We propose a blind joint equalization algorithm for M-QAM signals based on a widely linear filtering approach. The proposed scheme jointly compensates receiver IQ imbalance and polarization mixing, along with carrier recovery, followed by transmitter IQ imbalance compensation. We first investigate the proposed scheme’s tolerance to transceiver IQ Imbalance, polarization mixing, phase noise and frequency offset through numerical simulations for 32 GBd PM-16QAM and PM-64QAM signals and compare its performance with the conventional digital processing algorithms. Further, with the proposed algorithm, we experimentally demonstrate the improvement in Q2 value to up to ~ 1.22 dB for a 32 GBd PM-16QAM and ~ 3.72 dB for a 16 GBd PM-64QAM signal with a phase imbalance of 9o. We show that the MSE convergence of the proposed joint equalizer is much faster than conventional DSP algorithms. Deployment of such an equalizer in optical communication systems is beneficial due to its improved tolerance to multiple impairments, albeit with increased complexity.

This paper deals with a new smooth line-switching method that facilitates the network reconfiguration of islanded networks. Its distinct features include the ability to handle network asymmetries and to minimize the line current during the switching action. This is attained by developing a three-phase sensitivity-based method to determine the operating set-points of distributed generators (DGs) that minimize the current of the candidate line participating in the switching action. These set-points correspond to the positive-sequence powers as well as the negative- and zero-sequence currents of DGs. Furthermore, the network constraints such as voltage limits and power limits of DGs are always satisfied. Simulations are performed in a balanced 33-bus islanded network as well as in the unbalanced IEEE 8500-node network to evaluate the performance of the proposed method.

I suggest a method for biomedical imaging with heat using principal and independent components analysis. This method produces novel results suggesting physiologic mechanisms. When using thermal imaging to detect breast cancer, the dominant heat signature is of indirect heat transported by the blood away from the tumor location into the skin. Interpretation is usually based on vascular patterns and not by observing the direct cancerous heat. In this new method one uses a sequence of thermal images of the patient’s breast following external temperature change. Data are recorded and analyzed using independent component analysis (ICA) and principal component analysis (PCA). ICA separates the image sequence into new independent images having a common characteristic time behavior. Using the Brazilian visual lab mastology data set, I observed three types of component images: Images corresponding to a minimum change as a function of applied temperature or time, which suggests an association with the cancer generated heat, images in which a moderate temperature dependence is associated with veins affected by vasomodulation, and images of complex time behavior indicating heat absorption due to high perfusion of the tumor. All components appear to clearly and distinctly represent underlying physiology.

In this work, we report that the AFE/FE gate-stack can be utilized to engineer the S-curve for boosting the ION of NC-FinFET. By using a short-channel BSIM-CMG compatible AFE/FE stack NC-FinFET model, the capacitance matching and ON-state performance for AFE/FE stack NC-FinFETs are investigated. Our study indicates that, the AFE/FE gate-stack can be used to improve the capacitance matching in strong inversion at higher gate-bias. Therefore, impressively higher ON-state current (compared to single-layer) can be achieved. In reality, source-drain series resistance will make such high IDS impractical. The more likely strategy is to use lower VDD to achieve much lower power consumption (and reduced vertical fields). While the transient NC effect also needs to be carefully investigated, this study suggests significant long term benefits to VDD scaling if materials with certain AFE and FE properties are developed and introduced in IC manufacturing in the future.

OpenPLC is an open-source software that complies with IEC 61131-3 international standard and has been widely used by researchers to validate PLC logic execution. However, the software’s capability is limited as it is solely restricted to Modbus and DNP3 protocols. With the integration of intelligent electronic devices (IEDs) into modernized power grid based on IEC 61850 standards, it is necessary to incorporate functionalities that enable PLCs to communicate with IEDs. Therefore, in this paper we develop OpenPLC61850, which is an extended version of OpenPLC and is compatible with the IEC 61850 protocol. The architecture and the procedure involved with PLC logic execution, IED communication, and SCADA connectivity are explained in this paper. As the proposed software is also an open source, we believe that this would be helpful for researchers for conducting smart grid research.

In this paper, we propose a novel bilinear observer- based robust fault detection, isolation and adaptive fault estimation methodology to precisely estimate a class of actuator faults, namely bias in damper position and lock-in-place faults, in Variable-Air-Volume (VAV) terminal units of Heating Ventilation and Air-Conditioning (HVAC) systems. The proposed adaptive fault estimator is robust in the sense that the fault estimates are not affected by the unmeasured disturbance variable and that the effects of measurement noises on fault estimates are attenuated. The fault estimation algorithm with the integrated building control system improves occupants comfort and reduces the operation, maintenance, and utility cost, thereby reducing the impact on the environment. The effectiveness of the methodology for adaptive estimation of multiple or single VAV damper faults is successfully demonstrated through different simulation scenarios with SIMBAD (SIMulator of Building And Devices), which is being used in industries for testing and validation of building energy management systems.

In this paper, a sentiment analysis model based on the bi-directional GRU, Attention and Capusle fusion of BI-GRU+Attention+Capsule was designed and implemented based on the sentiment analysis task of the open film review data set IMDB, and combined with the bi-directional GRU, Attention and Capsule. It is compared with six deep learning models, such as LSTM, CNN, GRU, BI-GRU, CNN+GRU and GRU+CNN. The experimental results show that the accuracy of the BI-GRU model combined with Attention and Capusule is higher than the other six models, and the accuracy of the GRU+CNN model is higher than that of the CNN+GRU model, and the accuracy of the CNN+GRU model is higher than that of the CNN model. The accuracy of CNN model was successively higher than that of LSTM, BI-GRU and GRU model. The fusion model of BI-GRU +Attention+Capsule adopted in this paper has the highest accuracy among all the models. In conclusion, the fusion model of BI-GRU+Attention+Capsule effectively improves the accuracy of text sentiment classification.

Rotation of polarization is the one of problem in communication with linear polarization. In fact, it can reduce the received signal strength down to -20 dB in some cases. Circular polarization is one the accessible approaches for sorting this problem due to it can be immune against polarization rotation. In this paper, proposed antenna can perform in linear and circular polarization which can be switched according to the condition of signal which is being transmitted. Consequently, environmental disruptions could not decline the quality of communication. 1) The proposed antenna is categorized in reconfigurable antennas which have different methods for implementation. First of all, the proposed antenna is a multi-polarization reconfigurable antenna that can radiate circular polarization and linear polarization on the same structure at a specific frequency. Moreover, a simple feeding network for this antenna is implemented. This antenna utilizes common power dividers for feeding the square patch. Finally, switching circuits which are used for changing polarization in this antenna are by far simple than that of existing works.

Detecting Zero-Day intrusions has been the goal of Cybersecurity, especially intrusion detection for a long time. Machine learning is believed to be the promising methodology to solve that problem, numerous models have been proposed but a practical solution is still yet to come, mainly due to the limitation caused by the out-of-date open datasets available. In this paper, we propose an approach for Zero-Day intrusion detection based on machine learning, using flow-based statistical data generated by CICFlowMeter as training dataset. The machine learning classification model used is selected from eight most popular classification models, based on their cross validation results, in terms of precision, recall, F1 value, area under curve (AUC) and time overhead. Finally, the proposed system is tested on the testing dataset. To evaluate the feasibility and efficiency of tested models, the testing datasets are designed to contain novel types of intrusions (intrusions have not been trained during the training process). The normal data in the datasets are generated from real life traffic flows generated from daily use. Promising results have been received with the accuracy as high as almost 100%, false positive rate as low as nearly 0%, and with a reasonable time overhead. We argue that with the proper selected flow based statistical data, certain machine learning models such as MLP classifier, Quadratic discriminant analysis, K-Neighbor classifier have satisfying performance in detecting Zero-Day attacks.

Recently, the development of pre-trained language models has brought natural language processing (NLP) tasks to the new state-of-the-art. In this paper we explore the efficiency of various pre-trained language models. We pre-train a list of transformer-based models with the same amount of text and the same training steps. The experimental results shows that the most improvement upon the origin BERT is adding the RNN-layer to capture more contextual information for short text understanding. But the conclusion is: There are no remarkable improvement for short text understanding for similar BERT structures. Data-centric method[12] can achieve better performance.

Non linear deformation of spring based continuum manipulators cause difficulty in predicting the position of the tip. When we put different tools on the tip for various purposes, the difficulty further increases. Model less control of the manipulator has shown great success in tip positioning of these types of manipulators. One of the major drawbacks of model less control is the requirement of a large data set and time. Hence, this paper studies the effect of the implementation of MAML(Model-Agnostic Meta-Learning) for fast adaptation of different offset conditions. The effects have been studied in the simulation environment and on the real prototype. The continuum arm used for the experimentation is a tendon based non constant curvature spring based manipulator. An average error of 0.03m has been achieved on the prototype. MAML was successful in bringing down the relative tip positioning error of the manipulator from 7.02% to 1.55% in the simulation environment. It also showed success in bringing down the relative tip positioning error from 11.06% to 4.09% on real prototype. We also studied the effectiveness of the same in trajectory following.

Inland transportation, due to the importance of this business and competition between active organizations in this field, applying new technologies in management and making better decisions can be beneficial. We have presented three data mining techniques of clustering, association rules, and classification to investigate the factors affecting the cost and time of road and rail transportation. Using methods based on the K-means algorithm with comparing four clusterings, we have proposed Naive Bayes (probabilistic) classification to determine the total accuracy of transportation percent to 97.91%. Finally, classification tree algorithms such as Bayesian theory and random forest have been used, and the results and output rules have been compared. This article is comprehensive and new to use various effective parameters inland transportation. We will confirm its efficiency by using the criterion(5v)(which we will explain in its place) and then the results in the field. A larger one, called land transit, could be generalized between the two countries. In the end, we have discussed more in methodology and results

In this work, a multi-level 42-sided polygonal space vector structure (SVS) for suppression of lower order harmonics for Open-End Induction Motor(OEIM) drive applications is proposed. The proposed power circuit topology consists of two inverters feeding an Open-End Induction Motor from either side. The main inverter fed with a single DC link providing active power for motor operation is switched at low switching frequency. The secondary inverter fed with a capacitive supply is switched at high frequency to suppress lower order harmonics upto 39th order, up to the base speed of operation allowing maximum utilization of the DC link. The advantages of lower order harmonic suppression in motor phase voltage, for polygonal space vector structures are combined with multi-level inverter topology. This results in lower switching losses in low frequency switching main inverter and low voltage secondary inverter. Use of a single DC link facilitates fourquadrant operation of the inverter. The proposed scheme is validated for steady state and dynamic performance by experimental results.

Humans can easily parse and find answers to complex queries such as “What was the capital of the country of the discoverer of the element which has atomic number 1?” by breaking them up into small pieces, querying these appropriately, and assembling a final answer. However, contemporary search engines lack such capability and fail to handle even slightly complex queries. Search engines process queries by identifying keywords and searching against them in knowledge bases or indexed web pages. The results are, therefore, dependent on the keywords and how well the search engine handles them. In our work, we propose a three-step approach called parsing, tree generation, and querying (PTGQ) for effective searching of larger and more expressive queries of potentially unbounded complexity. PTGQ parses a complex query and constructs a query tree where each node represents a simple query. It then processes the complex query by recursively querying a back-end search engine, going over the corresponding query tree in postorder. Using PTGQ makes sure that the search engine always handles a simpler query containing very few keywords. Results demonstrate that PTGQ can handle queries of much higher complexity than standalone search engines.

This work proposes a design methodology to optimize multiple design metrics of a stand-alone PV/battery system at the same time. The relevance of each objective can be adjusted by the designer and this paper explores the correlations among them. An application example is proposed, where the objectives are the minimization of investment and operational cost, with a boundary set on the system reliability. The variables are six and represent the size of the generation, storage, and power conversion elements, as well as the converters selection. The example design is repeated with two battery types, Lead-Acid and Li-Ion. The use of a genetic algorithm reduces the computational power, allowing the quick execution of several optimizations with different settings.

Approach to bring efficient, generic, and reusable solutions for challenges while automating the logic validation of a card games, specifically built with unity engine.

Digital Twins may be employed for developing embedded software and can be classified in three subcategories by their level of integration with the corresponding physical objects/twins. We introduce and report on experience with a new category: the Digital Twin Prototype. Digital Twin Prototypes support engineers to test embedded software systems, without the need of a connection to a physical object. In CI/CD pipelines they can be used for integration testing and thus, allow for an agile verification and validation process. We developed and evaluated this approach to create an underwater network of ocean observation systems. The feasibility was shown in a demonstration mission in the Baltic Sea in October 2020.

Reconfigurable intelligent surface (RIS)-empowered communication is being considered as an enabling technology for sixth generation (6G) wireless networks. The key idea of RIS-assisted communication is to enhance the capacity, coverage, energy efficiency, physical layer security, and many other aspects of modern wireless networks. At the same time, mobile edge computing (MEC) has already shown its huge potential by extending the computation, communication, and caching capabilities of a standalone cloud server to the network edge. In this article, we first provide an overview of how MEC and RIS can benefit each other. We envision that the integration of MEC and RIS will bring an unprecedented transformation to the future evolution of wireless networks. We provide a system-level perspective on the MEC-aided RIS (and RIS-assisted MEC) that will evolve wireless network towards 6G. We also outline some of the fundamental challenges that pertain to the implementation of MEC-aided RIS (and RIS-assisted MEC) networks. Finally, the key research trends in the RIS-assisted MEC are discussed.