This project aimed to develop a solution which can assist visually impaired individuals in identifying currency denominations and detecting counterfeit currency. To achieve this, we used Teachable Machine for currency denomination detection and MATLAB for counterfeit currency detection. The resulting model was then integrated into an Android application, making it easily accessible to users. The project involved collecting and recording audio files for different currency denominations, which were then used for training the Teachable Machine model. The MATLAB model used edge detection and feature extraction techniques to detect counterfeit currency. The Android application provided an easy-to-use interface for users to detect the denomination of currency and identify its originality. By integrating these two models, we were able to achieve a solution that can be used by visually impaired individuals to easily identify currency and avoid counterfeit currency. In summary, this project presents a practical solution to address the challenges faced by visually impaired individuals in handling currency and detecting counterfeit currency.

The microgrid is located at distribution network side and generates power according to power demand in a specific region using several distributed generations such as wind, solar, fuel cell etc. Due to uncertainty in distributed generations, the frequency regulation is a formidable problem in islanded microgrid. Thus, a centralised linear quadratic regulator-based controller is designed for islanded microgrid in order to get good performance and zero deviation in terms of frequency deviation. The closed loop control law convergence is obtained using the Lyapunov stability theorem. In presence of distributed generation uncertainties, proposed controller performance is compared and it enhances closed loop system stability with reduction in over/under shoots, settling time and oscillations. In addition, also through well said controller regulates power generation from power from flywheel storage plant, fuel cell plant and battery storage plant to sustain minimum frequency deviations effectively. The performance, stability and ability to keep in synchronism with the proposed control scheme are validated on several distributed generations through MATLAB© simulations.

In today's era, human life is more stressful. Due to changes in lifestyle, professional stress, travelling, and food habits, people are more susceptible to various degenerative disorders like Manyastambha. Manyastambha is the clinical entity in which the back of the neck becomes stiff or rigid, and pain, Stambha in the cervical region and movement of the neck are hampered. It is the most common degenerative disease by which a larger group of the community has been affected. Manyastambha has been enumerated in eighty Nanatmaj Vyadhis as well as Urdhwajatrugata Vikaras. It can be clinically co-related with cervical Spondylosis in modern medicine. Cervical Spondylosis is a degenerative condition of the cervical spine. Ruk and Stambha are the primary symptoms. If severe, it may cause pressure on nerve roots with subsequent sensory or motor disturbances. Today is the era of modernization and fast life. Everybody is busy and living a stressful life. In the present observational study, housewives are more prone to develop Manyastambha (cervical spondylosis), followed by clerks, tailors, farmers and IT professionals.

Automatic modulation classification finds its application in many military and civil areas. It is an integral part of cognitive radio and software defined radio technologies. In this paper, LabVIEW based Field Programmable Gate Array (FPGA) implementation of modulation classification algorithm is proposed. Any modulation scheme among BPSK, QPSK, 8PSK, 8QAM, 16QAM and 4ASK is classified by alteration of oversampling factor and further error minimization between extracted constellation and ideal constellation of considered modulation schemes. Study results reveal that the developed method detects the above mentioned modulation schemes reliably above 12 dB SNR in Additive White Gaussian Noise (AWGN) channel. Comparative analysis of the proposed method with existing methods based on higher order statistics, mel-frequency ceptral coefficients and naive based modulation classification shows an overall improvement in classification accuracy.

In this work, experimental studies of electrically and magnetically induced re-orientational transitions in suspensions of cobalt ferrite (CoFe2O4) nanoparticles in 7CB (4 - n - heptyl - 4 ' - cyanobiphenyl) liquid crystal (LC) are presented. Two concentrations are studied (i:e 0.033wt% or sample A and 0.044wt% or sample B). The results obtained are compared with the pristine liquid crystal. Low threshold voltages and magnetic fields for the re-orientational transition are observed in the doped cases. The nanoparticles bonded to the LC in close proximity are orienting in a parallel manner with the bulk liquid crystal media and causing these changes. Effect of the polarity and position of the magnet on the re-orientational mechanism in comparison to the pristine sample is also studied. (c) 2022 Published by Elsevier B.V.

In the present era, electrochemical water splitting has been showcased as a reliable solution for alternative and sustainable energy development. The development of a cheap, albeit active, catalyst to split water at a substantial overpotential with long durability is a perdurable challenge. Moreover, understanding the nature of surface-active species under electrochemical conditions remains fundamentally important. A facile hydrothermal approach is herein adapted to prepare covellite (hexagonal) phase CuS nanoplates. In the covellite CuS lattice, copper is present in a mixed-valent state, supported by two different binding energy values (932.10 eV for CuI and 933.65 eV for CuII) found in X-ray photoelectron spectroscopy analysis, and adopted two different geometries, that is, trigonal planar preferably for CuI and tetrahedral preferably for CuII. The as-synthesized covellite CuS behaves as an efficient electro(pre)catalyst for alkaline water oxidation while deposited on a glassy carbon and nickel foam (NF) electrodes. Under cyclic voltammetry cycles, covellite CuS electrochemically and irreversibly oxidized to CuO, indicated by a redox feature at 1.2 V (vs the reversible hydrogen electrode) and an ex situ Raman study. Electrochemically activated covellite CuS to the CuO phase (termed as CuSEA) behaves as a pure copper-based catalyst showing an overpotential (η) of only 349 (±5) mV at a current density of 20 mA cm-2, and the TOF value obtained at η349 (at 349 mV) is 1.1 × 10-3 s-1. A low Rct of 5.90 Ω and a moderate Tafel slope of 82 mV dec-1 confirm the fair activity of the CuSEA catalyst compared to the CuS precatalyst, reference CuO, and other reported copper catalysts. Notably, the CuSEA/NF anode can deliver a constant current of ca. 15 mA cm-2 over a period of 10 h and even a high current density of 100 mA cm-2 for 1 h. Post-oxygen evolution reaction (OER)-chronoamperometric characterization of the anode via several spectroscopic and microscopic tools firmly establishes the formation of crystalline CuO as the active material along with some amorphous Cu(OH)2 via bulk reconstruction of the covellite CuS under electrochemical conditions. Given the promising OER activity, the CuSEA/NF anode can be fabricated as a water electrolyzer, Pt(-)//(+)CuSEA/NF, that delivers a j of 10 mA cm-2 at a cell potential of 1.58 V. The same electrolyzer can further be used for electrochemical transformation of organic feedstocks like ethanol, furfural, and 5-hydroxymethylfurfural to their respective acids. The present study showcases that a highly active CuO/Cu(OH)2 heterostructure can be constructed in situ on NF from the covellite CuS nanoplate, which is not only a superior pure copper-based electrocatalyst active for OER and overall water splitting but also for the electro-oxidation of industrial feedstocks.

Deep learning (DL) is a newly addressed area of research in the field of modulation classification. In this letter, a constellation density matrix (CDM) based modulation classification algorithm is proposed to identify different orders of ASK, PSK, and QAM. CDM is formed through local density distribution of the signal’s constellation generated using LabVIEW for a wide range of SNR. Two DL models, ResNet-50 and Inception ResNet V2 are trained through color images formed by filtering the CDM. Classification accuracy achieved demonstrates better performance compared to many existing classifiers in the literature.

Automatic recognition of modulation scheme in blind environment plays a key role in many communication applications. A hierarchical and local density (HLD) approach is proposed to classify eight modulation schemes in a two stage process. In the first stage, the domain of modulation schemes (FSK, ASK, PSK, and QAM) is identified. FSK is identified based on feature extracted through complex envelope of downconverted signal. ASK scheme is identified using linear regression error. PSK and QAM modulation schemes are recognized based on the ratio of sixth and fourth order cumulant. In the latter stage, the order of modulation (ASK, PSK, and QAM) is classified through its respective ideal constellation points. HLD can correctly identify 2ASK, 4ASK, and QPSK modulation schemes in AWGN channel above 8 dB SNR and the other modulation schemes (8ASK, 8PSK, 16QAM, and 64QAM) above 16dB SNR. HLD is implemented in NI labVIEW and validated on the signals generated through PXIe-5673 and received using NI PXIe-5661. The proposed HLD classifier does not require any training to set thresholds as compared to more complex SVM, KNN, and Naive Bayes Classifier based techniques and shows an improved accuracy.

Automatic modulation classification (AMC) has a wide range of applications in the military and civilian areas. In the military, it is used for the extraction of information from unknown intercepted signals and the generation of jamming signals. Civil applications include interference management and spectrum underutilization. To overcome the limitations of traditional methods like maximum likelihood (ML) and feature- based (FB), deep learning (DL) networks have been developed and are being evolved. Following this direction, a convolution neural network (CNN) based AMC method is proposed. The two dimensional Fast Fourier Transform (2D-FFT) is used as a classification feature and a less complex and efficient deep CNN model is designed to classify the modulation schemes of different orders of PSK and QAM. The developed method achieves adequate classification performance for considered five modulation schemes in the AWGN channel.

Blind signal modulation recognition is an essential block for designing a cognitive radio. Different algorithms are developed in the literature, but few are given with detailed implementation. This study proposes a software-defined radio based implementation of blind signal modulation recogniser (BSMR) on field-programmable gate array (FPGA), which works without any prior knowledge of the received signal. The algorithm estimates carrier frequency offset, symbol rate, symbol timing offset, and corrects the signal for these offsets to extract constellation points. It uses clustering structure formed by constellation signature in I/Q plane to detect the modulation for different orders of ASK, PSK, and QAM. The proposed algorithm is deployed on FPGA, using LabVIEW, for a reliable and reconfigurable platform. The algorithm is optimised to use minimum hardware resources and facilitate future up-gradation. The system developed by implementing the algorithm on NI-FlexRIO-7975 FPGA module with NI-5791 adapter detects modulation type in real time without any training. Signals for testing are generated using NI-PXIe-5673 (RF transmitter), and BSMR identifies the modulation type in 81.451 ms under additive white Gaussian noise channel.

Agriculture will face significant challenges in the 21st century, largely due to the need to increase global food supply under the declining availability of soil and water resources and increasing threats from climate change. Nonetheless, these challenges also offer opportunities to develop and promote food and livelihood systems that have greater environmental, economic and social resilience to risk. It is clear that success in meeting these challenges will require both the application of current multidisciplinary knowledge, and the development of a range of technical and institutional innovations.During last two decades, the atmospheric greenhouse gases (GHGs) concentrations have increased markedly. Carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) have increased from 280 ppm, 715 ppb and 270ppb during pre-industrial era (1750 AD) to 385 ppm, 1797 ppb and 322 ppb, respectively in 2008. As on today, The CO2 concentration has exceeded 400 ppm. Increase in temperature can increase crop evapotranspiration and soil nutrient mineralization and salinity, reduce crop duration, fertilizer use efficiency and may affect survival and distribution of pests. Already scarce water resources will be further stressed under expected climatic changes. In the scenario of sea-level rise, the saline area under sea inundation will also extend and influence the crop production. Thus, changing climate is likely to have a significant influence on agriculture and eventually the food security and livelihoods of a large rural population. This paper identifies possible climate change responses that address agricultural production at the plant, and farm, regional scales. Critical components required for the strategic assessment of adaptation capacity and anticipatory adaptive planning is identified and examples of adaptive strategies for a number of key agricultural sectors are provided. Adaptation must be fully consistent with agricultural rural development activities that safeguard food security and increase the provision of sustainable ecosystem services, particularly where opportunities for additional financial flows may exist, such as payments for carbon sequestration and ecosystem conservation. Climate change will affect agriculture and forestry systems through higher temperatures, elevated CO2 concentration, precipitation changes, increased weeds, pests, and disease pressure, and increased vulnerability of organic carbon pools. Benefits of adaptation vary with crop species, temperature and rainfall changes. Useful synergies for adaptation and mitigation in agriculture, relevant to food security exist and should be incorporated into development, and climate policy. Synergistic adaptation strategies to enhance agro-ecosystem and livelihood resilience, including in the face of increased climatic pressures. Ensuring food security without compromising sustainability of land resources under a rapidly growing population and changing climate is among the major challenges of this era. Smart strategies individually offers a magic bullet solution to the foregoing challenges and most of the promising technologies are founded on local knowledge, local and scientific knowledge must be integrated when choosing the most suitable climate-smart technologies and practices for any given agro-ecology.

In the present study, we report the palladium(ii)-catalyzed regioselective ortho-C-H bromination/iodination of challenging arylacetamide derivatives using N-halosuccinimides as halogenating agents. Diverse arylacetamides underwent the regioselective ortho-bromination and iodination of aromatic C-H bonds in the presence of a reactive benzylic C(sp3)-H bond without installing any bulky auxiliaries via unfavorable six-membered metallacycles. Weak coordination, the use of ubiquitous primary amides for challenging C-H functionalization, the simple catalytic system and the wide substrate scope are the key features of this transformation. Further, the halogenated amide derivatives were transformed into a variety of valuable synthons. Detailed mechanistic studies revealed some interesting aspects concerning the reaction pathway. We present for the first time strong evidence for the formation of imidic acid (in situ) from primary amides under Brønsted acid conditions that eventually aids in the stabilization of palladacycles of amide derivatives and drives regioselective C-X bond formation.

Background: The objective of the study was to assess the outcome of various modalities of treatment and evaluation of a low cost technique of percutaneous catheter drainage of liver abscess.Methods: A prospective study of patients with liver abscess was conducted in a tertiary care centre over a period of one year. Since the cost of commercially available catheter (pig tail type) for image guided percutaneous drainage of liver abscess is quite high (approx Rs 800) and considering the fact that a considerable population in eastern Uttar Pradesh is poverty stricken, this study includes an evaluation of low cost technique of percutaneous drainage of liver abscess as a pilot project. In the present study K-90 was used as “low cost drainage” and compared with pig tail catheter drainage.Results: Total 34 patients with liver abscess were enrolled in the study. 31 cases were male and 3 cases were female. 34 cases were subjected to catheter drainage (pig tail catheter, K-90) yielding varying quantities of pus from 300 ml to 2200 ml, depending on the size of the abscess. 15 out of 34 patients underwent tube drainage (K-90) by our innovative trochar cannula system using 5 mm laparoscopic metal trochar.Conclusions: Although, compared to pig tail drainage, K-90 tube drainage is associated with more number of minor complication and prolonged hospital stay, however looking to the advantages and greatly reduced cost its use is probably justified.

A novel method based on constellation structure is proposed to identify PSK and QAM modulation of different orders, in the slow and flat fading channel. The proposed method does not require training for threshold optimization and considers carrier frequency, symbol rate, and phase offset unknown. The symbol rate is estimated using the spectrum of the instantaneous phase of the complex baseband signal. Carrier frequency offset (CFO) is estimated and corrected from the downconverted signal and downsampled to the estimated symbol rate for extraction of constellation points. The phase offset is determined based on the symmetrical structure of constellation. The features extracted using k-medoids are used for classification of the final modulation scheme. Results show that the proposed algorithm outperforms some existing classifiers and offers lower computational complexity compared to algorithms based on subtractive clustering.

A facile and straightforward approach has been developed for the synthesis of alpha-amino diaryl ketones. This strategy utilizes simple, cheap, stable, non-toxic arylacetonitriles as a key starting material and is effectively promoted by N-Bromosuccinimide. This reaction proceeds via the involvement of three consecutive steps in a one-pot manner without isolating any intermediates and displays remarkable functional group tolerance. Gram-scale syntheses of alpha-amino ketone demonstrate the practicality of the protocol.

An efficient and straightforward method has been developed for the synthesis of polysubstituted phenanthridines from simple aryl iodides and alkyl/aryl nitriles via the palladium-catalyzed nucleophilic addition of aryl iodides to nitriles followed by cascade formation of C-C and C-N bonds viz. in situ generated imine directed sequential two fold C-H activation.

Herein we disclose the efficient Pd(II)-catalyzed and regioselective ortho C=H alkenylation of arylacetamide derivatives, viz. weakly coordinating aliphatic primary amides. This protocol utilizes ubiquitous free primary amides as the directing group and circumvents, two troublesome steps of installation and removal of an external auxiliary. This strategy directly enables the incorporation of a synthetically versatile olefin in the products in Moderate to good yields with regio- and. distereoselectivity. The alkenylated acetamides can be easily manipulated and further transformed into a. variety of useful derivatives.

An efficient one‐pot two‐step procedure for the synthesis of unsymmetrical (hetero)aryl 1,2‐diketones has been developed. The reaction proceeds through a palladium‐catalyzed nucleophilic addition of potassium aryltrifluoroborates to aliphatic nitriles followed by a copper‐catalyzed aerobic benzylic C–H oxygenation using molecular oxygen as a green oxidant. This represents the first example of the direct synthesis of unsymmetrical diaryl 1,2‐diketones from arylacetonitriles. This method utilizes inexpensive, stable, nontoxic, and readily available starting materials, is highly effective in the presence of both electron‐rich and electron‐poor nitriles and aryltrifluoroborates, and tolerates a wide variety of functional groups. The synthetic utility of this transformation was shown by increasing the scale of the reaction and by carrying out the one‐pot protocol for the preparation of quinoxaline and benzimidazole derivatives. A plausible reaction mechanism has also been proposed.

An efficient and environmentally friendly synthetic approach toward functionalized dihydropyrrole derivatives is reported. The developed protocol proceeds via chemoselective intramolecular N-C bond formation of alkylimidates through 1,5-hydrogen atom transfer from in situ generated imidate N-radicals. The major advantage of this designed strategy lies in the choice of starting materials, mild reaction conditions, high chemo- and diastereoselectivity, clean source of energy, and good functional group tolerance. Further, 4-iododihydropyrroles could be easily transformed into a variety of useful derivatives.