The utilization of large datasets in applications results in significant energy expenditures attributed to frequent data shifts between memory and processing units. In-Memory Computing (IMC) distinguishes itself by employing computations within a memory crossbar to perform logic operations, leading to enhanced computational speed and energy efficiency. This study introduces RASA-based subtractor, strategically improved for computation, and energy consumption. Subsequently, the proposed subtractor are employed to construct a comparator and facilitate pooling operations. The comparator is developed using the proposed subtractor, achieves the comparison in n steps for a n-bit comparator. Additionally, a n-bit min pooling operation for anxn (4 x 4) feature map requires 2-1 (15) steps. Energy consumption of the RASA design demonstrates hopped up performance, showcasing an average savings of 87.42% and 89.98% compared to the ASA and Muller C based subtractor.

Data security has become increasingly vital in modern computing systems, with the rise of sophisticated cyber threats and the widespread use of digital information. In-Memory Computing (IMC) has emerged as a promising approach to address data transfer bottlenecks and enhance computational efficiency. This work for the first time proposed an innovative method for in-memory data encryption utilizing the XOR-based Feistel cipher within a Static Random-Access Memory (SRAM) array. The XOR-based Feistel cipher offers simplicity and efficiency, making it an ideal candidate for in-memory encryption. We achieve data encryption without requiring the readout of the device state, leading to significant energy and delay savings over traditional computing-in-memory architectures. The effectiveness of the proposed method is demonstrated through extensive simulations and analysis, showcasing its robustness and efficiency in ensuring data security. Simulations results in 165 ps delay with a power consumption of 154 zW resulting in a PDP of 25.41 fJ. The proposed FC implementation features a 96.14% reduction in delay compared with the conventional FC computation.

Fingerprint is a unique pattern developed inside of the hand. It helps to identify a person and secure a lock for personal devices or data. This impression was processed in ways such as verification and identification. Verification is used to verify the two or more fingerprints and identification is used to identify the right human. Cryptography is another data security by using characters, numbers, symbols, etc.In this paper, we merge the cryptography, fingerprint, Delaunay triangulation and Vigenere cipher by using Heptagonal Fuzzy Numbers (HFN).

Soft Errors becoming more predominant due to the constant scaling down of the transistors which lead to a decrease in the critical charge (Qc) and noise margin of the memory cell. In this paper, radiation-hardened (RH) 12T Memory cell is proposed which is resilient to soft errors as well as improves the critical read and write access time. This memory cell exhibits better results in terms of critical charge Qc with improved write static noise margin (WSNM). The extensive Monte Carlo simulations in Industry Hardware Calibrated 65nm standard CMOS process demonstrates that the proposed cell achieves improved performance with respect to $\boldsymbol{0.70\times}$ read access time, $\boldsymbol{0.69}\times$ write access time, $\boldsymbol{4.57}\times$ WSNM, $\boldsymbol{1.07}\times Q_{c}$ as compared to NQ-10T at a supply voltage of 1V. $Qc$ of the proposed RH-12T outperforms 6T SRAM & Q-10T by $\boldsymbol{1.91}\times$ and $\boldsymbol{1.62\times}$ respectively. In terms of area comparison, the silicon area is $\boldsymbol{1\times}$ for both NQ-10T and the proposed 12T.

Human pose detection is an active research topic in the field of computer vision. Numerous methods and theories have been proposed for pose detection, however the research studies reveal that they fail to recognize due to interference within the segments, similar movements, and other external factors. In this paper, virtual gameplay is built based on the machine learning algorithm. A neural network model called DBLSTM is developed by utilizing the deep learning techniques for training and testing the proposed model. Here, the laptop camera is used as the motion capture device to track the movements of the human body. Data extracted from the image/videos by using OpenPose connects the obtained data with the virtual environment. The result analysis shows that the proposed SVM algorithm outperforms all other algorithms in terms of recognition rate.

Introduction: This chapter is intended to link the embracing strategy of ‘socially responsible investment’ with the apparent cause of economic destruction ‘financial crimes’. Today’s financial world is not always associated with ethics and morality, but it does not mean rising investments cause rising financial crimes. Socially responsible investing (SRI) has been rising, and many of today’s investors are interested in tracking ethically sound companies. Investors find a great way to invest around many investment opportunities, while socially responsible investors work with little social cause. This increasing literacy over SRI notably helps to reduce investments in unethical grounds which in turn reduces financial crimes.Design/methodology: This work is premised on desk research. Conceptual and documentary methods were used in the study. The tertiary data source has been used in the study to develop a template describing the working of SRI in fixing financial crimes.Findings: Findings of this study detail: a breakdown of industries that comes under SRI, channels of financial crimes, impact of SRI on financial crimes, and design an action plan for more effective environmental, social, and governance (ESG)-based investments to fix problems of financial crimes in the Indian economy.Practical implications: The model of SRI has unfolded these days. While the purpose of these funds differs, they generally swear off the weapons industry and avoid ‘sin stocks’. In-depth analysis of this study area enables building quality investment strategy among investors and thereby helps to combat financial crimes.

The main objective of this study is to produce interlock fabrics with two different types of material at the face and back and to evaluate the effectiveness of its air permeability and thermal properties, which, in turn, decide the comfort of the wearer. It is observed that the tightness factor of the fabric has a linear relationship with air permeability, thermal conductivity and Qmax. The polyester modal interlock fabric shows a higher Qmax value which provides a good warm-cool effect, that is important for sportswear applications.

Nitrogen/Sulphur/Zirconium doped cobalt ferrites were prepared by wet impregnation and coprecipitation techniques. The catalytic activities of the samples were evaluated for the production of triacetin using glycerol and acetic acid. The catalytic efficiency of cobalt ferrite increased as a result of doping by zirconium and non-metals. The highest triacetin yield obtained was 90.7

This study presents an innovative approach for smart traffic monitoring and detection using Tinkercad. Tinkercad is a web-based 3D modelling tool that allows users to create 3D designs, objects, and scenes using a variety of shapes and materials. The paper proposes the use of Tinkercad for the development of a system for the detection of traffic violations such as over-speeding, sudden lane change and illegal parking. The system is based on the use of 3D models of roads, cameras and sensors which are used to detect the traffic violations. The data collected by the sensors is then processed by a computer to detect the violations. This system can be used to effectively monitor and detect traffic violations in real-time and to take necessary measures to prevent them. The paper also discusses the implications of such a system on traffic safety and proposes an implementation plan. In earlier days, due to the low population density, traffic monitoring used to be simple. However, due to the tremendous increase in population, traffic monitoring has become a serious challenge. This study has created a system to track traffic. The proposed system utilized an ultrasonic sensor, a microprocessor, and a camera to build this device. When a vehicle crosses the road, this mechanism is actuated even when the red signal is turned on. When a vehicle attempts to pass a red light without stopping, the ultrasonic sensor detects the movement of the impediment using ultrasonic waves. Hence these ultrasonic sensor gives signals to the microcontroller. This microcontroller activates the alarm, which activates the camera to capture the image of the vehicle. This helps to obtain the data of the vehicle, which is crossed when the red light is ON. This data is collected by the camera which is along the microcontroller Hence this picture of a vehicle is saved by mentioning its date and time of crossed. As a result, this system enables to readily monitor traffic by using basic instruments.

For a commutative ring R with unity, the inclusion ideal graph In(R) is a graph whose vertices are all non-trivial ideals of R and two distinct ideals I and J are adjacent if and only if either \(I \subset J\) or \(J \subset I\). In this paper, we provide a formula for the size of In(R), when R is a finite product of fields. Also we examine the genus and crosscap of In(R) deliberately. Added with that, we analyse the book thickness of In(R).

Hippocampus segmentation on magnetic resonance imaging is more significant for diagnosis, treatment and analyzing of neuropsychiatric disorders. Automatic segmentation is an active research field. Previous state-of-the-art hippocampus segmentation methods train their methods on healthy or Alzheimer’s disease patients from public datasets. It arises the question whether these methods are capable for recognizing the hippocampus in a different domain. Therefore, this study proposes a precise computational method for hippocampus segmentation from MRI of brain to assist physicians in the diagnosis of Alzheimer’s disease (HCS-MRI-DAD-LBP). Initially, the input images are pre-processed by Trimmed mean filter for image quality enhancement. Then the pre-processed images are given to ROI detection, ROI detection utilizes Weber’s law which determines the luminance factor of the image. In the region extraction process, Chan–Vese active contour model (ACM) and level sets are used (UACM). Finally, local binary pattern (LBP) is utilized to remove the erroneous pixel that maximizes the segmentation accuracy. The proposed model is implemented in MATLAB, and its performance is analyzed with performance metrics, like precision, recall, mean, variance, standard deviation and disc similarity coefficient. The proposed HCS-MRI-DAD-LBP method attains in OASIS dataset provides high disc similarity coefficient of 12.64%, 10.11% and 1.03% compared with the existing methods, like HCS-DAS-MLT, HCS-DAS-RNN and HCS-DAS-GMM and in ADNI dataset provides high precision of 20%, 9.09% and 1.05% compared with existing methods like HCS-MRI-DAD-CNN-ADNI, HCS-MRI-DAD-MCNN-ADNI and HCS-MRI-DAD-CNN-RNN-ADNI, respectively.

ABSTRACT The Ag2O/TiO2 composite photocatalysts (PCs) were synthesised in a facile manner into applied photocatalytic decomposition of methylene blue (MB) dye under visible-light treatment. Then, physicochemical belongings of as-fabricated PCs were analytically categorised by powder XRD, FT-IR, HRSEM with an EDXS, HRTEM, UV-Vis DRS and PL spectroscopy. Associated with the pristine TiO2 and Ag2O, the united Ag2O/TiO2 NC exposed greater photo-decomposition actions (~95.4%) in MB dye under visible-light treatment. The photo-degradation rate constant of Ag2O/TiO2 heterostructured composite was about 0.03504 min−1, which is almost 1.8 and 1.2 times superior to pristine TiO2 and Ag2O NPs, individually. Besides, the as-fabricated Ag2O/TiO2 NC exhibits high reutilising stability for the next five consecutive consumptions. Hence it proposes that the liability for strong visible-light fascination capacity, narrow bandgap, effectual separation and hindered the recombination of photo-excited electron-hole (e−/h+) pairs by the construction of greatly dispersed Ag2O NPs.

Fused deposition modeling (FDM) is one of the additive manufacturing (AM) methods widely used in many divisions, especially medical implants and aerospace, due to capabilities to build complex 3D objects and geometries. However, quality and dimensional accuracy of the FDM parts are significantly influenced by the various FDM process parameters including filament wire material. In the present work, new filament wire material Thermoplastic Polyurethane (TPU) was utilized to produce FDM parts. Hence, deciding the optimum process parameters is very critical to produce the FDM parts with good surface quality (Ra) and dimensional accuracy (Δd) concurrently using TPU material. In this paper, the author has contributed to determine the optimum 3D printing process parameters to improve the quality and accuracy for the new filament wire material Thermoplastic Polyurethane (TPU) using multi-attribute decision making (MADM) methods namely Gray Relational Analysis (GRA) and technique for order preference by similarity to ideal solution (TOPSIS). Further, the results of GRA and TOPSIS techniques were compared and concluded that TOPSIS method substantially reduced the surface roughness to a value of 12% contrast to the GRA method whereas the dimensional deviation accuracy increased to 6.25% over the GRA method.

Nitrogen, sulphur and zirconium doped cobalt ferrites prepared by coprecipitation method were evaluated as catalysts for the production of cumene from benzene and isopropyl alcohol. The catalytic efficiency of cobalt ferrite towards the reaction increased due to the doping by zirconium and non-metals. The isopropylation activity is decidedly dependent on the number and strength of active surface sites that in turn depends on the nature and concentration of dopant. Reaction variables like temperature, reactant ratio and catalyst load affected the reaction rate considerably. On successive uses, the catalysts were found to be magnetically recoverable and stable.

Today nanoparticles and nanocomposite are used for removal of metals and biological substances. Magnetic oxide nanoparticles are a class of engineered materials with very small size and which can be operated under the influence of external magnetic field. This is mainly due to their very efficient contaminant removal capacity, fast reaction rate and most importantly due to magnetic properties which enables its easy recovery. These magnetic nanoparticles are commonly synthesized by different methods. These preparation methods have several limitations in terms of operating cost production rates and risk to the environment and humans. In addition to these, magnetic nanoparticles lose their reaction rate due to macromolecules formation and magnetic property and dispensability on exposure of atmosphere. Green synthesis of magnetic nanoparticles has remained a comparatively new research area. The problem of treating waste water is being very severe, particularly in developing countries with disposal of untreated waste water. The release of untreated waste water with nutrients and metal concentration leads to worldwide ecological problem in water bodies. The present study revealed Murrayakoenigii (curry leaves) used for producing magnetic nanoparticles in green synthesis method and these magnetic nanoparticles have the potential to remove nutrients like Phosphate ion and Nitrate ion from wastewater and magnetic nanoparticles were characterised by FTIR,XRD and SEM Techniques.

Smart automotive web applications facilitate the easy purchase of automotive accessories online and offer customers real-time automobile services. Such an application offers customers auto parts alongside a great user experience with first-rate quality and customer service. Our website project furnishes all the relevant information needed on the product, product type, model, company, order, items ordered, and the status of the order tracked. Auto parts retailers and online mechanics have long been able to rely on product quality and the niche nature of the said products to set themselves apart. The fierce growth of online competition, however, has been driving consumer-friendly branding strategies. Further, our website provides links to miscellaneous auto spares stores so customers can have access to a wide range of products to choose from and be able to compare prices as well. The user-friendly nature of the application’s UI makes it easy for customers to browse products and eliminates the need to re-specify the automobile type every time a user refreshes or opens another page.

To conquer the drawback of von Neumann architecture, research has been carried out on the computational methods in the memory array itself to achieve near-memory or in-memory computations (IMC). This paper for the first time proposed an analog IMC approach for full adder design using 8 ^+ T Static Random Access Memories (SRAM). In conventional FA, addition is executed as a sequence of digital boolean operations inside the memory array and there is a need for external logic gates to compute the FA outputs. The proposed analog adder exploits the bit-line voltage discharge (V _BL ) with respect to the data stored in the 8 ^+ T memory cell for the bit addition. The bit-line discharge voltage is accumulated using a voltage accumulation circuit (VAC) and acts as an input to an analog to digital converter (ADC). The digital output obtained is the Sum of a single bit FA. Multi-bit FA is computed from this single-bit analog FA. Extensive simulation results, referring to an industrial hardware-calibrated UMC 65-nm CMOS technology indicate 27 × improvement in power and 36 × improvements in throughput leading to a reduction of 972 × in energy-delay product.

The goal of this study is to enhance the emergency response department's reaction time to car accidents and an attempt to find solutions for quick accident notification. The project uses a “smart unit” implanted in the car to record the vehicle's data and send them to the vehicle owner or a third party at regular intervals. The smart device is made up of a variety of sensors that are properly safeguarded in a black box that remains safe even if the car is entirely damaged. Appropriate algorithms are created based on the data to provide alerts and initiate action. The system will aid customers in a variety of ways, including providing quick assistance in the event of an accident, tracking the car in the event of theft, and remotely deactivating the vehicle. The device will assist the user in a variety of ways, including providing quick assistance in the event of an accident, tracking the car in the event of theft, and remotely deactivating the vehicle. Furthermore, the device will simultaneously sending the information to the insurance companies shortly after the event occurs, ensuring that the information is delivered to the insurance companies far ahead of time. The output of the proposed research work includes a smart device to monitor the vehicle, the prototype could be a viable technology for car manufacturers to integrate and improve vehicle safety features and increase the dependability of vehicle accident detection and reporting system.