Automated Teller Machine Authentication Using Biometric

This paper presents a novel method of a secured card-less Automated Teller Machine (ATM) authentication based on the three bio-metrics measures. It would help in the identification and authorization of individuals and would provide robust security enhancement. Moreover, it would assist in providing identification in ways that cannot be impersonated. To the best of our knowledge, this method of Biometric_ fusion way is the first ATM security algorithm that utilizes a fusion of three biometric features of an individual such as Fingerprint, Face, and Retina simultaneously for recognition and authentication. These biometric images have been collected as input data for each module in this system, like a fingerprint, a face, and a retina module. A database is created by converting these images to YIQ color space, which is helpful in normalizing the brightness levels of the image hence mainly (Y component’s) luminance. Then, it attempt to enhance Cellular Automata Segmentation has been carried out to segment the particular regions of interest from these database images. After obtaining segmentation results, the featured extraction method is carried out from these critical segments of biometric photos. The Enhanced Discrete Wavelet Transform technique (DWT Mexican Hat Wavelet) was used to extract the features. Fusion of extracted features of all three biometrics features have been used to bring in the multimodal classification approach to get fusion vectors. Once fusion vectors ware formulated, the feature level fusion technique is incorporated based on the extracted feature vectors. These features have been applied to the machine learning algorithm to identify and authorization of multimodal biometrics for ATM security. In the proposed approach, we attempt at useing an enhanced Deep Convolutional Neural Network (DCNN). A hybrid optimization algorithm has been selected based on the effectiveness of the features. The proposed approach results were compared with existing algorithms based on the classification accuracy to prove the effectiveness of our algorithm. Moreover, comparative results of the proposed method stand as a proof of more promising outcomes by combining the three biometric features.     

Postnatal Protein Intake as a Determinant of Skeletal Muscle Structure and Function in Mice—A Pilot Study

Sarcopenia is characterised by an age-related decrease in the number of muscle fibres and additional weakening of the remaining fibres, resulting in a reduction in muscle mass and function. Many studies associate poor maternal nutrition during gestation and/or lactation with altered skeletal muscle homeostasis in the offspring and the development of sarcopenia. The aim of this study was to determine whether the musculoskeletal physiology in offspring born to mouse dams fed a low-protein diet during pregnancy was altered and whether any physiological changes could be modulated by the nutritional protein content in early postnatal stages. Thy1-YFP female mice were fed ad libitum on either a normal (20%) or a low-protein (5%) diet. Newborn pups were cross-fostered to different lactating dams (maintained on a 20% or 5% diet) to generate three groups analysed at weaning (21 days): Normal-to-Normal (NN), Normal-to-Low (NL) and Low-to-Normal (LN). Further offspring were maintained ad libitum on the same diet as during lactation until 12 weeks of age, creating another three groups (NNN, NLL, LNN). Mice on a low protein diet postnatally (NL, NLL) exhibited a significant reduction in body and muscle weight persisting up to 12 weeks, unlike mice on a low protein diet only prenatally (LN, LNN). Muscle fibre size was reduced in mice from the NL but not LN group, showing recovery at 12 weeks of age. Muscle force was reduced in NLL mice, concomitant with changes in the NMJ site and changes in atrophy-related and myosin genes. In addition, μCT scans of mouse tibiae at 12 weeks of age revealed changes in bone mass and morphology, resulting in a higher bone mass in the NLL group than the control NNN group. Finally, changes in the expression of miR-133 in the muscle of NLL mice suggest a regulatory role for this microRNA in muscle development in response to postnatal diet changes. Overall, this data shows that a low maternal protein diet and early postnatal life low-protein intake in mice can impact skeletal muscle physiology and function in early life while postnatal low protein diet favours bone integrity in adulthood.     

Nanoparticles as tool for enhanced ophthalmic delivery of vancomycin: a multidistrict-based microbiological study,solid lipid nanoparticles formulation and evaluation

Context: A microbiological multidistrict-based survey from different Egyptian governorates was conducted to determine the most prevalent causative agents of ocular infections in the Egyptian population. Antibiotic sensitivity testing was then performed to identify the most potent antimicrobial agent. Vancomycin (VCM) proved the highest activity against gram-positive Staphylococcus bacteria, which are the most commonly isolated causative agents of ocular infection. However, topically applied VCM suffers from poor ocular bioavailability because of its high molecular weight and hydrophilicity. Objective: The aim of the present study was to develop VCM-loaded solid lipid nanoparticles (SLNs) using water-in-oil-in-water (W/O/W) double emulsion, solvent evaporation technique to enhance ocular penetration and prolong ophthalmic residence of VCM. Method: Two consecutive full factorial designs (2⁴ followed by 3²) were adopted to study the effect of different formulation and process parameters on SLN formulation. The lipid type and structure, polyvinyl alcohol (PVA) molecular weight and concentration, sonication time, as well as lipid:drug ratio were studied as independent variables. The formulated SLN formulae were evaluated for encapsulation efficiency (EE%), particle size (PS), and zeta potential as dependent variables. Results: The statistically-optimized SLN formula (1:1 ratio of glyceryltripalmitate:VCM with 1% low molecular weight PVA and 1?min sonication time) had average PS of 277.25?nm, zeta potential of ?20.45, and 19.99% drug encapsulation. Scanning and transmission electron micrographs showed well-defined, spherical, homogenously distributed particles. Conclusion: The present study suggests that VCM incorporation into SLNs is successfully achievable; however, further studies with different nanoencapsulation materials and techniques would be valuable for improving VCM encapsulation.     

Experience with software conversion

Experience with a program to convert from LISP 1.6 to INTERLISP is described. The conversion program was designed with two goals in mind. First, it had to be capable of being executed in either of the languages's environments and it had to yield identical results. Second, the speed of the converted program was to be approximately the same as the original program. This meant that the conversion process must be completed prior to execution of the converted program. The various constraints and considerations imposed by these goals are examined. In addition, aside from problems in finding INTERLISP analogs for various LISP 1.6 constructs, careful consideration must also be paid to input/output functions, escape characters, global variables, representation of numbers and different string implementations.     

Modified Garden Balsan Optimization Based Machine Learning for Intrusion Detection

The Internet of Things (IoT) environment plays a crucial role in the design of smart environments. Security and privacy are the major challenging problems that exist in the design of IoT-enabled real-time environments. Security susceptibilities in IoT-based systems pose security threats which affect smart environment applications. Intrusion detection systems (IDS) can be used for IoT environments to mitigate IoT-related security attacks which use few security vulnerabilities. This paper introduces a modified garden balsan optimization-based machine learning model for intrusion detection (MGBO-MLID) in the IoT cloud environment. The presented MGBO-MLID technique focuses on the identification and classification of intrusions in the IoT cloud atmosphere. Initially, the presented MGBO-MLID model applies min-max normalization that can be utilized for scaling the features in a uniform format. In addition, the MGBO-MLID model exploits the MGBO algorithm to choose the optimal subset of features. Moreover, the attention-based bidirectional long short-term (ABiLSTM) method can be utilized for the detection and classification of intrusions. At the final level, the Aquila optimization (AO) algorithm is applied as a hyperparameter optimizer to fine-tune the ABiLSTM methods. The experimental validation of the MGBO-MLID method is tested using a benchmark dataset. The extensive comparative study reported the betterment of the MGBO-MLID algorithm over recent approaches.     

EF-MPR,a new energy eFficient multi-point relay selection algorithm for MANET

MultiPoint Relay (MPR) selection algorithm is a flooding technique for propagating a broadcast message inside an ad-hoc network which reduces the number of unnecessary broadcast messages in order to save more energy in the network, minimize the number of packet collisions, and speed up the propagation time. In this paper, we demonstrate that MPR selection is an application of Set Covering Problem (SCP). A few optimization methods are developed in this work to find the optimum solution including Simulated Annealing (SA), Tabu Search (TS), Genetic Algorithm (GA), and a new greedy algorithm. Extensive simulations are set up to evaluate the developed methods. The new algorithm is named Energy eFficient MPR or EF-MPR in short. The simulation results show that EF-MPR can reduce the number of MPR nodes up to 19%. Moreover, EF-MPR algorithm reduces the power-consumption of network up to 12% and speed up the propagation time by 9%.     

Intensive Benchmarking of D2D communication over 5G cellular networks: prototype,integrated features,challenges, and main applications

Wireless Networks - The evolving Fifth generation (5G) cellular wireless networks are envisioned to provide higher data rates, lower end-to-end latency, and lower energy consumption for devices. In...     

Effects of THC on driving performance, physiological state and subjective feelings relative to alcohol

BACKGROUND: The effects of marijuana or THC on driving has been tested in several studies, but usually not in conjunction with physiological and subjective responses and not in comparison to alcohol effects on all three types of measures. OBJECTIVE: To assess the effects of two dosages of THC relative to alcohol on driving performance, physiological strain, and subjective feelings. METHOD: We tested the subjective feelings and driving abilities after placebo, smoking two dosages of THC (13 mg and 17 mg), drinking (0.05% BAC) and 24 h after smoking the high dose THC cigarette, while monitoring physiological activity of the drugs by heart rate. Fourteen healthy students, all recreational marijuana users, participated in the study. RESULTS: Both levels of THC cigarettes significantly affected the subjects in a dose-dependent manner. The moderate dose of alcohol and the low THC dose were equally detrimental to some of the driving abilities, with some differences between the two drugs. THC primarily caused elevation in physical effort and physical discomfort during the drive while alcohol tended to affect sleepiness level. After THC administration, subjects drove significantly slower than in the control condition, while after alcohol ingestion, subjects drove significantly faster than in the control condition. No THC effects were observed after 24 h on any of the measures.     

Elemental composition of Nigerian coals

Three analytical techniques (ASTM, FNAA and INAA) have been used to determine the major elemental composition, the percentage concentrations of Si, Fe, Al, as well as the concentrations of the trace elements Ca, K, Cl, Mg, Na and Ti of coals from seven major deposits in Nigeria. The results show that Nigerian coals are of weak or noncoking grades with low sulphur (0.52-2.04%) and high hydrogen (3.7-6.60%) contents. The coals possess properties which make them suitable feedstocks in gasification processes. They may also be used as blends with coking coals for sintering. Their potentials in the production of form coke in metallurgical and domestic applications, in addition to their suitability as thermal coals for electricity generation and steam raising in industries, provide incentives for their utilization.     

Vibration Performance,Stability and Energy Transfer of Wind Turbine Tower via Pd Controller

In this paper, we studied the vibration performance, energy transfer and stability of the offshore wind turbine tower system under mixed excitations. The method of multiple scales is utilized to calculate the approximate solutions of wind turbine system. The proportional-derivative controller was applied for reducing the oscillations of the controlled system. Adding the controller to single degree of freedom system equation is responsible for energy transfers in offshore wind turbine tower system. The steady state solution of stability at worst resonance cases is studied and examined. The offshore wind turbine system behavior was studied numerically at its different parameters values. Furthermore, the response and numerical results were obtained and discussed. The stability is also analyzed using technique of phase plane and equations of frequency response. In addition, the numerical results and comparison between analytical and numerical solutions were obtained with MAPLE and MATLAB algorithms.