighted target coverage deployment of a rechargeable visual sensor network charged by an unmanned aerial vehicle (UAV) in 3D space, which should ensure network permanence and satisfy the budgets of both deployment and maintenance. In this problem, since UAV has a limited energy capacity, it is allowed to return to the depot for energy replenishment more than once in each charging period of wireless nodes so as to increase target coverage. After formulating this problem as a mixed integer nonlinear program, this work proposes a greedy heuristic and a particle swarm optimizer to approximately solve this NP-hard problem. Extensive simulation results reveal that the latter can deliver a better solution despite consuming more time than the former.

Full-view coverage realized by camera sensor networks (CSNs) is highly demanded for monitoring and recognizing objects appearing at target points. However, it aggravates the energy shortage in CSNs as caused by the need to generate and process much sensed data. Undoubtedly, enabling CSN nodes to be rechargeable and harvest energy from their surroundings is an effective method to overcome the energy limitation of a CSN and ensuring its perpetual operation. Moreover, using rechargeable nodes can avoid the replacement of batteries, and thus can reduce network maintenance cost. In this article, we investigate how to design and deploy a rechargeable CSN with the fewest nodes to achieve full-view coverage of all target points while guaranteeing its connectivity and perpetual operation. We first formulate the problem as an integer linear program and prove its NP-hardness, and then propose a greedy heuristic and a differential evolution algorithm to solve it. Extensive simulation results reveal that the latter is able to achieve a larger success rate and higher solution quality but spends more time than the former.

Mobile-edge computing provides an effective approach to reducing the workload of smart devices and the network delay induced by data transfer through deploying computational resources in the proximity of the devices. In a mobile-edge computing system, it is of great importance to improve the quality of experience of users and reduce the deployment cost for service providers. This article investigates a joint cloudlet deployment and task offloading problem with the objectives of minimizing energy consumption and task response delay of users and the number of deployed cloudlets. Since it is a multiobjective optimization problem, a set of tradeoff solutions ought to be found. After formulating this problem as a mixed-integer nonlinear program and proving its NP-completeness, we propose a modified guided population archive whale optimization algorithm to solve it. The superiority of our devised algorithm over other methods is confirmed through extensive simulations.

We present and experimentally validate two minimal compact memristive models for spiking neuronal signal generation using commercially available low-cost components. The first neuron model is called the Memristive Integrate-and-Fire (MIF) model, for neuronal signaling with two voltage levels: the spike-peak, and the rest-potential. The second model MIF2 is also presented, which promotes local adaptation by accounting for a third refractory voltage level during hyperpolarization. We show both compact models are minimal in terms of the number of circuit elements and integration area. Using the MIF and MIF2 models, we postulate the design of a memristive solid-state brain with an estimation of its surface area and power consumption. Analytical projections show that a memristive solid-state brain could be realized within (i) the surface area of the median human brain, 2,400cm2, (ii) the same volume of the median human brain, and (iii) a total power budget of approximately 20 W using a 3.5 nm technology. Distinct from the past decade of memristive neuron literature, our benchmarks are attained using generic commercially available memristors that are reproducible using off-the-shelf components. We expect this work can promote more experimental demonstrations of memristive circuits that do not rely on prohibitively expensive fabrication processes.

With the advance on wireless energy transfer, it is reliable and favorable to power a directional sensor network (DSN) by wireless charging. This paper investigates how to deploy a rechargeable DSN using a mobile charger (MC) with the least number of nodes for perpetual target coverage subject to the limited sensing angles of directional sensors and limited energy capacity of the MC. We prove that the proposed problem is NP-hard. Next, we formulate it as a mixed integer nonlinear program to determine the smallest subset of sites to place sensors and the working directions of sensing nodes. Then, we propose two algorithms, i.e., an energy-bounded minimum-cost deployment and a relaxed-linear-program and repairing-based deployment. The simulation results demonstrate that the latter has higher success rate and solution quality than the former at the expense of more computational time.