Core I7 Research Articles

Incremental and parallel proximal SVM algorithm tailored on the Jetson Nano for the ImageNet challenge

PurposeThis paper aims to propose the new incremental and parallel training algorithm of proximal support vector machines (Inc-Par-PSVM) tailored on the edge device (i.e. the Jetson Nano) to handle the large-scale ImageNet challenging problem.Design/methodology/approachThe Inc-Par-PSVM trains in the incremental and parallel manner ensemble binary PSVM classifiers used for the One-Versus-All multiclass strategy on the Jetson Nano. The binary PSVM model is the average in bagged binary PSVM models built in undersampling training data block.FindingsThe empirical test results on the ImageNet data set show that the Inc-Par-PSVM algorithm with the Jetson Nano (Quad-core ARM A57 @ 1.43 GHz, 128-core NVIDIA Maxwell architecture-based graphics processing unit, 4 GB RAM) is faster and more accurate than the state-of-the-art linear SVM algorithm run on a PC [Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32 GB RAM].Originality/valueThe new incremental and parallel PSVM algorithm tailored on the Jetson Nano is able to efficiently handle the large-scale ImageNet challenge with 1.2 million images and 1,000 classes.

Designing a Real-Time-Based Optical Character Recognition to Detect ID Cards

This research 0aims to design a Real-time ID card detection based on Optical Character Recognition (OCR). OCR detects and records information into CSV files using a camera. Hopefully, it can become one of the administrative solutions in Indonesia by using existing identity cards using OCR in real time. This research method was carried out independently in August 2021 using ID cards as objects. The tool involved was a 320x320 pixel webcam camera on an HP Intel Core i5 7th Gen notebook. The software used by Easy OCR was Pytorch-based. ID cards were detected using an algorithm by TensorFlow object detection with SSD MobileNet V2 FPNLite 320x320 as the pre-trained model of Tensorflow. The researchers collected ID card images using a webcam with various light conditions and orientations and label them using labeling. The researchers trained it with only 20 photos. After 3000 training steps, the researchers obtained about 0.17 loss and 0.95. Thus, the ID card detection tool using OCR runs well.

Improved conditional differential attacks on lightweight hash family QUARK

Nonlinear feedback shift register (NFSR) is one of the most important cryptographic primitives in lightweight cryptography. At ASIACRYPT 2010, Knellwolf et al. proposed conditional differential attack to perform a cryptanalysis on NFSR-based cryptosystems. The main idea of conditional differential attack is to restrain the propagation of the difference and obtain a detectable bias of the difference of the output bit. QUARK is a lightweight hash function family which is designed by Aumasson et al. at CHES 2010. Then the extended version of QUARK was published in Journal of Cryptology 2013. In this paper, we propose an improved conditional differential attack on QUARK. One improvement is that we propose a method to select the input difference. We could obtain a set of good input differences by this method. Another improvement is that we propose an automatic condition imposing algorithm to deal with the complicated conditions efficiently and easily. It is shown that with the improved conditional differential attack on QUARK, we can detect the bias of output difference at a higher round of QUARK. Compared to the current literature, we find a distinguisher of U-QUARK/D-QUARK/S-QUARK/C-QUARK up to 157/171/292/460 rounds with increasing 2/5/33/8 rounds respectively. We have performed the attacks on each instance of QUARK on a 3.30 GHz Intel Core i5 CPU, and all these attacks take practical complexities which have been fully verified by our experiments. As far as we know, all of these results have been the best thus far.

Libyan Vehicle License Plate Recognition with Support Vector Machine

Computer vision has become widely used in many aspects of our daily lives. There are a great number of applications that consider computer vision as a core part of them, such as those associated with law enforcement. This paper presents the design and implementation of a model for a vehicle building entrance, using a license plate recognition algorithm for Libyan vehicles. In addition, a small dataset of Libyan vehicles was created for research purposes. As with most recognition systems, there are mainly three stages to be distinguished: plate detection using vertical and horizontal histograms, character segmentation is performed through a connected-component labeling algorithm, and finally, optical character recognition (OCR) by using support vector machines (SVMs). An Arduino board was used to control the gate opening and closing processes according to the authorized vehicles stored in the database. Ultrasonic sensors were used to detect a vehicle stop at the gate. The system was programmed with MATLAB executed on a 2.20GHz Core i7 CPU, 8 GB RAM, Windows 10. Despite the limited size of the vehicle images dataset, the experiments showed promising performance in terms of average accuracy estimated at 83.3%, and the computation time was 5 seconds.

PMU-Events-Driven DVFS Techniques for Improving Energy Efficiency of Modern Processors

This paper describes the results of our measurement-based study, conducted on an Intel Core i7 processor running the SPEC CPU2017 benchmark suites, that evaluates the impact of dynamic voltage frequency scaling (DVFS) on performance (P), energy efficiency (EE) , and their product (PxEE) . The results indicate that the default DVFS-based power management techniques heavily favor performance, resulting in poor energy efficiency. To remedy this problem, we introduce, implement, and evaluate four DVFS-based power management techniques driven by the following metrics derived from the processor's performance monitoring unit: (i) the total pipeline slot stall ratio (FS-PS), (ii) the total cycle stall ratio (FS-TS), (iii) the total memory-related cycle stall ratio (FS-MS), and (iv) the number of last level cache misses per kilo instructions (FS-LLCM). The proposed techniques linearly map these metrics onto the available processor clock frequencies. The experimental evaluation results show that the proposed techniques significantly improve EE and PxEE metrics compared to the existing approaches. Specifically, EE improves from 44% to 92%, and PxEE improves from 31% to 48% when all the benchmarks are considered together. Furthermore, we find that the proposed techniques are particularly effective for a class of memory-intensive benchmarks – they improve EE from 121% to 183% and PxEE from 100% to 141%. Finally, we elucidate the advantages and disadvantages of each of the proposed techniques and offer recommendations on using them.

Initialization of profile and social network analyses robot and platform with a concise systematic review

This paper presents profile and social network analyses on concise systematic review corpora. It suggests two new robots and platforms for profile and social network analyses, that will serve previously proposed data, expert, and event-driven robots and platforms for energy and power industry. The literature is collected and stored in three topic clusters “location”, “investment”, and “DEMATEL” to prepare corpora. Twenty-five publications are selected in each sample corpus. A sample dataset of each corpus is prepared for thirty-one features such as “author’s full name and surname”, “applied methods”, and “publisher”. Afterward, “authors network matrices” are prepared in spreadsheet software. Data input files (*.csv) are prepared for each dataset. Gephi 0.9.2 201709241107 (free open-source software) is used for social network analyses with built-in layout and statistics algorithms on a desktop Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHz, 6,00 GB RAM personal computer in an offline and active cybersecurity software environment. Force Atlas, Force Atlas 2, Fruchterman–Reingold, OpenOrd, Yifan Hu, and Yifan Hu Proportional layout algorithms with Noverlap layout algorithm are run one by one. Runtimes range 2–120 s. All default statistic algorithms are run for several metrics like average degree, average weighted degree, betweenness centrality, closeness centrality, harmonic closeness centrality, eccentricity, and density. Authors in “location” cluster have a centralized network, but authors in “investment” and “DEMATEL” clusters have distributed networks. General profile analyses are conducted based on authors’ publications in the literature without any data and information on social media sites and platforms. Two new profile analysis metrics are proposed as “researcher’s past research focus index”, and “researcher’s future research focus prediction index”. Detailed profile analysis is performed for only Burak Omer Saracoglu. All analyses and findings are compared and summarized in the end.

Electronic packaging cabinets simplified modeling, simulation, and experimental validation for systems engineering

A simplified three-dimensional mathematical model for electronic packaging cabinets was derived from physical laws. Tridimensionality resulted from the domain division in volume elements (VEs) with uniform properties, each with one temperature, and empirical and theoretical correlations allowed for modeling their energetic interaction, thus producing ordinary differential equations (ODEs) temperatures versus time system. The cabinet (2048 mm × 1974 mm × 850 mm) thermal response with one heat source was measured. Data set 1 with a 1.6-kW power source was used for model adjustment by solving an inverse problem of parameter estimation (IPPE) having the cabinet internal average air velocities as adjustment parameters. Data set 2 obtained with a 3-kW power source validated model results. The converged mesh had a total of 7500 VE. The steady-state solution took between 16 and 19 s of CPU time to reach convergence and less than 3 min to obtain the 6500-s cabinet dynamic response under variable loading conditions, in an Intel CORE i7 computer. After validation, the model was used to study the impact of heat source height on system thermal response. Fundamentally, a sharp minimum junction temperature Tjct,min = 98.5 °C was obtained in the system hot spot at an optimal heat source height, which was 25.7 °C less than the highest calculated value within the investigated range (0.1 m < zjct < 1.66 m) for the 1.6-kW power setting, which characterizes the novelty of the research, and is worth to be pursued, no matter how complex the actual cabinet design may be.

Designing an energy management system for household consumptions with an off-grid hybrid power system

<abstract> <p>This paper analyzes the effect of meteorological variables such as solar irradiance and ambient temperature in addition to cultural factors such as consumer behavior levels on energy consumption in buildings. Reducing demand peaks to achieve a stable daily load and hence lowering electricity bills is the goal of this work. Renewable generation sources, including wind and Photovoltaics systems (PV) as well as battery storage are integrated to supply the managed home load. The simulation model was conducted using Matlab R2019b on a personal laptop with an Intel Core i7 with 16 GB memory. The model considered two seasonal scenarios (summer and winter) to account for the variable available energy sources and end-user electric demand which is classified into three demand periods, peak-demand, mid-demand, and low-demand, to evaluate the modeled supply-demand management strategy. The obtained results showed that the surrounding temperature and the number of family members significantly impact the rate of electricity consumption. The study was designed to optimize and manage electricity consumption in a building fed by a standalone hybrid energy system.</p> </abstract>

Efficient Homomorphic Encryption Accelerator With Integrated PRNG Using Low-Cost FPGA

With recent development in internet speed and reliability, cloud computing has become a more reliable solution for the user. In many cases where data privacy is critical, fully homomorphic encryption (FHE) can be a security solution for securing cloud computing. FHE enables computation on encrypted data, hence it ensures data privacy in case of cloud computing. One popular scheme of FHE is the BFV homomorphic encryption scheme, which is based on ring learning with error (RLWE) computation. The BFV scheme uses ring polynomials as the main object, hence its encryption, decryption, and evaluation require high-degree polynomial multiplication. In this paper, we present comprehensive design and implementation of a hardware architecture to accelerate encryption and decryption in BFV scheme. Our accelerator uses convolution approach for calculating a polynomial multiplication. To implement the convolution, we use a systolic array to calculate polynomial convolution followed by a simple delayed subtraction to calculate polynomial modulo reduction inside our accelerator’s core. Moreover, we use a built-in Gaussian pseudo-random number generator (PRNG) to generate Gaussian noise in the encryption operations. Finally, we implement the 1024 degrees BFV accelerator on the Xilinx PYNQ Z1 board and compare the encryption and decryption performances to other methods as well as a software implementation on Intel Core i7 with 8GB memory. Experimental results show that our accelerator outperforms the clock cycles of other methods with the same polynomial degrees 1024 up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$22\times $ </tex-math></inline-formula> . Moreover, our proposed Gaussian PRNG has better <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> correlation compared to the rotation-only-based PRNG. Finally, our accelerator accelerates up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9\times $ </tex-math></inline-formula> for encryption and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.5\times $ </tex-math></inline-formula> for decryption as well as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.8\times $ </tex-math></inline-formula> for overall compared to Microsoft SEAL on Intel Core i7 processor with 8GB memory. The proposed design is scalable for higher degrees polynomial multiplication and useful for security technology such as high-speed secure cloud computing, blind computing, and secure communication.

A Novel Deep Learning Based ANPR Pipeline for Vehicle Access Control

Computer Vision and Deep Learning technology are playing a key role in the development of Automatic Number Plate Recognition (ANPR) to achieve the goal of an Intelligent Transportation System (ITS). ANPR systems and pipelines presented in the literature often work on a specific layout of the number plate as every region has a unique plate configuration, font style, size, and layout formation. In this paper, we have developed a smart vehicle access control system considering a wide variety of plate formations and styles for different Asian and European countries and presented novel deep learning based ANPR pipeline that can be used for heterogeneous number plates. The presented improved ANPR pipeline detects vehicle front/rear view and subsequently localizes the number plate area using the YOLOv4 (You Only Look Once) object detection models. Further, an algorithm identifies the unique plate layout, which is either a single or double row layout in different countries, and the last step in the pipeline is to recognize the number plate label using a deep learning architecture (i.e., AlexNet or R-CNNL3). The results show that our trained YOLOv4 model for vehicle front/rear view detection achieves a 98.42% mAP score, and the number plate localization model achieves a 99.71% mAP score on a 0.50 threshold. The overall average plate recognition accuracy of our proposed deep learning-based ANPR pipeline using R-CNNL3 architecture achieved a single character recognition accuracy of 96%, while AlexNet architecture recognized a single character with a 98% accuracy. In contrast, the ANPR pipeline using the OCR method is found to be 90.94%, while latency is computed as 0.99 s/frame on Core i5 CPU and 0.42 s/frame on RTX 2060 GPU. The proposed ANPR system using a deep learning approach is preferred due to better accuracy, but it requires a high-performance GPU for real-time implementation. The presented pipeline is developed and implemented for smart vehicle access control, but it can be deployed for any ANPR application.

Near-real-time gradually expanding 3D land surface reconstruction in disaster areas by sequential drone imagery

The ability of drones to access disaster areas has been proven powerful and flexible for acquiring first-hand optical imagery data for environmental observation. However, such imagery data usually undergo postprocessing, and the three-dimensional (3D) products are mainly for accurate land surveys. The postprocessing procedure is too time-consuming to meet instant decision support and rescue response requirements. Therefore, this paper intends to develop a systematic workflow that is able to achieve on-the-fly 3D reconstruction in disaster areas by optical imagery sequentially acquired by drones. This study proposes a strategy to spatially link sequential images (SLSI) for image localization and suitable stereopair selection. In addition, the criteria for valid epipolar stereoapair determination are developed to make the 3D dense reconstruction more automatic and effective. The 3D digital land surface can be gradually reconstructed and expanded in the computer system while the drone is capturing new images. This paper utilizes the imagery dataset of collapsed buildings induced by the 2016 Kumamoto earthquake in Japan to simulate the more effective 3D reconstruction. Although the accuracy of the consequence is reported to be closely one meter, the mean data processing time for every image can achieve the level by approximately ten seconds while performing the proposed scheme on an iMac with Intel Core i5 and 16 GB random access memory (RAM). As a result, the efficiency and computational power needed are significantly reduced to support emergency applications soon after a disaster occurs.

Discrete event simulation applied to single queue management: a case study at a bank agency

Capacity and queue management are currently used in financial institutions. With decreasing bank units due to internet services, research in this field has focused on improving to utilize their employees efficiently and achieve service excellence. In developing countries like Brazil, the customer has become more bank-accounted due to government and labor requirements, such as the wage credit became mandatory in the wage account. The paper's aim is motivated by a real-life case study to simulate discrete events to improve queue management at a Brazilian bank branch with the Arena software simulation environment. The simulation model was designed, tested, and applied considering the Discrete Event Simulation (DES) replication for queuing strategies on a real-world banking scenario. The arrival and service times were collected from 115 customers in Ferraz de Vasconcelos/SP city. It was performed in version 15.10 (2018) of the Arena software, with processor Intel core i3 CPU dual-core 3.07 GHz and 8GB of RAM. The results indicate that the bank agency should consider providing 9 to 11 operators to attend customers considering the arrival and service rate.

Medical B-Mode Ultrasound Imaging Reconstruction Algorithms: Evaluation and Simulation

Ultrasound is one of the most important imaging modalities in medical practice. It is the most technique development with a lot benefits and with little challenges that include low imaging quality and high variability. Medical field is the most application that exploit the ultrasonic technique widely in body imaging, especially the real time Feature which take advantage of diagnostic time. This research makes a survey on the ultrasound image reconstruction and Display. First, the Ultrasound Imaging are reviewed as overall studying include the frequency ranges, advantages, main limitations, and applications. Second, the 2D or B-mode ultrasound imaging according to generation steps and mathematic studying are reviewed. Third, the stages of the 3D ultrasound object formation which is the data acquisition, data preprocessing, reconstruction method and 3D visualization, are discussed. Fourth, MATLAB Simulation to create B-Mode Ultrasound image with Synthetic phantom of a fetus in the third month of age, by using Field II software made specially for ultrasound environment, after initializing the ultrasound environment a phantom used to yield the B-Mode image. The complete B-Mode fetus scanned image creation need 5 hours and approximately 20 mins, that each line takes around 2min and 30 sec, with a processor 1.8 GHz Dual-Core Intel Core i5, MacOS. When the parameters of transducer have been changing then the clearness of the generated image the time taken will change in response.

Error Backpropagation Neural Network for Classifying Diabetic Diseases based on the Grading Method of Circular Region

Diabetic retinopathy grading is an important issue after detecting lesions of retina to estimate their risk and to take a suitable decision for treatment. Here, the grading of diabetic retinopathy is examined by consistent medical approaches to build a computer model for grading in automated way, which improve the efficiency of diabetic screening services. After the grading of diabetic retinopathy, Error Backpropagation Neural Network Learning Rule is used to give suggestions to a doctor for suitable treatment for the patient. Here, sixteen different cases are trained, and it takes about 8.368 seconds with 20820 iterations. The Neural network diagnosis four-treatment cases and they are urgent, moderate, mild and normal. It is also found from the results that Neural Network is very fast algorithm to give these decisions. In addition, the program that is used for carrying out processes is MATLAB Program version 2015, the computer is HP core i7.

A Magic Wand Selection Tool for Surface of 3D Model

Introduction: segmentation of 3d shapes is a fundamental problem in computer graphics and computer-aided design. It has received much plays attention in recent years. The analysis and research methods of 3d mesh models have established reliable mathematical foundations in graphics and geometric modeling. Compared with color and texture, shape features describe the shape information of objects from geometric structure features. And it an important role in a wide range of applications, including mesh parameterization, skeleton extraction, resolution modeling, shape retrieval, character recognitio,, robot navigation, and many others. Methods: The interactive selection surface of models is mainly used for shape segmentation. The common method is boundary-based selection, which requires user input some stokes near the edge of the selected or segmented region. Chen et al.introduced an approach to join the specified points form the boundaries for region segmentation on the surface. Funkhouser et al. improve the Dijkstra algorithm to find segmentation boundary contour. The graph cut algorithm use the distance between the surface and its convex hull as the growing criteria to decompose a shape into meaningful components. The watershed algorithm, widely used for image segmentation, is a region-growing algorithm with multiple seed points. Wu and Levine use simulated electrical charge distributions over the mesh to deal with the 3D part segmentation problem. Other methods using a watershed algorithm for surface decomposition. Results: We implemented our algorithm in C++ and Open MP and conducted the experiments on a PC with a 3.07 GHz Intel(R) Core(TM) i7 CPU and 6 GB memory. Our method can get a similar region under different interaction vertices in specific regions. Figure 6a and Figure 6b are the calculation results of tolerance region selection of this algorithm in a certain region of kitten model at two different interaction points, from which we can see the obtained regions are similar from different vertices in this region. Figure 6c and figure 6d are two different interactive points in the same region, and the region selection results are obtained by Region growing technique. Discussion: In this paper, we proposed a novel magic wand selection tool to interactive select surface of 3D model. The feature vector is constructed by extracting the HKS feature descriptor and means curvature of 3D model surface, which allows users to input the feature tolerance value for region selection and improves the self-interaction of users. Many experiments show that our algorithm has obvious advantages in speed and effectiveness. The interactive generation of region boundary is very useful for many applications including model segmentation. Conclusion: In consideration of a couple of requirements including user-friendliness and effectiveness in model region selection, we propose a novel magic wand selection tool to interactive selection surface of 3D models. First, we pre-compute the heat kernel feature and mean curvature of the surface, and then form the eigenvector of the model. Then we provide two ways for region selection. One is to select the region according to the feature of tolerance value. The other is to select the region that aligns with stroke automatically. Finally, we use the geometry optimization approach to improve the performance of the computing region con-tours. Extensive experimental results show that our algorithm is efficient and effective.

A low-resource digital infrastructure to streamline viewing and interpretation of radiographic images: A case study of Uganda's hospital-wide environment.

Picture Archiving and Communication Systems (PACS) are said to improve patient quality of care through timely access to radiological images by clinicians. However, they are costly to be considered for hospital wide environment in low income countries. Ordinary core i3 computer systems (PCs) can provide an affordable and faster alternative solution for PACS workstations. This comparative study assessed the diagnostic accuracy, image quality of ordinary PC systems versus PACS workstations and patient turnaround time (PTAT). Forty images were randomly obtained and viewed by four raters from both PACS and PC. The findings showed modest agreement among raters (kappa 0.644 for PACS and 0.5164 PC) with acceptable diagnostic accuracy for PC (AUC = 0.7990), 97.5% reproduction of images on PC and significant reduction in PTAT after a switch to PC (4.8 min), p < 0.001, suggesting that PC display can improve quality of health care services through timely access to radiographic images.

Time domain principal component analysis for rapid, real-time 2D MRI reconstruction from undersampled data.

A rapid real-time 2D accelerated method was developed for magnetic resonance imaging (MRI) using principal component analysis (PCA) in the temporal domain. This method employs a moving window of previous dynamic frames to reconstruct the current, real-time frame within this window. This technique could be particularly useful in real-time tracking applications such as in MR-guided radiotherapy, where low latency real-time reconstructions are essential. The method was tested retrospectively on 15 fully-sampled data sets of lung patient data acquired on a 3T Philips Achieva system. High frequency data are incoherently undersampled, while the central low-frequency data are always acquired to characterize the temporal fluctuations through PCA. The undersampling pattern is derived in such a way that all of k-space is acquired within a pre-determined number of frames. The missing data in the current frame are then filled in by fitting the temporal characterizations to the acquired undersampled data, using a pre-determined number of PCs. A subset of six patients was used to test the contour ability of the images. Various accelerations between 3x and 8x were tested along with the optimal number of PCs for fitting. A comparison was also performed with previous work from our group proposed by Dietz etal. as well as with a standard low resolution acquisition. In order to determine how the method would perform at lower signal to noise ratio (SNR), noise levels of 2×, 4×, and 6× were added to the 3T data. Metrics such as normalised mean square error and Dice coefficient were used to measure the reconstruction image quality and contour ability. The proposed method demonstrated good temporal robustness as consistent metrics were detected for the duration of the imaging session. It was found that the optimal number of PCs for temporal fitting was dependent on the acceleration rate. For the data tested, five PCs were found to be optimal at the acceleration rates of 3× and 4×. This number decreases to three at accelerations of 5× and 6× and further decreases to two at an acceleration rate of 8×, likely due to greater instability with fewer acquired data points. The use of too many PCs for fitting increased the chances of noisy reconstruction which affected contourability. The proposed 2D real-time MR acceleration method demonstrated greater robustness in the metrics over time when compared with previous real-time PCA methods using metrics such as normalised mean squared error, peak SNR and structural similarity up to an acceleration of 8x. Improved temporal robustness of image structure contourability and accurate definition was also demonstrated using several metrics including the Dice coefficient. Reconstruction of raw acquired data can be performed at approximately 50ms per frame using an Intel core i5 CPU. The method has the advantage of being very flexible in terms of hardware requirements as it can operate successfully on a single coil channel and does not require specialized computing power to implement in real-time.

Evaluation of antibacterial and antiviral barrier function of the conceptual model of a pneumatic helmet

Annotation. The global pandemic has necessitated the development of new respiratory protective equipment that seems to combine reliability and ergonomics. The article tests an innovative conceptual model of a pneumatic helmet with a laminar air supply system for its permeability to bacteria Micrococcus luteus and antistaphylococcal bacteriophages. The latter were fed in the form of an aerosol to the external filter of the working device with subsequent titration and seeding. Evaluation of bacterial infection is performed by counting the units that form colonies by multiplying the above indicator by the degree of dilution. The presence of bacteriophages and their number was determined by the Grace method with the calculation of plaque-forming units in the culture of Staphylococcus aureus. Statistical processing of the results was performed on a personal computer Intel Core i3 using one- and multi-factor analysis of variance (licensed software packages Microsoft Office 2016 and Statistica 10). In the full and partial configuration of the device (if there is only an external or internal filter), the penetration of microorganisms into the internal space is not detected. The results of the study confirmed the reliability of the protection system for 6 hours of continuous operation, which makes the conceptual model of a pneumatic helmet a more ergonomic alternative to the usual means of respiratory protection.

Stochastic estimation of total radiated power from PCB signal/PDN layout using EMI radiation resistance

In this article, a stochastic model is proposed for estimation of electromagnetic radiated emission from printed circuit board (PCB) layout. Considering the arbitrary nature of the PCB layout by radiation resistance, the radiated power density is estimated analytically. The model employs stochastic passing signals as layout trace excitation. The approach is conducted on a designed and fabricated test board. The total radiated power from the PCB layout can be as high 11.5 nW in 2.4GHz–2.5 GHz and 2.8uW in 1 MHz to 3 GHz. An important achievement of the model is its fast estimation of total radiated power for a given PCB layout, rather time/cost consuming experimental measurements and software simulations. For the designed test board, the full-wave simulation time lasts 5 h, while the proposed mathematic model gives the result within 30s on a device with Intel Core i5 processor and 8 GB RAM. The approach can be considered as a fast and preliminary verification step in estimation of radiated emission from PCB layout.

Virtual Reality Application For The Vestibular Ocular Motor Screen: A Comparison With A Novel Prototype.

Clinical assessment of sport related concussion (SRC) involves a multifaceted test battery reliant upon accurate baseline measures. Advances in technology have prompted the use of virtual reality (VR) platforms to improve enjoyment of baseline and post-injury assessments. Possible influence of VR sickness for some users may deter the use symptom provocation tests like the Vestibular/Ocular Motor Screening (VOMS) exam in VR. It is important to understand baseline performance of the VOMS in VR as it might influence overall symptom provocation. PURPOSE: The purpose of this study was to examine differences in the VOMS using a clinical prototype (PRO) versus using a virtual reality (VR) stimulus with respect to symptom provocation change score (SPCS) and near point convergence (NPC) average score in a healthy population. METHODS: Data was collected from 15 healthy subjects (Female = 9, male = 6, avg. age = 20.86 years) who completed the VOMS assessment using the PRO and VR method in the same sitting. Symptoms assessed consisted of headache, nausea, dizziness, and fogginess. Subjects were asked to state the severity of each symptom based upon a scale of 0-10. After a 5-minute break, the same subjects completed the VOMS with an HTC Vive Pro Eye HMD (90 Hz; 2880x1600 pixels). The headset was powered by an Acer Predator gaming laptop with a 7th Generation Intel Core i7 Quad-Core processor with 16GB of memory and NVIDIA GeForce GTX 1070 graphics card. The VOMS stimuli was created with the Unity Software package (Unity3D engine version 2019.1.6) in which the VOMS protocol was simulated in a VR environment. Mann-Whitney U tests assessed the differences in total SPCS and NPC average score from baseline between both methods. RESULTS: For SPCS, the results indicate that the PRO method (avg = 1.4 ± 1.77 symptoms) and the VR method (avg = 0.9 ± 1.18 symptoms) of administering the VOMS were not significantly different (p = 0.33, Cohen’s d = 0.33). With respect to NPC average, there was no significant difference (p = 0.19, Cohen’s d = 0.22) between the PRO method (avg = 2.33 ± 1.74 cm) and the VR method (avg = 3.08 ± 4.42 cm). CONCLUSION: These data suggests that using a VR system to administer the VOMS may not elicit additional symptoms and it may be a viable tool for the collection of baseline symptom provocation data in a healthy population.