This study summarizes the theory of laser-amplifier noise based on the master equation, the quantum-mechanical Langevin equation, and the rate equation. The three approaches are used differently depending on the application. Laser-amplifier noise in the direct-detection receiver is described by the master equation. Meanwhile, the quantum-mechanical Langevin equation is most effective for analyzing noise appearing in coherent optical communication systems. The rate equation approach provides a clear understanding of the intensity-noise characteristics of laser oscillators, including the amplitude squeezing.

Here we propose a novel mathematical method to construct an analog gauge using a weighted-sum combination of several sensor measurements, where the sensors have significant skedasticity in their response functions. We provide a theoretical derivation for the weights and an empirical demonstration of the method (skedastic boosting) on a real-world dataset with applications to gas concentration estimation.

The Internet of Things (IoT) is today’s rapidly growing technology that consists of a massive number of smart devices. The advent of IoT has brought about huge prospects of innovation and opportunities on the domestic as well as industry levels. However, there are vulnerabilities and weaknesses of IoT that inflame concerns about security and privacy issues. Enhancing the security of IoT networks is a critical issue and Intrusion Detection Systems (IDS) become the forefront solution in safeguarding the network from different malicious attacks. In this work, we tackle the emerging anomaly detection problem in IoT using Artificial Intelligence, more precisely a deep learning scheme called Convolutional Neural Network (CNN). The CSE-CIC-IDS 2018 dataset provided by the Canadian Institute of Cybersecurity was used for the experimental analysis. According to the study, the suggested approach outperforms the existing machine learning-based algorithms, with a low false alarm rate and better detection rate in the detection of attacks.

Discriminating motor imagery with electroencephalogram (EEG)-based brain-computer interface (BCI) poses a challenge as it involves an extensive data acquisition phase that demands a substantial amount of effort from the user. To address this issue, one approach is to use unsupervised domain adaptation, where classification models are constructed using data from multiple subjects, and only the unlabeled data from the target user is used for model calibration. However, since brain patterns from motor imagery vary between individuals, the reliability of each subject must be considered when multiple subjects are used to build the classification model. Thus in this paper, we propose Selective-MDA that performs domain adaptation on each source subject and selectively limits influences based on their domain discrepancies. To evaluate our approach, we assess our results with two public datasets, BCI Competition IV IIa and the Autocalibration and Recurrent Adaptation datasets. We further investigate the effect of source selection by comparing the discrimination performance when different numbers of source domains are selected based on discrepancy measures. Our results demonstrate that Selective-MDA not only integrates multi-source domain adaptation to cross-subject motor imagery discrimination but also highlights the impact of source domain selection when using data from multiple subjects for model training.

Misalignment can significantly affect the mutual inductance of a wireless power transfer (WPT) system, there by impacting the system’s power transfer capabilities and efficiency. Traditionally, to achieve optimal efficiency with constant output power under misalignment, individual closed loop feedback control needs to be used on both the transmitter and receiver sides. The receiver side phase shift (β) is controlled to maintain the constant output power, and the transmitter side phase shift (α) is controlled to achieve unity power factor for optimal efficiency. However, this necessitates additional high-bandwidth sensing circuitry, thereby increasing system complexity. This paper proposes a novel control approach for mitigating the impact of misalignment and load variation by employing a single receiver-side controller. A closed form analytical relationship between α and β has been established in this paper to maintain optimal efficiency while regulating output power. The proposed method obtains β from the receiver side controller and α from the derived relation. The proposed control method is validated using an experimental setup of a 1 kW bidirectional wireless power transfer (BiWPT) system. The simulation and experimental results of the BiWPT system under various loading and misalignment conditions are presented.

This paper discusses about the challenges in data communications and networking

The next generation communication systems demand a tremendous increase in the existing data rate for various applications. One solution to this issue is the use of Large Intelligent Surfaces (LIS) which consists of a panel on which reflecting elements are mounted. Their main role is to redirect the incident electromagnetic signal to the desire user. This in turn increases the strength of the received signal which increases the quality of reception, providing an enhanced Quality of Service (QoS). The Machine Learning Algorithms have been implemented for various aspects of LIS implementation like channel estimation, calculation of discrete phase shifts to name a few. Here, a Deep Learning (DL) model is proposed to evaluate the Signal to Interference Noise Ratio (SINR) for a LIS assisted communication system. The effect of Adam, SGD, RMSProp, Adamax and Adadelta optimizers on the DL model is studied. The loss function considered is Mean Squared Error (MSE). The simulation results indicate that the DL model with Adam Optimizer gives accuracy of 97.45% which is better as compared to other optimizers.

We explore the use of distributed PV power measurements for real-time short-term forecasting of the maximum potential power output of a utility-scale PV power plant, to support future incorporation of PV plants in automatic generation control (AGC) systems. PV plants operating under AGC may run in a curtailed state but must be capable of accurately forecasting their maximum potential non-curtailed power output, or potential high limit (PHL), 10-20 seconds in advance – despite variable environmental conditions. One forecast approach estimates PHL from a subset of inverters, designated reference inverters, which are never curtailed. We propose an alternative approach using in-situ I V tracers to measure maximum power point of reference modules distributed throughout a PV plant. This would allow for greater sampling of variations in irradiance, module temperature, soiling, and albedo, permitting better PHL prediction accuracy. To study potential benefits of this method, we analyzed a one-year data set from an approximately 100 MW PV power plant in the US Southeast, using non-curtailed string combiner outputs as a proxy for distributed module-level power measurements. We compare the reference inverter and distributed power measurement approaches. Results indicate that for highly flexible operation, substantial improvement in prediction accuracy can be achieved using distributed power measurements compared to using a low fraction of reference inverters. We review the study methodology, conclusions, and shortcomings and discuss prospective future work.

Objective: Exercise monitoring with low-cost wearables could improve the efficacy of remote physical-therapy prescriptions by tracking compliance and informing the delivery of tailored feedback. While a multitude of commercial wearables can detect activities of daily life, such as walking and running, they cannot accurately detect physical-therapy exercises. The goal of this study was to build open-source classifiers for remote physical therapy monitoring and provide insight on how data collection choices may impact classifier performance. Methods: We trained and evaluated multi-class classifiers using data from 19 healthy adults who performed 37 exercises while wearing 10 inertial measurement units on the wrist, pelvis, thighs, shanks, and feet. We investigated the effect of sensor density, location, type, sampling frequency, output granularity, feature engineering, and training-data size on exercise-classification performance. Results: Exercise groups (n=10) could be classified with 96% accuracy using a set of 10 inertial measurement units (IMUs) and with 89% accuracy using a single pelvis-worn IMU. Multiple sensor modalities (i.e., accelerometers and gyroscopes), high sampling frequencies, and more data from the same population did not improve model performance, but in the future data from diverse populations and better feature engineering could. Conclusions: Given the growing demand for exercise monitoring systems, our sensitivity analyses, along with open-source tools and data, should reduce barriers for product developers, who are balancing accuracy with product formfactor, and increase transparency and trust in clinicians and patients.

In survivable elastic optical networks, multi-path protection combined with traffic squeezing has recently gained attention. Link-disjoint multipath routing (LD-MPR) and bandwidth squeezing protection (BSP) when applied to the routing, modulation level and spectrum assignment problem are efficient strategies to address the excessive bandwidth demanded by protected services, spectral fragmentation and link-load balancing. LD-MPR enables the division of service transmission bit rate into independent flows whereas BSP tolerates traffic reduction under link failure. An open issue in the literature is how to efficiently divide demanded traffic among the link-disjoint routes under dynamic-traffic and BSP, while complying the SLA. We propose in this paper a multi-objective-  optimization genetic algorithm that defines how service transmission bit rate should be partitioned among the candidate link-disjoint routes. A customized partitioning definition is made for each source-destination node pair in the network, aiming the simultaneous minimization of network blocking probability and average squeezing transmission bit rate experienced by the services during single-link failure. We also propose, for the first time, a fixed-alternate routing using groups of disjoint paths (FARgdp). Complex dynamic-traffic network scenarios that simultaneously consider BSP, LD-MPR and FARgdp are addressed.

In this paper, we present a Jacobian vector correction (JVC) method for calibrating tactile force in EIT-based tactile sensors. The JVC method addresses the non-uniform sensitivity distribution of EIT reconstruction by constructing a scaling vector for sensitivity correction based on the Jacobian vector. A thresholding segmentation strategy is employed to enhance the correction results within a region of interest (ROI). The proposed method achieves approximately constant sensitivity across all locations and eliminates the need for extensive data collection during force calibration. We provide a straightforward sensitivity analysis and visualizations to improve comprehension of EIT measurement properties and the calibration methodology. The effectiveness of the JVC method is evaluated through phantom experiments using various target objects with different conductivities or sizes, and on a non-array EIT-based tactile sensor composed of a porous elastic polymer and ionic liquid. The results demonstrate the ability of the JVC method to accurately capture strength information without being constrained by location dependency. Furthermore, the practicality of our proposed method is validated by integrating two calibrated flexible tactile sensors (each with a 100 cm2 area) into a robot arm platform.

In this work, we propose a novel nano-scale architecture that performs matrix multiplications. Matrix multiplications are the basic operations of machine learning (ML) algorithms and, thus, the presented approach enables their application at the nano-scale, for example inside the human body in the Internet of Bio-Nano-Things (IoBNT). It is based on the molecule exchange between connected compartments and introducing chemical reactions in some of them. The matrix entries are solely defined by the volumes of the compartment. We provide a detailed mathematical description of the stochastic and dynamic behavior of the system. Moreover, we derive design guidelines for the proposed architecture. Finally, we validated the proposed approach through particle-based simulations.

Dynamic modeling and its control of two nonlinear processes are presented in this work. The processes emulate a clean water storage plant and are configured by interconnecting two tanks. According to the way, the tanks are interconnected, i.e., in cascade or in series, the dynamic behavior of the process changes from a non-interacting to an interacting system, both nonlinear. Three controllers (two linear and one nonlinear) have been designed and applied to the process. In order to evaluate the performance of the system, as a function of the controller applied, three figures of merit have been selected and compared. From the comparison results of these figures of merit, the advantages and drawbacks of each designed controller are identified. Finally, through simulation results, the feasibility of the system operation is verified.

Smart cities intelligently control the functionalities of the city through the use of various electronic methods, sensors, and advanced communication techniques. An intelligent transport system (ITS) is the backbone of smart cities and refers to a system in which numerous vehicles utilize a communication infrastructure to exchange vital information, such as traffic, congestion, and road conditions. One of the key elements of ITS is vehicle tracking or localization, which is the monitoring of a moving vehicle’s location using a Global Positioning System (GPS). Accurate vehicle localization is required because many safety and traffic management applications depend on the precise positioning of the vehicles. Intelligent reflecting surface (IRS) is a new technology that offers attractive features like improved performance gains, enhanced network coverage, and flexible deployment, and is considered a key enabler for the 6G-driven vehicle-to-everything (V2X) systems that provide a programmable wireless environment. In this paper, a comprehensive view of a 6G-driven vehicle localization mechanism utilizing IRS is provided. The work also lists the benefits of IRS-enabled sensing in 6G vehicular networks including enhanced security, overcoming blockages, and improved localization. A case study to highlight the advantages of the IRS in vehicle tracking is presented. Finally, we also present the research challenges and future work directions in IRS-enabled vehicle tracking.

The paper briefly explains on CAN protocol, On-board Diagnostics and about the establishment of wireless data acquisition in the automotive domain. This paper can really help people to understand the overall network of in-vehicle system and about the STM32 mcu.

A document by Eslam Mounier . Click on the document to view its contents.

The Internet of Things (IoT) paradigm has widespread applications across many fields in which private and sensitive user or environmental data are sensed and shared. Most present-day IoT applications depend on centralized cloud servers for authentication and access control. Validating the identity of a user and determining the legitimacy of his/her access requests require multiple rounds of data communications over the untrusted Internet, exposing sensitive data to potential attacks. Thus, protecting these data from security and privacy attacks and ensuring legitimate access is imperative. To address this challenge, we adopt an emerging technology called blockchain to propose a decentralized security framework called BloAC. It ensures secure access control in IoT networks without the intervention of the back-end cloud. We have used the Hyperledger Fabric, an open-source, permissioned blockchain platform, for implementing a prototype system using customized attribute-based access control (ABAC) policies. We have performed simulated and real test bed-based experiments to illustrate that BloAC outperforms the cloud server-based access control in latency and scalability. Finally, we conduct a security analysis to formally verify the ABAC policies used in BloAC and establish its robustness against attacks theoretically and using the AVISPA tool.

Clock domain crossing (CDC) is a critical aspect of digital design where data is transferred between different clock domains in a synchronous system. It entails making sure that the data is appropriately synchronized and transported between the source and destination clock domains without any loss or errors. Modern digital systems that employ numerous clock domains to accommodate various modules or components operating at different frequencies place a high value on CDC. Potential timing problems such as setup and hold time violations, clock skew, and metastability pose difficulties for clock domain crossing. To overcome these difficulties and guarantee dependable data transport, appropriate synchronization techniques and design considerations are used, including the usage of synchronization components like flip-flops and synchronizers. This paper presents the advantage gained by using VC Spyglass tool for easier CDC analysis. CDC is crucial to maintain system functionality, performance, and reliability, and it requires careful design, verification, and analysis to mitigate potential issues and ensure the correct operation of complex digital systems.