Technologies, Volume 10, Issue 4 (August 2022) – 23 articles

To improve the accuracy and real-time performance of vehicle recognition in an advanced driving-assistance system (ADAS), a vehicle recognition method based on radar and camera information fusion is proposed. Firstly, the millimeter-wave radar and camera are calibrated jointly, the radar recognition information is mapped on the camera image, and the region of interest is established. Then, based on operator edge detection, global threshold binarization is performed on the image of the region of interest (ROI) to obtain the contour information of the vehicle in front, and Hough transform is used to fit the vehicle contour edge straight line. Finally, a sliding window is established according to the symmetry characteristics of the fitting line, which can find the vehicle region with the highest symmetry and complete the identification of the vehicle. The experimental results show that compared to the original recognition region of the radar, the mean square error of this algorithm is reduced by 13.4 and the single frame detection time is reduced to 28 ms. It is proven that the algorithm has better accuracy and a faster detection rate, and it can solve the problem of an inaccurate recognition region caused by radar error. Full article

Synthetic micro/nanomotors (MNMs) are human-made machines characterized by their capacity for undergoing self-propelled motion as a result of the consumption of chemical energy obtained from specific chemical or biochemical reactions, or as a response to an external actuation driven by a physical stimulus. This has fostered the exploitation of MNMs for facing different biomedical challenges, including drug delivery. In fact, MNMs are superior systems for an efficient delivery of drugs, offering several advantages in relation to conventional carriers. For instance, the self-propulsion ability of micro/nanomotors makes possible an easier transport of drugs to specific targets in comparison to the conventional distribution by passive carriers circulating within the blood, which enhances the drug bioavailability in tissues. Despite the promising avenues opened by the use of synthetic micro/nanomotors in drug delivery applications, the development of systems for in vivo uses requires further studies to ensure a suitable biocompatibility and biodegradability of the fabricated engines. This is essential for guaranteeing the safety of synthetic MNMs and patient convenience. This review provides an updated perspective to the potential applications of synthetic micro/nanomotors in drug delivery. Moreover, the most fundamental aspects related to the performance of synthetic MNMs and their biosafety are also discussed. Full article

In the manufacturing of injection-molded plastic parts, it is essential to perform a non-destructive (and, in some applications, contactless) three-dimensional measurement and surface inspection of the injection-molded part to monitor the part quality. The measurement method depends strongly on the shape and the optical properties of the part. In this study, a high-precision (±5 µm) and high-speed system (total of 24 s for a complete part dimensional measurement) was developed to measure the dimensions of a piano-black injection-molded part. This measurement should be done in real time and close to the part’s production time to evaluate the quality of the produced parts for future online, closed-loop, and predictive quality control. Therefore, a novel contactless, three-dimensional measurement system using a multicolor confocal sensor was designed and manufactured, taking into account the nominal curved shape and the glossy black surface properties of the part. This system includes one linear and one cylindrical moving axis, as well as one confocal optical sensor for radial R-direction measurements. A 6 DOF (degrees of freedom) robot handles the part between the injection molding machine and the measurement system. An IPC coordinates the communications and system movements over the OPC UA communication network protocol. For validation, several repeatability tests were performed at various speeds and directions. The results were compared using signal similarity methods, such as MSE, SSID, and RMS difference. The repeatability of the system in all directions was found to be in the range of ±5 µm for the desired speed range (less than 60 mm/s–60 degrees/s). However, the error increases up to ±10 µm due to the fixture and the suction force effect. Full article

Persia was the early name for the territory that is currently recognized as Iran. Iran’s proud history starts with the Achaemenid Empire, which began in the 6th century BCE (c. 550). The Iranians provided numerous innovative ideas in breakthroughs and technologies that are often taken for granted today or whose origins are mostly unknown from the Achaemenid Empire’s early days. To recognize the history of computing systems in Iran, we must pay attention to everything that can perform computing. Because of Iran’s historical position in the ancient ages, studying the history of computing in this country is an exciting subject. The history of computing in Iran started very far from the digital systems of the 20th millennium. The Achaemenid Empire can be mentioned as the first recorded sign of using computing systems in Persia. The history of computing in Iran started with the invention of mathematical theories and methods for performing simple calculations. This paper also attempts to shed light on Persia’s computing heritage elements, dating back to 550 BC. We look at both the ancient and current periods of computing. In the ancient section, we will go through the history of computing in the Achaemenid Empire, followed by a description of the tools used for calculations. Additionally, the transition to the Internet era, the formation of a computer-related educational system, the evolution of data networks, the growth of the software and hardware industry, cloud computing, and the Internet of Things (IoT) are all discussed in the modern section. We highlighted the findings in each period that involve vital sparks of computing evolution, such as the gradual growth of computing in Persia from its early stages to the present. The findings indicate that the development of computing and related technologies has been rapidly accelerating recently. Full article

NASA’s Gateway to Astronaut Photography of Earth contains over 30,000 photos of ~2500 cataloged urban lightscapes (anthropogenic night light) taken from the International Space Station. A subset of over 100 of these multispectral DSLR photos are of sufficient spatial resolution, sharpness and exposure to be potentially useful for broadband spectral characterization of urban lightscapes. Spectral characterization of multiple urban lightscapes can provide a basis for quantifying intra and interurban variability in night light brightness, color and extent, as well as the potential for change analyses. A comparative analysis of simulated atmospheric transmissivity from the MODTRAN radiative transfer model indicates that the spectral slopes of transmissivity spectra are relatively insensitive model atmospheres, with variations in atmospheric path length and aerosol optical depth primarily affecting the bias of the spectrum rather than the slope. A mosaic of 18 intercalibrated, transmissivity-compensated RGB photos renders a spectral feature space bounded by four clearly defined spectral endmembers corresponding to white, yellow and red light sources, with brightness modulated by a dark background endmember. These four spectral endmembers form the basis of a linear spectral mixture model which can be inverted to provide estimates of the areal fraction of each endmember present within every pixel field of view. The resulting spectral feature spaces consistently show two distinct mixing trends extending from the dark endmember to flat spectrum (white–yellow) and warm spectrum (orange) sources. The distribution of illuminated pixels is strongly skewed toward a lower luminance background of warm spectrum street lighting with brighter lights, generally corresponding to point sources and major thoroughfares. Full article

The software selection process in the context of a big company is not an easy task. In the Business Intelligence area, this decision is critical, since the resources needed to implement the tool are huge and imply the participation of all organization actors. We propose to adopt the systemic quality model to perform a neutral comparison between four business intelligence self-service tools. To assess the quality, we consider eight characteristics and eighty-two metrics. We built a methodology to evaluate self-service BI tools, adapting the systemic quality model. As an example, we evaluated four tools that were selected from all business intelligence platforms, following a rigorous methodology. Through the assessment, we obtained two tools with the maximum quality level. To obtain the differences between them, we were more restrictive increasing the level of satisfaction. Finally, we got a unique tool with the maximum quality level, while the other one was rejected according to the rules established in the methodology. The methodology works well for this type of software, helping in the detailed analysis and neutral selection of the final software to be used for the implementation. Full article

Our objective is to design triple-band implantable antennas with wide bandwidths and appropriate sizes for biomedical applications. The targeted design frequencies are 400 MHz, 2.4 GHz, and the new Wi-Fi band of 5.7 GHz. Three triple-band antennas with bandwidth improvements are presented to insure all-time data connection. The proposed triple-band implantable antennas benefit from combining long-distance data transfer at lower frequency bands and a higher effective bandwidth, and high-speed communications at higher frequency bands, which will have flexibility for a variety of applications. A comprehensive explanation of the design procedure to achieve multiple-band implantable antennas is provided. Furthermore, miniaturization techniques are utilized to design antennas in compact sizes suitable for biomedical applications. In this paper, three-layer structures including skin, fat, and muscle are used for the designs, then antennas are placed in the chest, neck, head, and hand of different human voxels to compare antennas’ performance. Additionally, normal and overweight human effects on antenna performance were compared. Antennas have 2 to 6 dBi directivity for telemetry usage, and they are designed to satisfy the absorption limit for the human body to keep the Specific Absorption Rate (SAR) averaged over 1 g of tissue less than 1.6 W/kg and over 10 g of tissue less than 2 W/kg, according to IEEE standard. The antennas include fractal, meandered, and comb types with sizes of 1.4 mm × 10 mm × 10 mm, 3.04 mm × 10 mm × 17.25 mm, and 1.4 mm × 12 mm × 12 mm, respectively. The designed antenna showed an impedance bandwidth of 53 MHz to 120 MHz, 90 MHz to 320 MHz, and 300 MHz to 1200 MHz at the three bands. The meandered antenna was selected for validation of simulations, and its S parameters were measured in the equivalent liquid phantom of body tissues. Full article

The perception of the surrounding environment is a key requirement for autonomous driving systems, yet the computation of an accurate semantic representation of the scene starting from RGB information alone is very challenging. In particular, the lack of geometric information and the strong dependence on weather and illumination conditions introduce critical challenges for approaches tackling this task. For this reason, most autonomous cars exploit a variety of sensors, including color, depth or thermal cameras, LiDARs, and RADARs. How to efficiently combine all these sources of information to compute an accurate semantic description of the scene is still an unsolved task, leading to an active research field. In this survey, we start by presenting the most commonly employed acquisition setups and datasets. Then we review several different deep learning architectures for multimodal semantic segmentation. We will discuss the various techniques to combine color, depth, LiDAR, and other modalities of data at different stages of the learning architectures, and we will show how smart fusion strategies allow us to improve performances with respect to the exploitation of a single source of information. Full article

The increased mortality rates associated with antibiotic resistance has become a significant public health problem worldwide. Living beings produce a variety of endogenous compounds to defend themselves against exogenous pathogens. The knowledge of these endogenous compounds may contribute to the development of improved bioactive ingredients with antimicrobial properties, useful against conventional antibiotic resistance. Cowpea is an herbaceous legume of great interest due to its high protein content and high productivity rates. The study of genetic homology of vicillin (7S) from cowpea (Vigna unguiculata L.) with vicilins from soybean and other beans, such as adzuki, in addition to the need for further studies about potential biological activities of this vegetable, led us to seek the isolation of the vicilin fraction from cowpea and to evaluate the potential in vitro inhibitory action of pathogenic microorganisms. The cowpea beta viginin protein was isolated, characterized, and hydrolyzed in silico and in vitro by two enzymes, namely, pepsin and chymotrypsin. The antimicrobial activity of the protein hydrolysate fractions of cowpea flour was evaluated against Staphylococcus aureus and Pseudomonas aeruginosa, confirming the potential use of the peptides as innovative antimicrobial agents. Full article

The aim of this research is to discover the bond of entropy and the experience of video game immersion, using an Interpretative Phenomenological Analysis (IPA) to interpret the immersive experiences of players and how this bond of entropy and immersion could be transferred on other immersive technologies. The experiment was conducted on a selection of low-entropy scenes in three video games belonging to the genre of interactive drama. Six players were selected as the sample group for this research, based on their playthrough experiences of the games Heavy Rain (2010), Until Dawn (2015) and Dark Pictures Anthology: Man of Medan (2019) on the PlayStation platform. By monitoring the levels of entropy and immersion during their playthroughs, this research explores the potential of transferring immersion through the use of entropy from digital games to other immersive technologies. According to the research highlights and through data interpretation, entropy is found to be immensely influential upon achieving and maintaining narrative, physical and emotional immersion, and its effect could be further applied to other immersive technologies sharing a common ground with digital games, which features are further examined in finer detail in the current research. Full article

The significant volume of sharing of digital media has recently increased due to the pandemic, raising the number of unauthorized uses of these media, such as emerging unauthorized copies, forgery, the lack of copyright, and electronic fraud, among others. In particular, several applications integrate services or products such as music distribution, content management, audiobooks, streaming, and so on, which require users to demonstrate and guarantee their audio ownership. The use of acoustic fingerprint technology has emerged as a solution that is widely used to secure audio applications. This technique extracts and analyzes certain information that identifies the inherent properties of a partial or complete audio file. In this paper, we introduce two audio fingerprinting hardware architectures with a feature extraction system based on spectrogram saliency maps (SSM) and a brute-force search. The first of these conducts a search in 33 saliency maps of 32 × 32 pixels in size. After analyzing the first algorithm, a second architecture is proposed, in which the saliency map is reduced to 27 × 25 pixels, requiring 75.67% fewer hardware resources, lowering the power consumption by 64.58%, and improving the efficiency by 3.19 times via a throughput reduction of 22.29%. Full article

From a single meter that measures the entire home’s electrical demand, energy disaggregation calculates appliance-by-appliance electricity consumption. Non-intrusive load monitoring (NILM), also known as energy disaggregation, tries to decompose aggregated energy consumption data and estimate each appliance’s contribution. Recently, methodologies based on Artificial Intelligence (AI) have been proposed commonly used in these models, which can be expensive to run on a server or prohibitive when the target device has limited capabilities. AI-based models are typically computationally expensive and require a lot of storage. It is not easy to reduce the computing cost and size of a neural network without sacrificing performance. This study proposed an efficient non-parametric supervised machine learning network (ENSML) architecture with a smaller size, and a quick inference time without sacrificing performance. The proposed architecture can maximise energy disaggregation performance and predict new observations based on past ones. The results showed that employing the ENSML model considerably increased the accuracy of energy prediction in 99 percent of cases. Full article

This article deals with pick-up and delivery activities in a selected company that focuses on the distribution of products in the gastronomic sector of the market and suggests how to make the present approach more efficient. The introductory part of the article clarifies the meanings of basic concepts related to the issue of optimizing the logistics processes in the company. The crucial goal is to analyze the existing pick-up and delivery technology and then, in the application part of the article, to propose adequate measures in the context of streamlining these activities with their technical and economic evaluation. An analysis of current delivery routes, which are used for the distribution of gastronomic products, is first performed. Thereafter, the routes are optimized with the aim of minimizing the total distance traveled by using the Operations Research methods, namely: the Hungarian method, Vogel approximation method, nearest neighbor method and the Routin route planner which is based on a principle of the Greedy algorithm. At the end of the article, a technical and economical evaluation of the findings is discussed, wherein the individual results of optimization through selected methods are first compared and then, new optimized routes are selected. Full article

In recent years, the foment for sustainable and reliable micro energy grid (MEG) systems has increased significantly, aiming mainly to reduce the dependency on fossil fuels, provide low-cost clean energy, lighten the burden, and increase the stability and reliability of the regional electrical grid by having interconnected and centralized clean energy sources, and ensure energy resilience for the population. A resilient energy system typically consists of a system able to control the energy flow effectively by backing up the intermittent output of renewable sources, reducing the effects of the peak demand on the grid side, considering the impact on dispatch and reliability, and providing resilient features to ensure minimum operation interruptions. This paper aims to demonstrate a real-time simulation of a microgrid capable of predicting and ensuring energy lines run correctly to prevent or shorten outages on the grid when it is subject to different disturbances by using energy management with a fail-safe operation and redundant control. In addition, it presents optimized energy solutions to enhance the situational awareness of energy grid operators based on a graphical and interactive user interface. To expand the MEG’s capability, the setup integrates real implemented hardware components with the emulated components based on real-time simulation using OPAL-RT OP4510. Most hardware components are implemented in the lab to be modular, expandable, and flexible for various test scenarios, including fault imitation. They include but are not limited to the power converter, inverter, battery charger controller, relay drivers, programmable AC and DC loads, PLC, and microcontroller-based controller. In addition, the real-time simulation offers a great variety of power sources and energy storage such as wind turbine emulators and flywheels in addition to the physical sources such as solar panels, supercapacitors, and battery packs. Full article

In this paper, a numerical study has been undertaken on the susceptible-infected-recovered (SIR) epidemic model that encompasses the mechanisms of the evolution of disease transmission; a prophylactic vaccination strategy in the susceptible populations, depending on the infective individuals. We furnish numerical and graphical simulation combined with explicit series solutions of the proposed model using the New Iterative Method (NIM) and Modified New Iterative Method (MNIM). The analytic-numeric New Iterative Method failed to deliver accurate solution for the large time domain. A new reliable algorithm based on NIM, the coupling of the Laplace transforms, and the New Iterative method is called Modified New Iterative Method (MNIM) which is presented to enhance the validity domain of NIM techniques. The convergence analysis of the MNIM has also been illustrated. The simulation results show that the vaccination strategy can slow down the spread of the epidemic rapidly. Numerical results illustrate the excellent performance of the MNIM and show that the modified method is much more accurate than the NIM. Full article

The multivehicle routing problem (MVRP) is a variation of the classical vehicle routing problem (VRP). The MVRP is to find a set of routes by multiple vehicles that serve multiple customers at a minimal total cost while the travelling-time delay due to traffic congestion is tolerated. It is an NP problem and is conventionally solved by metaheuristics such as evolutionary algorithms. For the MVRP in a distribution network, we propose an optimal distribution path optimization method that is composed of a distribution sequence search stage and a distribution path search stage that exploits a divide-and-conquer strategy, inspired by the idea of dynamic programming. Several optimization objectives subject to constraints are defined. The search for the optimal solution of the number of distribution vehicles, distribution sequence, and path is implemented by using an improved genetic algorithm (GA), which is characterized by an operation for preprocessing infeasible solutions, an elitist’s strategy, a sequence-related two-point crossover operator, and a reversion mutation operator. The improved GA outperforms the simple GA in terms of total cost, route topology, and route feasibility. The proposed method can help to reduce costs and increase efficiency for logistics and transportation enterprises and can also be used for flow-shop scheduling by manufacturing enterprises. Full article

In this paper, the self-curing process was considered and found to be a better alternative to the conventional curing process for concrete structures in Ethiopia. It is well known that water plays a significant role in the curing process of preparing concrete in the construction industry. A good quality water is required for the conventional curing process, but that is scarce in Ethiopia. Curing concrete for bridges and roads is difficult in Ethiopia due to the poor quality and scarcity of water. In this study, Polyethylene Glycol (PEG) 600, a self-curing process, is considered as an alternative. Using the M40 Grade mix, four different percentages of PEG-600, 0.0, 0.5, 1.0, and 1.5 of cement weight, were studied, and the specimens were tested. Here, M40 grade stands for “a concrete mix with a characteristic compressive strength of 40 N/mm², i.e., 40 Newton per square millimeter”. Additionally, the mechanical strengths and properties of both conventional and self-cured processed concretes were calculated and compared. The present investigation concludes that PEG 600 offers significant results for self-curing concrete. The study procedure, results, and recommendations are presented in the text of the paper. Full article

In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices. Full article

The interaction of an electromagnetic beam with a sharp boundary of a dense cold semi-limited plasma was considered in the case of a normal wave incidence on the plasma surface. The possibility of the appearance of an electrostatic field outside the plasma was revealed, the intensity of which decreased according to the power law with a distance from the plasma and the center of the beam. It was possible to form cavities with a reduced electron density, being each electromagnetic resonators, which probed deeply into the dense plasma and couldexist in a stable state for a long period. Full article

In the area of Materials Science and Engineering, the tetrahedron comprising of processing, microstructure, properties and performance as four vertex corners is always key to develop new materials and to convert them to a useful shape for end application with the best properties possible [...] Full article

Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease in childhood. Seven children and young people (CYP) with a diagnosis of JIA and suspected active arthritis of a single knee joint were recruited for this proof-of-concept study. The presence of active arthritis was confirmed by clinical examination. Four tri-axial accelerometers were integrated individually in elastic bands and placed above and below each knee. Participants performed ten periodic flexion-extensions of each knee joint while lying down, followed by walking ten meters in a straight path. The contralateral (non-inflamed) knee joint acted as a control. Accelerometry data were concordant with the results of clinical examination in six out of the seven patients recruited. There was a significant difference between the accelerometry measured range of movement (ROM, p-value = 0.032) of the knees with active arthritis and the healthy contralateral knees during flexion-extension. No statistically significant difference was identified between the ROM of the knee joints with active arthritis and healthy knee joints during the walking test. The study demonstrated that accelerometry may help in differentiating between healthy knee joints and those with active arthritis; however, further research is required to confirm these findings. Full article

(1) Background: In addition to verbalizations, facial expressions advertise one’s affective state. There is an ongoing debate concerning the communicative value of the facial expressions of pain and of pleasure, and to what extent humans can distinguish between these. We introduce a novel method of analysis by replacing human ratings with outputs from image analysis software. (2) Methods: We use image analysis software to extract feature vectors of the facial expressions neutral, pain, and pleasure displayed by 20 actresses. We dimension-reduced these feature vectors, used singular value decomposition to eliminate noise, and then used hierarchical agglomerative clustering to detect patterns. (3) Results: The vector norms for pain–pleasure were rarely less than the distances pain–neutral and pleasure–neutral. The pain–pleasure distances were Weibull-distributed and noise contributed 10% to the signal. The noise-free distances clustered in four clusters and two isolates. (4) Conclusions: AI methods of image recognition are superior to human abilities in distinguishing between facial expressions of pain and pleasure. Statistical methods and hierarchical clustering offer possible explanations as to why humans fail. The reliability of commercial software, which attempts to identify facial expressions of affective states, can be improved by using the results of our analyses. Full article