Recycling cellular downlink energy for overlay self-sustainable IoT networks

This paper investigates the self-sustainability of an overlay Internet of Things (IoT) network that relies on harvest- ing energy from a downlink cellular network. Using stochastic geometry and queueing theory, we develop a spatiotemporal model to derive the steady state distribution of the number of packets in the bu ff ers and energy levels in the batteries of IoT devices given that the IoT and cellular communications are allocated disjoint spectrum. Particularly, each IoT device is modeled via a two-dimensional discrete-time Markov Chain (DTMC) that jointly tracks the evolution of data bu ff er and energy battery. In this context, stochastic geometry is used to derive the energy generation at the batteries and the packet transmission probability from bu ff ers taking into account the mutual interference from other active IoT devices. To this end, we show the Pareto-Frontiers of the sustainability region, which defines the network parameters that ensure stable network operation and finite packet delay. The results provide several insights to design self-sustainable IoT networks. Index Terms —Spatiotemporal models, stochastic geometry, queuing theory, energy harvesting, packet transmission success probability, two-dimensional discrete-time Markov chain, sta- bility conditions

On the generalization of the hazard rate twisting-based simulation approach

Estimating the probability that a sum of random variables (RVs) exceeds a given threshold is a well-known challenging problem. A naive Monte Carlo simulation is the standard technique for the estimation of this type of probability. However, this approach is computationally expensive, especially when dealing with rare events. An alternative approach is represented by the use of variance reduction techniques, known for their efficiency in requiring less computations for achieving the same accuracy requirement. Most of these methods have thus far been proposed to deal with specific settings under which the RVs belong to particular classes of distributions. In this paper, we propose a generalization of the well-known hazard rate twisting Importance Sampling-based approach that presents the advantage of being logarithmic efficient for arbitrary sums of RVs. The wide scope of applicability of the proposed method is mainly due to our particular way of selecting the twisting parameter. It is worth observing that this interesting feature is rarely satisfied by variance reduction algorithms whose performances were only proven under some restrictive assumptions. It comes along with a good efficiency, illustrated by some selected simulation results comparing the performance of the proposed method with some existing techniques

Energy efficient target detection through waveform selection for multi-sensor RF sensing based Internet of Things

—In this paper, we explore multi-sensor Radio Frequency (RF) sensing based Internet of Things (IoT) for surveillance applications. RF sensing techniques are the next generation technologies which offer distinct advantages over traditional means of sensing. Traditionally, Energy detection (ED) has been used for surveillance applications due to its low computational complexity. However, ED is unreliable due to high false detection rates. There is a need to develop surveillance strategies which offer reliable target detection rates. In this paper, we have proposed a multi-sensor RF sensing based target detection architecture for IoT. To perform surveillance within IoT, multiple sensor nodes are required to co-exist while performing the desired tasks. Interfering waveforms from the neighbouring sensor nodes have a significant impact on the target detection reliability of IoT. n this paper, a waveform selection criterion has been proposed to optimise the target detection reliability and power consumption within IoT in the presence of interfering waveforms

Application and modeling of GaN FET in 1MHz large signal bandwidth power supply for radio frequency power amplifier

In this paper, implementation and testing of non- commercial GaN HEMT in a simple buck converter for envelope amplifier in ET and EER transmission techn iques has been done. Comparing to the prototypes with commercially available EPC1014 and 1015 GaN HEMTs, experimentally demonstrated power supply provided better thermal management and increased the switching frequency up to 25MHz. 64QAM signal with 1MHz of large signal bandw idth and 10.5dB of Peak to Average Power Ratio was gener ated, using the switching frequency of 20MHz. The obtaine defficiency was 38% including the driving circuit an d the total losses breakdown showed that switching power losses in the HEMT are the dominant ones. In addition to this, some basic physical modeling has been done, in order to provide an insight on the correlation between the electrical characteristics of the GaN HEMT and physical design parameters. This is the first step in the optimization of the HEMT design for this particular application

Antisense pre-treatment increases gene therapy efficacy in dystrophic muscles

In preclinical models for Duchenne muscular dystrophy, dystrophin restoration during adeno-associated virus (AAV)-U7-mediated exon-skipping therapy was shown to decrease drastically after six months in treated muscles. This decline in efficacy is strongly correlated with the loss of the therapeutic AAV genomes, probably due to alterations of the dystrophic myofiber membranes. To improve the membrane integrity of the dystrophic myofibers at the time of AAV-U7 injection, mdx muscles were pre-treated with a single dose of the peptide-phosphorodiamidate morpholino (PPMO) antisense oligonucleotides that induced temporary dystrophin expression at the sarcolemma. The PPMO pre-treatment allowed efficient maintenance of AAV genomes in mdx muscles and enhanced the AAV-U7 therapy effect with a ten-fold increase of the protein level after 6 months. PPMO pre-treatment was also beneficial to AAV-mediated gene therapy with transfer of micro-dystrophin cDNA into muscles. Therefore, avoiding vector genome loss after AAV injection by PPMO pre-treatment would allow efficient long-term restoration of dystrophin and the use of lower and thus safer vector doses for Duchenne patients

Quantification of lentiviral vector copy numbers in individual hematopoietic colony-forming cells shows vector dose-dependent effects on the frequency and level of transduction

Lentiviral vectors are effective tools for gene transfer and integrate variable numbers of proviral DNA copies in variable proportions of cells. The levels of transduction of a cellular population may therefore depend upon experimental parameters affecting the frequency and/or the distribution of vector integration events in this population. Such analysis would require measuring vector copy numbers (VCN) in individual cells. To evaluate the transduction of hematopoietic progenitor cells at the single-cell level, we measured VCN in individual colony-forming cell (CFC) units, using an adapted quantitative PCR (Q-PCR) method. The feasibility, reproducibility and sensitivity of this approach were tested with characterized cell lines carrying known numbers of vector integration. The method was validated by correlating data in CFC with gene expression or with calculated values, and was found to slightly underestimate VCN. In spite of this, such Q-PCR on CFC was useful to compare transduction levels with different infection protocols and different vectors. Increasing the vector concentration and re-iterating the infection were two different strategies that improved transduction by increasing the frequency of transduced progenitor cells. Repeated infection also augmented the number of integrated copies and the magnitude of this effect seemed to depend on the vector preparation. Thus, the distribution of VCN in hematopoietic colonies may depend upon experimental conditions including features of vectors. This should be carefully evaluated in the context of ex vivo hematopoietic gene therapy studies

TAT-Mediated Transduction of MafA Protein In Utero Results in Enhanced Pancreatic Insulin Expression and Changes in Islet Morphology

Alongside Pdx1 and Beta2/NeuroD, the transcription factor MafA has been shown to be instrumental in the maintenance of the beta cell phenotype. Indeed, a combination of MafA, Pdx1 and Ngn3 (an upstream regulator of Beta2/NeuroD) was recently reported to lead to the effective reprogramming of acinar cells into insulin-producing beta cells. These experiments set the stage for the development of new strategies to address the impairment of glycemic control in diabetic patients. However, the clinical applicability of reprogramming in this context is deemed to be poor due to the need to use viral vehicles for the delivery of the above factors. Here we describe a recombinant transducible version of the MafA protein (TAT-MafA) that penetrates across cell membranes with an efficiency of 100% and binds to the insulin promoter in vitro. When injected in utero into living mouse embryos, TAT-MafA significantly up-regulates target genes and induces enhanced insulin production as well as cytoarchitectural changes consistent with faster islet maturation. As the latest addition to our armamentarium of transducible proteins (which already includes Pdx1 and Ngn3), the purification and characterization of a functional TAT-MafA protein opens the door to prospective therapeutic uses that circumvent the use of viral delivery. To our knowledge, this is also the first report on the use of protein transduction in utero

Practical nonlinear energy harvesting model in MIMO DF relay system with channel uncertainty

In this paper, we aim to maximize the end-to- end achievable rate of multiple-input multiple-output (MI MO) decode-and-forward (DF) where the relay is an energy harves t- ing (EH) node using the time switching (TS) scheme. The relay first harvests the energy from the source, then uses its harve sted energy to forward the information carrying signal from the source to the destination. The EH model at the relay is a nonlinear model. Also, we assume that the channel knowledge is imperfect at the relay and destination. We propose the struc ture of the optimal covariance matrices at the source (during EH a nd information decoding periods), the optimal covariance mat rix at the relay and the optimal EH time ratio. Through the simulati on results, we compare between di ff erent linear / nonlinear EH models and we show the gain / loss performance of the linear model compared to other nonlinear EH model

Normally-off AlGaN/GaN/AlGaN double heterostructure FETs with a thick undoped GaN gate layer

In this letter, we report on polarization charge engineering enabling normally-off operation for a double-heterostructure Al(0.26)Ga(0.74)N/GaN/Al(0.07)Ga(0.93)N-based field effect transistors (DHFETs) using a 35-nm-thick undoped GaN layer underneath the gate metallization. The combined effect of the negative polarization charge induced by the AlGaN back barrier and the undoped GaN gate layer ensures the total depletion of the channel, and provides a positive thres-hold voltage. The fabricated DHFET exhibits normally-off operation with a threshold voltage of 1.2 V, a maximum drain current density of 370 mA/mm, and a high ON/OFF current ratio of 10(7), at a gate bias of 7 V. A transistor with gate-drain distance of 6µm demonstrates 300 V off-state breakdown voltage

Recycling cellular energy for self-sustainable IoT networks: a spatiotemporal study

This paper investigates the self-sustainability of an overlay Internet of Things (IoT) network that relies on harvesting energy from a downlink cellular network. Using stochastic geometry and queueing theory, we develop a spatiotemporal model to derive the steady state distribution of the number of packets in the buffers and energy levels in the batteries of IoT devices given that the IoT and cellular communications are allocated disjoint spectrum. Particularly, each IoT device is modelled via a two-dimensional discrete-time Markov Chain (DTMC) that jointly tracks the evolution of the data buffers and energy battery. In this context, stochastic geometry is used to derive the energy generation at the batteries and the packet transmission success probability from buffers taking into account the mutual interference from other active IoT devices. To this end, we show the Pareto-Frontiers of the sustainability region, which define the network parameters that ensure stable network operation and finite packet delay. Furthermore, the spatially averaged network performance, in terms of transmission success probability, average queueing delay, and average queue size are investigated. For self-sustainable networks, the results quantify the required buffer size and packet delay, which are crucial for the design of IoT devices and time critical IoT applications