Automatic modulation recognition (AMR) technique plays an important role in the identification of modulation types of unknown signal of integrated sensing and communication (ISAC) systems. Deep neural network (DNN) based AMR is considered as a promising method. Considering the complexity of a typical ISAC system, devising the DNN manually with limited knowledge of its various classifications will be very tasking. This paper proposes a neural architecture search (NAS) based AMR method to automatically adjust the structure and parameters of DNN and find the optimal structure under the combination of training and constraints. The proposed NAS-AMR method will improve the flexibility of model search and overcome the difficulty of gradient propagation caused by the non-differentiable quantization function in the process of back propagation. Simulation results are provided to confirm that the proposed NAS-AMR method can identify the modulation types in various ISAC electromagnetic environments. Furthermore, compared with other fixed structure networks, our proposed method delivers the highest recognition accuracy, under the condition of low parameters and floating-point operations (FLOPs).

In the past decade, we have witnessed the emergence of a large number of different computer-based animations and simulations that have the goal to foster better learning of different physics topics. Past studies have shown many benefits of animations and simulations, but for their efficient usage it is very important that teachers are well educated in the teaching material and usage of selected visualizations. Furthermore, studies have proven that augmented reality technology has a potential to reduce cognitive load and improve the quality of physics lectures. Many of these visualizations are generally designed for targeted physics phenomena, and sometimes it is not easy to address specific students’ misconceptions. In this paper, we will present augmented reality animations and a simulation that can generally be useful for teaching about counterintuitive aspects of rolling motion, and specifically address students’ misconceptions about rolling friction and velocity in contact with the ground.

Specific emitter identification (SEI) is developed as a potential technology against attackers in cognitive radio networks and authenticate devices in Internet of Things (IoT). It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. Due to the strong capability of deep learning (DL) in extracting the hidden features of data and making classification decision, deep neural networks (DNNs) have been widely used in the SEI. Considering the insufficiently labeled training dataset and large unlabeled training dataset, we propose a novel SEI method using semi-supervised (SS) learning framework, i.e., metric-adversarial training (MAT). Specifically, two object functions (i.e., cross-entropy (CE) loss combined with deep metric learning (DML) and CE loss combined with virtual adversarial training (VAT)) and an alternating optimization way are designed to extract discriminative and generalized semantic features of radio signals. The proposed MAT-based SS-SEI method is evaluated on an open source large-scale real-world automatic-dependent surveillance-broadcast (ADS-B) dataset. The simulation results show that the proposed method achieves a better identification performance than four latest SS-SEI methods.

To address the problem of spectrum resources and transmitting power for vehicular networks, this paper proposes a resource allocation (RA) method based on dueling double deep-Q network (D3QN) reinforcement learning (RL). Due to the high mobility of the vehicle, the channel changes rapidly which makes it difficult to accurately collect high-accuracy channel state information at the base station and to perform centralized management. In response of this difficulty, we construct a multi-intelligence model, using Manhattan Grid Layout City Model as the basis of environment and with each vehicle-to-vehicle (V2V) link as an intelligence. They work together to interact with the environment, receive appropriate observations, get rewards, and finally learn to improve the allocation of power and spectrum to enable users to achieve a better entertainment experience and a safer driving environment. Experimental results demonstrate that with proper training mechanism and reward function construction, cooperation among multiple intelligence can be performed in a distributed manner, with improvements in both the capacity of total vehicle-to-infrastructure links and the effective payload delivery success rate of the V2V links compared to common Q-network.

Malware traffic classification (MTC) is a very important component of cyber security, and a number of the MTC techniques are based on deep learning (DL) with a strong capability of feature mining and classification. However, these DL-based MTC methods are heavily dependent on a large amount of network traffic samples. In the few-shot scenarios, these methods usually overfit and have poor classification performance. Considering that the update cycle of malware is faster and faster, and there are more and more types of malware, collecting enough training samples for all malware is very challenging, if not impossible. In this paper, a novel few-shot MTC(FS-MTC) method is proposed based on convolutional neural network (CNN) and model-agnostic meta-learning (MAML) algorithm. Specifically, the CNN is trained on samples from normal softwares by MAML rather than the conventional optimization methods, then the CNN is finetuned by a few samples from malware for MTC. Simulation results show that our proposed MAML-based FS-MTC can outperform the traditional MTC methods. The performance of our proposed method can reach up to 95.69%.

INTRODUCTION MATERIALS AND METHODS

This paper analyzes the problem of DC cable selection in photovoltaic (PV) plants. PV plants can have tens of kilometres of one-way cables that are important parts of the system. The currents flowing through these cables can reach values of several hundred amps. Losses incurred on DC cables are up to 1%, which can be significant when measuring power loss during the operating period. Reduction of these losses can be achieved by increasing the cross-section of the cable. The paper describes the requirements set by the standards for selecting cable cross-sections. An analytical criterion function that connects electricity losses and cable crosssection were deduced. This function depends on several parameters such as electricity price, cable price, the average number of sunny hours per year, average amount of electricity through cable, interest rate, loan repayment period, and plant operation period. Several cases with the analysis of the obtained results are presented.

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Background: History of pharmaceutical industry in Bosnia and Herzegovina (B&H) has its roots from 1951. Importance of domestic industry not just from economical aspect but also from public health perspective and as scientific base has not been evaluated previously. Objective: The aim of this article was to provide the review of the pharmaceutical industry developments in Bosnia and Herzegovina, its roots, current position and future perspectives.. Methods: Research of published scientific papers as well other documents and archives of pharmaceutical manufacturers has been conducted. We have also analysed market trends focusing on domestic producers. Results and Discussion: Over more than seventy years of B&H pharmaceutical industry has been developing. During Yugoslavia only two companies existed of which one, Bosnalijek is still present, while Sanofarm has been closed. After 1996, expansion of domestic manufacturers started and today six companies are present. They are mainly oriented to generic drugs production in different forms. Total market share of domestic producers in B&H is 20-25% which is relatively low comparing to other countries. Many of domestic manufacturers are exporting their products to some of the most demanding markets in Europe and Middle East. Conclusion: Long history of domestic drug manufacturers in B&H gives solid legacy for future developments. Importance of local producers has been confirmed during war in B&H and COVID-19 pandemic as a crisis situation, mainly from public health perspective and sustainable supply of essential medicines. Higher support by state and collaboration with academia in order to expand portfolio, especially in area of biologic medicines is required in future.

High-voltage direct current (HVDC) circuit breaker development and deployment strongly depend on the testing process, which ensures that the HVDC circuit breakers will satisfy design requirements. This article presents an HVDC circuit breaker test bench circuit configuration that can provide controllable large output currents to simulate different fault conditions for the current breaking test and high output voltage for the dielectric withstand test. The current breaking test circuit is based on multiple cascaded power converters connected in parallel to provide the necessary output current capability. Each cascaded power converter is composed of multiple cells that are operated by a phase-shifted pulsewidth-modulated signal for greater controllability and higher quality of the output waveform. The dielectric withstand test circuit is a simple high-voltage source with a low power rating that can also be used to charge the test bench and the internal circuitry of the circuit breaker that is to be tested. The proposed test bench ensures that fault conditions can be replicated accurately and offers greater flexibility by being able to test mechanical, semiconductor-based, or hybrid HVDC circuit breakers with different current and voltage ratings on the same hardware without any changes. The idea and the operating principle of the proposed test bench are verified experimentally on a downscaled system that consists of three cascaded power converters connected in parallel with three cells per cascaded power converter and with a total equivalent switching frequency of 92.5 kHz.