Assessment Of Quality Parameters Research Articles

Advancements and Challenges in Real-Time Electronic Vision Technologies for Canned Fish Quality Inspection: A Comprehensive Review

The global demand for high-quality canned fish products has driven the adoption of advanced inspection technologies to ensure consistency, safety, and compliance with industry standards. This paper provides a comprehensive review of real-time electronic vision technologies employed in the inspection of canned fish quality. It traces the evolution of the canned fish industry from manual inspection methods to sophisticated automated systems, emphasizing the role of technologies such as hyperspectral imaging, machine learning algorithms, and electronic vision systems. The effectiveness of these technologies in detecting defects, assessing quality parameters, and maintaining product integrity is critically analyzed. Despite their benefits, challenges such as high costs, the need for specialized skills, and integration complexities with existing production processes are significant barriers. This review addresses these challenges and proposes solutions, including cost-reduction strategies, workforce training, and the development of adaptable systems. The paper concludes by outlining future research directions, particularly in validating these technologies in real-world scenarios and enhancing their accessibility to the industry. The findings offer valuable insights for researchers and industry stakeholders aiming to advance the quality control of canned fish products through innovative technological solutions.

Harnessing Machine Learning Algorithms to Model the Association between Land Use/Land Cover Change and Heatwave Dynamics for Enhanced Environmental Management

As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km2. The increase in PM2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning.

Utilization of retentates for white brined cheese production: comparative evaluation of physico-chemical and sensory properties

The aim of the present study is (1) to enhance the protein fraction in cheese milk through membrane filtration and consequently (2) to separate the majority of whey as ‘ideal whey’ prior to the production of white brined cheese, and (3) to explore the potential utilization of retentates in the production of white brined cheese. The chemical, physical, textural and sensory properties of cheeses produced from microfiltration (MF) retentates were characterized to investigate the effects of cross-flow MF on white brined cheese. Polyether sulfone (MP005) membranes featuring a pore diameter of 0.05 μm and polyvinylidene fluoride (MV020) membranes with a pore diameter of 0.20 μm were utilized. The assessed quality parameters of cheeses derived from polyethersulfone membrane (MP005) retentates were similar to the control (p > 0.05), whereas the cheese obtained from polyvinylidene fluoride membrane (MV020) retentates exhibited notable differences (p < 0.05) from the control. The traditional whey generated after cheese-making, which typically requires substantial on-site processing and/or treatment, was reduced by 3.5–6.7 times using the MP005 retentates. Simultaneously, 58–70% of the cheese milk (w/w) was separated as ideal whey before the cheese-making process. MF shows potential as an eco-friendly technology suitable for use in cheese production processes. The potential utilization of MP005 for enriching the protein content in cheese milk holds promise for white cheese production.

Nutrient Composition and Quality Assessment of Royal Jelly Samples Relative to Feed Supplements.

Royal jelly is a substance secreted by the hypopharyngeal and mandibular glands of nurse honey bees, serving as crucial nutritional source for young larvae, queen honey bees, and also valuable product for humans. In this study, the effect of the feed supplements on the nutritional composition and qualities of royal jelly was investigated. Two types of royal jelly samples were acquired: one from honey bees fed with sugar syrup as a feed supplement and the other from honey bees fed with honey. The production, harvesting, and storage of all royal jelly samples followed standard procedures. Parameters for quality assessment and nutritional value, including stable carbon isotopic ratio, moisture content, 10-hydroxy-2-decenoic acid (10-HDA) level, carbohydrate composition, amino acid composition, and mineral contents, were analyzed. The results revealed that despite variability in moisture content and carbohydrate composition, fructose was lower (2.6 and 4.1 g/100 g as is for sugar-fed and honey-fed royal jelly, respectively) and sucrose was higher (7.5 and 2.7 g/100 g as is for sugar-fed and honey-fed royal jelly, respectively) in the sugar-fed group. The stable isotope ratio (-16.4608‱ for sugar-fed and -21.9304‱ for honey-fed royal jelly) clearly distinguished the two groups. 10-HDA, amino acid composition, and total protein levels were not significantly different. Certain minerals, such as potassium, iron, magnesium, manganese, and phosphorus were higher in the honey-fed group. Hierarchical analysis based on moisture, sugar composition, 10-HDA, and stable carbon isotopes categorized the samples into two distinct groups. This study demonstrated that the feed source could affect the nutritional quality of royal jelly.

Design and Implementation of a Self-Supervised Algorithm for Vein Structural Patterns Analysis Using Advanced Unsupervised Techniques

Compared to other identity verification systems applications, vein patterns have the lowest potential for being used fraudulently. The present research examines the practicability of gathering vascular data from NIR images of veins. In this study, we propose a self-supervision learning algorithm that envisions an automated process to retrieve vascular patterns computationally using unsupervised approaches. This new self-learning algorithm sorts the vascular patterns into clusters and then uses 2D image data to recuperate the extracted vascular patterns linked to NIR templates. Our work incorporates multi-scale filtering followed by multi-scale feature extraction, recognition, identification, and matching. We design the ORC, GPO, and RDM algorithms with these inclusions and finally develop the vascular pattern mining model to visualize the computational retrieval of vascular patterns from NIR imageries. As a result, the developed self-supervised learning algorithm shows a 96.7% accuracy rate utilizing appropriate image quality assessment parameters. In our work, we also contend that we provide strategies that are both theoretically sound and practically efficient for concerns such as how many clusters should be used for specific tasks, which clustering technique should be used, how to set the threshold for single linkage algorithms, and how much data should be excluded as outliers. Consequently, we aim to circumvent Kleinberg’s impossibility while attaining significant clustering to develop a self-supervised learning algorithm using unsupervised methodologies.

FFF print defect characterization through in-situ electrical resistance monitoring

Fused filament fabrication is a popular fabrication technique. Currently there is a need for in-situ monitoring modalities to gather real-time information on prints, both for quality control and closed-loop control. Despite current advancements, effective and affordable in-situ monitoring techniques for non-destructive defect detection of voids and bonding quality are still limited. This work demonstrates in-situ monitoring of fused filament fabrication through electrical resistance measurements as an alternative to thermal and optical methods. A new, easy-to-implement setup is demonstrated which measures the electrical resistance of a conductively doped filament between the nozzle and single or multi-electrodes on the bed. Defects can be located in an unprecedented way with the use of encoded axes in combination with the observed resistance variations throughout the part. A model of the anisotropic electrical conduction is used to interpret the measurements, which matches well with the data. Warping, inter-layer adhesion, under-extrusion and overhang sagging print defects can be observed in the measurements of parts with a complex geometry, which would be difficult to measure otherwise. Altogether in-situ electrical resistance monitoring offers a tool for optimising prints by online studying the influence of the print parameters for quality assessment and it opens up possibilities for closed-loop control.

Determination and Engineering of Li‐Ion Tortuosity in Electrode Toward High Performance of Li‐Ion Batteries

AbstractWith the progressively widespread application of lithium‐ion batteries, their energy density and power density are being pursued to the extremely high. However, both cannot be reached simultaneously at present. Tortuosity depicts the relationship in ionic diffusion between bulk electrolyte and the electrolyte in a porous electrode, provides information about channels within electrodes, and correlates the current dilemma between energy density and power capability of the lithium‐ion batteries. It is crucial to upgrade the fast charging and discharging performance of LIBs electrodes without losing volumetric energy density and using tortuosity as a guide is able to achieve this goal fundamentally. Meanwhile, tortuosity can serve as an evaluation parameter for production quality assessment, but this demands a fast, accurate, and cost‐effective measuring method. After the definition and measurement of tortuosity are provided, this article analyzes the structural influencing factors of tortuosity which have impacts on ion transportation and subsequently on tortuosity to facilitate a more systematic and comprehensive picture. Through the discussion of contradictions and influencing factors, a more refined transmission line model (the refined TLM) is perceived as the very first step to drive both aforementioned goals toward success and is discussed in the perspective part.

ARCnet: A Multi-Feature-Based Auto Radio Check Model

Radio checks serve as the foundation for ground-to-air communication. To integrate machine learning for automated and reliable radio checks, this study introduces an Auto Radio Check network (ARCnet), a novel algorithm for non-intrusive speech quality assessment in civil aviation, addressing the crucial need for dependable ground-to-air communication. By employing a multi-scale feature fusion approach, including the consideration of audio’s frequency domain, comprehensibility, and temporal information within the radio check scoring network, ARCnet integrates manually designed features with self-supervised features and utilizes a transformer network to enhance speech segment analysis. Utilizing the NISQA open-source dataset and the proprietary RadioCheckSpeech dataset, ARCnet demonstrates superior performance in predicting speech quality, showing a 12% improvement in both the Pearson correlation coefficient and root mean square error (RMSE) compared to existing models. This research not only highlights the significance of applying multi-scale attributes and deep neural network parameters in speech quality assessment but also emphasizes the crucial role of the temporal network in capturing the nuances of voice data. Through a comprehensive comparison of the ARCnet approach to traditional methods, this study underscores its innovative contribution to enhancing communication efficiency and safety in civil aviation.

Physics-guided spatio–temporal neural network for predicting dissolved oxygen concentration in rivers

The prediction of river water quality is key in water resource management. Data-driven machine learning models have been widely used for predicting river water quality. However, these models seldom consider the physical mechanisms of water quality variation, which degrades the accuracy and stability of the prediction results. Hence, we develop a physics-guided spatio–temporal neural network (PGSTNN) model to predict a critical parameter for water quality assessment, i.e. dissolved oxygen. Physical information regarding spatio–temporal interactions in a hydrological network is explicitly considered to construct the architecture of PGSTNN. Two physical rules of dissolved oxygen variation (i.e. Henry’s law and power-scaling law) are established for the loss function of PGSTNN to guarantee the physical consistency of the prediction results. Experiments on the 2020–2021 water quality dataset in Atlanta, USA show that PGSTNN outperforms seven baseline neural network models in terms of prediction accuracy and stability. PGSTNN typically brings at least 10% accuracy (e.g. root mean square error and mean absolute error) improvement over the comparison methods. The proposed PGSTNN may not only improve the emergency response ability of water resource management, but also provide useful ideas for integrating scientific knowledge with machine learning.

Evaluating Relationship between Surface Water Quality and Zooplankton Diversity in Coastal Areas of Tien Giang Province, Vietnam

The zooplankton diversity is the most important ecological parameter for water quality assessment, as these small creatures are sensitive to any changes in water quality, hence the objective of this study as to evaluate the relationship between surface water quality and zooplankton diversity in the coastal areas of Tien Giang province, Vietnam using appropriate standard methods. The results showed that the coastal water quality had high concentrations of suspended solids, ammonium and coliforms. A total of 71 zooplankton species with a total density ranging from 3,219,383 to 24,993,634 individuals/m3 were identified. Among the zooplankton species, Copepoda nauplius appeared at all monitoring stations, predominating with a density ranging from 740,800 to 7,555,250 individuals/m3. The values of the Shannon-Wiener diversity index (H') ranged from 1.54 to 2.37, reflecting the coastal water quality in the study area from polluted to mildly polluted. The coastal water variables of pH, salinity, DO, NH4+-N and coliform were closely correlated with the density of zooplankton species. The relationship between surface water quality and zooplankton diversity could be a basis for selecting biological indicators to monitor coastal water quality.

Long-term (2003−2021) evolution trend of water quality in the Three Gorges Reservoir: An evaluation based on an enhanced water quality index

The degradation of water quality induced by the construction of large-scale hydraulic projects is one of the primary public concerns; however, it is rarely addressed with long-term field observation data. Here, we reported the long-term (2003–2021) trends, seasonal patterns, and overall condition of water quality of the Three Gorges Reservoir (TGR) with an enhanced water quality index (WQI). Specifically, to emphasize the importance of the biological role in water quality assessment, chlorophyll-a (Chla) was incorporated into WQI, and then a novel workflow using machine learning approach based on Random Forest (RF) model was constructed to develop a minimal water quality index (WQImin). The enhanced WQI indicated an overall “good” water quality condition, exhibiting a gradually improving trend subsequent to the reservoir impoundment in 2003. Meanwhile, the assessment revealed that the water quality has discernible seasonal patterns, characterized by poorer conditions in the spring and summer seasons. Furthermore, the RF model identified Chla, dissolved oxygen (DO), ammonium nitrogen (NH4-N), water temperature (WT), pH, and total nitrogen (TN) as key parameters for the WQImin, with Chla emerging as the most important factor in determining WQImin in our study. Moreover, weighted WQImin models exhibited improved performance in estimating WQI. Our study emphasizes the importance of biological parameters in water quality assessment, and introduces a systematic workflow to facilitate the development of WQImin for accurate and cost-efficient water quality assessment. Furthermore, our study makes a substantial contribution to the advancement of knowledge regarding long-term trends and seasonal patterns in water quality of large reservoirs, which provides a foundational basis for guiding water quality management practices for reservoirs worldwide.

A method of image fusion using texture guidance based guided image filter and image statistics

A technique of image fusion by means of a guided image filter is proposed in this paper. It employs image smoothing with a guided filter by making use of texture information as guidance for the guided filter. Then, image statistics-based pixel weight computation is utilized for generating the weight maps of the source images from detail layer features. Finally, the source images are integrated based on the weighted average combining strategy. The efficacy of the proposed algorithm is tested and compared with several state-of-the-art image fusion methods in terms of several objective image quality assessment parameters. The experimental result suggests the efficacy of the proposed algorithm in image fusion.

Qualidade da atenção primária à saúde da pessoa idosa durante a pandemia da covid-19: uma revisão sistemática

Abstract Objective To evaluate the quality of primary healthcare services for older adults during the COVID-19 pandemic through studies evaluating health services. Method A systematic literature review was conducted. The search for articles covered the databases Web of Science, Embase, Scopus, and Medline/Pubmed. Original scientific articles with a case-control or cross-sectional design, involving populations that included older adults, focusing on primary healthcare, and assessing the quality of health services were included. Exclusion criteria were studies without specific analysis for older adults, studies evaluating health access or economic evaluation of health. The risk of bias analysis was conducted according to JBI protocols, and data synthesis was performed using SWiM guidelines. Results Seven studies were included in the review. The primary care services evaluated were mostly outpatient, with diverse quality assessment parameters related to individual care. The evaluation of quality identified a decrease in in-person consultations and visits, and an increase in the use of telehealth, with good satisfaction ratings among older adults. Conclusion The studies highlighted the difficulties maintaining and accessing in-person services during the COVID-19 pandemic, which directly contributed to a reduction in consultations and exams, usage of and access to health services, and the quality of primary healthcare.

The Influence of Steganographic Methods and QR Code Resolutions on Data Hiding in Cover Images

Abstract This work deals with digital image processing using the steganography technique in real life. Various steganography techniques face data authentication, data security, and copyright issues. Due to the fact that the problem of data security is currently very topical, these techniques are often sought after. This is related to the constant development of new algorithms that provide solutions that will safely hide the secret message in the image and at the same time will be resistant to various types of attacks. The motivation for writing this work lies in the technique of representing a secret message using a Quick Response code. Later in this work is comparison between our results and results of approach made by scientific community, which works with data hiding within steganographic principles. The secret message is written in the least significant bit plane of the cover image, which ensures that the modification of the image cannot be detected by the naked eye. The proposed solution is evaluated by mathematical quality assessment parameters Peak signal-to-noise ratio, Mean squared error, Normalized cross-correlation and Structural similarity index.

Impact of Physico-Chemical and Biological Parameters on Zooplankton Composition and Abundance in Freshwater Lake of Mulukanoor Karimnagar District, Telangana

The plankton constitutes the basic food sources of any aquatic ecosystem, which Supports fish and other aquatic animals. Zooplankton diversity is one of the most important ecological parameters in water quality assessment. Zooplankton is good indicator of the changes in water quality because they are strongly affected by environmental conditions and respond quickly to changes in water quality. In the present paper, Zooplankton diversity in relation to physico-chemical parameters of the Mulukanoor Lake located in Krimnagar District, Telangana state.The study was carried out for a period of one year from June 2015 to May 2016 to evaluate the relationship and influence between different water quality parameters and the abundance of zooplankton population. The physico-chemical parameters includes Temperature, Electrical Conductivity, Total Dissolved Solids, Water Transparency, PH, Dissolved Oxygen.Free Co2, Total Alkalinity, Total Hardness, Chloride, Biological Oxygen Demand, Phosphate, Nitrate and Ammonia were analyzed. All the values of these parameters were found within the prescribed standard limits. Therefore this lake has rich number of species biodiversity of aquatic animals. Four different groups of zooplankton were identified in this study which includes Rotifera, Cladocera, Copepoda and Ostracoda. Zooplankton diversity and abundance refers to variety within in the community. Numerically rotifers were dominant group throughout the study period. The season wise zooplankton analysis showed a maximum average abundance of species in Post-Monsoon season, minimum abundance in monsoon season due to the different environmental and inflow characteristics of the water body. The composition of zooplankton rotifers was dominant Rotifera(56%) followed by cladocera (23%), copepod (13%) and ostracoda (8%).Therefore it can be now concluded that this lake is highly potential to take up commercial fisheries.

Technology Selection for Wastewater Treatment in the Maldives

In the Maldives, the discharge of untreated wastewater into the sea poses a significant threat to public health and the marine environment. This study proposes technically feasible, sustainable, and economical wastewater treatment technologies appropriate for small islands in the Maldives. Out of the 187 residential islands in the Maldives, three islands (HA. Dhidhdhoo, HA. Hoarafushi, and HA. Ihavandhoo) were selected as study areas. This study characterized the wastewater from these islands using physiochemical and microbiological water quality assessment parameters. The Water and Wastewater Treatment Technologies Appropriate for Reuse (WAWTTAR), an open-source software developed by the Humbolt State University in 1993, was used to simulate the performance of different wastewater treatment processes to treat the characterized wastewater. The feasible alternatives identified from these simulations were analyzed using a Multi-Criteria Analysis (MCA), considering various indicators that influence the decision-making process. The wastewater quality assessment revealed that the wastewater generated in the three islands produced low concentrations of chemical and biological oxygen demand (COD and BOD), with concentrations ranging from 267 – 309 mg/L and 128 – 219 mg/L, respectively. Nitrate and phosphate concentrations were also low (5.7 – 18.0 mg/L and 2.8 – 14.9 mg/L, respectively), where these concentrations in Dhidhdhoo and Hoarafushi were already within the maximum allowable limits of 15 mg/L and 10 mg/L, respectively, for discharge into the deep sea. Based on the findings, secondary treatment technologies such as membrane bioreactors (MBR) and sequencing batch reactors (SBR) with Ultraviolet (UV) disinfection were found to be the most feasible options for treating the wastewater. These results were further checked for robustness using a sensitivity analysis. This study demonstrates that the proposed methodology is suitable for technology selection and can provide valuable information for policymakers and stakeholders in the country and serve as a basis for future studies.

Availability of Raw Material for Wood Based Cricket Bat Industry in Kashmir Valley, India

Kashmir Himalayas is known for its abundant supply of high quality wood suitable for cricket bat industry. The present study aimed to gather comprehensive data on the availability of raw material for the cricket bat industry in Anantnag and Pulwama districts of Kashmir during the specified time period (2017-2018). The main objective of this study was to evaluate the major problems faced by the industry and to assess quality parameters of cleft and the wood used for making these clefts. Shortage of quality raw material, absence of proper marketing channels and irregular electricity supply were the major factors responsible for production shortfall. The units used wood of Salix alba var. caerulea as main raw material while Populus deltoides wood was also used for low quality bats. For the study, latest available official records of Government of Jammu and Kashmir as well as questionnaire based sample survey data were used. The industry produced 15.10 crore clefts that consumed 17,28,532 ft 3 of wood. Sample survey revealed that in 2016, the selected 62 units procured 5,84,557 ft3 of wood with an average of 9,428.33 ft3 per unit.

Tumour area infiltration and cell count in endoscopic biopsies of therapy-naive upper GI tract carcinomas by QuPath analysis: implications for predictive biomarker testing

Guidelines regulate how many (tumour-bearing) tissue particles should be sampled during gastric cancer biopsy to obtain representative results in predictive biomarker testing. Little is known about how well these guidelines are applied, how the number of tissue particles correlates with the actual tumour-infiltrated area and how many absolute tumour cells are captured. The study included endoscopic biopsies of untreated carcinomas of the upper gastrointestinal (GI)-tract during the 2016–2020 review period. Archival (H&E)-stained histological sections were digitised and the tumour areas were manually annotated. The tumour-bearing tissue area and absolute carcinoma cell count per case were determined by image analysis and compared with a reference primary surgical specimen. Biopsies from 253 patients were analysed. The following mean values were determined: (a) tumour tissue particle number: 6.5 (range: 1–25, standard deviation (SD) = 3.33), (b) number of tumour-bearing tissue particles: 4.7 (range: 1–20, SD = 2.80), (c) tumour-infiltrated area: 7.5 mm2 (range: 0.18–59.46 mm2, SD = 6.67 mm2), (d) absolute tumour cell count: 13,492 (range: 193–92,834, SD = 14,185) and (e) tumour cell count in a primary surgical specimen (tumour size: 6.7 cm): 105,200,176. The guideline-recommended tissue particle count of 10 was not achieved in 208 patients (82.2%) and the required tumour-bearing tissue particle count of 5 was not achieved in 133 patients (52.6%). Tissue particle count, tumour-infiltrated area and tumour cell count were only weakly correlated. Most cases featured an infiltrated area ≥ 4.5 mm2 (156, 61.7%). Cases with more tissue particles showed only a moderate increase in infiltrated area and tumour cells compared to cases with fewer particles. Biopsies are often used to determine predictive biomarkers, particularly Her2/neu and PD-L1. Diagnostic standards to ensure representative material have been suggested in guidelines to reduce false-negative predictions. However, the real-world practice seems to substantially deviate from recommended standards. To the best of our knowledge, this is the first systematic study describing the relationships between endoscopic tissue fragment number, actual infiltrated tumour area and carcinoma cell number. The data question the tissue particle number as a quality assessment parameter. We advocate histopathological reports indicating on which basis statements on therapy-relevant biomarkers were made. Digital pathology has the potential to objectively quantify the tissue for documentation, quality assessment and future clinical studies.

Inexact radix-4 Booth multipliers based on new partial product generation scheme for image multiplication

Inexact computing, is a modern approach for the development of low power and high-performance digital circuits, which involves approximation stages to get the utmost accurate result and are very much applicable in some error-tolerant applications like image processing. Such applications are limited to human eyesight ability, which gives us the freedom to make the approximate output. In this manuscript, two inexact partial product generators, viz. IPPG1, IPPG2 are proposed for the design of radix-4 based 8 × 8 Booth multipliers IRBM1.1, IRBM1.2, respectively. Moreover, an inexact 4:2 compressor is also proposed to add such partial product bits generated by IPPG1 and IPPG2, and nomenclate them as IRBM2.1 and IRBM 2.2, respectively. IPPG1 and IPPG2 are designed by introducing errors in the Karnaugh’s map to reduce the circuit complexity and errors, respectively. Similarly, errors are introduced in the truth table of the exact 4:2 compressor to design a low power, high speed inexact 4:2 compressor. Error metrics estimation of such proposed inexact Booth multipliers and the state-of-the-art designs are performed using MATLAB and circuit performance parameters like area, computational delay and power dissipation are extracted using gpdk45 nm technology. While comparing, IRBM1.2 offers minimum error metrics than all other reported design so far. Moreover, in the circuit level prospect, IRBM1.1 consumes minimum power and IRBM2.2 is the fastest among its reported counter-parts. IRBM2.2 offers minimum power-delay-area (PDA) product over all of its reported counter-parts. For the validation of the proposed designs, an image multiplication, edge-detection using Sobel operator, and convolutional neural network-based application is incorporated and quality assessment parameters are measured using MATLAB.

Analysis of dual-energy mammography subtraction technique for the dose and image quality evaluation using 3D-printed breast phantom.

This study aimed to quantitatively assess the radiation dose using XR-QA2 and the image quality of the dual-energy subtraction mammography technique on an in-house phantom. The analysis was carried out to investigate the effect of targets/filters on dose value and image quality using an in-house phantom made of PLA + as an object representing compressed breasts. All irradiation parameters were performed in the craniocaudal position with manual mode. Mean glandular dose (MGD) was recorded, followed by the calculation of the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and modulation transfer function (MTF) for image quality assessment parameters. The results showed that the image quality was accepted at dose levels within the IAEA and BAPETEN tolerance limit for 60mm equivalent compressed breast using dual-energy mammography. Furthermore, the target/filter (W/Rh) reduced the dose by 1.03mGy compared to the Mo/Mo and Mo/Rh with an enhancement in image quality. This indicated that the target/filter (W/Rh) combination was optimal due to the image quality improvement obtained with lower MGD.