Optimal Security Proofs for Full Domain Hash,Revisited

RSA Full Domain Hash (RSA-FDH) is a digital signature scheme, secure against chosen message attacks in the random oracle model. The best known security reduction from the RSA assumption is non-tight, i.e., it loses a factor of \(q_s\), where \(q_s\) is the number of signature queries made by the adversary. It was furthermore proven by Coron (Advances in cryptology—EUROCRYPT 2002, Lecture notes in computer science, vol 2332. Springer, Berlin, pp 272–287, 2002) that a security loss of \(q_s\) is optimal and cannot possibly be improved. In this work, we uncover a subtle flaw in Coron’s impossibility result. Concretely, we show that it only holds if the underlying trapdoor permutation is certified. Since it is well known that the RSA trapdoor permutation is (for all practical parameters) not certified, this renders Coron’s impossibility result moot for RSA-FDH. Motivated by this, we revisit the question whether there is a tight security proof for RSA-FDH. Concretely, we give a new tight security reduction from a stronger assumption, the Phi-Hiding assumption introduced by Cachin et al. (Advances in Cryptology—EUROCRYPT’99. Lecture notes in computer science, vol 1592. Springer, Berlin, pp 402–414, 1999). This justifies the choice of smaller parameters in RSA-FDH, as it is commonly used in practice. All of our results (positive and negative) extend to the probabilistic signature scheme PSS (with message recovery).     

Bandwidth‐Efficient Selective Retransmission for MIMO‐OFDM Systems

In this work, we propose an efficient selective retransmission method for multiple‐input and multiple‐output (MIMO) wireless systems under orthogonal frequency‐division multiplexing (OFDM) signaling. A typical received OFDM frame may have some symbols in error, which results in a retransmission of the entire frame. Such a retransmission is often unnecessary, and to avoid this, we propose a method to selectively retransmit symbols that correspond to poor‐quality subcarriers. We use the condition numbers of the subcarrier channel matrices of the MIMO‐OFDM system as a quality measure. The proposed scheme is embedded in the modulation layer and is independent of conventional hybrid automatic repeat request (HARQ) methods. The receiver integrates the original OFDM and the punctured retransmitted OFDM signals for more reliable detection. The targeted retransmission results in fewer negative acknowledgements from conventional HARQ algorithms, which results in increasing bandwidth and power efficiency. We investigate the efficacy of the proposed method for optimal and suboptimal receivers. The simulation results demonstrate the efficacy of the proposed method on throughput for MIMO‐OFDM systems.     

Application layer QoS optimization for multimedia transmission over cognitive radio networks

In cognitive radio (CR) networks, the perceived reduction of application layer quality of service (QoS), such as multimedia distortion, by secondary users may impede the success of CR technologies. Most previous work in CR networks ignores application layer QoS. In this paper we take an integrated design approach to jointly optimize multimedia intra refreshing rate, an application layer parameter, together with access strategy, and spectrum sensing for multimedia transmission in a CR system with time varying wireless channels. Primary network usage and channel gain are modeled as a finite state Markov process. With channel sensing and channel state information errors, the system state cannot be directly observed. We formulate the QoS optimization problem as a partially observable Markov decision process (POMDP). A low complexity dynamic programming framework is presented to obtain the optimal policy. Simulation results show the effectiveness of the proposed scheme.     

Sensorless Control of a Permanent Magnet Synchronous Motor Using Artificial Neural Network Based Estimator—An Application of the Four-Switch Three-Phase Inverter

This article presents a space vector pulse width modulation based three-phase four-switch inverter for the sensor-less control of a three-phase permanent-magnet synchronous motor (PMSM). The PMSM was modeled in MATLAB SIMULINK using the dynamic model to extract signals required for the sensor-less control. The calculation of time for gate signals of the switches in the four-switch three-phase inverter has been explained in detail and implemented using the artificial neural networks. The ANN based SVPWM block takes reference voltage, frequency and modulation index as commands and provides three-phase voltages with variable frequency. An ANN based speed and position estimator was also implemented for the PMSM to eliminate mechanical sensors. The proposed system was implemented using the Texas Instruments' TMS320F2812 development kit and the National Instruments data acquisition module USB-6259. The results have been presented for the permanent magnet synchronous motor drive in under-modulation region.     

Forecast the Influenza Pandemic Using Machine Learning

Forecasting future outbreaks can help in minimizing their spread. Influenza is a disease primarily found in animals but transferred to humans through pigs. In 1918, influenza became a pandemic and spread rapidly all over the world becoming the cause behind killing one-third of the human population and killing one-fourth of the pig population. Afterwards, that influenza became a pandemic several times on a local and global levels. In 2009, influenza ‘A’ subtype H1N1 again took many human lives. The disease spread like in a pandemic quickly. This paper proposes a forecasting modeling system for the influenza pandemic using a feed-forward propagation neural network (MSDII-FFNN). This model helps us predict the outbreak, and determines which type of influenza becomes a pandemic, as well as which geographical area is infected. Data collection for the model is done by using IoT devices. This model is divided into 2 phases: The training phase and the validation phase, both being connected through the cloud. In the training phase, the model is trained using FFNN and is updated on the cloud. In the validation phase, whenever the input is submitted through the IoT devices, the system model is updated through the cloud and predicts the pandemic alert. In our dataset, the data is divided into an 85% training ratio and a 15% validation ratio. By applying the proposed model to our dataset, the predicted output precision is 90%.     

Prediction of Time Series Empowered with a Novel SREKRLS Algorithm

For the unforced dynamical non-linear statespace model, a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article. The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems. With the help of an ortho-normal triangularization method, which relies on numerically stable givens rotation, matrix inversion causes a computational burden, is reduced. Matrix computation possesses many excellent numerical properties such as singularity, symmetry, skew symmetry, and triangularity is achieved by using this algorithm. The proposed method is validated for the prediction of stationary and non-stationary MackeyGlass Time Series, along with that a component in the x-direction of the Lorenz Times Series is also predicted to illustrate its usefulness. By the learning curves regarding mean square error (MSE) are witnessed for demonstration with prediction performance of the proposed algorithm from where it’s concluded that the proposed algorithm performs better than EKRLS. This new SREKRLS based design positively offers an innovative era towards non-linear systolic arrays, which is efficient in developing very-large-scale integration (VLSI) applications with non-linear input data. Multiple experiments are carried out to validate the reliability, effectiveness, and applicability of the proposed algorithm and with different noise levels compared to the Extended kernel recursive least-squares (EKRLS) algorithm.     

Chitosan-Tripolyphosphate Nanoparticles Improves Oxidative Stability of Encapsulated Shrimp Oil throughout the Extended Storage

Shrimp oil is encapsulated in chitosan-tripolyphosphate nanoparticles (CSNPs) prepared by a dual-step process involving emulsification of oil followed by entrapment in the chitosan-tripolyphosphate matrix. CSNPs loaded with shrimp oil at varied levels show different encapsulation efficiencies (32.34–67.54%), mean particle diameters (110.29–278.11 nm), and zeta potential (18.93–33.77 mV). Scanning electron micrographs reveal that CSNPs are spherical or ellipsoidal in shape without flocculation. Differential scanning calorimetry and Fourier transform infrared spectroscopy results further substantiate the entrapment of shrimp oil within the CSNPs. Shrimp oil loaded in CSNPs have better oxidative stability and quality, compared to the free oil plausibly due to the synergistic effect between enclosure of oil by CSNPs and antioxidative property of chitosan. Polyunsaturated fatty acids and astaxanthin are more retained in CSNP encapsulated oil than the free oil over the storage of 8 weeks, indicating high potency of CSNPs in preventing the losses in the nutritional value and active component of shrimp oil. Practical Applications: Shrimp oil is an exemplary source of astaxanthin and n-3 fatty acids with potential health benefits. Shrimp oil encapsulation in chitosan nanoparticles is a simple and promising technique to protect the oil from oxidation and rancidity with no significant loss of polyunsaturated fatty acids or astaxanthin. Shrimp oil loaded-chitosan nanoparticles are thermodynamically stable and disperse readily in water, making it highly favorable for fortification in variety of foods, particularly beverages. This technique is cost effective, since chitosan is abundantly available at low cost.     

Stabilizing Bottom Side of Perovskite via Preburying Cesium Formate toward Efficient and Stable Solar Cells

The fragile bottom side of perovskite films is demonstrated to be harmful to the efficiency and stability of perovskite solar cells (PSCs) because the carrier extraction and recombination can be significantly influenced by the easily formed strain, voids, and defects on the bottom side. Nevertheless, the bottom side of perovskite films is usually overlooked because it remains a challenge to directly characterize and modify the bottom side. Herein, a facile and effective strategy is reported to stabilize the bottom side via preburying cesium formate (CsFo) into the SnO₂ electron transport layer (ETL). It is found that the synergistic effect of cesium cation (Cs⁺) and formate anion (HCOO⁻) causes strain relaxation, void elimination, and defects’ reduction, which further facilitate the charge extraction. Consequently, the champion power conversion efficiency (PCE) of formamidinium (FA)-based PSCs is increased from 23.34% to 24.50%. Meanwhile, the ultraviolet (UV), thermal, and operational stability are also enhanced. Finally, formamidinium–cesium (FACs)-based PSCs are investigated to confirm the effectiveness of this preburied CsFo strategy, and the optimal device exhibits a champion PCE of 25.03% and a remarkably high fill factor (FF) of 85.65%.     

Effects of red rice flour addition on the rheological,textural, sensory and bioactive properties of wheat flour-based pan breads

Red rice, rich in fibre and phenolic contents, is one of the underutilised grains. Therefore, the present study developed the wheat/red rice flour (RRF) composites followed by its utilisation in pan breads. It led to an increase in ash and fibre contents of resultant flour combinations, but gluten network became weaker. The values for water absorption were unaffected with RRF replacements ≤15% of wheat flour. Moreover, the proximate estimates of RRF added breads demonstrated increased moisture and fibre contents. Substituted bread combinations were harder with increased gumminess and springiness values; however, cohesion decreased. The per cent change in cohesion and hardness of individual breads upon storage was lesser in composites compared to control suggesting decline in staling phenomenon. Nutritional assessment of both RRF containing flour and breads showed higher total phenolic content and DPPH radical scavenging activity compared to their respective controls. Composite breads also demonstrated reduced glycaemic index suggesting its importance for diabetic patients.