Parameter Identification of Systems with Noise in Input and Output

In the problem of system parameters identification, most treatments made previously have assumed that the input to the system was free from noise. However, there would be many instances, where it would be more practical to assume the input to be accompanied with additive noise. Such a case is considered in the present paper, and the asymptotic unbiased estimate is obtained under certain conditions. The extended matrix approach with the ordinary least square method is used for the estimation of the parameters of the systems and the noise filter. Order identification is also discussed for this system with input noise. An application of the obtained solution to a numerical example shows that it gives a satisfactory result, both in parameter identification and in order identification

Optimization of deep learning features for age-invariant face recognition

This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods

A Longitudinal Study of Small Group Interaction in Social Virtual Reality

Now that high-end consumer phones can support immersive virtual reality, we ask whether social virtual reality is a promising medium for supporting distributed groups of users. We undertook an exploratory in-the-wild study using Samsung Gear VR headsets to see how existing social groups that had become geographically dispersed could use VR for collaborative activities. The study showed a strong propensity for users to feel present and engaged with group members. Users were able to bring group behaviors into the virtual world. To overcome some technical limitations, they had to create novel forms of interaction. Overall, the study found that users experience a range of emotional states in VR that are broadly similar to those that they would experience face-to-face in the same groups. The study highlights the transferability of existing social group dynamics in VR interactions but suggests that more work would need to be done on avatar representations to support some intimate conversations

FACTS allocation considering loads uncertainty, steady state operation constraints, and dynamic operation constraints

This study proposes an algorithm to allocate different types of flexible AC transmission system (FACTS) in power systems. The main objective of this study is to maximize profit by minimizing the system’s operating cost including FACTS devices (FDs) installation cost. Dynamic and steady state operating restrictions with loads uncertainty are included in the problem formulation. The overall problem is solved using both teaching learning based optimization (TLBO) technique for attaining the optimal allocation of the FDs as main-optimization problem and matpower interior point solver (MIPS) for optimal power flow (OPF) as the sub-optimization problem. The validation of the proposed approach is verified by applying it to test system of 59-bus; Simplified 14-Generator model of the South East Australian power system

Population based optimization algorithms improvement using the predictive particles

A new efficient improvement, called Predictive Particle Modification (PPM), is proposed in this paper. This modification makes the particle look to the near area before moving toward the best solution of the group. This modification can be applied to any population algorithm. The basic philosophy of PPM is explained in detail. To evaluate the performance of PPM, it is applied to Particle Swarm Optimization (PSO) algorithm and Teaching Learning Based Optimization (TLBO) algorithm then tested using 23 standard benchmark functions. The effectiveness of these modifications are compared with the other unmodified population optimization algorithms based on the best solution, average solution, and convergence rate

A secured message transmission protocol for vehicular ad hoc networks

Vehicular Ad hoc Networks (VANETs) become a very crucial addition in the Intelligent Transportation System (ITS). It is challenging for a VANET system to provide security services and parallelly maintain high throughput by utilizing limited resources. To overcome these challenges, we propose a blockchain-based Secured Cluster-based MAC (SCB-MAC) protocol. The nearby vehicles heading towards the same direction will form a cluster and each of the clusters has its blockchain to store and distribute the safety messages. The message which contains emergency information and requires Strict Delay Requirement (SDR) for transmission are called safety messages (SM). Cluster Members (CMs) sign SMs with their private keys while sending them to the blockchain to confirm authentication, integrity, and confidentiality of the message. A Certificate Authority (CA) is responsible for physical verification, key generation, and privacy preservation of the vehicles. We implemented a test scenario as proof of concept and tested the safety message transmission (SMT) protocol in a real-world platform. Computational and storage overhead analysis shows that the proposed protocol for SMT implements security, authentication, integrity, robustness, non-repudiation, etc. while maintaining the SDR. Messages that are less important compared to the SMs are called non-safety messages (NSM) and vehicles use RTS/CTS mechanism for NSM transmission. Numerical studies show that the proposed NSM transmission method maintains 6 times more throughput, 2 times less delay and 125% less Packet Dropping Rate (PDR) than traditional MAC protocols. These results prove that the proposed protocol outperforms the traditionalMAC protocols

Generalized optimal placement of PMUs considering power system observability, communication infrastructure, and quality of service requirements

This paper presents a generalized optimal placement of Phasor Measurement Units (PMUs) considering power system observability, reliability, Communication Infrastructure (CI), and latency time associated with this CI. Moreover, the economic study for additional new data transmission paths is considered as well as the availability of predefined locations of some PMUs and the preexisting communication devices (CDs) in some buses. Two cases for the location of the Control Center Base Station (CCBS) are considered; predefined case and free selected case. The PMUs placement and their required communication network topology and channel capacity are co-optimized simultaneously. In this study, two different approaches are applied to optimize the objective function; the first approach is combined from Binary Particle Swarm Optimization-Gravitational Search Algorithm (BPSOGSA) and the Minimum Spanning Tree (MST) algorithm, while the second approach is based only on BPSOGSA. The feasibility of the proposed approaches are examined by applying it to IEEE 14-bus and IEEE 118-bus systems

Variations in genetic and chemical constituents of Ziziphus spina-christi L. populations grown at various altitudinal zonation up to 2227m height

AbstractAltitudinal gradient-defined specific environmental conditions could lead to genetics and chemical variations among individuals of the same species. By using RAPD, ISSR, GC–MS and HPLC analysis, the genetic and chemical diversity of Ziziphus spina-christi plants at various altitudinal gradient namely; Abha (2227.86m), Dala Valley (1424m), Rakhma Valley (1000m), Raheb Valley (505m) and Al-Marbh (147m) were estimated. RAPD markers revealed that the highest similarity value (40.22%) was between Raheb Valley and Al-Marbh while the lowest similarity (10.08%) was between Abha and Raheb Valley. Based on ISSR markers the highest similarity value (61.54%) was also between Raheb Valley and Al-Marbh, while the lowest similarity (26.84%) was between Abha and Rakhma Valley. GC–MS results showed the presence of various phytochemical constituents in each population. The dendrogram based on chemical compounds separated the Z. spina-christi grown at the highest elevations (Abha) from the populations in lower elevations. HPLC analysis showed that the leaves of Z. spina-christi plant contain considerable amount of vitamins including B1, B12, B2 and folic acid. In conclusion, there is a close relation between altitudinal gradients, genetic diversity and chemical constituents of the leaves of Z. spina-christi plants

Silica-Coated Magnetic Nanoparticles for Vancomycin Conjugation

Drug resistance is a global health challenge with thousands of deaths annually caused by bacterial multidrug resistance (MDR). Efforts to develop new antibacterial molecules do not meet the mounting needs imposed by the evolution of MDR. An alternative approach to overcome this challenge is developing targeted formulations that can enhance the therapeutic efficiency and limit side effects. In this aspect, vancomycin is a potent antibacterial agent that has inherent bacterial targeting properties by binding to the D-Ala-D-Ala moiety of the bacterial peptidoglycan. However, the use of vancomycin is associated with serious side effects that limit its clinical use. Herein, we report the development of vancomycin-conjugated magnetic nanoparticles using a simple conjugation method for targeted antibacterial activity. The nanoparticles were synthesized using a multistep process that starts by coating the nanoparticles with a silica layer, followed by binding an amide linker and then binding the vancomycin glycopeptide. The developed vancomycin-conjugated magnetic nanoparticles were observed to exhibit a spherical morphology and a particle size of 16.3 ± 2.6 nm, with a silica coating thickness of 5 nm and a total coating thickness of 8 nm. The vancomycin conjugation efficiency on the nanoparticles was measured spectrophotometrically to be 25.1%. Additionally, the developed formulation retained the magnetic activity of the nanoparticles, where it showed a saturation magnetization value of 51 emu/g, compared to 60 emu/g for bare magnetic nanoparticles. The in vitro cell biocompatibility demonstrated improved safety where vancomycin-conjugated nanoparticles showed IC50 of 183.43 μg/mL, compared to a much lower value of 54.11 μg/mL for free vancomycin. While the antibacterial studies showed a comparable activity of the developed formulation, the minimum inhibitory concentration was 25 μg/mL, compared to 20 μg/mL for free vancomycin. Accordingly, the reported formulation can be used as a platform for the targeted and efficient delivery of other drugs