Experimental Assessment of the Effects of Building Materials on Wi-Fi Signal 2.4 GHz and 5 GHz

We are all witnesses to the widespread use of wireless LANs (WLAN) and their easy implementation in indoor environments. Wi-Fi is the most popular technology for the WLAN. However, interference caused by building materials is a common, yet often overlooked, contributor to poor Wi-Fi performance. This interference occurs due to the nature of radio wave propagation and the characteristics of the wireless communication system. Therefore, during the implementation of these networks, one must consider the quasi-static nature of the Wi-Fi signal and its dependence on the influence of various building materials on the propagation of these waves. This paper presents the effects of building materials and structures on indoor environments for Wi-Fi 2.4 GHz and 5 GHz. To establish the interdependencies between factors influencing electric field levels, measurements were conducted in an experimental Wi-Fi network at different distances from the access point (AP). The results obtained show that the electric field strength of the Wi-Fi signal decreases depending on the distance, the building materials, and the transmitted frequency. Concrete material had the most significant impact on the strength of the electric field in Wi-Fi, while glass had a relatively minor effect on reducing it. Wi-Fi operates within the radio frequency spectrum, typically utilizing frequencies in the 2.4 GHz and 5 GHz bands. Additionally, measurements revealed that Wi-Fi signal penetration is more pronounced at lower frequencies (2.4 GHz) as opposed to the Wi-Fi signal 5 GHz. The findings can be used to address the impact of building materials and structures on indoor radio wave propagation, ultimately ensuring seamless Wi-Fi signal coverage within buildings.

Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

Adaptive blind gain correction of time-interleaved ADCs forwide-band communication applications

High spectral efficiency is essential in design of multimedia communication systems such as L-band mobile in addition to various requirements of transmission quality. Time-interleaved A/D converter （TI-ADC） is an effective candidate to implement wide-band ADC with relatively slow circuits accounting for digital spectrum management. However, practical performance of TI-ADC is largely limited because of mismatches between different channels originated from manufacturing process variations. In this paper, a blind adaptive method is proposed to correct gain mismatch errors in TI-ADC, and it is verified through simulations on a two-channel TI-ADC. In proposed method, gain mismatch error is estimated and corrected in an adaptive scheme. Proposed compensated T1-ADC architecture is structurally very simple and hence suitable for realiza- tion in integrated circuits. Besides, proposed digital compensation algorithm not only is computationally efficient but also provides an improvement of 32.7 dB in the performance of two-channel TI ADC.

Design of 1.33 μm and 1.55 μm Wavelengths Quantum Cascade Photodetector

In this paper, a quantum cascade photodetector based on intersubband transitions in quantum wells with ability of detecting 1.33 μm and 1.55 μm wavelengths in two individual current paths is introduced. Multi quantum wells structures based on III-Nitride materials due to their large band gaps are used. In order to calculate the photodetector parameters, wave functions and energy levels are obtained by solving 1-D Schrodinger–Poisson equation self consistently at 80 ?K. Responsivity values are about 22 mA/W and 18.75 mA/W for detecting of 1.33 μm and 1.55 μm wavelengths, respectively. Detectivity values are calculated as 1.17 × 107 (Jones) and 2.41 × 107 (Jones) at wavelengths of 1.33 μm and 1.55 μm wavelengths, respectively.

Optoelectronic properties analysis of silicon light-emitting diode monolithically integrated in standard CMOS IC

Si p^+n junction diodes operating in the mode of avalanche breakdown are capable of emitting light in the visible range of 400-900 nm. In this study, to realize the switching speed in the GHz range, we present a transient model to shorten the carrier lifetime in the high electric field region by accumulating carriers in both p and n type regions. We also verify the optoelectronic characteristics by disclosing the related physical mechanisms behind the light emission phenomena. The emission of visible light by a monolithically integrated Si diode under the reverse bias is also discussed. The light is emitted as spatial sources by the defects located at the p-n junction of the reverse-biased diode. The influence of the defects on the electrical behavior is manifested as a current-dependent electroluminescence.

Design and analysis of resonant cavity-enhanced quantum ring inter-subband photodetector with resonant tunneling barriers for terahertz detection

The effect of resonant cavity structure on the performance operation of In As/Ga As quantum ring intersubband photodetector is studied for detection of terahertz radiations range.In order to confinement of optical field w ithin active region and consequently enhancement in responsivity of device,tw o periods of Al2O3/Ga As distributed bragg reflectors are used as bottom dielectric mirror and a thin layer of Au material as top mirror of device.For further improvement in detectivity,Al0.3Ga0.7As/In0.3Ga0.7As resonant tunneling barriers are included in absorption layers to reduce dark current of device.Proposed photodetector show s a peak responsivity of about 0.4(A/W)and quantum efficiency of 1.2%at the w avelength of 80μm(3.75 THz).Furthermore,specific detectivity(D*)of device is calculated and results are compared to conventional quantum ring inter-subband photodetector.Results predict a D*of^1011(cm.Hz1/2/W)for device at T=80 K and V=0.4 V w hich is tw o orders of magnitude higher than that of conventional QRIPs.

Adaptive Expanding Ring Search Based Per Hop Behavior Rendition of Routing in MANETs

Routing protocols in Mobile Ad Hoc Networks(MANETs)operate with Expanding Ring Search(ERS)mechanism to avoid ooding in the network while tracing step.ERS mechanism searches the network with discerning Time to Live(TTL)values described by respective routing protocol that save both energy and time.This work exploits the relation between the TTL value of a packet,trafc on a node and ERS mechanism for routing in MANETs and achieves an Adaptive ERS based Per Hop Behavior(AERSPHB)rendition of requests handling.Each search request is classied based on ERS attributes and then processed for routing while monitoring the node trafc.Two algorithms are designed and examined for performance under exhaustive parametric setup and employed on adaptive premises to enhance the performance of the network.The network is tested under congestion scenario that is based on buffer utilization at node level and link utilization via back-off stage of Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA).Both the link and node level congestion is handled through retransmission and rerouting the packets based on ERS parameters.The aim is to drop the packets that are exhausting the network energy whereas forward the packets nearer to the destination with priority.Extensive simulations are carried out for network scalability,node speed and network terrain size.Our results show that the proposed models attain evident performance enhancement.

Comparative Performance Evaluation of Large FPGAs with CNFET-and CMOS-based Switches in Nanoscale

Routing resources are the major bottlenecks in improving the performance and power consumption of the current FPGAs. Recently reported researches have shown that carbon nanotube field effect transistors(CNFETs) have considerable potentials for improving the delay and power consumption of the modern FPGAs. In this paper, hybrid CNFET-CMOS architecture is presented for FPGAs and then this architecture is evaluated to be used in modern FPGAs. In addition, we have designed and parameterized the CNFET-based FPGA switches and calibrated them for being utilized in FPGAs at 45 nm, 22 nm and 16 nm technology nodes.Simulation results show that the CNFET-based FPGA switches improve the current FPGAs in terms of performance, power consumption and immunity to process and temperature variations. Simulation results and analyses also demonstrate that the performance of the FPGAs is improved about 30%, on average and the average and leakage power consumptions are reduced more than 6% and 98% respectively when the CNFET switches are used instead of MOSFET FPGA switches. Moreover, this technique leads to more than 20.31%smaller area. It is worth mentioning that the advantages of CNFET-based FPGAs are more considerable when the size of FPGAs grows and also when the technology node becomes smaller.

Data Augmentation and Random Multi-Model Deep Learning for Data Classification

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML models.The increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen examples.Deep learning(DL)models have a lot of parameters,and they frequently overfit.Effectively,to avoid overfitting,data plays a major role to augment the latest improvements in DL.Nevertheless,reliable data collection is a major limiting factor.Frequently,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in batches.In this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for classification.We present a methodology for using Generative Adversarial Networks(GANs)to generate images for data augmenting.Through experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model efficiency.Experimenting across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors

Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions.An integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge.This study presents an IIoT-based sensor node for industrial motors.The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms.The initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple sensors.The proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main brokers.The broker’s bridging allows for data backup in case of connection loss.The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial environments.The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults.The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.

A New Scheme to Direct Torque Control of Interior Permanent Magnet Synchronous Machine Drives for Constant Inverter Switching Frequency and Low Torque Ripple

DTC （direct torque control） can produce quick and robust response, but it has the problems of large torque ripples and inconstant inverter switching frequency. This paper introduces a modified direct torque control based on the SVM （space vector modulation） for IPMSM （interior permanent magnet synchronous motor） drive. Two PI （proportional-integral） controllers regulate the flux and torque, respectively, and the inverter is controlled by the SVM technique in the proposed DTC system. Simulation results show that the performance of the proposed DTC system has been improved with respect to the conventional DTC. The DTC system can effectively reduce the flux and torque ripples.

Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various applications.Therefore,a novel localisation algorithm is proposed for noisy range measurements in IIoT networks.The position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position estimation.Therefore,the AMs selection algorithm for the localisation of BMs in the IIoT network is introduced.Only those AMs will participate in the localisation process,which increases the accuracy of the final location estimate.Then,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement noise.The results are compared with the state‐of‐the‐art and verified through numerous simulations.

Effective Diagnosis of Lung Cancer via Various Data-Mining Techniques

One of the leading cancers for both genders worldwide is lung cancer.The occurrence of lung cancer has fully augmented since the early 19th century.In this manuscript,we have discussed various data mining techniques that have been employed for cancer diagnosis.Exposure to air pollution has been related to various adverse health effects.This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer.We have introduced data mining in lung cancer to air pollution,and our approach includes preprocessing,data mining,testing and evaluation,and knowledge discovery.Initially,we will eradicate the noise and irrelevant data,and following that,we will join the multiple informed sources into a common source.From that source,we will designate the information relevant to our investigation to be regained from that assortment.Following that,we will convert the designated data into a suitable mining process.The patterns are abstracted by utilizing a relational suggestion rule mining process.These patterns have revealed information,and this information is categorized with the help of an Auto Associative Neural Network classification method(AANN).The proposed method is compared with the existing method in various factors.In conclusion,the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.

Two-dimensional Parameter Relationships for W UMa-type Systems Revisited

Reviewing the empirical and theoretical parameter relationships between various parameters is a good way to understand more about contact binary systems.In this investigation,two-dimensional(2D)relationships for P–MV(system),P–L1,2,M1,2–L1,2,and q–Lratiowere revisited.The sample used is related to 118 contact binary systems with an orbital period shorter than 0.6 days whose absolute parameters were estimated based on the Gaia Data Release 3 parallax.We reviewed previous studies on 2D relationships and updated six parameter relationships.Therefore,Markov chain Monte Carlo and Machine Learning methods were used,and the outcomes were compared.We selected 22 contact binary systems from eight previous studies for comparison,which had light curve solutions using spectroscopic data.The results show that the systems are in good agreement with the results of this study.

Hybrid robust fault detection and isolation of satellite reaction wheel actuators

In this paper,a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults,external disturbances,and parametric uncertainties.The proposed methodology incorporates a residual generation module,including a bank of filters,into an intelligent residual evaluation module.First,residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances.The residual evaluation module is developed based on the suggested series and parallel forms.Further,a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance.A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances,manoeuvres,uncertainties,and noises.The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.

Optimal PD/PID control of smart base isolated buildings equipped with piezoelectric friction dampers

Long-period pulses in near-field earthquakes lead to large displacements in the base of isolated structures.To dissipate energy in isolated structures using semi-active control,piezoelectric friction dampers（PFD） can be employed.The performance of a PFD is highly dependent on the strategy applied to adjust its contact force.In this paper,the seismic control of a benchmark isolated building equipped with PFD using PD/PID controllers is developed.Using genetic algorithms,these controllers are optimized to create a balance between the performance and robustness of the closed-loop structural system.One advantage of this technique is that the controller forces can easily be estimated.In addition,the structure is equipped with only a single sensor at the base floor to measure the base displacement.Considering seven pairs of earthquakes and nine performance indices,the performance of the closed-loop system is evaluated.Then,the results are compared with those given by two well-known methods：the maximum possive operation of piezoelectric friction dampers and LQG controllers.The simulation results show that the proposed controllers perform better than the others in terms of simultaneous reduction of floor acceleration and maximum displacement of the isolator.Moreover,they are able to reduce the displacement of the isolator systems for different earthquakes without losing the advantages of isolation.

Robust Leader-Following Output Regulation of Uncertain Multi-Agent Systems With Time-Varying Delay

In this paper, the robust analysis and design of leader-following output regulation for multi-agent systems described by general linear models is given in presence of timevarying delay and model uncertainty. To this aim, a new regulation protocol for the closed-loop multi-agent system under a directed graph is proposed. An important specification of the proposed protocol is to guarantee the leader-following output regulation for uncertain multi-agent systems with both stable and unstable agents. Since many signals can be approximated by a combination of the stationary and ramp signals, the presented results work for adequate variety of the leaders. The analysis and design conditions are presented in terms of certain matrix inequalities. The method proposed can be used for both stationary and ramp leaders. Simulation results are presented to show the effectiveness of the proposed method.

Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

Localization of sensor nodes in the internet of underwater things(IoUT)is of considerable significance due to its various applications,such as navigation,data tagging,and detection of underwater objects.Therefore,in this paper,we propose a hybrid Bayesian multidimensional scaling(BMDS)based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical,magnetic induction,and acoustic technologies.These communication technologies are already used for communication in the underwater environment;however,lacking localization solutions.Optical and magnetic induction communication achieves higher data rates for short communication.On the contrary,acoustic waves provide a low data rate for long-range underwater communication.The proposed method collectively uses optical,magnetic induction,and acoustic communication-based ranging to estimate the underwater sensor nodes’final locations.Moreover,we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound(H-CRLB).Simulation results provide a complete comparative analysis of the proposed method with the literature.

Soft Robotic Glove Controlling Using Brainwave Detection for Continuous Rehabilitation at Home

The patients with brain diseases(e.g.,Stroke and Amyotrophic Lateral Sclerosis(ALS))are often affected by the injury of motor cortex,which causes a muscular weakness.For this reason,they require rehabilitation with continuous physiotherapy as these diseases can be eased within the initial stages of the symptoms.So far,the popular control system for robot-assisted rehabilitation devices is only of two types which consist of passive and active devices.However,if there is a control system that can directly detect the motor functions,it will induce neuroplasticity to facilitate early motor recovery.In this paper,the control system,which is a motor recovery system with the intent of rehabilitation,focuses on the hand organs and utilizes a brain-computer interface(BCI)technology.The final results depict that the brainwave detection for controlling pneumatic glove in real-time has an accuracy up to 82%.Moreover,the motor recovery system enables the feasibility of brainwave classification from the motor cortex with Artificial Neural Networks(ANN).The overall model performance reveals an accuracy up to 96.56%with sensitivity of 94.22%and specificity of 98.8%.Therefore,the proposed system increases the efficiency of the traditional device control system and tends to provide a better rehabilitation than the traditional physiotherapy alone.

A low-voltage and energy-efficient full adder cell based on carbon nanotube technology

Scaling problems and limitations of conventional silicon transistors have led the designers to exploit novel nano-technologies. One of the most promising and feasible nano-technologies is CNT(Carbon Nanotube) based transistors. In this paper, a high-speed and energy-efficient CNFET(Carbon Nanotube Field Effect Transistor) based Full Adder cell is proposed for nanotechnology. This design is simulated in various supply voltages, frequencies and load capacitors using HSPICE circuit simulator. Significant improvement is achieved in terms of speed and PDP(Power-Delay-Product) in comparison with other classical and state-of-the-art CMOS and CNFET-based designs, existing in the literature. The proposed Full Adder can also drive large load capacitance and works properly in low supply voltages.