Optimization of the overall success probability of the energy harvesting cognitive wireless sensor networks

Wireless energy harvesting can improve the performance of cognitive wireless sensor networks (WSNs). This paper considers radio frequency (RF) energy harvesting from transmissions in the primary spectrum for cognitive WSNs. The overall success probability of the energy harvesting cognitive WSN depends on the transmission success probability and energy success probability. Using the tools from stochastic geometry, we show that the overall success probability can be optimized with respect to: 1) transmit power of the sensors; 2) transmit power of the primary transmitters; and 3) spatial density of the primary transmitters. In this context, an optimization algorithm is proposed to maximize the overall success probability of the WSNs. Simulation results show that the overall success probability and the throughput of the WSN can be significantly improved by optimizing the aforementioned three parameters. As RF energy harvesting can also be performed indoors, hence, our solution can be directly applied to the cognitive WSNs that are installed in smart buildings

Self Attention with Temporal Prior: Can We Learn More from Arrow of Time?

Many of diverse phenomena in nature often inherently encode both short and long term temporal dependencies, short term dependencies especially resulting from the direction of flow of time. In this respect, we discovered experimental evidences suggesting that {\it interrelations} of these events are higher for closer time stamps. However, to be able for attention based models to learn these regularities in short term dependencies, it requires large amounts of data which are often infeasible. This is due to the reason that, while they are good at learning piece wised temporal dependencies, attention based models lack structures that encode biases in time series. As a resolution, we propose a simple and efficient method that enables attention layers to better encode short term temporal bias of these data sets by applying learnable, adaptive kernels directly to the attention matrices. For the experiments, we chose various prediction tasks using Electronic Health Records (EHR) data sets since they are great examples that have underlying long and short term temporal dependencies. The results of our experiments show exceptional classification results compared to best performing models on most of the task and data sets

Magnon topology and thermal Hall effect in trimerized triangular lattice antiferromagnet

The non-trivial magnon band topology and its consequent responses have been extensively studied in two-dimensional magnetisms. However, the triangular lattice antiferromagnet (TLAF), the best-known frustrated two-dimensional magnet, has received less attention than the closely related Kagome system, because of the spin-chirality cancellation in the umbrella ground state of the undistorted TLAF. In this work, we study the band topology and the thermal Hall effect (THE) of the TLAF with (anti-)trimerization distortion under the external perpendicular magnetic field using the linearized spin wave theory. We show that the spin-chirality cancellation is removed in such case, giving rise to the non-trivial magnon band topology and the finite THE. Moreover, the magnon bands exhibit band topology transitions tuned by the magnetic field. We demonstrate that such transitions are accompanied by the logarithmic divergence of the first derivative of the thermal Hall conductivity. Finally, we examine the above consequences by calculating the THE in the hexagonal manganite YMnO$_3$, well known to have anti-trimerization.Comment: 6 + 7 pages, 3 + 5 figures, 0 + 1 table; Journal reference adde

Energy harvesting non-orthogonal multiple access system with multi-antenna relay and base station

In this paper, we consider downlink non-orthogonal multiple access cooperative communication system. The base station (BS) serves two types of users, which are named relay user (RU) and far user (FU). The BS and RU are equipped with multiple transmit antennas. The RU harvests energy from the BS transmissions to perform the relaying operation for the FU. We have considered 1) amplify-forward; 2) decode-forward; and 3) quantize-map-forward relaying protocols at the RU. As the BS and RU have multiple antennas, therefore we consider 1) beamforming and 2) random antenna selection strategies at the BS and RU. Closed form expressions for the outage probability are provided for the aforementioned relay protocols and antenna strategies. Further, we show that for certain data rate range of the relay and FU the quantize-map-forward relaying protocol can perform better than the other two relaying protocols

IT Capabilities, Process-Oriented Dynamic Capabilities, and Firm Financial Performance

More and more publications are highlighting the value of IT in affecting business processes. Recognizing firm-level dynamic capabilities as key to improved firm performance, our work examines and empirically tests the influencing relationships among IT capabilities (IT personnel expertise, IT infrastructure flexibility, and IT management capabilities), process-oriented dynamic capabilities, and financial performance. Process-oriented dynamic capabilities are defined as a firm’s ability to change (improve, adapt, or reconfigure) a business process better than the competition in terms of integrating activities, reducing cost, and capitalizing on business intelligence/learning. They encompass a broad category of changes in the firm’s processes, ranging from continual adjustments and improvements to radical one-time alterations. Although the majority of changes may be incremental, a firm’s capacity for timely changes also implies its readiness to execute radical alterations when the need arises. Grounded on the theoretical position, we propose a research model and gather a survey data set through a rigorous process that retains research validity. From the analysis of the survey data, we find an important route of causality, as follows: IT personnel expertise -\u3e IT management capabilities -\u3e IT infrastructure flexibility -\u3e process-oriented dynamic capabilities -\u3e financial performance. Based on this finding, we discuss the main contributions of our study in terms of the strategic role of IT in enhancing firm performance

Self-attention with temporal prior: can we learn more from the arrow of time?

Many diverse phenomena in nature often inherently encode both short- and long-term temporal dependencies, which especially result from the direction of the flow of time. In this respect, we discovered experimental evidence suggesting that interrelations of these events are higher for closer time stamps. However, to be able for attention-based models to learn these regularities in short-term dependencies, it requires large amounts of data, which are often infeasible. This is because, while they are good at learning piece-wise temporal dependencies, attention-based models lack structures that encode biases in time series. As a resolution, we propose a simple and efficient method that enables attention layers to better encode the short-term temporal bias of these data sets by applying learnable, adaptive kernels directly to the attention matrices. We chose various prediction tasks for the experiments using Electronic Health Records (EHR) data sets since they are great examples with underlying long- and short-term temporal dependencies. Our experiments show exceptional classification results compared to best-performing models on most tasks and data sets

Reliable, Efficient, and Power Optimized Control-Channel Selection Scheme for Cognitive Radio Networks

This paper presents a centralized control-channel selection scheme for cognitive radio networks (CRNs) by exploiting the variation in the spectrum across capacity, occupancy, and error rate. We address the fundamental challenges in the design of the control-channel for CRNs: (1) random licensed users (LUs) activity and (2) the economical and vulnerability concerns for a dedicated control-channel. We develop a knapsack problem (KP) based reliable, efficient, and power optimized (REPO) control-channel selection scheme with an optimal data rate, bit error rate (BER), and idle time. Moreover, we introduce the concept of the backup channels in the context of control-channel selection, which assists the CRs to quickly move on to the next stable channel in order to cater for the sudden appearance of LUs. Based on the KP and its dynamic programming solution, simulation results show that the proposed scheme is highly adaptable and resilient to random LU activity. The REPO scheme reduces collisions with the LUs, minimizes the alternate channel selection time, and reduces the bit error rate (BER). Moreover, it reduces the power consumed during channel switching and provides a performance, that is, competitive with those schemes that are using a static control-channel for the management of control traffic in CRNs

Leveraging Edge Computing for Minimizing Base Station Energy Consumption in Multi-Cell (N)OMA Downlink Systems

In this paper, we suggest that the combination of edge computing in the form of data compression with communication at the base stations (BSs) for transmissions to their associated multiple downlink users (DUs) is advantageous for minimizing the total energy consumption. We assume that the individual DUs have minimum rate requirements along with outage probability constraints. Then, we set the resource allocation to minimize the total energy consumption (the sum of compression energy and transmission energy) for the BSs with orthogonal and non-orthogonal multiple access (OMA and NOMA) transmission schemes, while taking into account the quality of service (QoS) constraints of individual DUs. The formulated optimization problems are non-convex and difficult to solve. Therefore, the energy minimization problems are decomposed into smaller problems and low-complexity solutions are obtained. Specifically, for the single-cell scenario we use Lagrange duality theory and Karush–Kuhn–Tucker conditions to obtain closed-form global optimal solutions. It is revealed that the optimal resource allocation at the BS is determined by a DU-specific parameter, named path-loss factor. This finding is then used to obtain the optimal resource allocation for the multi-cell scenario and two iterative algorithms, with guaranteed convergence, are proposed to solve the energy minimization problems for NOMA and OMA transmission schemes. Next, the effectiveness of the proposed approaches are demonstrated with the help of simulation results. It is found that the BSs can exploit the flexibilities in minimum rate requirements and outage probability requirements, and compress the data of individual DUs before transmission in an attempt toward reducing the total consumed energy.Peer reviewe

Uterine and placental expression of TRPV6 gene is regulated via progesterone receptor- or estrogen receptor-mediated pathways during pregnancy in rodents

Transient receptor potential cation channel, subfamily V, member 6 (TRPV6) is an epithelial Ca2+ channel protein expressed in calcium absorbing organs. In the present study, we investigated the expression and regulation of uterine and placental TRPV6 during gestation in rodents. Uterine TRPV6 peaked at pregnancy day (P) 0.5, P5.5 and, P13.5 and was detected in uterine epithelium and glands of rats, while placental TRPV6 mRNA levels increased in mid-gestation. Uterine and placental TRPV6 mRNA levels in rats appear to cyclically change during pregnancy, suggesting that TRPV6 may participate in the implantation process. In addition, uterine TRPV6 mRNA is only expressed in placenta-unattached areas of the uterus, and uterine TRPV6 immunoreactivity was observed in luminal and glandular epithelial cells. In the placenta, TRPV6 was detected in the labyrinth and spongy zone. These results may indicate that TRPV6 has at least two functions: implantation of the embryo and maintenance of pregnancy. To investigate the pathway(s) mediating TRPV6 expression in rodents, anti-steroid hormone antagonists were injected prior to maximal TRPV6 expression. In rats, TRPV6 expression was reduced by RU486 (an anti-progesterone) through progesterone receptors, and ICI 182,780 (an anti-estrogen) blocked TRPV6 expression via estrogen receptors in mice. The juxtaposition of uterine and placental TRPV6 expressed in these tissues supports the notion that TRPV6 participates in transferring calcium ions between the maternal and fetal compartments. Taken together, TRPV6 gene may function as a key element in controlling calcium transport in the uterus between the embryo and the placenta during pregnancy