International Journal of Electrical Engineering and Computing
International Journal of Electrical Engineering and Computing (IJEEC) is a peer reviewed, open access scientific journal, published by the University of East Sarajevo, Faculty of Electrical Engineering, Bosnia and Herzegovina, and the publishing, design and marketing company Academic Mind, Belgrade, Serbia. The journal was created to promote the academic, professional community and research development visibility, spreading original and relevant articles in the wide range of subfields related to the electrical engineering and computer science. The papers reporting and containing original theoretical and/or practice oriented research and articles of interdisciplinary nature are all welcome.
University of East Sarajevo, Faculty of Electrical Engineering, East Sarajevo, Bosnia and Herzegovina
Operational planning and security management in modern power systems under regional coordination conditions
Deregulation of the electricity market has brought not only numerous benefits but also significant challenges in the domain of operational planning and management of the power system. The increase in cross-border trade required improved coordination and data exchange among Transmission System Operators (TSOs). In response, Regional Security Coordinators (RSCs) were established to centralise the coordination of TSOs' operations and provide forecasts of the power system state over short- and medium-term time intervals. This paper aims to provide insight into the key aspects of RSC operations, emphasizing their significant role in planning, coordinating, and implementing security strategies at the regional and Pan-European level. Although the data presented in this paper is mainly theoretical due to the confidentiality of information managed by RSCs, this paper nevertheless serves as a valuable resource given that the concept of RSCs has been insufficiently addressed in professional literature and scientific studies.
Copyright (c) 2025 International Journal of Electrical Engineering and Computing
Residue Number System-Based Hash Function: Design and Cryptographic Analysis
In this paper, we propose a novel cryptographic hash function based on the Residue Number System (RNS). The design leverages the inherent parallelism of RNS to enhance computational efficiency and resilience against certain classes of attacks. Through modular processing across independent residues, the function achieves notable improvements in hardware acceleration capabilities, making it particularly suitable for resource-constrained environments. To assess the robustness of the proposed function, we conduct a series of tests including the distribution of Hamming weights, bit balance evaluation, and avalanche effect analysis. The results demonstrate that the hash function produces uniformly distributed outputs, exhibits strong avalanche characteristics, and maintains high resistance to input structure correlation. These properties confirm the potential of the proposed RNS-based approach for secure and efficient hashing in modern cryptographic applications.
Two and Three Dimensional Support Vector Algorithm for Students’ Classification
In an era where technology with its development is constantly reshaping the field of professional practice, it is evident that educating engineers nowadays is a challenging task. This is supported by the increasingly popular teaching methods in which traditional learning methods are combined with modern technologies, all with the aim of greater motivation for learning and work among the students. The subject of research in this paper is specifically one teaching method, adapted for teaching at technical faculties, which uses machine learning methods (support vector method) in its background. The introduced method recognizes the importance of assessing students' abilities and knowledge because a good classification of students allows the teacher to plan the lesson more easily and choose the approach they believe will give the best results.
Population Genetic Analysis of Allele Frequencies for Hereditary Thrombophilia in Pregnant Women Using Artificial Intelligence
Thrombophilia is an inherited condition characterized by an increased tendency for blood clot formation, affecting approximately 8-11% of the European population. It is typically inherited in an autosomal dominant manner and includes approximately 10 different subtypes, classified based on genetic factors. This condition often leads to complications in pregnant women, including spontaneous miscarriage, fetal deformities, and an increased risk of heart attack, stroke, pulmonary embolism, and deep vein thrombosis. The goal of this study was to conduct a detailed genetic analysis of thrombophilia using population genetics methods (Hardy–Weinberg equilibrium, Shannon index, genotype frequencies) and to develop an AI-based predictive model for high-risk genotypes, evaluated via confusion matrices. The research focused on assessing allele frequencies, genetic diversity, and deviations from Hardy-Weinberg equilibrium in a cohort of 2,760 pregnant women to enhance the understanding of the genetic basis of this condition. Our analysis revealed significant correlations between coagulation factor genes and identified distinctive patterns of genetic diversity across 12 thrombophilia-associated markers. The results provide key insights into genetic variations and their potential implications for pregnancy complications. This study opens new perspectives for improving early detection and more effective risk management of thrombophilia during pregnancy.
Population Genetics; Allele Frequencies; Thrombophilia; Genotypic frequency; Pregnancy; Shannon index; Hardy-Weinberg Equilibrium (HWE); Artificial intelligence;
CodeCrafter – Efficient Code Generator for Modern Single-page Web Applications
Web applications and REST API based applications can be seen as a most common type of application today and their number is constantly increasing. Due to the pressure to produce more code and be faster at producing it, developers have been using various programming languages, libraries and frameworks suitable for such applications. Two of the most popular technologies are Spring on the back-end, and React on the front-end. While each technology provides a satisfiable functionality, the amount of code that needs to be produced is non-trivial, especially in cases where the application has to provide even basic security and auditing functionalities. One solution for this issue is use of code generator tools that produce both back- and front-end code from a model. While a wide variety of generators exist, none of the analyzed ones fulfill all the requirements of a modern web application. In this paper, we present CodeCrafter, a web-based code generation application that produces Spring and React code based on relational data model in DDL or JSON format, and provides developers with simple and efficient tool to generate fully functional foundational code. We compare it to existing code generation tools and measure its performance as a function of number of tables in the database. We show that CodeCrafter produces code in very short amount of time even for very complex databases, while providing features and functionalities not present in other analyzed tools. We also give a short overview of possible use in conjunction with LLM based coding tools.
Optimization and Construction of a Deep Learning Model for Breast Cancer Segmentation
The research focuses on optimizing and developing a breast cancer segmentation model utilizing multi-GPU arrays to maximize hardware resource utilization. By exploring various architectural strategies, the study aims to enhance resource allocation efficiency despite existing limitations. Detailed evaluations using mammography datasets have demonstrated significant improvements in tumor detection capabilities. This technology holds the potential to revolutionize breast cancer detection, a critical advancement given the global impact of the disease. Training data analysis confirms the scalability of these results across diverse hardware configurations, ensuring high efficiency and reliability. The study employs modern architectures, contributing valuable insights to the field of breast cancer segmentation and advancing medical imaging technologies. The development of efficient and clinically viable solutions is imperative in contemporary medical image analysis. While state-of-the-art deep learning architectures offer impressive capabilities, their substantial computational demands pose barriers to widespread clinical adoption. This research addresses the need for solutions that efficiently process large datasets while maintaining diagnostic accuracy, facilitating integration into clinical workflows and reducing operational costs.
machine learning; AI; image segmentation
Copyright (c) 2024 International Journal of Electrical Engineering and Computing
Lightning protection of the 110 kV substation at the wind park Ivan Sedlo against transients caused by direct lightning strikes into connected overhead line
This paper deals with the lightning protection of 110 kV substation at the wind park Ivan Sedlo against transients caused by direct lightning strikes into connected 110 kV overhead line. Lightning protection level (LPL) I as per standard IEC 62305-1 is selected for this substation to reduce probability of equipment failure to low level. Calculations are done in the EMTP-ATP software. Three critical scenarios are analysed: shielding failure at the connected 110 kV overhead line, lightning strike to the top of first tower in front of the substation and lightning strike to the top of gantry tower at the entrance to the substation. It is presented that LPL I for analysed configuration of substation can be achieved by installing two sets of surge arresters. First set of surge arresters is installed in the transmission line bay, while second set of surge arresters is installed in the transformer bay. Transferred lightning transients through the power transformer are also calculated. It is presented that one additional set of surge arresters is necessary at the 30 kV side of the power transformer, to protected it against transferred lightning transients. Energy duty of all three sets of surge arresters is calculated. At the 110 kV voltage level energy stress of surge arresters is significant and strongly depends on the substation grounding resistance value. Estimated energy stress of set of surge arresters at the 30 kV voltage level is very low.
lightning protection; overhead line; shielding failure; substation; surge arrester; wind park
MQTT Protocol in Smart Home Environments: Principles of Operation and Application
Internet of Things as a revolutionary concept in technology of modern days has led to the popularization of “smart” things. It introduces low-cost and reliable solutions to provide independent operations of devices and machines. Smart home systems have become one of the most important implementations as they significantly improve the quality of people’s lives. Since communication plays a key role in these systems, this article provides insights into Message Queuing Telemetry Transport protocol application in smart home systems. Although it is not the only protocol used, MQTT stands out in terms of reliability, efficiency, and simplicity. The article provides major MQTT characteristics, advantages and its application in a particular smart home system for ambient parameters monitoring and control. The system integrates NodeMCU based sensor node with mobile and web applications via MQTT protocol. Results of the performance analysis of MQTT use in this system are given. Tests have shown great connection establishment speed, a high percentage of successfully sent messages, and a large number of messages sent per minute. Thanks to the flexibility of the MQTT protocol, the system is easy to upgrade and expand.
A System for Remotely Monitoring and Controlling Temperature within an Egg Artificial Incubator
Abstract— According to a report by the Food and Agriculture Organization of the United Nations, poultry meat is the leading contributor to global growth in total meat production. This growth is facilitated by the short production cycle of poultry, advancements in genetics, animal healthcare, feeding methods, breeding techniques, and improved environmental conditions, enabling producers to adapt quickly to market demands. Proper hatching practices are critical to maximizing the number of healthy chicks and minimizing their mortality rate. There are two primary methods of hatching: natural incubation, where hens incubate eggs by sitting on them, and artificial incubation, which uses incubators. In artificial incubation, monitoring and controlling key environmental factors, particularly temperature and humidity, are essential. Maintaining the incubator's temperature within an optimal range is crucial, with literature recommending a range of 37°C to 38°C and an ideal temperature of 37.8°C. This paper introduces a system for remotely monitoring and controlling temperature within an artificial incubator. The system integrates hardware and software based on the Internet of Things (IoT) framework. For this purpose, a proportional-integral-derivative (PID) controller is designed to maintain the desired temperature, with temperature data stored in a database and accessible online. The proposed system offers a cost-effective yet highly efficient solution, primarily designed for small-scale hatcheries. With modifications, it can be adapted for broader agricultural applications, including greenhouses, silos, and stables. By contributing to digital agriculture, the system supports sustainable farming practices, which are essential for the future of agriculture.
Cost vs. Performance: Raspberry Pi and ESP32 in Face Detection Tasks
A cost-effective, scalable solution that leverages low-cost embedded systems for computationally intensive tasks like face detection is critical for the growing demands of IoT applications, motivating the development of innovative hybrid architectures. This paper proposes such a framework, combining the ESP32 CAM as a distributed image capture unit with light preprocessing and the Raspberry Pi as a centralized processing unit. By addressing the limitations of standalone implementations—low computational capability of the ESP32 CAM and high deployment costs of the Raspberry Pi—our framework achieves a balance between affordability and performance. We present the analysis of the devices, evaluating metrics such as frame rate, detection accuracy, and cost-efficiency. The results highlight the potential of the hybrid system to significantly lower costs while enabling scalable, real-time face detection in IoT scenarios. This study contributes to the ongoing research by proposing an adaptable, resource-optimized framework suitable for diverse use cases, from smart surveillance to retail monitoring, paving the way for more efficient IoT-based vision systems.
Raspberry Pi, ESP32 CAM, face detection, microcontrollers, IoT, frame rate, detection accuracy, cost-efficiency
A new electrode structure of IrOx/Bi-doped SrRuO3 for highly reliable La-doped Pb (Zr, Ti)O3-based ferroelectric memories
We successfully developed a lanthanum (La)-doped Pb (Zr,Ti)O3 (PLZT) based ferroelectric capacitor (FC) using a new electrode material of bismuth (Bi) doped SrRuO3 (B-SRO) aiming at reduction of energy consumption of ferroelectric random access memory (FeRAM) by suppressing the leakage current of its FC. Our employed B-SRO layer is effective for suppressing the leakage current due to reducing atomic interdiffusions of Iridium and lead between IrOx top electrode (TE) and PLZT. Space charge limited conduction (SCLC) is dominant in the leakage current of the FC with B-SRO, while defect assisted conduction possibly includes in the leakage current of FC without B-SRO in addition with the SCLC. Switchable polarization, depending on the B-SRO thickness, has largest value for 1.0-1.5 nm thick B-SRO. Excellent imprint and switching (fatigue) endurances is proven on the FC with 1 nm thick B-SRO.
Ferroelectric memory; FRAM; FeRAM; Bi doped SrRuO3
Realization of adaptive beamformer on open-source hardware
Realization of an efficient system for extracting useful audio signals in the presence of interferences is an important engineering problem in the field of acoustics. This paper proposes a method for extracting sound signals from a specific direction of incidence. A microphone array consisting of eight microphones was used, coupled with space-time signal processing executed in real time on hardware. The microphone signals are filtered by an adaptive beamformer, whose coefficients are optimized in real time on open-source hardware using the Least Mean Square algorithm. This paper describes the characteristics and limitations of the used microphone array. Testing the system through simulation indicates that extracting narrowband and wideband signals using the presented adaptive beamforming method is theoretically possible. This paper aims to experimentally evaluate the algorithm under real-world conditions, as well as identify the limitations of extracting wideband signals. Adaptation of the filter coefficients and filtering the signals from the microphones was implemented on Bela hardware, specialized for processing audio signals. This experiment highlights the limitations that arise from extracting a wideband signal using this type of hardware in real conditions. By comparing the useful and filtered signals in both time and frequency domains, the quality of the filtering was analyzed. Our experiments suggest that the current hardware specifications are a limiting factor for successful wideband signal filtering in real conditions, as they limit the maximum possible order of the adaptive filter, which proved to be insufficient.
adaptive beamforming, Bela hardware, microphone array, real time processing, signal extraction
A New Design Approach to Discrete-Time Sliding Mode Controllers With Disturbance Compensation
Abstract— A new design approach to discrete time sliding-mode-based controllers with disturbance compensation is proposed in this paper. The approach is applicable for linear time invariant plants with matched disturbances. Starting from the known methods for disturbance estimation (i) using discrete time nominal plant model and (ii) using original sliding mode control design method, full integration of those two algorithms into single one is proposed. Besides, the proposed algorithm can be additionally simplified for a small sampling time. The simplified algorithm does not directly depend on the equivalent control but only on the present and previous values of the sliding variable and previous value of the control. The obtained results are compared with the corresponding system with disturbance estimator based on sliding variable measurement. It is established that both methods give identical results in the nominal case. The method is illustrated in a positional servo system design. Comparative analysis of different methods is done by computer simulation.
electrical drives; sliding mode control; speed control; position control; discrete-time variable structure control
Disturbance Compensation in Discrete-Time Sliding Mode Control – A Comparative Study
This work is intended to compare three methods for disturbances estimation and compensation in discrete-time variable structure systems with sliding modes. All methods detect disturbance, which appear in control channel, with time delay of one sampling perio. The first method is based on nominal discrete-time plant model and can be used in any type of discrete-time control systems. The second and the third methods detect disturbance by measurement of sliding function, and it is applicable in discrete-time sliding mode control systems only. The main task of this work is to check efficiency of the given methods in the presence of unmodeled inertial dynamics in actuators and position and velocity sensors of a positional servo system. It is shown that all three methods give near identical results in the nominal case, while the second and the third methods are superior in the presence of unmodelled dynamics. The third method introduces zig-zag motion in the nominal case. Results of research are illustrated by computer simulation of an example of positional servo system.
An Integrated System for Smart and Efficient Irrigation of Crop Fields
The widespread adoption of new technologies has led to the development of various innovative solutions in agriculture. Crop monitoring using drones for early detection of plant diseases, autonomous machinery for soil preparation, and various robotic systems for harvesting and sorting fruits are just some examples of technology application to increase productivity in agricultural production. This paper describes an integrated system solution for irrigating agricultural production plots aimed at increasing production efficiency while efficiently utilizing necessary resources. All system components (hardware and software) are custom designed, taking into account real-world aspects and constraints in the field. The paper provides a detailed description of all functional units of the system, as well as preliminary experiences with the operation of the implemented prototype system. We also provides some initial characterization of the soil moisture probe that is designed in-house by running extensive LTSpice simulations.
Application of Internet of Things Hardware in Control Systems
This paper discusses a new concept of control systems, characterized as the Internet of Things Control Systems (IoTCS), which is based on the control techniques of networked control systems and the concept of the Internet of Things. To obtain IoTCS, the Internet of Things hardware, which belongs to embedded systems and boards hardware class, is used. The example of temperature control shows how the concept of IoTCS can be practically realized with the selected Internet of Things hardware. The operation of the implemented IoTCS is based on the principle of a networked control system in which the Arduino Uno board is equivalent to a communication network. Temperature control is enabled by a PID controller designed in Simulink, which shows that software packages traditionally used for the synthesis and analysis of control systems can be effectively used for the synthesis and analysis of control systems based on the Internet of Things hardware.
control systems; Internet of Things; Internet of Things hardware; temperature control; PID controller
Copyright (c) 2021 International Journal of Electrical Engineering and Computing
Application of Particle Swarm Optimization for Classical Engineering Problems
In the design of mechanical elements, designers usually consider certain objectives that are related with cost, time, quality and reliability of product, depending on the requirements. In this paper, parametric optimization of spring design problem, pressure vessel design problem, cantilever beam design problem, cone coupling design problem and welded beam design problem has been carried out using Particle Swarm Optimization (PSO for short). The pseudo code for this algorithm was written using Matlab R2018a software suite. Results of the PSO algorithm will be compared to results obtained by the Differential Evolution (DE), Modified Ant Colony Algorithm, (MACA), Grasshopper Optimization Algorithm (GOA), Water Cycle Algorithm (WCA), Cucko Search (CS) , Genetic Algorithm (GA), Ant Lion Optimization (ALO), Firefly Algorithm (FA) and Method of Moving Asymptotes (MMA), depending of solutions found in literature. The source codes of PSO algorithm are publicly available at https://seyedalimirjalili.com.
optimization, pso algorithm, spring, pressure vessel, cantilever beam, cone coupling, welded beam
High Frequency Radio Surveillance System
The radio system for high frequency spectrum monitoring and signal processing used in direction finding is presented in this paper. This main function is realized using 6-channel HF radio signal receiver. The high degree of flexibility is achieved by the application of antenna multicoupler unit which allows that signals from several antennas may be processed in the system. The system signal processing especially in the demodulator is based on the most modern components of software defined radio. User handling is simple over intuitive user interface and system functional parameters may be easily changed.
antenna multicoupler unit; 6-channel HF radio signal receiver; demodulator; RTL-SDR –Software Defined Radio, SCPI protocol
Simulating the Power Output of Large Bifacial Photovoltaic Plants
Bifacial photovoltaic modules have gained increasing attention in the last decade due to their potential to achieve higher annual energy yield in comparison to conventional monofacial modules. Since the higher energy production is also accompanied by higher investment costs, it is necessary to conduct a careful techno-economic analysis in order to provide the investors an answer about their accurate cost efficiency. The achievable energy output of a bifacial photovoltaic power plant is influenced by many factors such as module geometry, row spacing, orientation of the modules and ground albedo. Since the methodology for prediction of the energy yield has not yet been standardized, the main target of this paper was to create one version of a comprehensive standalone energy calculator that would serve as a useful performance assessment tool for designers and investors. The developed software tool was tested on several characteristic scenarios and the obtained results were compared with the results provided by two freely available online calculators.
Power Flow Prediction for a Steel Plant with a Dynamic Regression Model
We present a power flow forecast for an industrial complex consisting of more than 20 different companies. The predominant consumer of electricity in the industrial complex is a steelwork company with an electric arc furnace. For the mentioned production system, we have developed a robust power flows forecast for a month in the future, with a high level of confidence in the forecast. A steelwork with an electric arc furnace is a very specific example of an energy consumer. Other companies in the industrial complex are not connected to the steel plant technologically, but they are on the same energy connection. They have a weekly power flow profile significantly different from the steel plant. To calculate the forecast model and perform the forecast of power flows on the common power connection, we need only two inputs of data: Historical measurements of power flows and the number of loads of the electric arc furnace in the following days. These are input data in a forecast model that we get without any problems. We first showed a prediction with classical linear regression. The next model to predict was the seasonal ARIMA model with a regressor, also called a dynamic regression model. The dynamic regression model improved the prediction by 15% compared to static linear regression, according to the RMSE measure. This was followed by an improvement in the dynamic regression forecasting model by considering the seasonality 7/5 in the time series. We did this with a model with superimposed noise. This model incorporates the ARMA methodology and considers superimposed noise. With this model, we improved the forecasting by 30% to linear regression. Logically, the filter model of the prediction also improved, gaining more Lag coefficients, and losing a constant. Qualitatively, the result is a forecast of power flow for one month with prediction error MAPE 8% and measure R2 is 0.9.
Electrical Drives Control via Discrete-Time Variable Structure Systems with Sliding Mode
Modern control techniques of electrical drives (EDs) use robust control algorithms. One of such algorithms is variable structure control (VSC) with sliding mode (SM). SM control needs more information on the controlled plant than the conventional PI(D) control. Valid mathematical model of the controlled plant is necessary for the SM controller design. Generalized mathematical model of two-phase electrical machine and its adaptation to direct current (DC) and induction motor (IM) are given in this paper, employed in the cascade control structure. Also, the basic SM control theory and discrete-time controller design approach, developed by the authors, are given. Finally, experimentally realized examples of speed and position control of DC and IM are given as an illustration of the efficiency of the promoted EDs controller design via discrete-time VSC.
Blockchain Adoption in Aviation: Opportunities and Challenges
In the world of aviation, current evidence suggests that data management systems are majorly centralized and at the same time suffer some noted shortfalls. Whereas, under this system of operation, it is hard to guarantee trust, especially data provenance, in addition to other shortfalls. More so, there are issues of lack of sufficient governance of data immutability, transparency, auditability, and traceability; which present debilitating drawbacks to the current regime of centralized data management systems in aviation industries. In effect, the shortfalls and lapses of the reigning data management –– the aviation centralized data management systems–– incentivizes the adoption of other foolproof data management system. In plain terms, technology models now favor and encourage Blockchain as a result of its ‘heavy-weight’ of strength over the weaknesses of other systems of data management, especially the centralized data management in aviation. Blockchain is a new ‘bride’ courted by popular technologies for its positive disruptive tendencies. Blockchain as a new normal disruptive technology promises potency and reliability in data trust, traceability, transparency, auditability, and immutability. Blockchain further emphasizes immutability for stored and exchanged data, in transaction environments, in a decentralized and secure manner. That is, in a manner whereby a trusted third-party involvement is not required. For this reason, this paper discusses and highlights the key characteristics that differentiate and denominate Blockchain. It dwells more upon Blockchain application in aviation, in its several sectors such as passenger verification, In-flight Entertainment (IFE) Monetization, Smart Contact, Digital Identity, Distribution, Payment, and so on. Also, the paper did not conclude without pointing out areas of inconclusive research in Blockchain technology to stimulate further research studies leading to new understanding, knowledge, ideation, and idea incubation.
Copyright (c) 2022 International Journal of Electrical Engineering and Computing
Subsystem with Ultrasonic and Passive Infrared Sensors for Pedestrian Detection
Despite the rapid development of technologies for the detection of pedestrians in traffic, there are still significant problems with autonomous vehicle management systems. Often these problems are caused by different external conditions, weather disasters, different light levels, day and night, and more. All these factors can affect the incorrect assessment of the system and the performed detection of the pedestrian object. The pedestrian fatalities have risen recently, even as vehicles are equipped with sophisticated avoidance technology. Vehicle-based sensors can fail to identify pedestrians especially when those pedestrians are small, too far or too close to the vehicle, or partially occluded by nearby objects. The pedestrians have variable physical characteristics and appear in a variety of environments with different background features. The paper analyzes the possibilities and problems that arise in the detection and recognition of pedestrians. Pedestrian safety is one of the important issues of traffic safety in urban areas. The application of various technologies in vehicles provides enormous opportunities to reduce accidents in which pedestrians are most often injured. The development of intelligent pedestrian recognition systems can be divided into two directions. Development of hardware part related to different types of sensors, and development of software part based on artificial neural networks. The paper analyzes various technologies used in pedestrian detection in automated vehicles with an emphasis on hardware development. A lowcost system based on two types of sensors, ultrasonic sensor and passive infrared, is proposed. Aspects of the application of such sensors with possibilities of implementation in complex computer vision systems ar analyzed. The most common classes of sensors presently used in automated driving applications include visible-light cameras (VLC), light detection and ranging (LiDAR).
Automated Vehicle; Pedestrian Detection; Ultrasonic Sensor; Passive Infrared Sensor
Condition, Development, Potential and Application of Electronic Treatment
The report provides a historical overview of the topic, examines the physical basis of electron beam production, thermal emission and treatment area, types of electron beam treatments and the state of technology, types, development and potential of equipment and its application in practice. The advantages and disadvantages are analyzed and the main conclusion is made that the wavelength of the beam determines and is commensurate with the resolution and size of the processed components.
electron beam treatment; beam wavelength; resolution