Constrained Codes for Joint Energy and Information Transfer

In various wireless systems, such as sensor RFID networks and body area networks with implantable devices, the transmitted signals are simultaneously used both for information transmission and for energy transfer. In order to satisfy the conflicting requirements on information and energy transfer, this paper proposes the use of constrained run-length limited (RLL) codes in lieu of conventional unconstrained (i.e., random-like) capacity-achieving codes. The receiver's energy utilization requirements are modeled stochastically, and constraints are imposed on the probabilities of battery underflow and overflow at the receiver. It is demonstrated that the codewords' structure afforded by the use of constrained codes enables the transmission strategy to be better adjusted to the receiver's energy utilization pattern, as compared to classical unstructured codes. As a result, constrained codes allow a wider range of trade-offs between the rate of information transmission and the performance of energy transfer to be achieved.Comment: 27 pages, 14 figures, Submitted Submitted in IEEE Transactions on Communication

Interactive Joint Transfer of Energy and Information

In some communication networks, such as passive RFID systems, the energy used to transfer information between a sender and a recipient can be reused for successive communication tasks. In fact, from known results in physics, any system that exchanges information via the transfer of given physical resources, such as radio waves, particles and qubits, can conceivably reuse, at least part, of the received resources. This paper aims at illustrating some of the new challenges that arise in the design of communication networks in which the signals exchanged by the nodes carry both information and energy. To this end, a baseline two-way communication system is considered in which two nodes communicate in an interactive fashion. In the system, a node can either send an "on" symbol (or "1"), which costs one unit of energy, or an "off" signal (or "0"), which does not require any energy expenditure. Upon reception of a "1" signal, the recipient node "harvests", with some probability, the energy contained in the signal and stores it for future communication tasks. Inner and outer bounds on the achievable rates are derived. Numerical results demonstrate the effectiveness of the proposed strategies and illustrate some key design insights.Comment: 29 pages, 11 figures, Submitted in IEEE Transactions on Communications. arXiv admin note: substantial text overlap with arXiv:1204.192

Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction

Tracking congestion throughout the network road is a critical component of Intelligent transportation network management systems. Understanding how the traffic flows and short-term prediction of congestion occurrence due to rush-hour or incidents can be beneficial to such systems to effectively manage and direct the traffic to the most appropriate detours. Many of the current traffic flow prediction systems are designed by utilizing a central processing component where the prediction is carried out through aggregation of the information gathered from all measuring stations. However, centralized systems are not scalable and fail provide real-time feedback to the system whereas in a decentralized scheme, each node is responsible to predict its own short-term congestion based on the local current measurements in neighboring nodes. We propose a decentralized deep learning-based method where each node accurately predicts its own congestion state in real-time based on the congestion state of the neighboring stations. Moreover, historical data from the deployment site is not required, which makes the proposed method more suitable for newly installed stations. In order to achieve higher performance, we introduce a regularized Euclidean loss function that favors high congestion samples over low congestion samples to avoid the impact of the unbalanced training dataset. A novel dataset for this purpose is designed based on the traffic data obtained from traffic control stations in northern California. Extensive experiments conducted on the designed benchmark reflect a successful congestion prediction

The Symmetric Minimal Surface Equation

For positive functions $u\in C^{2}(\Omega)$, where $\Omega$ is an open subset of $\mathbb{R}^{n}$, the Symmetric Minimal Surface Equation (SME), is $\sum_{i=1}^{n}D_{i}\bigl(\frac{D_{i}u}{\sqrt{1+|Du|^{2}}}\bigr)=\frac{m-1}{u\sqrt{1+|Du|^{2}}}$. Geometrically, the SME expresses the fact that the ``symmetric graph'' $SG(u)$, defined by $SG(u)=\bigl\{(x,\xi)\in \Omega\times\mathbb{R}^{m}:|\xi|=u(x)\bigr\}$, is a minimal (i.e.\ zero mean curvature) hypersurface in $\Omega\times\mathbb{R}^{m}$. A function $u\in C^{1}(\Omega)$ is said to be a singular solution if $u^{-1}\{0\}\neq \emptyset$, and if $u=\lim_{j\to\infty}u_{j}$, uniformly on each compact subset of $\Omega$, where each $u_{j}$ is a positive $C^{2}(\Omega)$ solution of the SME. The present paper develops are theory of singular solutions of the SME, including existence, H\"older and Lipschitz estimates for bounded solutions, and a compactness and regularity theory. We also prove that the singular set $u^{-1}{\{0\}}$ is codimension at most 2

Joint Interference Alignment and Bi-Directional Scheduling for MIMO Two-Way Multi-Link Networks

By means of the emerging technique of dynamic Time Division Duplex (TDD), the switching point between uplink and downlink transmissions can be optimized across a multi-cell system in order to reduce the impact of inter-cell interference. It has been recently recognized that optimizing also the order in which uplink and downlink transmissions, or more generally the two directions of a two-way link, are scheduled can lead to significant benefits in terms of interference reduction. In this work, the optimization of bi-directional scheduling is investigated in conjunction with the design of linear precoding and equalization for a general multi-link MIMO two-way system. A simple algorithm is proposed that performs the joint optimization of the ordering of the transmissions in the two directions of the two-way links and of the linear transceivers, with the aim of minimizing the interference leakage power. Numerical results demonstrate the effectiveness of the proposed strategy.Comment: To be presented at ICC 2015, 6 pages, 7 figure

Determining a piston's top dead center (TDC) in an automobile using installed piezoelectric on a vibrating beam

Smart structures and MEMS are considered important field recently, due to the importance and high capabilities in measurement and the power of reaction and response to changes of the surroundings. This research concerns two fields of practical vision in the automotive industry and discuss the energy recycled industrial vibrations. The goal is to use the PVDF piezoelectric elements in car flywheel for energy harvesting, and compare and replace it with the revolution sensor. This research is totally based on empirical tests data and the result that the use of piezoelectric polymer sensors can harvest energy, was find performing far better than inductive sensors.Keywords: Piezoelectric element; Vibration energy harvesting; smart structures; Revolution sensor; Flywhee

Energy Efficiency Improvement in Surface Mining

This chapter aims to provide an overview of energy efficiency in the mining industry with a particular focus on the role of fuel consumption in hauling operations in mining. Moreover, as the most costly aspect of surface mining with a significant environmental impact, diesel consumption will be investigated in this chapter. This research seeks to develop an advanced data analytics model to estimate the energy efficiency of haul trucks used in surface mines, with the ultimate goal of lowering operating costs. Predicting truck fuel consumption can be accomplished by first identifying the significant factors affecting fuel consumption: total resistance, truck payload, and truck speed. Second, developing a comprehensive analysis framework. This framework involves generating a fitness function from a model of the relationship between fuel consumption and its affecting factors. Third, the model is trained and tested using actual data from large surface mines in Australia, obtained through field research. Finally, an artificial neural network is selected to predict haul truck fuel consumption. The visualized results also clarify the general minimum areas in the plotted fuel consumption graphs. These areas potentially open a new window for researchers to develop optimization models to minimize haul truck fuel consumption in surface mines

Improve Energy Efficiency in Surface Mines Using Artificial Intelligence

This chapter demonstrates the practical application of artificial intelligence (AI) to improve energy efficiency in surface mines. The suggested AI approach has been applied in two different mine sites in Australia and Iran, and the achieved results have been promising. Mobile equipment in mine sites consumes a massive amount of energy, and the main part of this energy is provided by diesel. The critical diesel consumers in surface mines are haul trucks, the huge machines that move mine materials in the mine sites. There are many effective parameters on haul trucks’ fuel consumption. AI models can help mine managers to predict and minimize haul truck energy consumption and consequently reduce the greenhouse gas emission generated by these trucks. This chapter presents a practical and validated AI approach to optimize three key parameters, including truck speed and payload and the total haul road resistance to minimize haul truck fuel consumption in surface mines. The results of the developed AI model for two mine sites have been presented in this chapter. The model increased the energy efficiency of mostly used trucks in surface mining, Caterpillar 793D and Komatsu HD785. The results show the trucks’ fuel consumption reduction between 9 and 12%

The Effect of Morphine on the Incidence of Postoperative Nausea and Vomiting after Strabismus Surgery with Propofol

Background: Corrective strabismus surgery is associated with moderate pain and a very high incidence of postoperative nausea and vomiting (PONV). In our treatment centers Morphine is used a lot and it’s associated with high incidence of PONV and we have few alternative analgesics. The aim of this study was to compare the incidence of postoperative emesis with intravenous morphine in patients undergoing corrective strabismus surgery with propofol anesthesia with the control group. Methods: In a prospective, double-blind, randomized study, 126 patients who were candidates for strabismus surgery (either sex or any age) were randomly assigned to receive morphine or placebo. None of the patients received any premedication and a standardized anesthesia technique was used for all the patients. The incidence of PONV in patients within 24 hours after the surgery was compared. Results: During 24 hours after strabismus surgery, 29 (46%) patients in the morphine group and 27 (42.9%) in the control group had nausea. The frequency of vomiting was 11 (17.5%) patients in morphine group and 9 (14.3%) in the control group. There was no significant difference between the two groups regarding nausea episodes (P=0.85) and vomiting episodes (P= 0.8). Conclusion: According to the results of this study, morphine does not increase PONV after strabismus surgery