High-K dielectric sulfur-selenium alloys.

Upcoming advancements in flexible technology require mechanically compliant dielectric materials. Current dielectrics have either high dielectric constant, K (e.g., metal oxides) or good flexibility (e.g., polymers). Here, we achieve a golden mean of these properties and obtain a lightweight, viscoelastic, high-K dielectric material by combining two nonpolar, brittle constituents, namely, sulfur (S) and selenium (Se). This S-Se alloy retains polymer-like mechanical flexibility along with a dielectric strength (40 kV/mm) and a high dielectric constant (K = 74 at 1 MHz) similar to those of established metal oxides. Our theoretical model suggests that the principal reason is the strong dipole moment generated due to the unique structural orientation between S and Se atoms. The S-Se alloys can bridge the chasm between mechanically soft and high-K dielectric materials toward several flexible device applications

A novel PKC activating molecule promotes neuroblast differentiation and delivery of newborn neurons in brain injuries

Neural stem cells are activated within neurogenic niches in response to brain injuries. This results in the production of neuroblasts, which unsuccessfully attempt to migrate toward the damaged tissue. Injuries constitute a gliogenic/non-neurogenic niche generated by the presence of anti-neurogenic signals, which impair neuronal differentiation and migration. Kinases of the protein kinase C (PKC) family mediate the release of growth factors that participate in different steps of the neurogenic process, particularly, novel PKC isozymes facilitate the release of the neurogenic growth factor neuregulin. We have demonstrated herein that a plant derived diterpene, (EOF2; CAS number 2230806-06-9), with the capacity to activate PKC facilitates the release of neuregulin 1, and promotes neuroblasts differentiation and survival in cultures of subventricular zone (SVZ) isolated cells in a novel PKC dependent manner. Local infusion of this compound in mechanical cortical injuries induces neuroblast enrichment within the perilesional area, and noninvasive intranasal administration of EOF2 promotes migration of neuroblasts from the SVZ towards the injury, allowing their survival and differentiation into mature neurons, being some of them cholinergic and GABAergic. Our results elucidate the mechanism of EOF2 promoting neurogenesis in injuries and highlight the role of novel PKC isozymes as targets in brain injury regeneration

A Literature Review on Cloud Computing Adoption Issues in Enterprises

Part 3: Creating Value through ApplicationsInternational audienceCloud computing has received increasing interest from enterprises since its inception. With its innovative information technology (IT) services delivery model, cloud computing could add technical and strategic business value to enterprises. However, cloud computing poses highly concerning internal (e.g., Top management and experience) and external issues (e.g., regulations and standards). This paper presents a systematic literature review to explore the current key issues related to cloud computing adoption. This is achieved by reviewing 51 articles published about cloud computing adoption. Using the grounded theory approach, articles are classified into eight main categories: internal, external, evaluation, proof of concept, adoption decision, implementation and integration, IT governance, and confirmation. Then, the eight categories are divided into two abstract categories: cloud computing adoption factors and processes, where the former affects the latter. The results of this review indicate that enterprises face serious issues before they decide to adopt cloud computing. Based on the findings, the paper provides a future information systems (IS) research agenda to explore the previously under-investigated areas regarding cloud computing adoption factors and processes. This paper calls for further theoretical, methodological, and empirical contributions to the research area of cloud computing adoption by enterprises

Performance of aquatic plant species for phytoremediation of arsenic-contaminated water

This study investigates the effectiveness of aquatic macrophyte and microphyte for phytoremediation of water bodies contaminated with high arsenic concentration. Water hyacinth (Eichhornia crassipes) and two algae (Chlorodesmis sp. and Cladophora sp.) found near arsenic-enriched water bodies were used to determine their tolerance toward arsenic and their effectiveness to uptake arsenic thereby reducing organic pollution in arsenic-enriched wastewater of different concentrations. Parameters like pH, chemical oxygen demand (COD), and arsenic concentration were monitored. The pH of wastewater during the course of phytoremediation remained constant in the range of 7.3–8.4, whereas COD reduced by 50–65 % in a period of 15 days. Cladophora sp. was found to survive up to an arsenic concentration of 6 mg/L, whereas water hyacinth and Chlorodesmis sp. could survive up to arsenic concentrations of 2 and 4 mg/L, respectively. It was also found that during a retention period of 10 days under ambient temperature conditions, Cladophora sp. could bring down arsenic concentration from 6 to <0.1 mg/L, Chlorodesmis sp. was able to reduce arsenic by 40−50 %; whereas, water hyacinth could reduce arsenic by only 20 %. Cladophora sp. is thus suitable for co-treatment of sewage and arsenic-enriched brine in an algal pond having a retention time of 10 days. The identified plant species provides a simple and cost-effective method for application in rural areas affected with arsenic problem. The treated water can be used for irrigation

Fast Convergence Joint Optimization of PAPR Reduction and Digital Predistortion in the Next-Generation Broadcasting Systems

AbstractThe vast majority of designs on peak-to-average power ratio (PAPR) reduction and PA linearization schemes in broadcasting systems can be found in literature dealing with both of them in a separate manner on problem formulation, optimization objectives, and implementation issues without considering their mutual influence. Their overall performance might be suboptimal even if each of them has been optimized independently due to possible conflicts as both techniques are interdependent. This paper proposes an adding signal method that jointly achieves PAPR reduction and PA linearization simultaneously, and no extra processing is required at the receiver. The simulation results show that the proposed scheme offers a good performance/complexity trade-off requiring fewer iterations than recent methods.Abstract The vast majority of designs on peak-to-average power ratio (PAPR) reduction and PA linearization schemes in broadcasting systems can be found in literature dealing with both of them in a separate manner on problem formulation, optimization objectives, and implementation issues without considering their mutual influence. Their overall performance might be suboptimal even if each of them has been optimized independently due to possible conflicts as both techniques are interdependent. This paper proposes an adding signal method that jointly achieves PAPR reduction and PA linearization simultaneously, and no extra processing is required at the receiver. The simulation results show that the proposed scheme offers a good performance/complexity trade-off requiring fewer iterations than recent methods

A Novel Application of Polynomial Solvers in mmWave Analog Radio Beamforming

Abstract Beamforming is a signal processing technique where an array of antenna elements can be steered to transmit and receive radio signals in a specific direction. The usage of millimeter wave (mmWave) frequencies and multiple input multiple output (MIMO) beamforming are considered as the key innovations of 5th Generation (5G) and beyond communication systems. The mmWave radio waves enable high capacity and directive communication, but suffer from many challenges such as rapid channel variation, blockage effects, atmospheric attenuations, etc. The technique initially performs beam alignment procedure, followed by data transfer in the aligned directions between the transmitter and the receiver [1]. Traditionally, beam alignment involves periodical and exhaustive beam sweeping at both transmitter and the receiver, which is a slow process causing extra communication overhead with MIMO and massive MIMO radio units. In applications such as beam tracking, angular velocity, beam steering etc. [2], beam alignment procedure is optimized by estimating the beam directions using first order polynomial approximations. Recent learning-based SOTA strategies [3] for fast mmWave beam alignment also require exploration over exhaustive beam pairs during the training procedure, causing overhead to learning strategies for higher antenna configurations. Therefore, our goal is to optimize the beam alignment cost functions e.g., data rate, to reduce the beam sweeping overhead by applying polynomial approximations of its partial derivatives which can then be solved as a system of polynomial equations. Specifically, we aim to reduce the beam search space by estimating approximate beam directions using the polynomial solvers. Here, we assume both transmitter (TX) and receiver (RX) to be equipped with uniform linear array (ULA) configuration, each having only one degree of freedom (d.o.f.) with Nt and Nr antennas, respectively.Abstract Beamforming is a signal processing technique where an array of antenna elements can be steered to transmit and receive radio signals in a specific direction. The usage of millimeter wave (mmWave) frequencies and multiple input multiple output (MIMO) beamforming are considered as the key innovations of 5th Generation (5G) and beyond communication systems. The mmWave radio waves enable high capacity and directive communication, but suffer from many challenges such as rapid channel variation, blockage effects, atmospheric attenuations, etc. The technique initially performs beam alignment procedure, followed by data transfer in the aligned directions between the transmitter and the receiver [1]. Traditionally, beam alignment involves periodical and exhaustive beam sweeping at both transmitter and the receiver, which is a slow process causing extra communication overhead with MIMO and massive MIMO radio units. In applications such as beam tracking, angular velocity, beam steering etc. [2], beam alignment procedure is optimized by estimating the beam directions using first order polynomial approximations. Recent learning-based SOTA strategies [3] for fast mmWave beam alignment also require exploration over exhaustive beam pairs during the training procedure, causing overhead to learning strategies for higher antenna configurations. Therefore, our goal is to optimize the beam alignment cost functions e.g., data rate, to reduce the beam sweeping overhead by applying polynomial approximations of its partial derivatives which can then be solved as a system of polynomial equations. Specifically, we aim to reduce the beam search space by estimating approximate beam directions using the polynomial solvers. Here, we assume both transmitter (TX) and receiver (RX) to be equipped with uniform linear array (ULA) configuration, each having only one degree of freedom (d.o.f.) with Nt and Nr antennas, respectively

A Novel Application of Polynomial Solvers in mmWave Analog Radio Beamforming

Beamforming is a signal processing technique where an array of antenna elements can be steered to transmit and receive radio signals in a specific direction. The usage of millimeter wave (mmWave) frequencies and multiple input multiple output (MIMO) beamforming are considered as the key innovations of 5th Generation (5G) and beyond communication systems. The technique initially performs a beam alignment procedure, followed by data transfer in the aligned directions between the transmitter and the receiver. Traditionally, beam alignment involves periodical and exhaustive beam sweeping at both transmitter and the receiver, which is a slow process causing extra communication overhead with MIMO and massive MIMO radio units. In applications such as beam tracking, angular velocity, beam steering etc., the beam alignment procedure is optimized by estimating the beam directions using first order polynomial approximations. Recent learning-based SOTA strategies for fast mmWave beam alignment also require exploration over exhaustive beam pairs during the training procedure, causing overhead to learning strategies for higher antenna configurations. In this work, we first optimize the beam alignment cost functions e.g. the data rate, to reduce the beam sweeping overhead by applying polynomial approximations of its partial derivatives which can then be solved as a system of polynomial equations using well-known tools from algebraic geometry. At this point, a question arises: 'what is a good polynomial approximation?' In this work, we attempt to obtain a 'good polynomial approximation'. Preliminary experiments indicate that our estimated polynomial approximations attain a so-called sweet-spot in terms of the solver speed and accuracy, when evaluated on test beamforming problems.Comment: Accepted for publication in the SIGSAM's ACM Communications in Computer Algebra, as an extended abstrac

Hardware-friendly Power Amplifier Linearization in Next-Generation Broadcasting Systems

AbstractIt is essential to mitigate power amplifier (PA) nonlinear (NL) effects to achieve energy-efficient radio communications. To restore the transmit signal quality, digital pre-distortion (DPD) is widely used. Recently, fast convergence DPD (FC-DPD) which offers good PA linearization has been proposed for next-generation broadcasting systems. However, it suffers from complexity issues and this paper addresses that drawback and we propose a low-complex version to make it hardware-friendly. We achieve significant complexity reduction by simplifying the Jacobian computation needed in the FC-DPD algorithm. This scheme can be extended to any memory-less PA model or a measured PA fitted to a polynomial model. We have provided proof that the proposed technique has linear complexity and the simulation results indicate that the proposed scheme achieves performance close to the FC-DPD algorithm. This method can be applied to other transmission systems as well.Abstract It is essential to mitigate power amplifier (PA) nonlinear (NL) effects to achieve energy-efficient radio communications. To restore the transmit signal quality, digital pre-distortion (DPD) is widely used. Recently, fast convergence DPD (FC-DPD) which offers good PA linearization has been proposed for next-generation broadcasting systems. However, it suffers from complexity issues and this paper addresses that drawback and we propose a low-complex version to make it hardware-friendly. We achieve significant complexity reduction by simplifying the Jacobian computation needed in the FC-DPD algorithm. This scheme can be extended to any memory-less PA model or a measured PA fitted to a polynomial model. We have provided proof that the proposed technique has linear complexity and the simulation results indicate that the proposed scheme achieves performance close to the FC-DPD algorithm. This method can be applied to other transmission systems as well

Machine Learning-Aided Piece-Wise Modeling Technique of Power Amplifier for Digital Predistortion

AbstractWe propose a new power amplifier (PA) behavioral modeling approach, to characterize and compensate for the signal quality degrading effects induced by a PA with a machine learning (ML) aided piece-wise (PW) modeling approach. Instead of using a single pruned Volterra model, we use multiple small-size pruned Volterra models by classifying the input data into different classes. For that purpose, an ML classifier model is trained by extracting some crucial features from both the input signal statistics and the PA operating point. The simulation results indicate that our approach contributes to an improved performance/complexity trade-off than a single generalized memory polynomial (GMP) model in terms of PA behavior modeling and linearization.Abstract We propose a new power amplifier (PA) behavioral modeling approach, to characterize and compensate for the signal quality degrading effects induced by a PA with a machine learning (ML) aided piece-wise (PW) modeling approach. Instead of using a single pruned Volterra model, we use multiple small-size pruned Volterra models by classifying the input data into different classes. For that purpose, an ML classifier model is trained by extracting some crucial features from both the input signal statistics and the PA operating point. The simulation results indicate that our approach contributes to an improved performance/complexity trade-off than a single generalized memory polynomial (GMP) model in terms of PA behavior modeling and linearization

Polynomial Solvers for mmWave Radio Beamforming

Abstract Millimeter (mmWave) beamforming is an integral component of fifth-generation (5G) and beyond radio commu-nications. 5G beamforming involves the initial beam selection procedure using a codebook with multiple radio beam directions. Conventional codebook-based alignment schemes involve exhaustive sweeping over the predefined beam directions, the number of which increases significantly with large numbers of antennas resulting in undesirable latency and communications signal overhead. In this paper, we propose a novel algebraic-based codebook using Gröbner basis polynomial solvers to reduce the signal overhead during beam alignment. We also analyze the complexity-performance tradeoff between the proposed algebraic-based codebook and the exhaustive-based beam alignment across different monomial thresholds, multiple antenna configurations and radio contextual location information. Our results show that the proposed approach reduces the beam-search overhead at an average complexity reduction ratio of 73.95% with a performance tradeoff error of 32.25%.Abstract Millimeter (mmWave) beamforming is an integral component of fifth-generation (5G) and beyond radio commu-nications. 5G beamforming involves the initial beam selection procedure using a codebook with multiple radio beam directions. Conventional codebook-based alignment schemes involve exhaustive sweeping over the predefined beam directions, the number of which increases significantly with large numbers of antennas resulting in undesirable latency and communications signal overhead. In this paper, we propose a novel algebraic-based codebook using Gröbner basis polynomial solvers to reduce the signal overhead during beam alignment. We also analyze the complexity-performance tradeoff between the proposed algebraic-based codebook and the exhaustive-based beam alignment across different monomial thresholds, multiple antenna configurations and radio contextual location information. Our results show that the proposed approach reduces the beam-search overhead at an average complexity reduction ratio of 73.95% with a performance tradeoff error of 32.25%