Processing System Research Articles

Optimization research of handwritten digit recognition algorithm based on hardware neural networks

In the modern era of digitization, the widespread application of handwritten numerical data has led to an exponential increase in data volume, necessitating efficient real-time recognition and processing systems. This project is grounded in the foundational structure of neural networks, with a specific focus on integrating these networks into hardware through Very-Large-Scale Integration (VLSI). The primary objective is to achieve faster processing speeds, lower power consumption, and real-time operations. The central emphasis of this study is the design and implementation of a hardware neural network system tailored specifically for handwritten digit recognition. Drawing from neural network theory, we explore the construction of a hardware-based handwritten digit classifier. The fundamental model employed is the single-layer perceptron neuron model. Upon receiving 28x28 pixel grayscale images, the image classifier utilizes pre-trained weights, activation functions, and a maximum selector to compare and output recognition results for numerical digits. The concrete implementation is realized using the Verilog hardware description language, coupled with algorithm optimization strategies to enhance performance and efficiency. This research endeavor aims to provide an effective hardware solution for real-time handwritten digit recognition in the digital age.

A generative adversarial network approach for removing motion blur in the automatic detection of pavement cracks

AbstractAdvancements in infrastructure management have significantly benefited from automatic pavement crack detection systems, relying on image processing enhanced by high‐resolution imaging and machine learning. However, image and motion blur substantially challenge the accuracy of crack detection and analysis. Nevertheless, research on mitigating motion blur remains sparse. This study introduces an effective image processing system adept at deblurring and segmentation, employing a generative adversarial network (GAN) with UNet as the generator and Wasserstein GAN with Gradient Penalty (WGAN‐gp) as the loss function. This approach performs exceptionally in deblurring pavement crack images and improves segmentation accuracy. Models were trained with sharp and artificially blurred images, with WGAN‐gp surpassing other loss functions in effectiveness. This research innovatively suggests assessing deblurring quality through segmentation accuracy in addition to peak signal‐to‐noise ratio (PSNR) and structural similarity (SSIM), revealing that PSNR and SSIM may not fully capture deblurring effectiveness for pavement crack images. An extensive evaluation of various generators, including UNet, lightweight UNet, TransUNet, DeblurGAN, DeblurGAN‐v2, and MIMO‐UNet, identifies the superior performance of UNet on simulated motion blur. Validation with actual motion‐blurred images confirms the effectiveness of the proposed model. These findings demonstrate that GAN‐based models have great potential in overcoming motion blur challenges in pavement crack detection systems, marking a notable advancement in the field.

CRISPRi-based circuits to control gene expression in plants.

The construction of synthetic gene circuits in plants has been limited by a lack of orthogonal and modular parts. Here, we implement a CRISPR (clustered regularly interspaced short palindromic repeats) interference (CRISPRi)-based reversible gene circuit platform in plants. We create a toolkit of engineered repressible promoters of different strengths and construct NOT and NOR gates in Arabidopsis thaliana protoplasts. We determine the optimal processing system to express single guide RNAs from RNA Pol II promoters to introduce NOR gate programmability for interfacing with host regulatory sequences. The performance of a NOR gate in stably transformed Arabidopsis plants demonstrates the system's programmability and reversibility in a complex multicellular organism. Furthermore, cross-species activity of CRISPRi-based logic gates is shown in Physcomitrium patens, Triticum aestivum and Brassica napus protoplasts. Layering multiple NOR gates together creates OR, NIMPLY and AND logic functions, highlighting the modularity of our system. Our CRISPRi circuits are orthogonal, compact, reversible, programmable and modular and provide a platform for sophisticated spatiotemporal control of gene expression in plants.

A review in advanced digital signal processing systems

Digital Signal Processing (DSP) is a powerful technology that helps in making sense of various signals, like sounds and images, using computers. This review paper explains the meaning of DSP, shows how it works to process and enhance signals. It explores the wide range of signal processing methods, categorizing them from basic noise reduction to advanced machine learning algorithms, and how they are used today to improve the quality of audio, images, medical data, and other control systems. The paper further examines into signal processing techniques, providing a comprehensive understanding of the diverse methodologies employed in DSP applications. Additionally, it addresses not only the advancements but also the drawbacks associated with advanced DSP systems, offering insightful recommendations for overcoming challenges and optimizing performance. This review also includes categories of DSP methods, providing a structured overview of the different approaches within the field. It offers a clear and concise understanding of DSP, its practical uses, and its exciting potential in the digital age

Sliding mode observer-based fault diagnosis and continuous control set fault tolerant control for PMSM with demagnetization fault

A fault detection, estimation and fault tolerant control scheme for demagnetization fault in the permanent magnet synchronous motor systems is proposed in this paper. When demagnetization fault occurs, the system will deviate from the normal state severely. To facilitate the calculation, the demagnetization fault is converted to the dq coordinate system for processing and the resistance change is modeled as the system uncertainty, and the effect of the uncertainty on the system is analyzed. Then a fault detection observer is designed to detect the fault occurrence time, and a sliding mode observer based fault estimation algorithm is designed to estimate the fault and disturbance information. The observer parameters are determined by linear matrix inequality. When the fault information is obtained, a continuous control set fault tolerant control scheme is presented to minimize the impact of fault as far as possible. Finally, experimental results provide evidence of the feasibility of the suggested algorithm.

Research on electronic optical monitoring technology and system design for vacuum electron beam welding process

Electron beam welding technology is increasingly used in industrial manufacturing due to its high power density and the purity of welded joints. In vacuum environments, traditional optical and machine vision monitoring methods are susceptible to contamination by metal vapors, which hinders the monitoring effect. Due to the limitations of optical monitoring methods, electronic optical imaging technology is a new alternative monitoring method that has been introduced into the electron beam processing in recent years, but the complexity of the industrial electron beam welding environment makes it relatively less studied in the field of electron beam welding. In this study, a YAG scintillator detector is proposed to collect the backscattered electron information generated by the interaction of electrons with the sample, and an FPGA real-time processing system is used to realize the weld quality monitoring in the industrial electron beam welding processes. The distribution law of backscattered electrons in space is simulated by Monte Carlo method, and the results show that the number of distribution in the 40°∼60° angle region is more, the backscattering coefficient increases with the increase of the incident angle of the electron beam, and the backscattering coefficient of the samples with large atomic numbers is relatively high. The field monitoring experiments show that the designed system exhibits good sensitivity to the characteristic morphology, and the imaging resolution reaches more than 200 μm, and the weld hole defects are clearly visible. A good linear correlation between the current change curve during scanning and the actual shape profile is shown, while the image resolution is better when the spot current is 1 mA.

Microfluidic Mechanoporation: Current Progress and Applications in Stem Cells.

Intracellular delivery, the process of transporting substances into cells, is crucial for various applications, such as drug delivery, gene therapy, cell imaging, and regenerative medicine. Among the different approaches of intracellular delivery, mechanoporation stands out by utilizing mechanical forces to create temporary pores on cell membranes, enabling the entry of substances into cells. This method is promising due to its minimal contamination and is especially vital for stem cells intended for clinical therapy. In this review, we explore various mechanoporation technologies, including microinjection, micro-nano needle arrays, cell squeezing through physical confinement, and cell squeezing using hydrodynamic forces. Additionally, we highlight recent research efforts utilizing mechanoporation for stem cell studies. Furthermore, we discuss the integration of mechanoporation techniques into microfluidic platforms for high-throughput intracellular delivery with enhanced transfection efficiency. This advancement holds potential in addressing the challenge of low transfection efficiency, benefiting both basic research and clinical applications of stem cells. Ultimately, the combination of microfluidics and mechanoporation presents new opportunities for creating comprehensive systems for stem cell processing.

An Overview On: Exam Ease with Online Exam Portal

Our online Examination System is a special kind of program where examination of the coed are often taken during a correct and efficient manner. Online Examination System consists of some Question(MCQ) types & and also the student are to give the right answers of these question by selecting the suitable choice. Once a solution is submitted (it won't possible to return back to it question). An internet examination system could be a web-based examination system where examinations are given online either through the net or intranet employing a automatic data processing system. The principle objective of this online examination system is to effectively evaluate the scholar thoroughly through a completely automated system that not only reduces the specified time but also obtains fast and accurate results

Exergetic and exergoeconomic analyses of a large-scale industrial beer processing system

This research evaluated the performance of components and sections involved in industrial beer production using exergetic and exergoeconomics methodologies. The system was segmented into five production sections, and three energy input types were considered. The exergetic studies showed an operational exergetic efficiency of 3.33%, with an overall exergetic destruction rate of 5.54 MW and a specific destruction rate of 9.72 kW/hl for beer production. The overall improvement potential and sustainability index were estimated at 4.98 MW and 1.03, respectively. The brewhouse and packaging hall were identified as the sections with the highest production inefficiency, 58.73% and 30.40%, respectively. The exergoeconomic studies revealed a cost rate of 0.1704 USD/s for beer production, with the wort kettle, filling and cocking machine, Kieselguhr candle filter, whirlpool, and brite beer tank identified as the top five significant components in descending order. The efficiency of the system was critically affected by the activities in the packaging hall, particularly those involving energy inputs that cannot be recovered or attributed to the processed stream, beer. Further research is required to determine the cost savings of optimization measures identified from additional steam throttling, downsizing of some main pumps, and exergy loss during heating of wort and beer chilling processes.

Case study using APDMS and RPEMS for SAWL pipes and benefits for offshore pipelay

This paper reviews an Automatic Pipe Dimension Measurement System (APDMS) and a Robotic Pipe End Measurement System (RPEMS) which can measure 19 and 9 parameters with the help of 72 and 6 measurement sensors respectively. APDMS and RPEMS are integrated into one optical measurement system, using laser triangulation, coupled with Systems, Applications and Products in Data Processing (SAP) integration and its own calibration modulus which provides an accurate solution to all pipe measurements. Two longitudinal submerged arc welded offshore projects were executed for an offshore pipe lay with close dimensional tolerances measured using the APDMS for one project and RPEMS for the other project. The radii data, assessed from the newly calculated common centre of all cross-sectional data, was used to line up the two adjacent pipes in a faster and more accurate way. By knowing the shape of the pipe ends based on this radii data, the best relative rotation of these two pipes can be determined and any misalignment will be verified and will stay within the requirements. The extensive database available for each pipe helped to match the line pipes with similar or close out-of-roundness at pipe ends resulting in good fitment of the pipes and ultimately quality welds, together with more pipe joints per day, resulting in a faster completion of the project. This paper shows the significance of using both systems.

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations’ energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station’s performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station’s performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system’s simulation results are provided and scrutinized to confirm the concept’s suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations.

Insights into Grain Milling and Fractionation Practices for Improved Food Sustainability with Emphasis on Wheat and Peas.

Cereal grains and pulses are staple foods worldwide, being the primary supply of energy, protein, and fiber in human diets. The current practice of milling and fractionation yields large quantities of byproducts and waste, which are largely downgraded and end up as animal feeds or fertilizers. This adversely affects food security and the environment, and definitely implies an urgent need for a sustainable grain processing system to rectify the current issues, particularly the management of waste and excessive use of water and energy. The current review intends to discuss the limitations and flaws of the existing practice of grain milling and fractionation, along with potential solutions to make it more sustainable, with an emphasis on wheat and peas as common fractionation crops. This review discusses a proposed sustainable grain processing system for the fractionation of wheat or peas into flour, protein, starch, and value-added components. The proposed system is a hybrid model that combines dry and wet fractionation processes in conjunction with the implementation of three principles, namely, integration, recycling, and upcycling, to improve component separation efficiency and value addition and minimize grain milling waste. The three principles are critical in making grain processing more efficient in terms of the management of waste and resources. Overall, this review provides potential solutions for how to make the grain processing system more sustainable.

Cellulose gelation in aqueous hydroxide solutions by CO2(g): Fact and theory

Understanding how the solvent structure affects the stability of the dissolved state and the following precipitation is important for designing future dissolution-coagulation systems for cellulose processing. In this study, two morpholinium hydroxides with different alkyl chain lengths, namely N,N-dimethylmorpholinium hydroxide (NDMMOH(aq)) and N‑butyl‑N-methyl morpholinium hydroxide (BMMorOH(aq)), were studied and compared with the previously thoroughly investigated cellulose solvent benzyltriemethylammonium hydroxide (Triton B(aq)) which is well-known for its superior ability to stabilize the dissolved state. Cellulose solutions in each solvent were characterized by NMR, flow and frequency sweeps, while cellulose coagulation by CO2(g) was followed by in situ FTIR, pH and temperature measurements. The coagulated systems were characterized by CP/MAS 13C NMR, flow, and frequency sweep measurements. Complementary molecular dynamic (MD) simulations were performed to gain a deeper insight into the observed gelation behavior. The intrinsic viscosity indicated more extended cellulose chains in the solvents with more hydrophobic moieties (Triton B and BMMorOH). Even though the course of coagulation did not show any significant differences during monitoring, both the properties of the obtained gels and the MD simulations indicated differences in formation and properties of the coagulated materials that could be related both to the choice of solvent and coagulant.

Evaluation of the time, labor, and money required for manual and automated nucleic acid (RNA) isolation for the detection of SARS-COV-2 by QRT-PCR using the qiamp viral RNA mini kit and kingfisher flex

We undertook this cross-sectional investigation to assess the time, manpower, and average run cost per sample using manual Qiamp Viral RNA micro kit (Qiagen) and automated kingfisher flex instrument extraction methods for SARS-Cov-2 identification.The study used 120 Viral Transport Media-collected nasopharyngeal/ oropharyngeal swabs.Magnetic bead-based RNA extraction was performed using the Thermo Fisher Scientific kingfisher flex instrument and manual Extraction was Silica membrane-based Qiagen spin column kits. The TaqPath™ COVID-19 Combo Kit from Thermo Fisher Scientific was used for detecting SARS-CoV-2 target genes.Human technique took 40 minutes longer than automation. It cost more to automate than to manually labor. These disparities in time, effort, and cost affect laboratory operations, offering pros and cons for each method. This suggests that positive or negative was consistent regardless of viral load or RNA concentration.The study found that automated RNA extraction yielded better results compared to manual extraction. The automated sample processing system saved time, people, and money. In resource-limited or low-throughput labs, manual extraction may be preferable. Manual methods are laborious, require more hands-on time, and risk cross-contamination and technical blunders.

Object Detection for Unmanned Aerial Vehicles: A Comprehensive Review

Goal: Researchers studying artificial intelligence have focused a lot of emphasis on computer vision in drones. Drones with intelligence can tackle a lot of issues in real time. For the purpose of monitoring particular surroundings, computer vision tasks like object identification, object tracking, and object counting are important. It becomes increasingly difficult to do, though, due to elements like motion blur, occlusion, camera angle, and altitude. Methodology: A thorough assessment of the literature on object identification and tracking with unmanned aerial vehicles (UAVs) in relation to various applications has been done for this research. This study highlights the research gaps and provides a summary of the results of previous studies. Contribution: Detailed and categorized object identification techniques are used in UAV photos. A selection of UAV datasets tailored to object identification tasks is provided. Summaries of current research projects in various applications are provided. In order to alleviate highlighted research limitations, a secure onboard processing system on a strong object detection framework in precision agriculture is finally presented.

To What Extent do Race, Gender and Social Class Impact the Criminal Justice Processing System in Different Countries Around the World?

To What Extent do Race, Gender and Social Class Impact the Criminal Justice Processing System in Different Countries Around the World?

Enabling pulse shape discrimination with commercial ASICs

Fast electronic readout for high-channel density scintillator-based systems is needed for radiation tracking and imaging in a wide range of applications, including nuclear physics, nuclear security and nonproliferation. Programmable electronics, like FPGAs and ASICs, provide a fast way of conditioning and processing the signal in real time. In this paper, we present a pulse shape discrimination (PSD) method based on the shaping circuit of a commercially available ASIC, the Citiroc1A by CAEN Technologies. We used two different shaping times per detector channel to calculate a shaping parameter that enables PSD. Using our new method, neutron and gamma-ray pulses detected by a d12-stilbene scintillator can be effectively discriminated at light output values greater than 0.15MeVee. While not achieving the PSD performance of traditional offline charge integration, our method does not require the transfer of data to a separate system for further processing and enables the direct deployment of high-channel density multi-particle detection systems. Moreover, the availability of a wider range of shaping times than those on the Citiroc1A can potentially further improve the PSD performance.

Application of Bayesian networks based on Sequential Monte Carlo simulation and physical model in fault diagnosis of horizontal three-phase separator system

In offshore production platforms, horizontal three-phase separator is common process equipment. Its main function is to complete the dehydration and degassing of crude oil. Separator system is more complex, and its failure may cause significant economic losses and disastrous consequences. Therefore, it is critical to accurately and quickly identify where and why faults occur in the separator system. In this study, separator fault diagnosis model based on Bayesian networks is developed. Moreover, Sequential Monte Carlo simulation and physical model are introduced to overcome field problems such as missing separator failure data and the inability of experts to provide accurate empirical knowledge. Using this model, 13 faults in a separator in an offshore crude oil processing system are successfully diagnosed. Meanwhile, the proposed model is compared with deep neural network, convolutional neural network, and deep residual network, with accuracy rates of 100%, 91.34%, 87.99%, and 94.62%, respectively. Then, the diagnostic accuracy of each model for different faults is also compared in this paper under various signal-to-noise ratios. The results show that the method proposed in this paper has better noise immunity compared to the other three models. Therefore, the accuracy and robustness of the proposed model is further demonstrated. Finally, to analyze the fault-tolerance of the proposed model, 2–3 error evidence is randomly entered. The results show that the proposed model has better fault tolerance compared to data-driven Bayesian networks.

Automated analyses of dried blood spots collected by volumetric microsampling devices

BackgroundDried blood spot (DBS) sampling on cellulose cards suffers from varying blood haematocrit levels and from chromatographic effects, which have a direct impact on quantitative DBS analyses. Commercial volumetric microsampling devices were, therefore, introduced to mitigate these effects, however, these devices are not compatible with automated DBS processing systems and must be processed manually. ResultsCapillary electrophoresis (CE) instruments use fused-silica (FS) capillaries for precise and accurate liquid handling as well as for injection, separation, and quantitative analyses of liquid samples. These inherent features of an Agilent 7100 CE instrument were employed for the automated processing (elution and homogenization) of DBSs collected by hemaPEN® volumetric devices (2.74 μL of capillary blood per spot). The hemaPEN® samples were processed directly in CE vials by consecutive transfers of 56 μL of methanol and 14 μL of deionized water through the FS capillary in a sequence of 39 DBSs with repeatability of the liquid transfers better than 1.4 %. The resulting DBS eluates were homogenized by a quick air flush through the capillary and analyzed by the same capillary and CE instrument. Creatinine was selected as a clinically relevant model analyte and its endogenous concentrations in DBSs were determined by CE with capacitively coupled contactless conductivity detection (CE-C4D) in a background electrolyte solution consisting of 50 mM acetic acid and 0.1 % (v/v) Tween 20 (pH 3.0). The overall repeatability of the automated DBS processing and CE-C4D analyses of 39 DBSs was ≤7.1 % (peak areas) and ≤0.6 % (migration times), the calibration curve was linear in the 25–500 μM range (R2 = 0.9993) and covered all endogenous blood creatinine levels, the limit of detection was 5.0 μM, and sample throughput was >12 DBSs per hour. DBS ageing for 60 days and varying blood haematocrit levels (20–70 %) did not affect creatinine quantitative results (≤6.9 % for peak areas). Inter-capillary and inter-instrument repeatability was ≤7.7 % (peak areas) and ≤3.4 % (migration times) and demonstrated an excellent transferability of the proposed analytical concept among laboratories. Significance and noveltyThis contribution is the first-ever report on the use of a single off-the-shelf analytical instrument for fully automated analyses of DBSs collected by commercial volumetric microsampling devices and holds great promise for future unmanned quantitative DBS analyses.

Optimizing an Autonomous Robot’s Path to Increase Movement Speed

The goal of this study is to address the challenges associated with identifying and planning a mobile land robot’s path to optimize its speed in a stationary environment. Our focus was on devising routes that navigate around obstacles in various spatial arrangements. To achieve this, we employed MATLAB R2023b for trajectory simulation and optimization. On-board data processing was conducted, while obstacle detection relied on the omnidirectional video processing system integrated into the robot. Odometry was facilitated by engine encoders and optical flow sensors. Additionally, an external video system was utilized to verify the experimental data pertaining to the robot’s movement. Last but not least, the algorithms and hardware equipment used enabled the robot to go along the path at greater speeds. Limiting the amount of time and energy required to travel allowed us to avoid obstacles.