A survey of Advanced Spectrum Sensing Techniques in Cognitive Radio Networks

Radio spectrum resource demand has increased extraordinarily due to emerging broadband wireless applications which have resulted to critical spectrum shortage problem.  Cognitive radio technology is promising technology that can effectively use unutilized licensed spectrum and can solve spectrum shortage problem. Spectrum sensing is the key element of cognitive radio network to find unused spectrum. Hence effective and accurate spectrum sensing is compulsory for cognitive radio network. This paper is a survey of various advanced spectrum sensing techniques. This paper covers basics of spectrum sensing along with its classification and challenges of spectrum sensing

Automatic Classification of Medicinal Plants Using State-Of-The-Art Pre-Trained Neural Networks

Now a days every mankind is suffering due to infections. Ayurveda, the science of life helped to take preventive measures which boost our immunity.  It is plant-based science. Many medicinal plants found useful in daily life of common people for boosting immunity. Identifying the plant species having medicinal plant is challenging, it requires botanical expert. In the process of manual identification, botanical experts use various plant features as the identification keys, which are examined adaptively and progressively to identify plant species. The shortage of experts and trained taxonomist created global taxonomic impediment problem which is one of the major challenges.  Various researchers have worked in the field of automatic classification of plants since the last decade. The leaf is considered as primary input as it is available throughout the whole year. The research paper mainly focuses on the study of transfer learning approach for medicinal plant classification, which reuse already developed model at the starting point for model on a second task. Transfer learning approach is a black box approach used for image classification and many more applications by extracting features from an image. Some of the transfer learning models are MobileNet-V1, VGG-19, ResNet-50, VGG-16. Here it uses Mendeley dataset of Indian medicinal plant species which is freely available. Output layer classifies the species of leaves. The result provides evaluation and variations of above listed features extracted models. MobileNetV1 achieves maximum accuracy of 98%

Accuracy Optimization of Centrality Score Based Community Detection

Various concepts can be represented as a graph or the network. The network representation helps to characterize the varied relations between a set of objects by taking each object as a vertex and the interaction between them as an edge. Different systems can be modelled and analyzed in terms of graph theory. Community structure is a property that seems to be common to many networks. The division of the some objects into groups within which the connections or relations are dense, and the connections with other objects are sparser. Various research and data points proves that many real world networks has these communities or groups or the modules that are sub graphs with more edges connecting the vertices of the same group and comparatively fewer links joining the outside vertices. The groups or the communities exhibit the topological relations between the elements of the underlying system and the functional entities. The proposed approach is to exploit the global as well as local information about the network topologies. The authors propose a hybrid strategy to use the edge centrality property of the edges to find out the communities and use local moving heuristic to increase the modularity index of those communities. Such communities can be relevantly efficient and accurate to some applications. DOI: 10.17762/ijritcc2321-8169.15073

Hypoglycemic effects of Lagenaria siceraria, Cynodon dactylon and Stevia rebaudiana extracts

Introduction: The aim of the current analysis was to judge the hypoglycemic action of the phyto-extracts of Lagenaria siceraria, Cynodon dactylon and Stevia rebaudiana using suitable in vitro approaches. Methods: The hypoglycemic activity of the phyto-material extracts was evaluated by employing various in-vitro methods namely glucose diffusion, amylolysis kinetics and glucose adsorption capacity. Results: The extracts of L. siceraria, C. dactylon and S. rebaudiana exhibited glucose dialysis retardation indices (GDRI) of 48.14%, 37.03% and 29.62%, respectively at 60 minutes which were reduced to 15.78%, 10.52% and 18.42%, respectively at 120 minutes. All the plant extracts used in the study adsorbed glucose and their adsorptions markedly enhanced with increase in sugar concentration. Conclusion: From the outcome of the assay it can be concluded that the extracts of L. siceraria, C. dactylon and S. rebaudiana have hypoglycemic activity as observed in various in-vitro assays. However, the beneficial actions require to be verified by adopting various in vivo techniques along with clinical trials for their efficient use as potential remedial moiety

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Timing Considerations in Logic Arrays and Their Importance to Self Timed Digital Circuits

This paper presents a method for the design of self timed circuits on an integrated circuit that takes advantage of certain temporal constraints that are realizable in logic arrays. This method of design recognizes two distinct environments for circuits, the local environment and the global environment and further recognizes that the assumptions regarding the temporal characteristics of the system that may be valid in the local environment may not be valid in the global environment. This paper shows how self timed circuits can be systematically designed on a single chip using assumptions reasonable for components on a single chip. This paper is based on our work on Structured Logic Arrays (SLAs) and first explains the intricacies of the temporal constraints implemented in the structured array and then shows how one can take advantage of these constraints in the design of self timed circuits. In this structure, for example, it is possible to design an asynchronous sequential state machine with non adjacent transitions without getting into hazardous conditions. What is presented is related to Petri nets and their realization in circuits. The circuits that are designed using this method are very regular in structure and are efficient in utilization of chip area. Furthermore, fairly large integrated circuits can be designed relatively fast using this method. Examples of some chip designs are presented

An Asynchronous Logic Array

A new asynchronous logic array for the general synthesis of asynchronous digital circuits is presented. The parallel and asynchronous nature of the array gives the realized systems the speed and characteristics of hardwired circuits even though they are implemented in a uniform diode array with appropriate terminating circuits. The logic array is particularly suited for implementing control structures and should help extend the field of micro-control to asynchronous and parallel computers

DEVELOPMENT OF AN AUTOMATIC VARIABLE RATE SPRAYING SYSTEM BASED ON CANOPY CHARACTERIZATION USING ARTIFICIAL INTELLIGENCE

Spraying on tree crops must consider the canopy's structural features to maximize its effectiveness. The main drawbacks to VRI technology include the complexity of successfully implementing it and the lack of evidence that it assures better performance in net profit or water savings. Hence, a novel framework based on canopy characterization was presented in this research for an automatic variable-rate spraying system. The first phase was collecting the data, and the next was cleaning it to eliminate redundant information. The pre-treated data are then entered into the Crest- Stride-wise Regression Framework we devised, where we extract the canopy features and evaluate additional parameters. In addition, our proposed model automatically predicts the nozzle's flow rate and pressure based on a threshold value. Thus, this research shows that the recommended strategy achieves 99.98% accuracy, 99.99% precision, 99.99% F1 score, and 99.99% recall. As a result, our study enables safer and more efficient spraying distribution in the agricultural sector