Currency Denomination Research Articles

Currency Detection Application for Visually Impaired People

This introduces an image-processing-based mobile application that recognizes currency denominations and is sensitive to voice feedback, assisting visually impaired people. This research bridges a quite broad gap in the available assistive technology, as it is relatively fast and robust in providing a currency-handling solution

Carry Trades and FX Risk Buffers: Foreign Currency Debt of Emerging Market Firms

Abstract The surge in foreign currency (FC) corporate debt in emerging economies has sparked concerns about macroeconomic stability, heightened by speculation about non-financial firms engaging in carry trades. Using firm-level data on the currency denomination of both assets and liabilities, we find evidence of firms' carry trades: firms save in local currency liquid assets and earn higher interest income after issuing short-term FC debt. They also set aside FC liquid assets as FX risk buffers. A large degree of heterogeneity in incentives is observed. Notably, listed firms participate more in carry trades and allocate less FX risk buffers than non-listed firms.

Convolutional neural networks method for folded naira currency denominations recognition and analysis

Sorting banknotes automatically in payment facilities or freewill donation boxes involves many tasks such as recognizing banknote denomination, fitness classification, and counterfeit detection. Different studies have addressed these problems using foreign currencies. In other words, very few researches have been conducted on naira banknote denomination recognition. A few studies did not consider folded naira banknote denomination recognition. To overcome this, we propose a Machine Learning Based Folded Banknote Denomination Recognition System. The proposed system automatically extracts relevant features and characteristics of the naira currency denomination in different positions, orientations, and folds. This automatic representation of the naira currency denomination enables the proposed system to reduce over-reliance on suggested characteristics, improve performance accuracy, and generalize the proposed system to new problems. Moreover, the proposed system was modeled using object-oriented analysis and design methodology, and implemented in the Keras framework interfaced with Tensorflow. Then, the Softmax classification algorithm was used to classify each Naira banknote denomination. The proposed machine learning-based folded banknote denomination recognition achieved an average accuracy of 95%, which shows the generalization ability of the proposed system to recognize folded banknote denominations in different orientations, positions, and folded forms.

Global Capital Allocation

We survey the literature on global capital allocation. We begin by reviewing the rise of cross-border investment, the shift toward portfolio investment, and the literature focusing on aggregate patterns in multilateral and bilateral positions. We then turn to the recent literature that uses micro data to document patterns in global capital allocations. We focus on the importance of the currency of denomination of assets in international portfolios and the role that tax havens and offshore financial centers play in intermediating global capital. We conclude with directions for future research in this area.

Examination of Higher Denomination Naira Notes Exchanged in Veritas University Cafeteria for Fungal Contamination

Aims: This study is aimed examination of higher denomination of naira notes exchanged at Veritas University cafeteria for fungal contamination. Study Design: The factorial experimental design was employed for this study. Place and Duration of Study: Department of Microbiology, Veritas University, Abuja, between February 2023 to July, 2023. Methodology: A total of 60 Nigerian currency notes, (denominations: N100, N200, N500, N1000), were randomly collected from the Veritas University Cafeteria, and subjected to analysis. Each Naira note samples were aseptically soaked and thoroughly mixed separately, in 20 ml of peptone water in a 250 ml beaker for 15 minutes. This was done to dislodge the cells into the Peptone water. Using sterile forceps, the currency notes were carefully removed for preservation from each beaker and the contents of each beaker was sealed with foil paper and incubated for 24hrs at 37C. Standard microbiological methods were followed for the isolation and characterization of the fungal species. Results: The result of the findings showed that, all the different naira notes had fungal contamination. The fungal count ranged from 1 x 107 to 6.4 x 109 while the ₦100 currency denomination had the highest TVC (6.4 × 109 cfu/ml) and the ₦200 currency denomination had the least TVC (1.0 × 107 cfu/ml). The result of the findings also revealed the presence of a diverse range of fungal species which include the genera; Aspergillus, Penicillium, Candida, Neurospora, Trichothecium, Rhizopus and Cladosporium. The percentage occurrence of fungal isolates revealed Aspergillus spp to be the most occurred except in Samples D where Neurospora crasa was the most present fungi. Conclusion: The fungal species isolated from the naira notes include, Neurospora crasa, Aspergillus, Candida spp, Trichothecium spp, Penicillium spp, Rhizopus spp, Mucor spp. Aspergillus spp had the highest occurrence in all the samples except Sample D. These results emphasize the necessity of frequent monitoring in order to avoid any health hazards related to fungal contamination, as well as the implementation of good hygienic procedures in the handling and management of cash.

Seeing with Sound: Neural Networks and Deep Learning for Indian Currency Recognition

Visually impaired individuals face significant challenges in distinguishing between different denominations of Indian currency notes, which hinders their ability to perform routine tasks such as shopping and financial management. Traditional methods of currency recognition, such as tactile features or optical scanners, are often burdensome and inconvenient. This groundbreaking system utilizes a deep learning model developed from a vast dataset of Indian rupee notes captured in diverse environments, including various orientations, stages of wear-and-tear, and lighting conditions, ensuring robust training. By leveraging Convolutional Neural Networks (CNNs), the system ensures a high degree of accuracy in identifying notes and generates distinct audio cues corresponding to each denomination, simplifying the differentiation process for visually impaired individuals. The use of neural networks enables the system to continuously refine its recognition capabilities, adapting and improving its identification methods with additional data, including updated note variations or unexpected scenarios. Upon widespread availability, this technology has the potential to revolutionize how blind individuals in India manage their finances, providing them with increased autonomy and confidence in everyday transactions. The proposed system offers several advantages over traditional currency recognition methods: it is non-invasive, requiring no physical contact with the currency notes, thus enhancing convenience; its portability enables implementation on mobile devices, facilitating access for users on the go; and it prioritizes user-friendliness by providing real-time audio feedback, further simplifying the currency identification process for visually impaired individuals. Overall, this innovative solution represents a significant stride towards empowering the visually impaired community in India, offering newfound freedom and self-assurance in managing their monetary affairs.

A Review on Currency Classification and Image to Text Conversion Methodologies

Currency classification and Image to Text OCR are essential technologies that find applications in various domains, including finance, retail, and automation. The approach outlined in this paper has the potential to detect currencies from multiple countries. However, for practical implementation purposes, the focus is solely on Indian paper currencies. This system offers the advantage of convenient currency checking at any time and location, leveraging Convolutional Neural Networks (CNN) for effective implementation. Extensive testing was conducted on each denomination of Indian currency, resulting in an impressive 95% accuracy rate. To further refine accuracy, a classification model was developed, incorporating all pertinent factors discussed in the paper. Notably, the unique features of paper currency play a pivotal role in the recognition process. By emphasizing these elements and harnessing CNN technology, the proposed system demonstrates significant promise in accurately detecting and validating Indian paper currencies. It stands poised to serve various applications effectively. On the other hand, Image to Text OCR focuses on extracting text from images, enabling the conversion of non- editable documents into searchable and editable formats. Both technologies contribute to automation and efficiency in handling diverse visual information. Optical Character Recognition (OCR) is a technologydesigned to recognize and interpret both printed and handwritten characters by scanning text images. This process involves segmenting the text image into regions, isolating individual lines, and identifying each character along with its spacing. After isolating individual characters from the text image, the system conducts an analysis of their texture and topological attributes. This involves examining corner points, unique characteristics of various regions within the characters, and calculating the ratio of character area to convex area Prior to initiating recognition, the system creates templates that store the distinctive features of uppercase and lowercase letters, digits, and symbols. These templates serve as reference models for comparison during the recognition phase. During recognition, the system matches the extracted character's texture and topological Features with those stored in the templates to determine the exact character. This matching process involves comparing features of the extracted character with templates of all characters, measuring similarity, and ultimately recognizing the character accurately.

تحســــين أداء آلــــة عـــد الأمـــــوال باستبدال المحرك التيار المتردد بمحرك تيار مستمر، مع دمج حساس الأشعة تحت الحمراء والأردوينو

In the implementation of a money counting mechanism for a money-counting machine, I attempted to control the machine by replacing its control panel with Arduino control. This allowed for monitoring the movement and counting of money, controlling its stopping and starting, and taking readings from its sensors to employ it as a money counting machine. The process was very successful and yielded impressive results despite encountering some problems that were resolved. Among these issues was the difficulty in controlling the AC motor responsible for the mechanical movement of pulling the money. It was replaced with a DC motor, which offered greater flexibility in terms of stopping, starting, and speed control. This was very beneficial in ensuring the process of determining the desired amount of money by the counting machine. Replacing the AC motor with a DC motor in the money counting machine and using it in this project was a unique experience. Despite continuous research on related topics, I found no one who had used this idea. The results were highly reliable and stable. The following will discuss the results and explain the process. Arduino was primarily used in this process, with movement control using a DC motor driver and the existing infrared sensors in the money counting machine, along with other sensors such as the currency denomination sensor and counterfeit detection sensor. These components will be listed in this paper to clarify the process and how satisfactory results were achieved. Keywords: Arduino-based control systems, DC motor technology, Cash counting machine,AC motor, Infrared sensors.

De-dollarization? Not so fast

De-dollarization refers to the reduction of the reliance of foreign countries on the US dollar. This phenomenon generates concern about the U.S. dollar as a global currency. We construct new data on the currency denomination of central bank currency reserves, foreign exchange transaction volume, denomination of global debt securities, and the invoicing of trade. This paper presents empirical evidence suggesting that these concerns are misplaced, finding US dollar dominance remains unchanged up through late 2023, nearly two years after the 2022 Russian invasion of Ukraine and several years after the 2020 COVID-19 pandemic. Meanwhile euro and renminbi influence have since declined. These findings have implications for reserve currency resilience, U.S. dollar dominance, U.S. sanctions policy, international spillovers of U.S. monetary policy, and U.S. government borrowing costs.

Long-run asymmetric effects of financial risks on Sukuk market development: empirical evidence from Malaysia

Purpose The purpose of this study is to empirically investigate the asymmetric effects of financial risk on Sukuk market development for a sample of Malaysian countries over the period of 2010–2021. Design/methodology/approach This study refers to the International Country Risk Guide (ICRG) in determining the financial risk factors to be studied in addition to the Malaysia financial stress index (FSI) to capture changes in financial risk level. The authors use the nonlinear autoregressive distributed lag (NARDL) model to tackle the nonlinear relationships between identified financial risk variables and Sukuk market development. Findings The results suggest the existence of a long-run relationship between foreign debt service stability, international liquidity stability (ILS), exchange rate stability (ERS) and financial stress level with the Sukuk market development in Malaysia. Indeed, higher ILS and ERS will boost Sukuk market size, whereas higher foreign debt services and financial stress are negatively related to Sukuk market development. Findings also indicate that the long-run positive and negative impacts of identified financial risk components on Sukuk market development are statistically different. Taking into account the role of the Sukuk market in facilitating Malaysia’s economic growth, the country should aim to keep the foreign debt-to-GDP ratio at a sustainable level. Research limitations/implications This study points to three possible directions for future research. The first is the differential impact of financial risk components on Sukuk issuance for different Sukuk structures. As more data becomes available in the future, this area could be further explored by conducting the above analysis for different combinations of Sukuk structures and currency denominations. In addition, future researchers could also consider exploring the variability of financial risk impacts through comparative studies of the leading Sukuk-issuing countries to account for differences in regulatory frameworks and supporting infrastructure. Practical implications This study provides valuable practical and policy implications for strengthening the growth of the Sukuk market. While benefiting from the diversification benefits of funding sources to finance private or government projects and developments, Malaysia should remain vigilant to global economic conditions, foreign exchange markets and financial stress levels, as all of these factors may significantly influence investor sentiment and the rate of return offered by Sukuk issuance. Originality/value The use of the NARDL approach, which investigates the long-run effects of financial risk factors on Sukuk market development in Malaysia, makes this study a valuable addition to the literature, as there has been little research into the asymmetric effects of those variables on Sukuk market development using samples from emerging Asian markets.

Smart Currency Counter Machine

This project presents the development of a cost-effective and efficient Smart Currency Counter Machine utilizing the Arduino Uno microcontroller for the specific purpose of accurately counting 50- and 20-Rupees notes. In an era where currency counting accuracy is of paramount importance, this system offers a reliable solution for businesses and individuals.[1] The machine employs a combination of image processing techniques and sophisticated sensors to distinguish between various currency denominations, specifically the 50- and 20-Rupees notes.[3] The Arduino Uno's computational capabilities and versatility make it an ideal choice for controlling the system, while the use of open-source libraries and affordable components ensures accessibility to a wider range of users. Preliminary testing demonstrates that the Smart Currency Counter Machine provides fast, accurate, and user-friendly currency counting, contributing to improved financial record-keeping and efficiency in cash handling operations.[4]

High-resolution imaging of magnetic fields of banknote anti-counterfeiting strip using fiber diamond probe

Counterfeiting of modern banknotes poses a significant challenge, prompting the use of various preventive measures. One such measure is the magnetic anti-counterfeiting strip. However, due to its inherent weak magnetic properties, visualizing its magnetic distribution has been a longstanding challenge. In this work, we introduce an innovative method by using a fiber optic diamond probe, a highly sensitive quantum sensor designed specifically for detecting extremely weak magnetic fields. We employ this probe to achieve high-resolution imaging of the magnetic fields associated with the RMB 50 denomination anti-counterfeiting strip. Additionally, we conduct computer simulations by using COMSOL Multiphysics software to deduce the potential geometric characteristics and material composition of the magnetic region within the anti-counterfeiting strip. The findings and method presented in this study hold broader significance, extending the RMB 50 denomination to various denominations of the Chinese currency and other items that employ magnetic anti-counterfeiting strips. These advances have the potential to significantly improve and promote security measures in order to prevent the banknotes from being counterfeited.

Nature as the Fecund Female in Joseph Conrads Heart of Darkness

Abstract: The Currency Detection Android Application with Image Processing is an innovative solution designed to empower visually impaired individuals and assist travelers in identifying and managing paper currency notes with accuracy and convenience. This application leverages advanced image processing techniques, including edge detection, color analysis, texture recognition, and optical character recognition (OCR), to extract crucial features from currency notes. Machine learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are employed to recognize currency denominations based on these extracted features. The application provides real-time audio feedback, announcing the recognized denomination, and features a user-friendly interface with voice-guided instructions, catering to both visually impaired and sighted users. A comprehensive currency library ensures coverage of major world currencies, with regular updates to accommodate new designs and security features. Offline functionality further enhances accessibility. The project's scope encompasses primary functionalities, target audience, supported currencies, technological considerations, and acknowledges project limitations.

Cash Is Alive: How Economists Explain Holding and Use of Cash

Research on holding and use of cash involves many aspects, such as reasons for holding and hoarding cash, transactional demand for cash, cash management, type of spending paid with cash, type of consumer who pays cash, merchant acceptance, how consumers get cash, currency denominations, legal aspects, cash substitutes, and cost of cash. The purpose of this article is to introduce the reader to some of the research economists do on consumer holding and use of cash. (JEL D11, D12, E26, E31, E41, E42)

Limits to Fiscal and Monetary Policy in Small Open Economies

ABSTRACT In this paper we investigate the impact of the balance-of-payments constraint on output, income distribution and policy space in a small economy open to both commercial and financial flows. We do so by extending a Sraffian supermultiplier growth model to consider current and capital accounts and endogenous money. Expansionary fiscal policies increase output and its long run growth rate but have a negative impact on the balance-of-payments. Based on these results, we derive the value of government spending that maximises output while maintaining external equilibrium. Monetary policy also influences the balance-of-payments by attracting foreign financial flows through higher interest rates. This relationship is non-monotonic, since these flows can finance the external deficit resulting from a high output level but can also lead to a greater interest burden. This trade off allows us to find the level of the interest rate that maximises external space. It also implies that, with limits, raising interest rates might allow for higher real wages. Lastly, we examine the effects of the currency denomination of debt. We find that, in case of a depreciation, debt in domestic currency allows for a less contractionary adjustment than foreign currency-denominated debt but does not lead to higher economic growth.

Fake Currency Detection for Differently Abled People

Abstract: For people with visual impairments, identifying various currency denominations and fake currency is difficult. Even though special symbols are engraved on various denominations in India, the process is time-consuming for people with disabilities. The need for a handheld device to distinguish between different denominations and detect counterfeit notes stemmed from the lack of identification and fake detection tools. In this project, the features of the images are compared to all of the currency reference images. If the difference is less than a certain threshold, the numeric portion of the currency is extracted and compared; if it matches, the matched currency denomination is recognized. The 50, 100, 500, 2000, 20, and 10 Indian currency denominations are accepted. Using raspberry pi, the proposed model will automatically identify counterfeit currency and denomination in this project. Basic image processing, template matching, aspect ratio identification, the dominant colour, and feature extraction are all used to distinguish between counterfeit and denomination. Finally, to turn English text into a six-digit braille code, we use solenoid-based valves that are controlled by a Raspberry Pi.

Bilateral optimal denomination problem

A common problem about currency denomination is the change-making problem. In this paper, a generalized denomination-related problem inspired by the optimal denomination problem is discussed, namely the bilateral optimal denomination problem. The solution maximizes a certain monetary value and thus can be applied in pricing strategies. Two key issues need to be addressed. One is how to properly formulate the problem in mathematical language. The other is how to compute the corresponding result with an algorithm. The paper learns from existing denomination algorithms and provides a solution to the problem from construction to implementation. Some limitations are needed to improve the practicality of the solution. An example of threshold discounts is used to illustrate the real-life application of the proposed problem.

Effect of Lower Currency Denomination on Business to Consumers in Era of Cashless Society: A Study of Maiduguri Metropolis Nigeria

E-commerce transactions and cashless payments is a common denominator in financial transactions in Maiduguri Metropolis by business to commerce (B2C). The study investigated the effect of lower denomination currency (LCD) on B2C in the era of cashless society. The Metropolis is structured into four clusters of B2C. The study is on the effects of financial accessibility, financial innovation, maximum sales revenue and cash handling culture of B2C. Using four-likert scale and multiple linear regression the results showed that the financial accessibility revealed a high cost of handling cash book and that network issue affects the volume of business while financial innovation reduces the need for LCD and cost of holding cash. The results further showed that maximum sales revenue is a trade-off between liquidity and profitability; either way, it is a high liquidity or otherwise. On the effect of cash handling culture, the result showed that B2C does not require large amount of cash to operate and it does not require a professional to handle its cash reconciliation. The study recommended for improvement of financial accessibility, innovation and inclusion on B2C. The paper called on the CBN to be cautious in its call to B2C traders to key-in onto e-Naira in order not to destroy B2C retailers.

Advanced Blind helper Android Application Using Text-to-speech synthesis

Abstract: This project aims to develop an Android application that enables blind people to detect currency denominations and make phone calls using handwritten text recognition and voice commands. The application uses image processing techniques and a trained TensorFlow model to recognize the denomination of banknotes, and an ML model to recognize handwritten text and convert it to a phone number. The user can initiate a phone call by saying a voice command, and the application provides audio feedback throughout the process.

A Review on Designing of Fake Currency Detection System

The production of duplicate and fake currency has increased with a tremendous amount with the advancement in color print technology. Fake currency is a threat which cannot be easily recognized. Currency detection is a critical problem in various applications such as ATMs, vending machine and currency detection machines. With the increasing globalization of the world economy there is growing demand for accurate and reliable currency detection systems. The proposed system has practical applications in areas such as financial transactions, currency exchange and International trade. It is designed to be fast, accurate and scalable, capable of handling a wide range of currencies. Our research paper introduces a currency detection project that utilizes computer vision techniques to detect and classify banknotes. The proposed system uses image processing and machine learning algorithm to recognize the currency denomination and authenticity. The dataset for training and testing the proposed system is collected and labeled to ensure the accuracy and reliability of the system. Practical outcomes prove that this system aims high precision rates for both currency denomination and authenticity detection, making it an efficient and reliable solution for currency detection. Keywords: Currency Detection, Demonetization, Image Processing, features, System.