Search

This study introduces the novel communication topology, namely RadRCom, integrating radar and relay-assisted communication systems for single antenna configuration as a proof of concept. While simultaneous radar and communication operations within the same spectrum gain momentum, our work advances this concept by incorporating relay assistance, particularly crucial in applications like vehicle-to-anything (V2X) communication. The inclusion of relays significantly enhances communication system performance, addressing challenges such as interference management between radar, relay, and communication nodes. This topology attracts three design challenges such as optimal radar waveform, relay parameters and communication system parameters. However, the key bottlenecks are the interference from radar to the relay and communication receiver and similarly the one from communication transmitter and relay node to the radar. Therefore, the work addresses these challenges simultaneously meeting the quality of service. Our proposed RadRCom system optimizes radar waveform and relay parameters to improve signal-to-interference noise ratio (SINR) at the radar and mean square error (MSE) of data transmission. We introduce two frameworks for parameter design, i.e, radar-centric and relay-centric ones. We take a sub-optimal iterative approach to address the computational complexity. Numerical simulations are performed to evaluate the performances of the proposed RadRCom system with the proposed algorithms.

Open resonant cavity antenna (ORCA) and its recent advances promise attractive features and possible applications, although the designs reported so far are solely based on the classical electromagnetic (EM) theory and general perception of EM circuits. This work explores machine learning (ML)-assisted antenna design techniques aiming to improve and optimize its major radiation parameters over the maximum achievable operating bandwidth. A state-of-the-art method, e.g., parallel surrogate model-assisted hybrid differential evolution for antenna synthesis (PSADEA) has been exercised upon a reference ORCA geometry revealing a fascinating outcome. This modifies the shape of the cavity which was not predicted by EM-based analysis as well as promising significant improvement in its radiation properties. The PSADEA-generated design has been experimentally verified indicating 3dB-11dB improvement in sidelobe level along with high broadside gain maintained above 17 dBi over the 18.5% impedance bandwidth of the ORCA. The new design has been theoretically interpreted by the theory of geometrical optics (GO). This investigation demonstrates the potential and possibilities of employing artificial intelligence (AI)-based techniques in antenna design where multiple parameters need to be adjusted simultaneously for the best possible performances.

One of the best ways to handle the ambiguity and unpredictable nature of decision-making is through fuzzy logic, and one of the most recent developments in this area is the concept of spherical fuzzy sets. Since the squared total of membership, non-membership, and hesitation degrees should be between 0 and 1, and each degree should be defined in [0, 1], the hesitation of the decision-maker(s) about an attribute can be conveyed more thoroughly. The ambiguity of a fuzzy set is computed with the help of an entropy measure, and the available entropy measures for spherical fuzzy sets have various limitations. So, in this study, we suggest an innovative entropy measurement for spherical fuzzy sets and demonstrate its capacity to satisfy the axiomatic requirements. We compared the proposed entropy metric and all currently available spherical fuzzy entropy metrics, considering different factors, including attribute weight computation, linguistic hedges, and ambiguity computation. With the help of the proposed entropy metric, we introduce the Complex Proportional Assessment method for spherical fuzzy sets and illustrate it with a numerical example.

Cybersecurity is a vital technology and measures intended to protect networks, computers, information, and programs from threats and illegal access, modification, or damage. A security model covers a network and a computer safety method. Each system has antivirus software, firewalls, and an intrusion detection system (IDS). IDS helps in discovering and identifying illegal system behavior such as usage, copying, alteration, and damage. By estimating network traffic anomalies and patterns, deep learning (DL) models can enhance the detection abilities of IDS when compared to traditional rule-based methods. These models learn complex representations from data, authorizing them to recognize subtle and developing attack patterns. Techniques like recurrent neural network (RNN) and convolutional neural network (CNN) can be applied to progress consecutive or spatial features in network data, correspondingly. This manuscript empowers Cybersecurity by utilizing an Enhanced Rat Swarm Optimizer with a Deep Stack-Based Ensemble Learning (ERSO-DSEL) model. The ERSO-DSEL approach leverages feature selection (FS) with EL strategies to boost cybersecurity. In the ERSO-DSEL system, Z-score normalization is employed to measure the input data. Besides, an improved equilibrium optimizer (IEO) based FS approach is applied to choose a set of features. For cyberattack recognition, the ERSO-DSBEL approach uses the DSEL approach comprising three models namely deep neural network (DNN), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM). Furthermore, the hyperparameter selection of these DL models takes place using the ERSO system. The solution result of the ERSO-DSBEL model is executed on a benchmark IDS database. A wide-contrast study reported that the ERSO-DSBEL model accomplishes an enhanced accuracy outcome of 99.67% over other models of cybersecurity.

Renewable distributed energy resources are the solution to rising power demand, but their integration is a difficult technological undertaking that requires careful planning. Distributed energy resources are mostly incorporated in the final level of power system networks, where harmonic content swings are highest. At the planning stage, the highest potential power injection, i.e., hosting capacity, is estimated while taking into account the main anticipated operational challenges, one of which is undesirable harmonics. But it is not possible to include all operational harmonic orders precisely for accurate hosting capacity estimation. The presence of harmonics affects the hosting capacity of the network to a considerable extent. In this work, operational derated hosting capacity is proposed to enhance the situational awareness for calculating the change in hosting capacity of the network due to the presence of operational dominant harmonics, as compared to the ideal condition. Additionally, the effect of harmonics on the power factor of the network nodes are also investigated thoroughly, abiding the limits of IEEE-519-2014. Because in any distribution network, unbalance cannot be neglected, operational derated hosting capacity is also extended for testing unbalanced conditions within the network. Indicators such as voltage, current, harmonic distortion, and power factor are studied thoroughly to analyze the derated hosting capacity. To justify the effectiveness of the proposed work operational derated hosting capacity is compared with the existing literature. A reconfigured IEEE-33 bus distribution network is considered for assessment. The authors utilized MATLAB along with Typhoon HIL real-time platform for modeling as well as for validation of the results.

This paper presents a novel method for real-time identification of four parameters of the permanent magnet synchronous machines (PMSM) namely stator resistance, d-axis inductance, q-axis inductance and the rotor flux linkage. The proposed method is based on the utilization of the deep neural network to solve the problems of the existing model-based parameter estimation methods, which are caused by the non-linearity of the inverter and the inaccuracy of the measured rotor position. Extensive numerical simulations and experimental studies have been conducted to evaluate the robustness and the accuracy of the proposed online parameters identification solution, compared with the conventional methods such as recursive least square, extended Kalman filter and Adaline neural network.

Medicinal plants play a vital role in treating human and livestock ailments. A majority of the population living across the world relies on medicinal plants as a primary source of healthcare. The rural as well as the urban habitats depend on medicinal plants. Medicinal plants may be of root type or shoot type. Onion is one of the root types of medicinal plants that has abundant health benefits as well as economic value. Due to increasing urbanization, the availability of agricultural land is decreasing rapidly which is causing an increase in the demand for medicinal plants in a reduced cultivation area. This has led to the cultivation of soilless smart precision farming methods. Sensors are used to monitor and control the environmental factors affecting the growth and yield of agricultural produce. Among all the vertical farming methods, hydroponics is found to be the best way for cultivating herbaceous plants. This article deals with the comparison of growth responses of hydroponically grown onion (Allium cepa) in a Deep-Water Culture setup, as compared to that cultivated in soil. A systematic literature survey of onion grown in a hydroponic cultivation system is carried out. Finally, the growth responses of hydroponic and soil-grown onions are checked and compared with the results obtained from the ‘AquaCrop’ simulator. Results of the actual and simulator are verified and validated, and it is found that the actual and the simulation results are similar.

Osteosarcoma is the most normal kind of cancer that arises in bones, which appears on the surface to resemble earlier types of bone cells that assist in forging new bone tissues, but the tissue in osteosarcoma is weaker and softer than normal bone tissue. The usage of automated techniques for the detection of osteosarcoma has the potential to mitigate the obligations and burdens confronted by pathologists owing to its abundant quantity of cases. Artificial intelligence (AI) has an emerging progress in diagnostic pathology. In recent years, numerous studies using deep learning (DL) techniques to histopathological images (HI) have been published. While several studies claim higher accuracy, they might lack generalization and fall into the pitfall of overfitting owing to the wide range of HI. The study objective is to enhance the diagnosis and detection of osteosarcoma by employing computer-assisted detection (CAD) and diagnoses (CADx). Technique like convolutional neural networks (CNN) make better prognoses for patient conditions and considerably reduce the surgeon’s workload. CNN needs to be trained on the massive quantity of data to accomplish a remarkable performance. Therefore, the study presents a novel Group Teaching Optimization Algorithm with Deep Learning-Driven Osteosarcoma Detection on Histopathological Images (GTOADL-ODHI) technique. The purpose of the GTOADL-ODHI technique is to examine the HIs for the detection and classification of osteosarcoma. To accomplish this, the GTOADL-ODHI algorithm applies the Gaussian filtering (GF) method for image pre-processed to become rid of the noise. Besides, the capsule network (CapsNet) model is utilized for the extractor of the feature vector. Furthermore, the hyperparameter selection of the CapsNet approach takes place using the GTOA. Finally, the self-attention bidirectional long short-term memory (SA-BiLSTM) model can be employed for osteosarcoma recognition and classification. The widespread experimental analysis of the GTOADL-ODHI method is tested on the benchmark datasets. The simulation validation reported the optimum solution of the GTOADL-ODHI algorithm related to existing systems concerning distinct aspects.

Blockchain is a ground-breaking technology that has changed how we manage and store protected data. It is a decentralized ledger that enables safe, open, and unchangeable record-keeping. It relies on a distributed network of nodes rather than a single central authority to check and verify transactions, guaranteeing that each entry is correct and unchangeable. Transactions in a blockchain network are grouped into blocks, which are then linked together in a chronological and immutable chain. Block size is a critical parameter in blockchain technology, which refers to the maximum size of each block in the chain that is not benchmarked yet. However, we cannot just change the block size of the blockchain. It is challenging and will create security issues. The Block size is crucial because it affects the number of transactions processed per second, the confirmation time, and overall network efficiency. The confirmation time should be faster to ensure stable earnings for the miners. Moreover, it needs help with broader applications due to high transaction fees and long verification times. We have proposed a reinforcement learning model named ROBB that can efficiently create a block considering the current network state and previous transactions. At first, the problem was converted into a reinforcement learning environment to solve using multiple reinforcement algorithms. We developed a blockchain simulator to replicate the network environment. To transform it into a reinforcement learning environment, we integrated it with OpenAI Gym. The simulator was trained by generating random transactions. Finally, we designed a reward function that enables the simulator to hold transactions and create blocks with the pending transactions when it determines that the environment is favorable. In the final results, ROBB successfully minimized the waiting time for transactions and utilized the blocks to their full potential. Additionally, it optimized the block space, building upon the findings of previous researchers. From the research, we can see that our proposed models show impressive results with 100% block utilization and 1.8s average waiting time while creating the least number of blocks.

Terahertz (THz) communication is a highly promising solution for addressing the ultra-high data rate requirements of future sixth-generation (6G) wireless systems. However, the unfavorable frequency-dependent channel characteristics of THz communication pose a significant challenge in realizing this promising technology. In this paper, we present a novel solution to this challenge by proposing a wideband THz system featuring a unique THz-specific digital baseband waveform design tailored to the distinctive channel characteristics of the THz spectrum. To address the frequency-dependent challenges posed by the wideband THz channel and the spectral efficiency loss inherent in conventional orthogonal frequency division multiplexing (OFDM) systems due to cyclic prefix overhead, we propose a THz system with beamforming incorporating a multi-band OFDM (MB-OFDM) based waveform design using a filter-bank based channel model that slices the wideband channel into smaller frequency-independent subbands for ultra-wideband THz communication. Furthermore, this work introduces a novel squint model taking into account the printed-circuit-board (PCB) impairments and circuit imperfections, which have not been considered before. We also propose an all-digital mitigation scheme with this model. We propose a channel estimation algorithm using a three-level adaptive method with an auto-regressive (AR) model for exploiting the frequency dependency among the intra and inter-narrow subbands. Apart from these key contributions, we propose a THz environment-aware precoder to mitigate the wideband effect in the THz channel, specifically the subband-specific path loss and environment-related effect, and balance the overall gains across the band. We have also proposed to optimize the number of subbands and zero-padding in MB-OFDM waveform based on the minimum bit error rate (MBER) criterion. Numerical simulations are carried out to evaluate the performance of the proposed schemes demonstrating their feasibility and effectiveness for wideband THz communication with results showing a lower peak-to-average power ratio (PAPR) for the proposed waveform as compared to conventional OFDM.

The ascent of electric vehicle (EV) technology as a leading solution for green transportation is accompanied by advancements in charging infrastructure and automation. A notable hindrance is the low level of automation in charging procedures. In response to this, Automatic Charging Robots (ACR) have emerged, equipped transitioning from the manual operation to an automated plugging and unplugging operation. However, for this process to be executed flawlessly, these robots necessitate a charging port detection system with a precise navigation system to ensure accurate insertion of the charging gun into the designated charging port. This paper presents a sophisticated system, AViTRoN (Advanced Vision Track Routing and Navigation), which is developed for Automated Charging Robots in the context of Electric Vehicle (EV) charging. AViTRoN integrates advanced technologies to enable efficient charging port detection, navigation, and seamless user interaction. Utilizing the YOLOv8 deep learning model, AViTRoN ensures real-time charging port type detection using the data from a 3D depth sensor and an IR sensor within the Robot Operating System (ROS) framework. The 3D depth sensor provides detailed spatial information, while the IR sensor detects subtle environmental changes, enhancing the system’s accuracy during operation. AViTRoN also incorporates a charging completion notification mechanism, sending instant alerts to users via GSM/GPRS communication upon the conclusion of the charging cycle, thereby enhancing user convenience and experience.

The structured matrix completion problem (SMCP) is ubiquitous in several signal processing applications. In this article, we consider a fixed pattern, namely, the Hankel-structure for the SMCP under quantum formalism. By exploiting its structure, a lower-gate-complexity quantum circuit realization of a Hankel system is demonstrated. Further, we propose a quantum simulation algorithm for the Hankel-structured Hamiltonian with an advantage in quantum gate-operation complexity in comparison with the standard quantum Hamiltonian simulation technique. We show its application in eigenvalue spectrum estimation for signal processing applications. An error bound associated with this proposed quantum evolution is proposed with the consideration of spectrum estimation and measurement uncertainty. Numerical results are reported adopting random matrix theory in its fold to evaluate the efficacy of the proposed architecture and algorithm for large-dimensional systems, including an example application in delay estimation for ranging operations in a wireless communication system.

An accurate mathematical ECG model helps comprehend the heart’s workings, which in turn helps identify various heart-related abnormalities. A typical ECG waveform consists of recurrent QRS complexes at almost regular intervals. In the most general scenario, the amplitude, the period after which it repeats, and the shape of the QRS complex may change from pulse to pulse. This paper proposes a mathematical model for the ECG signals capable of capturing these features. Since statistical analysis of the the general model is more complex, we make certain assumptions to make the problem analytically tractable. With these assumptions at our disposal, we provide closed-form expressions for time-varying covariance/spectra of the process generated by the proposed model. In order to demonstrate the approximation capability of the model, that is, the ability to model ECG signals, we synthesize ECG signals for different health states by first extracting the QRS complex of that health state and using the mean time period also extracted from the realization of the same health state. We find that the degree of non-stationarity of the real-world ECG and the one generated by the model match closely. In reality, the signal for each state appears to be a close match. Based on our experimental findings, we observe that the measure of the degree of non-stationarity interestingly falls in different ranges, depending upon the type of heart-related abnormality. This observation makes our conjecture that this measure can serve as a biomarker for various related abnormalities.

The air-writing recognition task entails the computer’s ability to directly recognize and interpret user input generated by finger movements in the air. This form of interaction between humans and computers is considered natural, cost-effective, and immersive within the domain of human-computer interaction (HCI). While conventional air-writing recognition has primarily focused on recognizing individual characters, a recent advancement in 2022 introduced the concept of writing in the air (WiTA) to address continuous air-writing tasks. In this context, we assert that the Transformer-based approach can offer improved performance for the WiTA task. To solve the WiTA task, this study formulated an end-to-end air-writing recognition method called TR-AWR, which leverages the Transformer model. Our proposed method adopts a holistic approach by utilizing video frame sequences as input and generating letter sequences as outputs. To enhance the performance of the WiTA task, our method combines the vision transformer model with the traditional transformer model, while introducing data augmentation techniques for the first time. Our approach achieves a character error rate (CER) of 29.86% and a decoding frames per second (D-fps) value of 194.67 fps. Notably, our method outperforms the baseline models in terms of recognition accuracy while maintaining a certain level of real-time performance. The contributions of this paper are as follows: Firstly, this study is the first to incorporate the Transformer method into continuous air-writing recognition research, thereby reducing overall complexity and attaining improved results. Additionally, we adopt an end-to-end approach that streamlines the entire recognition process. Lastly, we propose specific data augmentation guidelines tailored explicitly for the WiTA task. In summary, our study presents a promising direction for effectively addressing the WiTA task and holds potential for further advancements in this domain.

In this work, we present a novel clustering approach which is inheriting the best characteristics of Unsupervised Functional Link Artificial Neural Network (UFLANN) and Genetic Algorithms (GAs) for uncovering clusters embedded in dataset represented through $(X)_{Nxd}$ , where X consists of $N$ data points with $d$ -dimensions. With an aim to realize natural clusters in a linear space UFLANN mapped the input vectors from lower dimension to higher dimension with a greater hope to achieve linearity in higher dimensional space. As a result, UFLANN introduces the problem of curse of dimensionality in the given datasets. However, it has been realized that the problems like sparse data and distance concentration associated with curse of dimensionality cast this problem to again a very complex problem. Hence to address some of the issues of curse of dimensionality, we have used GAs for selecting optimal number of features in the higher dimension for UFLANN to discover clusters embedded in the dataset. The proposed approach herein after named as GAUFLANN has been experimentally evaluated by using the metrics like (i) Davies-Bouldin Index, ii) Silhouette Score, and iii) Completeness score on different synthetic and real datasets. Our experimental study confirms that GAUFLANN is evidently scoring better in DB-index, Silhouette score, and Completeness score than the clustering methods like K-means, Hierarchical-Agglomerative (Average Linkage), and UFLANN across the datasets like Circles, Moons, Iris, and CORD-19.

Transferring an all-electric spacecraft from a launch injection orbit to the geosynchronous equatorial orbit (GEO) using a low thrust propulsion system presents a significant challenge due to the long transfer time typically spanning several months. To address the challenge of determining such long time-scale orbit-raising maneuvers to GEO, this paper presents a novel technique to compute transfers starting from geostationary transfer orbit (GTO) and super-GTO. The transfer is complex, involving multiple eclipses and revolutions. To tackle this challenge, we introduce a cascaded deep reinforcement learning (DRL) model to guide a low-thrust spacecraft towards the desired orbit by determining an appropriate thrust direction at each state. To ensure mission requirements, a gradient-aided reward function incorporating the orbital elements, guides the DRL agent to obtain the optimal flight time. The obtained results demonstrate that our proposed approach yields optimal or near-optimal time-efficient spacecraft orbit-raising. DRL implementation is important for spacecraft autonomy; in this context, we demonstrate that our DRL-based trajectory planning provides significantly better transfer time as compared to state-of-the-art approaches that allow for automated trajectory computation.

Red palm weevil (RPW) is a pest that can cause severe damage to plantations and affects palm trees. Classical approaches to detection depend on visual analysis, which is inaccurate and time-consuming. Hence, deep learning techniques emerge as a potential solution used for automating the process of detection and presenting efficient and precise results. The initial detection of the RPW remains a difficult task for good production as the identification will protect palm trees infected from the RPW. So advanced technologies like artificial intelligence (AI) and computer vision (CV) can be used in preventing the spread of the RPW on palm trees. Various scholars still working on identifying a precise method for the classification, identification, and localization of the RPW pest. This article develops an automated Red Palm Weevil Detection using Gorilla Troops Optimizer with Deep Learning (RPWD-GTODL) method. The goal of the presented RPWD-GTODL approach lies in the accurate detection and localization of the RPW effectually. To accomplish this, the presented RPWD-GTODL technique initially uses the Gabor filtering (GF) technique to pre-process the images. For RPW detection, the RPWD-GTODL technique uses a Mask RCNN object detector with MobileNetv2 as a backbone network. Moreover, the detection performance of the RPWD-GTODL technique can be boosted by the design of the GTO algorithm for the hyperparameter selection of the MobileNetv2 model. The performance validation of the RPWD-GTODL technique is tested using the RPW dataset and the results demonstrate the enhanced performance on RPW detection process with maximum accuracy of 99.27%.

Toeplitz matrix reconstruction algorithms (TMRAs) are one of the central subroutines in array processing for wireless communication applications. The classical TMRAs have shown excellent accuracy in the spectral estimation for both uncorrelated and coherence sources in the recent era. However, TMRAs incorporate the classical eigenvalue decomposition technique for estimating the eigenvalues of the Toeplitz-structured covariance matrices that demand very high computational complexity for large arrays. We demonstrate a low-complexity quantum simulation framework exploiting the structured Hamiltonian of Toeplitz and circulant variants. In this framework, we consider two approaches for the estimation of the eigenvalue spectrum of a given Toeplitz-structured matrix: first, an analytical framework with Jordan form-based sparse-decomposition of a dense-Toeplitz matrix, and second, an approximation method for the conversion of a Toeplitz matrix into a circulant matrix embedding quantum subroutines. We have also compared the efficacy of the proposed algorithms with standard Hamiltonian simulation and quantum phase estimation techniques for different quantum time resolutions and gate complexities. We show quantum gate-complexity analysis for our proposed algorithms. Considering the large dimensions of the Toeplitz matrix, we have employed random matrix theory in deriving the error bounds for the estimated eigenvalues. The numerical results are obtained partly in a classical computer and in an IBM quantum simulator.

This work addresses a concern of aperture coupled microstrip patch which is commonly overlooked. Such feeding configuration is typically believed to be immune to high cross-polar (XP) radiations, which indeed is a paradox. It actually offers considerably low XP only across H-plane, but concerning high XP over its diagonal or skewed planes (azimuth $\approx 45^{\circ }$ -70°). An aperture feed, therefore, hardly reveals any advantageous feature in terms of the overall XP discrimination. Such a major shortcoming of aperture-fed microstrip, to the best of our knowledge, has been addressed and successfully resolved in this article for the first time. It explores a way of mitigating near field issues based on theoretical analysis and has proposed a simple strategic approach to reform the same for a rectangular patch. A representative design, theoretical justification, and experimental studies with an S-band prototype have been presented. XP suppression by 11dB has been experimentally achieved in the diagonal (D-) plane with no considerable changes in its H- or E-plane. That eventually attains an overall XP discrimination by nearly 27dB from the perspective of 3D radiation scenario. The proposed technique hardly affects the co-polar radiations or gain of its traditional design. Moreover, this is satisfactorily functioning for a $2\times 2$ sub-array with a remarkable co-to-cross isolation by about 34dB over the entire radiation planes.

The vast expansion of the Internet of Things (IoT) devices and related applications has bridged the gap between the physical and digital world. Unfortunately, security remains a major challenge and the lack of secure links have fueled the increased attacks on IoT devices and networks. Due to its inherent scalability, Public Key Infrastructure (PKI) is the well-known and classic approach to bring public-key certificate based security to IoT. Even though the standard X.509 explicit certificates can be viable solution, they are inefficient and too large for resource constrained IoT networks and therefore, smaller, faster and more efficient Elliptic Curve Qu Vanstone (ECQV) implicit certificates can be employed for establishing authenticated connections in IoT. Moreover, the existing certificate-based authentication proposals in standardized IoT networks have either been deployed at the transport or physical layers. Thus, these proposals fail to provide true end-to-end security to messages at the application layer in the presence of intermediate CoAP proxies. This challenging aspect is addressed in this proposal by focusing on the certificate-based authentication at the application layer to ensure true end-to-end security of messages. Additionally, IoT application layer security protocols like EDHOC lacks mechanism for authenticated distribution of public keys and thus, there is a need for lightweight authentication based cryptographic primitive for establishing secure key agreement in IoT. This paper introduces a design and implementation of a lightweight ECQV implicit certificate and use them for authenticated key exchange in EDHOC at the application layer. We also design a lightweight profile with a novel encoding mechanism for ECQV implicit certificate, called L-ECQV. To prove its viability, L-ECQV has been implemented and evaluated on Contiki operating system. Our evaluation results show that the proposed L-ECQV certificate approach reduces energy consumption by 27%, message overhead of EDHOC handshake by 52%, and shows improvements in certificate validation time. The security analysis demonstrates that proposed L-ECQV certificates for EDHOC protocol is secure against a number of attack vectors present in the IoT network. This novel combination of ECQV certificates with EDHOC key exchange leads to a secure and lightweight authenticated key agreement in IoT networks.

Recently, novel applications in the space of artificial intelligence (AI) such as solving constraint optimization problems, probabilistic inferencing, contextual adaptation, and continual learning from noisy data are gaining momentum to address relevant real-world problems. A majority of these tasks are compute and/or memory intensive. While traditional deep learning has been fueled by the utilization of graphic processing units (GPUs) to accelerate algorithms primarily in the cloud, today we see a surge in the development of application/domain-specific integrated circuits and systems that aim at providing an order of magnitude improvement over traditional GPU-based approaches in terms of energy efficiency and latency. This growing branch of research taps into the realms of neuronal dynamics, collective computing using dynamical systems, harnessing stochasticity to enable probabilistic computing, and even draws inspiration from quantum computing. We envision such specialized application/domain-specific systems to perform complex tasks such as solving NP-hard optimization problems, performing reasoning and cognition in the presence of uncertainty with superior energy-efficiency (and/or area and latency improvements) compared to conventional GPU-based approaches and von Neumann computing using traditional silicon-based devices, circuits, and architectures. Of special interest is to utilize such nontraditional computing approaches to reduce the time to obtain solutions for computationally challenging problems that otherwise tend to grow exponentially with problem size. To support this vision, there needs to be fundamental advances in both nontraditional devices and circuits/architectures. Recent works have shown that novel circuit topologies and architectures involving non-Boolean, oscillatory, spiking, probabilistic, or quantum-inspired computing are more suited toward tackling applications such as solving constraint optimization problems, performing energy-based learning, performing Bayesian learning and inference, lifelong continual learning, and solving quantum-inspired applications such as Quantum Monte Carlo. A flurry of current research highlights that compared to traditional silicon-based devices, emerging nanodevices utilizing novel quantum materials such as complex oxides, ferroelectric materials, and spintronic materials can allow the realization of these novel circuits and architectures with lower foot-print area, higher energy efficiency, and lower latency.

Quantifying a system’s time-invariance deviation may reveal how it represents and processes the signals. The main objective of this paper is to quantify the departure of a time-varying system from being time-invariant. In particular, the paper quantifies the time variance of filter banks using kernel representation. We define the $\ell ^{2}$ measure for the degree of time variance ( $\mathrm {MDTV}^{\ell _{2}}$ ) based on the discrete-time domain form of the kernel representation and the $\ell ^{\infty }$ measure ( $\mathrm {MDTV}^{\ell _{\infty }}$ ) based on the matrix representation of the LPTV kernel. We show that off-diagonal matrix elements can be used to deduce the time-varying behavior of an LPTV system, proving that time-invariant and time-varying components can be separated. The matrix form of the LTI system, which is closest to an LPTV system, has been derived. This research offers a new kernel-based definition for time variance for any time-varying system and provides explicit expressions for MDTV for three distinct scenarios, namely, a single channel (SC), a uniform filter bank (UFB), and a non-uniform filter bank (NUFB). For each scenario, we determine the LPTV kernel and impulse response of the nearest LTI system to plot the MDTV versus decimation factor. Also, MDTV generated by an SC and a UFB with identical low pass filters in each channel is identical. We also observe that two-channel perfect reconstruction (PR) filter banks result in zero MDTV. Finally, we have compared the proposed approach with earlier work and demonstrated its better efficiency in terms of computational complexity.

Neural networks based cryptography has seen a significant growth since the introduction of adversarial cryptography which makes use of Generative Adversarial Networks (GANs) to build neural networks that can learn encryption. The encryption has been proven weak at first but many follow up works have shown that the neural networks can be made to learn the One Time Pad (OTP) and produce perfectly secure ciphertexts. To the best of our knowledge, existing works only considered communications between two or three parties. In this paper, we show how multiple neural networks in an adversarial setup can remotely synchronize and establish a perfectly secure communication in the presence of different attackers eavesdropping their communication. As an application, we show how to build Secret Sharing Scheme based on this perfectly secure multi-party communication. The results show that it takes around 45,000 training steps for 4 neural networks to synchronize and reach equilibria. When reaching equilibria, all the neural networks are able to communicate between each other and the attackers are not able to break the ciphertexts exchanged between them.

Levelized cost of energy (LCOE) is a commonly used metric to assess the cost-to-benefit ratio over the lifetime of an energy resource, such as photovoltaics (PV); however, power electronics engineers tend to rely on metrics such as efficiency and power density, which do not guarantee lifetime cost optimality. Recent work has shown that an LCOE-focused optimization approach can yield improved system designs, leading to improved lifetime performance with balanced lifetime cost and energy generation. This paper outlines an LCOE optimization framework for PV power electronics that uses geometric programming. The large number of circuit parameters and nonlinear nature of the system equations pose significant barriers. Our approach allows for decoupling the design variables, which, in turn, enables superior computational efficiency and a near-optimal solution. By incorporating the power electronics design process and magnetic loss mechanism into the convex design framework, the optimization engine yields practically implementable parameters for a PV converter that minimizes LCOE. An optimization example for a cascaded modular PV inverter architecture is presented that suggests 3.35% LCOE improvement can be achieved by the new power electronics and the advanced optimization. The proposed optimization framework can be applied to other power generation systems to evaluate the effect of the power electronics design on system lifetime costs and efficiency.

In this paper, we present a simple metasurface based multiband reflective polarization converter for both linear and circular polarizations. We show that, on one hand, the proposed structure can convert the polarization of linearly polarized waves to the orthogonal direction at four frequency bands — 4.3 GHz, 7.2 GHz, 12.3 GHz, and 15.15 GHz. On the other hand, this metasurface can achieve linear to circular or circular to linear polarization conversion at 4 GHz, 4.75–5.95 GHz, 8.35–8.8 GHz and 14.35–14.6 GHz frequency bands. Such multiband operation originates from multiple resonances occurring in the structure based on meandered square ring and diagonal split strip resonator. Moreover, the polarization transforming capability is stable for oblique incident angle up to $15^{o}$ , for both transverse electric (TE) and transverse-magnetic (TM) polarizations. Furthermore, the proposed structure acts as a meta-reflector that maintains the handedness of the circular polarization upon reflection. Finally, a prototype of the proposed meta-structure is fabricated and measured for both normal and oblique incidence of electromagnetic waves. All the results present excellent resonant stability with respect to the different polarization and incident angles. In addition, we have also performed the tolerance analysis of different material parameters to understand the robustness of the structure. Due to the ability of the structure to perform three functionalities through a single layout, the proposed design can pave its way in different microwave applications such as satellite, radar, and 5G communications.

Presents information on a study which described a blind deconvolution method which allows the deconvolution of broad-band signals. Application of blind deconvolution on data obtained from liquid chromatography and deep level transient spectroscopy; Examples; Conclusions.

SQUID magnetometry and electron magnetic resonance (EMR) spectroscopy at 9.28 GHz were employed to probe magnetism and surface spin disorder in Fe3 O4 nanoparticles. Transmission electron microscopy (TEM) and X-ray diffraction (XRD) analysis showed magnetite particles with size ≃8 nm. Magnetization (M) versus temperature studies yielded blocking temperature TB ≃ 170 K in H = 50 Oe. For T > TB, M scales approximately as H/T with μP ≃ 5400 μB as the magnetic moment/particle. At 5 K, the magnitudes of hysteresis loop parameters viz. coercivity Hc ≃ 37 Oe and saturation magnetization M8 = 62 emu/g are considerably smaller than the reported values for larger 150 nm Fe3 O4 particles. In EMR studies, a single symmetric line at g ≃ 2.11 with linewidth ΔH = 850 Oe is observed at 300 K with the g-value and ΔH increasing with decreasing T. These results are interpreted in terms of a disordered surface spin layer of 0.5 nm thickness. [ABSTRACT FROM AUTHOR]

In this paper, we compare the magnetic relaxation of DNA-coated γFe2 O3 with that of parent 7-nm γFe2 O3 particles by measuring the temperature dependence of AC susceptibilities (χ′ and χ″) at frequency f = 0.1, 1, 10, 100, and 1000 Hz and electron magnetic resonance (EMR) at 9.28 GHz. Changes in the blocking temperature TB (where χ′ peaks) with frequency are analyzed using the relation: TB = Ta + [T0/ln(f0/f)] derived from Vogel-Fulcher relaxation, where Ta(T0) measures the effective energy barrier (interparticle interaction). The fit to the data yields f0 = 1.3 × 1016 Hz, Ta = 2760 K(2960 K), and T0 = 40 K(20 K) for the uncoated (DNA-coated) γFe2 O3. From the relative shift ΔTm of Tm (the temperature where χ″ peaks) with change Δ log f in frequency, the parameterΦ = ΔTm/(TmΔ log f) is determined to be 0.04(0.06) for the uncoated (coated) ΔFe2 O3. These results show only minimal changes in the magnetic dynamics of γFe2 O3 upon DNA coating. [ABSTRACT FROM AUTHOR]

There is a concerted effort to develop vaccines to combat the public health crisis caused by COVID-19. Health experts are raising questions concerning the clinical trials, price and distribution of the vaccines among the public. Policymakers must design regulations to allocate vaccines equitably based on epidemiological as well as economic considerations. Allocation strategies must factor in the extent and duration of immunity the vaccines yield from future infections. We present a time-varying linear optimization-based approach, which incorporates epidemiological factors, such as population density, susceptible count and infected ratio as well as transportation costs, to disseminate vaccines among zones. Our approach also employs an update rule on the epidemiological statistics spawning from the SEIRD model to learn the extent of immunity provided by the vaccines. Our experiments are performed on the map of New York State with realistic mobility using spatial and ordinary differential equation-based SEIRD models. We show that the proposed approach allocates vaccines optimally while preventing zones from resource starvation. It adapts its vaccine recommendation to meet the prespecified epidemiological and economic policy requirements of zones, and is highly amenable to accommodate multiple immunity ratios, vaccine types and the time-span of acquired vaccine immunity. Moreover, we demonstrate that one can learn the latent immunity ratios of the vaccines to make more informed recommendations with time. Our ordinary differential equation-based analysis on the real demographic and infection data of New York State show that a small fraction of zones tends to exhibit a high resource demand due to their vulnerability to the pandemic spread. Our vaccine allocation strategy incorporates infection ratio as one of the criteria and is applicable to conceive a drug allocation strategy. The results suggest that incorporating the vulnerability score of zones to the epidemic spread into vaccine allocation may enhance recommendations and aid policy making.

Decomposition of digital signals and images into other basis or dictionaries than time or space domains is a very common approach in signal and image processing and analysis. Such a decomposition is commonly obtained using fixed transforms (e.g., Fourier or wavelet) or dictionaries learned from example databases or from the signal or image itself. In this work, we investigate in detail a new approach of constructing such a signal or image-dependent bases inspired by quantum mechanics tools, i.e., by considering the signal or image as a potential in the discretized Schroedinger equation. To illustrate the potential of the proposed decomposition, denoising results are reported in the case of Gaussian, Poisson, and speckle noise and compared to the state of the art algorithms based on wavelet shrinkage, total variation regularization or patch-wise sparse coding in learned dictionaries, non-local means image denoising, and graph signal processing.

Information centric networking (ICN) shifts the focus of existing internet architecture from host-oriented to content-oriented model by enabling in-network caching and content-based forwarding. These ICN features help to increase network performance by decreasing content discovery delay, content server load, and network congestion. To route a content interest inside network such that content can be fetched with minimal time is a challenging task in ICN. The performance of the ICN routing protocol can be significantly improved if the decisions related to content chunk placement and request forwarding are taken in a cooperative fashion. This paper describes a novel strategy for co-operative caching joint request forwarding in ICN, focusing on decreasing content retrieval latency. To do so, the caching strategy leverages the concept of connected dominating set (CDS) for creating a virtual backbone network to eliminate caching redundancy and reduce content discovery delay. It considers content chunk placement and request forwarding tasks as strongly co-related procedures. It exploits the caching information so that the request is forwarded to a content router (CR) with the maximum likelihood of carrying needed data using the betweenness centrality (BC) of CR. The proposed approach also uses the Markov chain-based model to estimate CS hit likelihood and use it as decisional parameter while forwarding the interest packet. This mechanism helps to fetch the content within the shortest possible time duration. The CCJRF-ICN adopts the Dijkstra’s shortest path routing and works in collaboration with the CDS-driven caching joint forwarding mechanism. The simulation study of CCJRF-ICN is done inside ns-3 based ndnSIM-2.0 simulator with performance measures like content store (CS) hit ratio, content discovery delay, mean hop distance, network load, and network overhead. The simulation outcomes demonstrate that CCJRF-ICN outperforms the state-of-the-art strategies for realistic topologies (GEANT, US-26, Euro-28) and shows improvement up to 5-35% against stated performance measures.

Many of the technological advances we enjoy today have been inspired by biological systems due to their ease of operation and outstanding efficiency. Designing technological solutions based on biological inspiration has become a cornerstone of research in a variety of areas ranging from control theory and optimization to computer vision, machine learning, and artificial intelligence. Especially in the latter few areas, biologically relevant solutions are becoming increasingly important as we look for new ways to make artificial systems more efficient, intelligent, and overall effective.

Emphysema is a lung disease that occurs due to abnormal alveoli expansion. This chronic disease causes difficulty in breathing which can lead to lung cancer. The progressive destruction of emphysema can be assessed by Computed Tomography (CT) scans and pulmonary function tests. The severity of the disease may extend to a stage where one can risk their life emphasizing the early detection of emphysema. Primary diagnosis can be done using spirometry and CT for early detection of the disease reducing the mortality rates. Difficulties associated with different diagnostic procedures and inter and intra-observer variations have made blooming researches on more computer-aided techniques. This paper intends to develop a computer-aided technique using the improved deep learning strategy. The initial process is image pre-processing, which is performed by histogram equalization and median filtering. Further, the Fuzzy C Means (FCM) clustering is used for segmentation. After segmentation, a new Adaptive Local Ternary Pattern (ALTP) is used for extracting the pattern descriptor, which is further utilized for classification. As a new contribution, the Parameter Optimized-Faster Region Convolutional Neural Network (PO-FRCNN) is developed for performing the diagnosis. The enhancement of pattern formation and deep classification is accomplished by the Improved Red Deer Algorithm (IRDA), which helps to tune the significant parameters that have a positive influence on the accurateness. The benchmark and real-time dataset are used for performing the experimentation. The results show that the proposed method yields the best result and can effectively diagnose emphysema when compared to state-of-the-art techniques.

Mobile crowd computing (MCC) that utilizes public-owned (crowd’s) smart mobile devices (SMDs) collectively can give adequate computing power without any additional financial and ecological cost. However, the major challenge is to cope with the mobility (or availability) issue of SMDs. User’s unpredicted mobility makes the SMDs really unstable resources. Selecting such erratic resources for job schedule would result in frequent job offloading and, in the worst case, job loss, which would affect the overall performance and the quality of service of MCC. In a Local MCC, generally, a set of users are available for a certain period regularly. Based on this information, the chances of a user being available for a certain duration from a given point of time can be predicted. In this paper, we provide an effective model to predict the availability of the users (i.e., their SMDs) in such an MCC environment. We argue that before submitting a job to an SMD, the stability of it is to be assessed for the duration of execution of the job to be assigned. If the predicted availability period is greater than the job size, then only the job should be assigned to the SMD. An accurate prediction will minimize the unnecessary job offloading or job loss due to the early departure of the designated SMD. We propose an advanced convolutional feature extraction mechanism that is applied to LSTM and GRU-based time-series prediction models for predicting SMD availability. To collect user mobility data, we considered a research lab scenario, where real mobility traces were recorded with respect to a Wi-Fi access point. We compared the prediction performances of convolutional LSTM and GRU with the basic LSTM and GRU and ARIMA in terms of MAE, RMSE, R2, accuracy, and perplexity. In all the measurements, the proposed convolutional LSTM exhibited considerably better prediction performance.

The modern world and modern technology want 5G communication to satiate their urgent need of multi-gigabits data transmission in small cells. For this aspiration, a bidirectional orthogonal frequency division multiplexing (OFDM) based millimeter wave over and terahertz wave over fiber system is proposed and demonstrated for 5G access fronthaul. A comparative study between millimeter-wave (MMW) and terahertz wave (THzW) regarding 5G communication is also analyzed. With the assistance of L and C band quantum dash laser diode, MMW and THzW are generated. For downstream (DS), 10 Gbps/12.5Gbps (for MMW/THzW) OFDM data in L band is transmitted over 50-km/25-km single mode fiber (SMF) and 100-m free space optics (FSO) distance, on the other side for upstream (US) transmission, 6.5 Gbps/10 Gbps OFDM data in C band is communicated over 50-km/25-km SMF plus 100-m FSO link. The selection of L and C band for DS and US transmission provide the advantages to reduce the Rayleigh back scattering effect in our bidirectional system not only that it also helps to mitigate the intensity-noise as compared to the conventional amplified spontaneous emission source. This research work gives an overview for the reliability of MMW and THzW system regarding 5G communication. The capability of our system becomes build up by obtaining low bit error rate (under FEC limit), proper error vector magnitude (

A current trend of research focuses on artificial intelligence based cryptography which although proposed almost thirty years ago could not attract much attention. Abadi and Anderson’s work on adversarial cryptography in 2016 rejuvenated the research area which now focuses in building neural networks that are able to learn cryptography using the idea from Generative Adversarial Networks (GANs). In this paper, we survey the most prominent research works that cover neural networks based cryptography from two main periods. The first period covers the oldest models that have been proposed shortly after 2000 and the second period covers the more recent models that have been proposed since 2016. We first discuss the implementation of the systems from the earlier era and the attacks mounted on them. After that, we focus on post 2016 era where more advanced techniques are utilized that rely on GANs in which neural networks compete with each other in order to achieve a goal e.g. learning to encrypt a communication. Finally, we discuss security analysis performed on adversarial cryptography models.

A new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme is proposed in this paper, by embedding a recently introduced adaptive denoiser using the Schroedinger equation’s solutions of quantum physics. The potential of the proposed model is studied for Poisson image deconvolution, which is a common problem occurring in number of imaging applications, such as limited photon acquisition or X-ray computed tomography. Numerical results show the efficiency and good adaptability of the proposed scheme compared to recent state-of-the-art techniques, for both high and low signal-to-noise ratio scenarios. This performance gain regardless of the amount of noise affecting the observations is explained by the flexibility of the embedded quantum denoiser constructed without anticipating any prior statistics about the noise, which is one of the main advantages of this method. The main novelty of this work resided in the integration of a modified quantum denoiser into the PnP-ADMM framework and the numerical proof of convergence of the resulting algorithm.

The high number ofsensors, ten in total, required by the traditional finite control set model predictive control (FCS-MPC) strategy of a four-leg inverter with LC output filter increases the complexity and cost of the system and limits the practical application and commercialization. To simplify the implementation, reduce the cost and enhance the reliability, a novel current sensorless predictive load voltage control scheme, with only four voltage sensors and no current sensors, is proposed in this work. A new dynamic model of the LC filtered four-leg inverter is constructed in the stationary ($\alpha \beta \gamma$) reference frame for the proposed load voltage and capacitor current observer. The dual-boundary-layer nonlinear feedback is combined with a linear feedback term to enhance the robustness while retaining the accuracy of the observer. Extensive experiments are performed to confirm the effectiveness of the proposed controller. The results validate that the proposed sensor-less FCS-MPC with only four voltage sensors achieves an equivalent performance to the conventional FCS-MPC with ten sensors.

Precision agriculture is the collection of hardware and software technologies that allow a farmer to make informed, differentiated decisions regarding agricultural operations such as planting, fertilizing, pest control, and harvesting. In recent years, advances in agricultural machinery and the emergence of agricultural robots continuously increased the resolution at which differentiated treatment is possible. This creates a corresponding need for information at a fine spatial and temporal resolution. Autonomous multi-robot systems (e.g., unmanned ground and aerial vehicles) are some of the most promising approaches for such information collection in open-air farms. In this paper, we survey the current state and challenges of multi-robot information gathering for precision agriculture, with a special focus on maximizing information and ensuring the security of the collected data while simultaneously keeping energy consumption in check.

Human speech is not only a verbose medium of communication but it also conveys emotions. The past decade has seen a lot of research going on with speech data which becomes especially important for human-computer interaction and also healthcare, security, and entertainment. This paper proposes the TLEFuzzyNet model, a three-stage pipeline for emotion recognition from speech. The first stage includes feature extraction by data augmentation of speech signals and extraction of Mel spectrograms, followed by the use of three pretrained transfer learning CNN models namely, ResNet18, Inception_v3, and GoogleNet whose prediction scores are fed to the third stage. In the final stage, we assign Fuzzy Ranks using a modified Gompertz function which gives the final prediction scores after considering the individual scores from the three CNN models. We have used the Surrey Audio-Visual Expressed Emotion (SAVEE), the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), and the Berlin Database of Emotional Speech (EmoDB) datasets to evaluate the TLEFuzzyNet model which has achieved state-of-the-art performance and is hence a dependable framework for Speech emotion recognition(SER). All the codes are available using GitHub link: https://github.com/KaramSahoo/SpeechEmotionRecognitionFuzzy

Many combinatorial optimization problems can be mapped onto the ground-state search problem of an Ising model. Exploiting the continuous-time dynamics of a network of coupled phase-transition nano-oscillators (PTNOs) allows building an Ising Hamiltonian solver for obtaining optimum or near-optimum solution with a large speed-up over discrete-time iterative digital hardware. Here, we provide insights into the continuous-time dynamics of such a PTNO-based Ising Hamiltonian solver. We highlight the formation of stable attractor states in the phase space of the coupled PTNO network using second-harmonic injection locking (SHIL) that corresponds to the minima of the Ising Hamiltonian. We show that the emergent synchronized dynamics of the PTNO network is maximized near the critical point of oscillator phase bistability beyond which the dynamics is limited by freeze-out effects. Such dynamical freeze-out severely limits the performance of the PTNO-based Ising solver from obtaining the global optimum. We highlight an improvement in the success probability of reaching the ground state by introducing an annealing scheme with linearly increasing SHIL amplitude compared with a constant SHIL. Finally, we estimate the “effective temperature” of the PTNO-based Ising solver by comparing it with the Markov chain Monte Carlo simulations. The PTNO-based Ising solver behaves like a low-temperature Ising spin system, indicating its effectiveness for optimization tasks.

Healthcare sector is probably the most benefited from the applications of nanotechnology. The nanotechnology, in the forms of nanomedicine, nanoimplants, nanobiosensors along with the internet of nano things (IoNT), has the potential to bring a revolutionizing advancement in the field of medicine and healthcare services. The primary aim of this paper is to explore the clinical and medical possibilities of these different implementations of nanotechnology. This paper provides a comprehensive overview of nanotechnology, biosensors, nanobiosensors, and IoNT. Furthermore, multilevel taxonomies of nanotechnology, nanoparticles, biosensors, nanobiosensors, and nanozymes are presented. The potential medical and clinical applications of these technologies are discussed in details with several examples. This paper specifically focuses on IoNT and its role in healthcare. In addition to describing a general architecture of IoNT for healthcare, the communication architecture of the IoNT is also explained. The challenges in the successful realization of IoNT are also discussed critically, along with a special discussion on internet of bio-nano things (IoBNT) and its potential in making IoNT more compatible to human body.

Scene text spotting aims at simultaneously localizing and recognizing text instances, symbols, and logos in natural scene images. Scene text detection and recognition approaches have received immense attention in computer vision research community. The presence of partial occlusion or truncation artifact due to the cluttered background of scene images creates an obstacle in perceiving the text instances, which makes the process of spotting very complex. In this paper, we propose a light-weight scene text spotter that can address the issue of cluttered environment of scene images. It is an end-to-end trainable deep neural network that uses local part information, global structural features, and context cue information of oriented region proposals for spotting text instances. It helps to localize in scene images with background clutters, where partially occluded text parts, truncation artifacts, and perspective distortions are present. We mitigate the problem of misclassification caused by inter-class interference by exploring inter-class separability and intra-class compactness. We also incorporate multi-language character segmentation and word-level recognition in a light-weight recognition module. We have used six publicly available benchmark datasets in different smart devices to illustrate the efficacy of the network.

The Internet of Things (IoT) exemplifies a large network of sensing and actuating devices that have penetrated into the physical world enabling new applications like smart homes, intelligent transportation, smart healthcare and smart cities. Through IoT, these applications have consolidated in the modern world to generate, share, aggregate and analyze large amount of security-critical and privacy sensitive data. As this consolidation gets stronger, the need for security in IoT increases. With first line of defense strategies like cryptography being unsuited due to the resource constrained nature, second line of defense mechanisms are crucial to ensure security in IoT networks. This paper presents a comprehensive study of existing second line of defense mechanisms for standardized protocols in IoT networks. The paper analyzes existing mechanisms in three aspects: Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS) and Intrusion Response Systems (IRS). We begin by providing an overview of standardized protocol stack, its layers and defensive security systems in IoT. From there, we build our narrative by presenting an extended taxonomy of IDS, IPS and IRS classifying them on their techniques, deployment, attacks, datasets, evaluation metrics and data pre-processing methods. We then thoroughly review, compare and analyze the research proposals in this context, considering the unique characteristics involved in these systems. Based on the extensive analysis of the existing defensive security systems, the paper also identifies open research challenges and directions for effective design of such systems for IoT networks, which could guide future research in the area.

The goal of this work is to forecast human activities that may require robot assistance. Each activity consists of consecutive actions. Each action is bounded by initial and final state and is created by the motion trajectory. The states are defined in the training phase. The vision and depth sensors are used for data collection. The data are processed and the structured database is built. This base is used for making prediction. The method allows us to forecast the trajectories of nominally possible motion goals (prognosing of an action). The probability functions support the selection of possible motion goal. Then the possible motion trajectory is created which predicts the ongoing action. The activity is predicted on the basis of already completed action sequences and using knowledge about possible sequences stored in the database. The core of the reasoning process are: the probability functions, the action graphs (describing the activities) and the structured database. The approach was evaluated using four datasets: CAD 60, CAD-120, WUT-17, and WUT-18. The efficiency of the presented solution compared to the other existing state-of-the-art methods is also investigated.

In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems. In this paper, a new metaheuristic algorithm, called the Border Collie Optimization is introduced. The algorithm is developed by mimicking the sheep herding styles of Border Collie dogs. The Border Collie's unique herding style from the front as well as from the sides is adopted successfully in this paper. In this algorithm, the entire population is divided into two parts viz., dogs and sheep. This is done to equally focus on both exploration and exploitation of the search space. The Border Collie utilizes a predatory move called eyeing. This technique of the dogs is utilized to prevent the algorithm from getting stuck into local optima. A sensitivity analysis of the proposed algorithm has been carried out using the Sobol's sensitivity indices with the Sobol g-function for tuning of parameters. The proposed algorithm is applied on thirty-five benchmark functions. The proposed algorithm provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm. The performance of the proposed algorithm is analytically and visually tested by different methods to judge its supremacy. Finally, the statistical significance of the proposed algorithm is established by comparing it with other algorithms by employing Kruskal-Wallis test and Friedman test.

The ability to launch, or otherwise deploy, a cylindrical body such as an unmanned vehicle, from an outer tube in an underwater environment continues to be of interest to the marine community. Applications of such technologies include the use of small unmanned underwater vehicles (UUV's) deployed from deep ocean craft to support oil exploration efforts or inspection of damaged infrastructure. Historically, these payloads are deployed from their containment tube through the use of a water slug generated from a pump which pushes the vehicle into the open ocean environment.The objectives of this project are to identify and demonstrate a method to launch a cylindrical body from the launch tube utilizing an electromagnetic scheme. It was found that the solenoid coil launching mechanism was the most effective for an underwater launcher. A design was created, modeled in simulations, and finally built and tested to prove its validity. It was found that the solenoid coil launcher could effectively expel a payload with ample exit velocity, low noise, and could be reloaded several times within a minute.

We report the development of a biped-robot system with real-time surface recognition and walking-speed adjustment to control the robot motion during walking on different types of surfaces. Four types of test surfaces (i.e. rough foam (RF), smooth foam (SF), thin carpet (TC) and smooth table (ST)) are considered in the system verification. For surface-property recognition we use ultra-thin-membrane force sensors, mounted under the robot feet, and a classification circuit, implemented on an Arduino Uno board. The walking-speed adjustment is performed with an external control circuit, which receives the surface-recognition signal from the classification circuit and sends a feedback signal to the robot controller (i.e. RCB-4HV) for adjusting the walking speed accordingly. We applied the nearest-neighbor-classification algorithm with the Euclidean-distance measure and a set of reference data, to distinguish between the four test surfaces based on the robot's real-time walking pattern. The mean absolute value (MAV) feature descriptor is used to generate four different types of reference walking pattern, corresponding to the four different surfaces. In our experiments it is observed, that the ST surface performs best in terms of average surface-recognition latency (SRL) (~3.6 sec) during walking on same surface. On the other hand, the surface transition from TC to SF showed minimum surface-transition latency (STL) (~8.2 sec) with correct speed change from 135 to 160 robot-motor-configuration frames per stride (frames/stride), while the transition from SF to TC surfaces showed maximum STL (~11.6 sec) including speed change from 160 to 135 frames/stride. The obtained results are useful for development of the next generation of surface-recognition and speed-adjustment systems, implemented in humanoid robots to enable balanced and stable walking in environments with multiple changed surface properties.