The Iowa Gambling Task (IGT) has become the classical experiment with many studies of cognitive decision models. In this work, we explore whether Autoregressive Integrated Moving Average (ARIMA) models and Recurrent Neural Networks (RNN) in time series analysis can be applied to extract the decision features of IGT participants. The simulation results of IGT show that both models can capture the selection characteristics of participants and make subsequent selection prediction accordingly. Furthermore, the RNN containing selection features with different preferences can represent the corresponding participants to participate in the IGT experiment.
{"title":"ARIMA and RNN for Selection Sequences Prediction in Iowa Gambling Task","authors":"Yuemeng Guo, Sensen Song, Hanbo Xie, Xiaoxue Gao, Jianlei Zhang","doi":"10.1109/AISP53593.2022.9760558","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760558","url":null,"abstract":"The Iowa Gambling Task (IGT) has become the classical experiment with many studies of cognitive decision models. In this work, we explore whether Autoregressive Integrated Moving Average (ARIMA) models and Recurrent Neural Networks (RNN) in time series analysis can be applied to extract the decision features of IGT participants. The simulation results of IGT show that both models can capture the selection characteristics of participants and make subsequent selection prediction accordingly. Furthermore, the RNN containing selection features with different preferences can represent the corresponding participants to participate in the IGT experiment.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"254 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75762352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agricultural economics plays a vital role in the economics sector of development as because large portion of a country’s population depends on agriculture sector. Higher agricultural productivity also increases the income of the rural population, raising demand for industrial output. Almost 70 percent of the India’s population depends on the agriculture sector. Agricultural development makes a critical contribution to overall economic growth in many developing countries. As farmers’ incomes rise, so does their demand both for farm inputs and services, and for non-farm goods. Increased agricultural production also leads to increased demand for processing facilities. There are many factors, which slow this development. So Smart farming is a management concept using modern technology to increase the quantity and quality of agricultural products. Today’s agriculture routinely uses sophisticated technologies such as robots, temperature and moisture sensors, aerial images, and GPS technology. These advanced devices and precision agriculture and robotic systems allow businesses to be more profitable, efficient, safer, and more environmentally friendly. The main objective of this paper is to design a smartphone controlled green house with advanced monitoring system for controlling various parameters like temperature control, sob moisture & humidity control of any agricultural process. The prototype presented in this paper can monitor temperature, soil and humidity through sensors, IOT & ISP.
{"title":"Smart Green House for Controlling & Monitoring Temperature, Soil & Humidity Using IOT","authors":"Akash Saha, Priyanka Sarkar Das, Bipasha Chakrabarti Banik","doi":"10.1109/AISP53593.2022.9760541","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760541","url":null,"abstract":"Agricultural economics plays a vital role in the economics sector of development as because large portion of a country’s population depends on agriculture sector. Higher agricultural productivity also increases the income of the rural population, raising demand for industrial output. Almost 70 percent of the India’s population depends on the agriculture sector. Agricultural development makes a critical contribution to overall economic growth in many developing countries. As farmers’ incomes rise, so does their demand both for farm inputs and services, and for non-farm goods. Increased agricultural production also leads to increased demand for processing facilities. There are many factors, which slow this development. So Smart farming is a management concept using modern technology to increase the quantity and quality of agricultural products. Today’s agriculture routinely uses sophisticated technologies such as robots, temperature and moisture sensors, aerial images, and GPS technology. These advanced devices and precision agriculture and robotic systems allow businesses to be more profitable, efficient, safer, and more environmentally friendly. The main objective of this paper is to design a smartphone controlled green house with advanced monitoring system for controlling various parameters like temperature control, sob moisture & humidity control of any agricultural process. The prototype presented in this paper can monitor temperature, soil and humidity through sensors, IOT & ISP.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"94 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84357332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-12DOI: 10.1109/AISP53593.2022.9760525
Divyesh Ranpariya, Parin Parikh, Manish I. Patel, Ruchi Gajjar
Pneumonia is a lung infection caused by bacteria, viruses, or fungi. It is one of the deadliest lung diseases among children under the age of five. An expert or radiologist can usually diagnose the condition using X-ray images of the chest. The use of machine learning in medical image processing helps to improve detection accuracy. This study aims to develop and present a combined Deep Learning model for classifying patients with Pneumonia disease based on chest X-rays. Three separate models are trained for the chest X-ray dataset in the proposed implementation, the first of which is the custom Convolutional Neural Network model. The other two models are Xception and EfficientNetB4. Various data augmentation and pre-processing methods are used, along with hyperparameter tuning. A combined model is created by assigning weights to the trained models based on their recall and accuracy values, and the classification results are obtained by a polling mechanism at the output, which gives an accuracy of 98.00%. The proposed work outperforms the existing literature in terms of several performance parameters.
{"title":"A CNN based Hybrid Model for Pneumonia Classification Using Chest X-ray Images","authors":"Divyesh Ranpariya, Parin Parikh, Manish I. Patel, Ruchi Gajjar","doi":"10.1109/AISP53593.2022.9760525","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760525","url":null,"abstract":"Pneumonia is a lung infection caused by bacteria, viruses, or fungi. It is one of the deadliest lung diseases among children under the age of five. An expert or radiologist can usually diagnose the condition using X-ray images of the chest. The use of machine learning in medical image processing helps to improve detection accuracy. This study aims to develop and present a combined Deep Learning model for classifying patients with Pneumonia disease based on chest X-rays. Three separate models are trained for the chest X-ray dataset in the proposed implementation, the first of which is the custom Convolutional Neural Network model. The other two models are Xception and EfficientNetB4. Various data augmentation and pre-processing methods are used, along with hyperparameter tuning. A combined model is created by assigning weights to the trained models based on their recall and accuracy values, and the classification results are obtained by a polling mechanism at the output, which gives an accuracy of 98.00%. The proposed work outperforms the existing literature in terms of several performance parameters.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"36 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85166304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays, ‘text in image’ steganography is utilised in a variety of applications like military, surveillance, and remote sensing etc., in order to keep the secret information secure. This paper is presenting the unique steganography algorithm for improving data security, visual quality, quality metrics and withstand the attacks of the stego image. In proposed algorithm, the security of the confidential information is improved by utilizing the SLICE encryption algorithm. It is used to generate the cipher data from the confidential data. The alexnet is introduced in this paper to detect the facial area in human cover image. A total of 1987 images are used to train the network and got maximum accuracy. Contour-let transform is utilized to decomposition of alexnet output. The “random pixel embedding” (RPE) technique is utilized to embed the confidential data in facial area of the sub-band. The combination of alexnet and contourlet transform is used to generate good visual quality of the stego image. The proposed algorithm produces a stego image that has better visual quality, security, metric-values and withstand attacks compared to recent methods.
{"title":"Secure ‘Text in Image’ Steganography Scheme Based on Alexnet and Contour-Let Transform","authors":"Lingamallu Naga Srinivasu, Vijayaraghavan Veeramani","doi":"10.1109/AISP53593.2022.9760538","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760538","url":null,"abstract":"Nowadays, ‘text in image’ steganography is utilised in a variety of applications like military, surveillance, and remote sensing etc., in order to keep the secret information secure. This paper is presenting the unique steganography algorithm for improving data security, visual quality, quality metrics and withstand the attacks of the stego image. In proposed algorithm, the security of the confidential information is improved by utilizing the SLICE encryption algorithm. It is used to generate the cipher data from the confidential data. The alexnet is introduced in this paper to detect the facial area in human cover image. A total of 1987 images are used to train the network and got maximum accuracy. Contour-let transform is utilized to decomposition of alexnet output. The “random pixel embedding” (RPE) technique is utilized to embed the confidential data in facial area of the sub-band. The combination of alexnet and contourlet transform is used to generate good visual quality of the stego image. The proposed algorithm produces a stego image that has better visual quality, security, metric-values and withstand attacks compared to recent methods.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"2 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85308998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-12DOI: 10.1109/AISP53593.2022.9760575
Zonunmawii, L. L. K. Singh, S. Chattopadhyay, Abhijyoti Ghosh
A circular patch antenna is one of the popular candidates in the planer antenna family. Circular patch antennas are used in many modern applications. A circular patch antenna with a strip loaded at the top is proposed and investigated for enhancement of co-polarization gain as well as the separation between co-polarized to cross-polarized radiation. The rectangular strip is placed in such a way that the overall patch dimension is not increasing. The length of the strip is same as the diameter of the circular patch. A copolarization gain of 7.5 dBi with co-polarization to crosspolarization separation of 23 dB is attains through the investigated structure.
{"title":"Rectangular Strip Loaded Circular Patch Antenna for Simultaneous Improvement of Co polar Gain and Co Polarization to Cross Polarization radiation Separation","authors":"Zonunmawii, L. L. K. Singh, S. Chattopadhyay, Abhijyoti Ghosh","doi":"10.1109/AISP53593.2022.9760575","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760575","url":null,"abstract":"A circular patch antenna is one of the popular candidates in the planer antenna family. Circular patch antennas are used in many modern applications. A circular patch antenna with a strip loaded at the top is proposed and investigated for enhancement of co-polarization gain as well as the separation between co-polarized to cross-polarized radiation. The rectangular strip is placed in such a way that the overall patch dimension is not increasing. The length of the strip is same as the diameter of the circular patch. A copolarization gain of 7.5 dBi with co-polarization to crosspolarization separation of 23 dB is attains through the investigated structure.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"136 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80608407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-12DOI: 10.1109/AISP53593.2022.9760561
N. Reddy, D. Panda
In this article, we have investigated the Gate misalignment effects on the performance of the Double gate hetero dielectric TFET(DGH-TFET) biosensor device in terms of subthreshold swing(SS), ON-current (I$_{mathbf{on}}$), OFF Current (I$_{mathbf{off}}$), Threshold voltage (Vth) and the ratio of ON-current (Ion) to the OF-current (I$_{mathbf{on}} {/mathbf{I}}_{mathbf{off}}$). For the first time, we have thoroughly investigated the misalignment of gate electrodes effect on the sensitivity of the double gate TFET based biosensor where actually considered the symmetric gate structure with no deviation in the gate alignment, however practically which is not possible. With due respect to the investigation carried in this work along with the sensitivity analysis, it observed that position and the alignment of both top and bottom gate show significant impact on the sensitivity of the biosensor. The misalignment of the double gate is executed by altering the position of the top gate in overlapped state and underlapped state concerning the bottom gate of the proposed double gate TFET biosensor. The underlapped gate structure degrades the performance device compared to the symmetric aligned gate structure, but the overlapped gate structure improves the device’s overall performance. The investigation is carried by taking the misalignment effect in the range of 10 nm in both overlapped and underlapped cases. The underlapped alignment gate structure falls of the subthreshold sensitivity by 30%, where the overlapped gate structure become the advantage for shoot up the sensitivity of the doublet gate hetero dielectric TFET biosensor by 25% and the underlapped gate structure improves the threshold voltage sensitivity 45% on an average.
{"title":"Dielectric Modulated Double Gate Hetero Dielectric TFET (DM-DGH-TFET) Biosensors: Gate Misalignment Analysis on Sensitivity","authors":"N. Reddy, D. Panda","doi":"10.1109/AISP53593.2022.9760561","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760561","url":null,"abstract":"In this article, we have investigated the Gate misalignment effects on the performance of the Double gate hetero dielectric TFET(DGH-TFET) biosensor device in terms of subthreshold swing(SS), ON-current (I$_{mathbf{on}}$), OFF Current (I$_{mathbf{off}}$), Threshold voltage (Vth) and the ratio of ON-current (Ion) to the OF-current (I$_{mathbf{on}} {/mathbf{I}}_{mathbf{off}}$). For the first time, we have thoroughly investigated the misalignment of gate electrodes effect on the sensitivity of the double gate TFET based biosensor where actually considered the symmetric gate structure with no deviation in the gate alignment, however practically which is not possible. With due respect to the investigation carried in this work along with the sensitivity analysis, it observed that position and the alignment of both top and bottom gate show significant impact on the sensitivity of the biosensor. The misalignment of the double gate is executed by altering the position of the top gate in overlapped state and underlapped state concerning the bottom gate of the proposed double gate TFET biosensor. The underlapped gate structure degrades the performance device compared to the symmetric aligned gate structure, but the overlapped gate structure improves the device’s overall performance. The investigation is carried by taking the misalignment effect in the range of 10 nm in both overlapped and underlapped cases. The underlapped alignment gate structure falls of the subthreshold sensitivity by 30%, where the overlapped gate structure become the advantage for shoot up the sensitivity of the doublet gate hetero dielectric TFET biosensor by 25% and the underlapped gate structure improves the threshold voltage sensitivity 45% on an average.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"1 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89379678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-12DOI: 10.1109/AISP53593.2022.9760683
S. Romera Joan, T. Manimekalai, T. Laxmikandan
In this paper we propose an Artificial Neural Network (ANN) based approach to reduce the computational complexity on solving the combinatorial optimization problem of resource allocation in a downlink multicarrier non-orthogonal multiple access (MC-NOMA) network aided by coordinated direct and relay transmission (CDRT) in the presence of underlay cognitive radio (CR) users. The combinatorial optimization involves optimal user pairing, relay selection, subcarrier pairing and assignment which, when solved by exhaustive search, incurs a high computational complexity and processing delay. We show that an ANN trained by stochastic gradient descent (SGD) based supervised learning algorithm can do the same with low complexity and can provide more than 50% reduction in processing delay.
{"title":"Machine Learning Aided Resource Allocation in a Downlink Multicarrier NOMA network with Coordinated Direct and Relay Transmission","authors":"S. Romera Joan, T. Manimekalai, T. Laxmikandan","doi":"10.1109/AISP53593.2022.9760683","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760683","url":null,"abstract":"In this paper we propose an Artificial Neural Network (ANN) based approach to reduce the computational complexity on solving the combinatorial optimization problem of resource allocation in a downlink multicarrier non-orthogonal multiple access (MC-NOMA) network aided by coordinated direct and relay transmission (CDRT) in the presence of underlay cognitive radio (CR) users. The combinatorial optimization involves optimal user pairing, relay selection, subcarrier pairing and assignment which, when solved by exhaustive search, incurs a high computational complexity and processing delay. We show that an ANN trained by stochastic gradient descent (SGD) based supervised learning algorithm can do the same with low complexity and can provide more than 50% reduction in processing delay.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"124 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87956427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-12DOI: 10.1109/AISP53593.2022.9760534
Akula Jaswanth, N. Gupta, A. Mishra, Y. Hum
Change detection is an essential task to study the changes of earth surfaces in remote sensing. It is extensively being investigated in SAR images nowadays. However, SAR images suffer from the speckle noise, which is a major drawback. To address the speckle noise problem, we propose the convolutional neural network with curvelet transform. As curvelet transform can suppress the noise, it is applied in preclassification, where the difference image is transformed using curvelet. Further, transformed image is classified by hierarchical fuzzy c-means (FCM) clustering, where each pixel is classified into different classes like changed class and unchanged class. From the preclassification, patches centered at the pixels belonging to these classes are generated as the training samples. Moreover, these training samples are passed through median filter before sending them to the convolutional neural network (CNN). The median filter helps in the reduction of noise. After the training of the CNN model, the trained model classifies the image pixels and provides the final binary change map. The experimental results obtained from the two SAR datasets confirm the effectiveness of the proposed method.
{"title":"Change Detection of SAR images based on Convolution Neural Network with Curvelet Transform","authors":"Akula Jaswanth, N. Gupta, A. Mishra, Y. Hum","doi":"10.1109/AISP53593.2022.9760534","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760534","url":null,"abstract":"Change detection is an essential task to study the changes of earth surfaces in remote sensing. It is extensively being investigated in SAR images nowadays. However, SAR images suffer from the speckle noise, which is a major drawback. To address the speckle noise problem, we propose the convolutional neural network with curvelet transform. As curvelet transform can suppress the noise, it is applied in preclassification, where the difference image is transformed using curvelet. Further, transformed image is classified by hierarchical fuzzy c-means (FCM) clustering, where each pixel is classified into different classes like changed class and unchanged class. From the preclassification, patches centered at the pixels belonging to these classes are generated as the training samples. Moreover, these training samples are passed through median filter before sending them to the convolutional neural network (CNN). The median filter helps in the reduction of noise. After the training of the CNN model, the trained model classifies the image pixels and provides the final binary change map. The experimental results obtained from the two SAR datasets confirm the effectiveness of the proposed method.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"172 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79527360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-12DOI: 10.1109/AISP53593.2022.9760687
Bujjibabu Penumutchi, Harichandraprasad Satti, Y. Ykuntam
RADAR (Radio Detection and Ranging) is an electromagnetic device used for detecting and locating objects from their reoccurrence signals. The received signal is then dealt with for information abstraction, like., target detection besides the velocity of the target. In CW radars the frequency measurement is done by de-modulating the received signal with respect to a transmitting. The matching velocity can be anticipated by passing the Doppler spectra through a filter bank. Finding the frequency in a pulse radar system is difficult than in CW radar. Thus, a better approach is the Doppler Processing state machine. The received signal is processed for required information. Detection is done by an algorithm called CFAR (Constant False Alarm Rate). In CFAR, a certain power threshold is determined. If the threshold is too high, then fewer targets are detected and conversely, if the threshold is too low then the false detection rate will increase. This threshold-based algorithm detects false targets in addition to original ones and to overcome this, a method called Cell Averaging Constant False Alarm Rate (CACFAR) would be used. Another parameter velocity is determined by Doppler frequency. The architecture is implemented in MATLAB/SIMULINK using Xilinx System Generator. To implement this model, three processing modules are required. Upon successful simulation, respective Verilog HDL code is generated and that code is run to observe design constraints like area, power, and delay. For CA-CFAR module at 2.5GHz frequency, the On-Chip power is 8.763W. At 0.95V, low On-chip power of 3.932W was observed at the frequency of 4GHz.
雷达(Radio Detection and Ranging，简称RADAR)是一种电磁装置，用于根据物体的再出现信号对其进行探测和定位。然后对接收到的信号进行信息抽象处理，如。，目标检测，除了目标的速度。在连续波雷达中，频率测量是通过相对于发射信号对接收信号进行解调来完成的。通过多普勒谱通过滤波器组可以预测匹配速度。在脉冲雷达系统中，频率的确定比在连续波雷达系统中困难。因此，更好的方法是多普勒处理状态机。对接收到的信号进行处理以获取所需的信息。检测是由一种叫做CFAR(恒定虚警率)的算法完成的。在CFAR中，确定了一定的功率阈值。如果阈值过高，则检测到的目标较少，反之，如果阈值过低，则误检率会增加。这种基于阈值的算法除了检测原始目标外还检测假目标，为了克服这一点，将使用一种称为单元平均恒定虚警率(CACFAR)的方法。另一个参数速度是由多普勒频率决定的。该体系结构在MATLAB/SIMULINK中使用Xilinx System Generator实现。要实现这个模型，需要三个处理模块。仿真成功后，生成相应的Verilog HDL代码，并运行该代码以观察设计约束，如面积，功率和延迟。对于频率为2.5GHz的CA-CFAR模块，片上功率为8.763W。在0.95V时，在4GHz频率下观察到3.932W的低片上功率。
{"title":"An efficient Pulse Doppler Radar block level modeling with Xilinx System Generator","authors":"Bujjibabu Penumutchi, Harichandraprasad Satti, Y. Ykuntam","doi":"10.1109/AISP53593.2022.9760687","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760687","url":null,"abstract":"RADAR (Radio Detection and Ranging) is an electromagnetic device used for detecting and locating objects from their reoccurrence signals. The received signal is then dealt with for information abstraction, like., target detection besides the velocity of the target. In CW radars the frequency measurement is done by de-modulating the received signal with respect to a transmitting. The matching velocity can be anticipated by passing the Doppler spectra through a filter bank. Finding the frequency in a pulse radar system is difficult than in CW radar. Thus, a better approach is the Doppler Processing state machine. The received signal is processed for required information. Detection is done by an algorithm called CFAR (Constant False Alarm Rate). In CFAR, a certain power threshold is determined. If the threshold is too high, then fewer targets are detected and conversely, if the threshold is too low then the false detection rate will increase. This threshold-based algorithm detects false targets in addition to original ones and to overcome this, a method called Cell Averaging Constant False Alarm Rate (CACFAR) would be used. Another parameter velocity is determined by Doppler frequency. The architecture is implemented in MATLAB/SIMULINK using Xilinx System Generator. To implement this model, three processing modules are required. Upon successful simulation, respective Verilog HDL code is generated and that code is run to observe design constraints like area, power, and delay. For CA-CFAR module at 2.5GHz frequency, the On-Chip power is 8.763W. At 0.95V, low On-chip power of 3.932W was observed at the frequency of 4GHz.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"291 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90623831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid growth in computational density and better computing, as well as the rise of worldwide knowledge transfer, have driven significant technological progress in the field of Biotechnology over the past few years. This is one of the primary technological advances expected to further transform this industry into low-cost single-board computers. So far, there hasn’t been a comprehensive analysis of the current adoption of these devices, as well as a general guide to assist researchers in incorporating them into their study. This study focuses on Raspberry Pi applicability in disciplines such as biotechnology, biosensors, bioprinters, biological, biosignal, bioaerosol, bioengineering, biochemical, biometrics and bioreactor. Since its initial introduction in 2012, the Raspberry Pi has gained popularity among a wide range of disciplines, as well as biologists in the lab, field and classrooms. A wide range of applications are available, from basic solutions such as nest box monitoring, wildlife camera capture, high-throughput behavioral recording, large-scale plant phenotyping, underwater video surveillance, closed-loop operant, to customized custom-built devices such as autonomous ecosystem monitoring. Despite the diversity of its applications, the Raspberry Pi has received only limited attention from the scientific community.
{"title":"Applications of Raspberry Pi in Bio-Technology: A Review","authors":"Suseela Vappangi, Naveen Kumar Penjarla, Sudha Ellison Mathe, Hari Kishan Kondaveeti","doi":"10.1109/AISP53593.2022.9760691","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760691","url":null,"abstract":"The rapid growth in computational density and better computing, as well as the rise of worldwide knowledge transfer, have driven significant technological progress in the field of Biotechnology over the past few years. This is one of the primary technological advances expected to further transform this industry into low-cost single-board computers. So far, there hasn’t been a comprehensive analysis of the current adoption of these devices, as well as a general guide to assist researchers in incorporating them into their study. This study focuses on Raspberry Pi applicability in disciplines such as biotechnology, biosensors, bioprinters, biological, biosignal, bioaerosol, bioengineering, biochemical, biometrics and bioreactor. Since its initial introduction in 2012, the Raspberry Pi has gained popularity among a wide range of disciplines, as well as biologists in the lab, field and classrooms. A wide range of applications are available, from basic solutions such as nest box monitoring, wildlife camera capture, high-throughput behavioral recording, large-scale plant phenotyping, underwater video surveillance, closed-loop operant, to customized custom-built devices such as autonomous ecosystem monitoring. Despite the diversity of its applications, the Raspberry Pi has received only limited attention from the scientific community.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"45 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90631902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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