In this paper, we analyze (for the first time in literature) the secrecy performance of a two-hop cooperative network in which all the nodes are of the energy-harvesting self-sustaining type. Both source and relay harvest energy from a power beacon (PB) and employ the harvest-use (HU) architecture. We consider optimal combining of the direct and relayed signals at the destination and eavesdropper. We do not assume any channel state information (CSI) at the source. We derive an approximate closed-form expression for secrecy outage probability assuming selective decode-and-forward (SDF) protocol at the relay. We demonstrate that secrecy outage is a convex function of the transmit power of PB. Further, the security-reliability trade-off (SRT) is analyzed to highlight the trade-off between outage and intercept probability. Computer simulations validate the derived analytical expressions

This study aims to address the increasing complexity of cyber attacks and the sensitivity of wind speed data by exploring the combination of Artificial Intelligence (AI) and cyber security measures for improved wind speed prediction accuracy and security. The collected wind speed dataset is pre-processed and encrypted using the ring learning with errors (ring-LWE) encryption algorithm, which is known for its high level of security. The encrypted data is then input to the Ensemble Empirical Mode Decomposition (EEMD) to split the encrypted signal and reduce the impact of encryption noise. The combination of Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) models are also applied to each intrinsic mode function (IMFs) of the decomposition result. Furthermore, the parallel programming framework is also employed during the training phase, significantly improving the processing speed and efficiency. The results of the predictions are decrypted and compared with the testing set, and other works to evaluate the proposed model. The Mean Absolute Percent Error (MAPE) ranges from 7% to 10%, indicating a high level of accuracy. The findings of the study have practical implications for wind energy production and the development of smart grid systems. The study highlights the importance of considering both accuracy and security when using AI and suggests future directions for research in this area, including the development of more advanced encryption algorithms and the integration of AI and cyber security into real-world wind energy systems.

8- bit digital phase shifter is design in 65nm CMOS technology and is packaged using QFN. This work is aimed to study the effects of the wire bonds without any compensation or matching work being done to the chip. It is observed that the insertion loss variation and return losses are affected much but the phase performances remains more or less same as the design.

Accurate detection of QRS complex in electrocardiogram (ECG) signals is essential for reliable estimation of the heart rate. However, traditional QRS detection algorithms often have low performance in the presence of various types of noise and signal abnormalities and require additional memory resources to track undetected peaks. In this work, we propose a novel QRS complex detection algorithm based on the root mean square (RMS) shifting concept. The concept of RMS shifting consists to remove an amount proportional to the RMS of the pre-processed signal for moving all the P and T waves of the electrocardiogram toward the negative part of the y-axis and keep only the R peaks in the positive part. Then, all the roots can be softly detected using the corollary of the intermediate value theorem known as Bolzano’s theorem. The detection of R peaks is ensured by Rolle’s theorem. Our proposed model has been implemented and evaluated on a diverse set of ECG datasets and its performances are comparable to that of spectral analysis based on the FFT algorithm widely used nowadays. For the construction of this model, we used a sample of an electrocardiogram signal from the MIT/BIH Arrhythmia database stored and provided by Simulink. The peaks detected by our algorithm have been verified and confirmed by the well-known Pan-Tompkins Algorithm used by MATLAB. Then, Our model has been applied to a publicly shared electrocardiogram database provided by a Japanese physiological laboratory. The comparison of the heart rate estimated by the proposed method and the spectral analysis method shows a low absolute error average (0.89 bpm), a low relative error average (1.37%), a low root mean square error (1.05 bpm), and a correlation coefficient very close to 1 (0.9938). We also measured the CPU time to assess the performance of our proposed method and we found that our algorithm is twice as fast as the conventional method. Therefore, we inferred that our model is reliable for estimating heart rate for electrocardiography applications.

There is some confusion in the radar community on the handling of phase noise in radar because current definitions give rise to divergent integrals. It is shown that the spectrum of a phase noise modulated signal rather than the spectrum of the phase noise modulating function is the applicable function for coherent radar system design. A heuristic model is proposed to derive the finite power signal spectrum from the infinite power phase spectrum. The well-known phase delay differencing function that is used to weigh the base-band phase noise before integration is also discussed and applied to typical spectra to obtain well behaved analytical descriptions of phase noise that lead to convergent integrals for the autocorrelation and noise power.

Most deep clustering methods despite providing complex networks to learn better from data, use a shallow clustering method. These methods have difficulty in finding good clusters due to the lack of ability to handle between local search and global search to prevent premature convergence. In other words, they do not consider different aspects of the search and it causes them to get stuck in the local optimum. In addition, the majority of existing deep clustering approaches perform clustering with the knowledge of the number of clusters, which is not practical in most real scenarios where such information is not available. To address these problems, this paper presents a novel automatic deep sparse clustering approach based on an evolutionary algorithm called Multi-Trial Vectorbased Differential Evolution (MTDE). Sparse auto-encoder is first applied to extract embedded features. Manifold learning is then adopted to obtain representation and extract the spatial structure of features. Afterward, MTDE clustering is performed without prior information on the number of clusters to find the optimal clustering solution. The proposed approach was evaluated on various datasets, including images and time-series. The results demonstrate that the proposed method improved MTDE by 18.94% on average and compared to the most recent deep clustering algorithms, is consistently among the top three in the majority of datasets. Source code is available on Github: https://github.com/parhamhadikhani/ADSMTDE_Clustering.

Video anomaly detection aims to identify unusual activity in videos. Recently, reconstruction and future frame prediction-based approaches have been frequently used to detect anomalies. However, due to the high generalization capability of deep neural networks, the reconstruction-based algorithms recreate the abnormal pixels with the normal ones. Therefore, we have developed a novel framework based on future prediction, capable of learning the video data's bidirectional motion with the help of a transformer-based siamese network. During the forward pass, the siamese network predicts the future sequence and learns the forward motion. In contrast, the backward movement is realized by predicting the inverse sequence. The model has trained on normal video data adversarially. In addition to adversarial loss, inverse loss and soft-DTW loss have been incorporated to make the model more objective. Extensive experiments have been conducted on the benchmark datasets to validate the efficacy of model.

Radars have been increasingly implemented in modern vehicles. Their presence opens opportunities for developing new positioning and collision avoidance solutions, to cite a few. However, radar scans are noisy and sparse, which challenges the robustness of the developed solutions. In this paper, we propose a novel method to structure radar scans to create point descriptors. The structured data is used in convolutional neural networks that explore orthogonal views of the radar point cloud. Experimental results demonstrate that the model performs well in estimating the forward velocity of the vehicle using only the radar scans, providing estimations at a higher data rate than odometers available in the vehicles.

Photonic RF transversal signal processors, which are equivalent to reconfigurable electrical digital signal processors but implemented with photonic technologies, have been widely used for modern high-speed information processing. With the capability of generating large numbers of wavelength channels with compact micro-resonators, optical microcombs bring new opportunities for realizing photonic RF transversal signal processors that have greatly reduced size, power consumption, and complexity. Recently, a variety of signal processing functions have been demonstrated using microcomb-based photonic RF transversal signal processors. Here, we provide detailed analysis for quantifying the processing accuracy of microcomb-based photonic RF transversal signal processors. First, we investigate the theoretical limitations of the processing accuracy determined by tap number, signal bandwidth, and pulse waveform. Next, we discuss the practical error sources from different components of the signal processors. Finally, we analyze the contributions of the theoretical limitations and the experimental factors to the overall processing inaccuracy both theoretically and experimentally. These results provide a useful guide for designing microcomb-based photonic RF transversal signal processors to optimize their accuracy.

Integrated local multi-energy systems are recognized as a promising bottom-up alternative to achieve the ambitious energy and climate goals set by the European Commission for 2030. The nature of integrated multi-energy systems presumes a very complex interaction of different aspects from various energy sectors, which historically functioned more or less independently. Therefore, configuration and development of an integrated local energy system under the energy community concept is an extremely demanding task, requiring a sound interdisciplinary knowledge base. The present study shares results from an activity in H2020 project eNeuron aiming to cover this gap, by introducing the Integrated Local Energy Community definition and providing a deep-dive review of multiple factors influencing the future development of these emerging systems, reviewing the three levels: political priorities, technical overview and limitations plus barriers. The paper has been accepted by ISC2 2022 Conference

This paper presents a review study of energy communities (ECs) in Europe, and discusses the future development of such communities in Europe – both related to energy technologies, energy carriers, regional conditions (North, Central and South of Europe), emerging regulatory development etc. From the analysis, it emerged that the future ECs in Europe will focus on utilizing local renewable energy sources (sun, wind, run of river, biogas, biomass), for covering all or part of the energy consumption for end-use customers. The ECs can be a sustainable alternative to large power plants based on fossil fuels. This is also linked to the emerging regulatory developments started when European Commission introduced such concepts in the “Clean Energy for all Europeans” package. The main goal of eNeuron H2020 project is to develop innovative tools for the optimal design and operation of ECs, integrating distributed energy resources and multiple energy carriers at different scales. Paper accepted at  EEM2022 Conference

In this paper, we propose a new clustering module that can be trained jointly with existing neural network layers.  Specifically, we have designed a generic clustering module with a competitive update mechanism. The module consists of a Gaussian unit and a maximum pooling layer. The Gaussian unit forward propagation conforms to the joint Gaussian distribution and contains two sets of trainable parameters. It requires no tedious setup and has a plug-and-play feature. To improve the  representation capability of the network, we used an auto-encoder to extract the hidden semantics of the input features and combined the clustering module with the auto-encoder to construct an end-to-end unsupervised clustering neural network. We refer to this as HGL_CAE(High-dimensional Gaussian distribution layers combined with convolutional autoencoders).The network is highly adaptable to different input feature dimensions and can cope with situations where the number of clusters  cannot be determined in advance. We conducted experiments on the MNIST dataset and the Fashion_MNIST dataset with a clustering accuracy of 93.38% and 72.83% respectively. It is highly competitive with existing methods.

A generic current control system for modular multilevel cascade converters (MMCCs) is proposed. It is based on (i) input/output-linearization (I/O-linearization), (ii) feedforward disturbance compensation and (iii) parallelized proportional-integral-resonant controllers to compensate for arbitrarily many harmonics (including positive and negative sequence). The controller design is performed in state space. It is generic and flexible and combines pole placement and LQR tuning. Moreover, it allows to take all system parameters such as cluster or output (grid) inductances or resistances individually into account during controller design and tuning. Moreover, the typically imposed simplifying assumptions on system dynamics (e.g., identical parameters) or operating conditions (e.g., balanced loading) can be overcome. The proposed control system is validated for a dual-star bridge-cell MMCC. The simulation results illustrate its excellent control performance under ideal but also non-ideal conditions, where conventional approaches usually fail.

This paper derives symbolic models for end-to-end RIS-assisted FSO systems built on the fading model’s probability density functions (PDFs) characterizing the sub-channels. We study some of the most popular models and derive closed-form expressions of PDFs, cumulative distributions, and moment-generating functions that are further exploited to analyze systems’ performance. By comparing scenarios in which the sub-channels are similar and different, we provide a clear understanding of how the overall RIS-assisted FSO channel behaves, along with comparative results based on commonly used models.

This article is about our latest development in the field of automated software debugging. As experienced developers in the software industry, we have encountered the challenges of debugging large-scale applications firsthand. Our major software product, Maxwell, which has been supporting businesses for over 10 years, contains over 14 million lines of code, making debugging a daunting and time-consuming task. To tackle this issue, we have been working on automating the debugging process, which will not only save valuable time but also improve efficiency, especially for new developers who may not have the same level of experience with the product. Our MDugger prototype has shown great promise in shortening software development time and improving efficiency based on the pilot project. We believe that our findings will greatly benefit the software community and we are eager to share our experience and knowledge with you.

Panoramic Radiography and Cone Beam Computed Tomography (CBCT) are the most used imaging techniques for implant treatment and oral surgery. However, it cannot be used frequently due to the high cost and radiation exposure of CBCT. Whereas Panoramic x-rays do not provide the high dimensional information necessary for planning surgery. Our research aims to produce an automated method for reconstructing a 3D dental structure from a single panoramic X-ray. This paper presents a robust preprocessing pipeline of CBCT data which are exploited to train a deep learning model for reconstructing the 3D dental structure from a single 2D panoramic X-ray image. It includes a method of generating a pair of synthetic panoramic X-rays and the corresponding flattened 3D volume as ground truth from CBCT data.

To insure a stable and reliable operation of the existing and future grids, it is important to study the stability of the power electronic based converters’ controls which can replace synchronous generation. Most existing studies use a Thévenin equivalent model of the grid to test the grid following controls. The present article demonstrates the limits of these studies. An alternative setup is proposed and is shown to provide more insight into the underlying mechanisms of the grid following control behaviour in weak grids, using a linearized state space model. These insights are exploited to propose further improvements to the control. These improvements are shown to extend the stability of the control while reducing the interactions among converters.

In the application of visual navigation, submap-based VSLAM has become one of the most robust monocular solutions in recent years, which is able to resume tracking by multi-submap maintenance and merging. However, due to the lack of long-term data association, the global consistency of submaps cannot be guaranteed in the existing work. Considering the fact that long-term data association does not have to be produced in realtime, we propose a VSLAM system with realtime and non-realtime hybrid style, RUMI-SLAM. Inspired by the rumination of mammalians that processes food in various stomaches and absorbs it in one stomach, RUMI-SLAM performs distributed submap building and centralized submap management. Building additional submaps in parallel leads to enriched mapping elements and enhanced data association across submaps, especially in challenging situations such as interleaved blurry and texture-poor frames. The experimental results demonstrate the superiority of RUMI-SLAM over the existing VSLAM systems, especially the robustness to challenging situations. We also provide real-robot experiments to demonstrate our RUMI-SLAM in the application of visual navigation. Our study provides a novel submap-based VSLAM framework, which achieves a robust and globally consistent performance.#This paper has been accepted by IEEE TIE