In this paper, an enhanced greedy bit and power allocation algorithms for orthogonal frequency division multiplexing (OFDM) communication systems are introduced. These algorithms combine low complexity greedy power al...In this paper, an enhanced greedy bit and power allocation algorithms for orthogonal frequency division multiplexing (OFDM) communication systems are introduced. These algorithms combine low complexity greedy power allocation algorithms with a simplified maximum ratio combining (MRC) precoding technique at the transmitter for maximizing the average data throughput of OFDM communication systems. Results of computer simulations show that precoding is an effective technique for improving the throughput performance of the proposed bit and power allocation algorithms.展开更多
The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia technology.High-quality images require well-tuned communication ...The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia technology.High-quality images require well-tuned communication standards.The Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and integrity.The data need a space away fromunauthorized access,modification,or deletion.These requirements are to be fulfilled by digital image watermarking and encryption.This paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication standard.It introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver side.The proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD watermarking.In addition,image encryption is implemented based on chaos and DNA encoding.The encrypted watermarked images are then transmitted through the wireless SC-FDMA system.The linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted images.Two subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this paper.The study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked images.In addition,different variants of SC-FDMA based on the Discrete Wavelet Transform(DWT),Discrete Cosine Transform(DCT),and Fast Fourier Transform(FFT)are considered and compared for the image communication task.The simulation results and comparison demonstrate clearly that DWT-SC-FDMAis better suited to the transmission of the digital images in the case of PedestrianAchannels,while the DCT-SC-FDMA is better suited to the transmission of the digital images in the case of Vehicular A channels.展开更多
With the emergence of the COVID-19 pandemic,the World Health Organization(WHO)has urged scientists and industrialists to exploremodern information and communication technology(ICT)as a means to reduce or even eliminat...With the emergence of the COVID-19 pandemic,the World Health Organization(WHO)has urged scientists and industrialists to exploremodern information and communication technology(ICT)as a means to reduce or even eliminate it.The World Health Organization recently reported that the virus may infect the organism through any organ in the living body,such as the respiratory,the immunity,the nervous,the digestive,or the cardiovascular system.Targeting the abovementioned goal,we envision an implanted nanosystem embedded in the intra living-body network.The main function of the nanosystem is either to perform diagnosis and mitigation of infectious diseases or to implement a targeted drug delivery system(i.e.,delivery of the therapeutic drug to the diseased tissue or targeted cell).The communication among the nanomachines is accomplished via communication-based molecular diffusion.The control/interconnection of the nanosystem is accomplished through the utilization of Internet of bio-nano things(IoBNT).The proposed nanosystem is designed to employ a coded relay nanomachine disciplined by the decode and forward(DF)principle to ensure reliable drug delivery to the targeted cell.Notably,both the sensitivity of the drug dose and the phenomenon of drug molecules loss before delivery to the target cell site in long-distance due to the molecules diffusion process are taken into account.In this paper,a coded relay NM with conventional coding techniques such as RS and Turbo codes is selected to achieve minimum bit error rate(BER)performance and high signal-to-noise ratio(SNR),while the detection process is based on maximum likelihood(ML)probability and minimum error probability(MEP).The performance analysis of the proposed scheme is evaluated in terms of channel capacity and bit error rate by varying system parameters such as relay position,number of released molecules,relay and receiver size.Analysis results are validated through simulation and demonstrate that the proposed scheme can significantly improve delivery performance of the desirable drugs in the molecular communication system.展开更多
The role of religious groups in the political field has arised in Africa in 1990s and despite being less visible it is still active. In fact, this is the climax of a very old process in Africa. Religion always partici...The role of religious groups in the political field has arised in Africa in 1990s and despite being less visible it is still active. In fact, this is the climax of a very old process in Africa. Religion always participates in politics and vice versa. It is therefore not surprising that religious groups take into account political preoccupations in their communication processes. Sometimes they get involved in political debates, particularly when the questions reveal very important choices for the Nation, as it was the case in 1960s and 1990s. But mostly, they get involved in the social field which is, for them, the key to have one foot in political field. However, beyond these considerations, this paper intends to show how the use of media by religious institutions is closely related to their understanding of the relationship between the religion as an institution, and the society.展开更多
This paper presents results from investigation of cultural transformations exhibited by Wasukuma youth in regard to getting fiancées in Ngasamo ward, Busega district, Simiyu region, Tanzania. The Main Objective w...This paper presents results from investigation of cultural transformations exhibited by Wasukuma youth in regard to getting fiancées in Ngasamo ward, Busega district, Simiyu region, Tanzania. The Main Objective was to assess the manner former Wasukuma young men used mega-stone objects in selecting prospective fiancées and compare with the current trend of using media in some areas of Bariadi district, Simiyu region. Specific Objectives included the following: to relocate tangible cultural heritage resources (mega-stones) used by Wasukuma young men in former times for getting fiancées in Simiyu region;to identify electronic media used by Wasukuma young men of today to communicate in a bid to get fiancées in Simiyu region;and to provide suggestions for pertinent protection, conservation as well as presentation of cultural heritage resources. Such investigation was carried out through surveys that included field observation, documentation together with records for Global Positioning System (GPS) coordinates per surveyed locality and key informant interviews. Results from the study identified granite rock boulders that were used as grinding stones for cereals such that they formed grinding hollows. Besides production of flour for making food like stiff porridge or soft porridge, such grinding hollows were used by youth of former times to identify hard working young ladies who could be useful for becoming life partners. Such cultural heritage assets need sustainable preservation as well as conservation plans in line with Antiqui-ties Act, Antiquities Rules and Monuments of 1980, Cultural Policy of 1997 together with Antiquities Policy of 2008. On the other hand, today’s youth in Bariadi area, Simiyu region and elsewhere in Usukuma areas, for instance, Kwimba district in Mwanza region used such mega-stones with the same purposes. However, currently, youth are using Information Communication Technology (ICT), for example, electronic media through television, mobile phones and the like to communicate with young ladies so as to build a permanent bond that could culminate to marriage.展开更多
Authentication of the digital image has much attention for the digital revolution.Digital image authentication can be verified with image watermarking and image encryption schemes.These schemes are widely used to prot...Authentication of the digital image has much attention for the digital revolution.Digital image authentication can be verified with image watermarking and image encryption schemes.These schemes are widely used to protect images against forgery attacks,and they are useful for protecting copyright and rightful ownership.Depending on the desirable applications,several image encryption and watermarking schemes have been proposed to moderate this attention.This framework presents a new scheme that combines a Walsh Hadamard Transform(WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding(DRPE).First,on the sender side,the secret medical image is encrypted using DRPE.Then the encrypted image is watermarking based on WHT.The combination between watermarking and encryption increases the security and robustness of transmitting an image.The performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index(SSIM),Peak Signal-to-Noise Ratio(PSNR),Normalized cross-correlation(NC),and Feature Similarity Index(FSIM).展开更多
Effective medical diagnosis is dramatically expensive,especially in third-world countries.One of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of me...Effective medical diagnosis is dramatically expensive,especially in third-world countries.One of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant challenge.Hence,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific tasks.Unfortunately,experimentation and utilization of different models of transfer learning do not achieve satisfactory results.In this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related diseases.This model is based on classification,denoising autoencoder,and transfer learning.Firstly,pre-processing is employed to prepare the medical images.It depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis process.Then,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect pneumonia.The proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray images.Finally,a comparative study is introduced for the classification performance with and without the denoising process.The proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and testing.To demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN models.The achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the existing diagnosis models.展开更多
Corona Virus Disease-2019(COVID-19)continues to spread rapidly in the world.It has dramatically affected daily lives,public health,and the world economy.This paper presents a segmentation and classification framework ...Corona Virus Disease-2019(COVID-19)continues to spread rapidly in the world.It has dramatically affected daily lives,public health,and the world economy.This paper presents a segmentation and classification framework of COVID-19 images based on deep learning.Firstly,the classification process is employed to discriminate between COVID-19,non-COVID,and pneumonia by Convolutional Neural Network(CNN).Then,the segmentation process is applied for COVID-19 and pneumonia CT images.Finally,the resulting segmented images are used to identify the infected region,whether COVID-19 or pneumonia.The proposed CNN consists of four Convolutional(Conv)layers,four batch normalization layers,and four Rectified Linear Units(ReLUs).The sizes of Conv layer used filters are 8,16,32,and 64.Four maxpooling layers are employed with a stride of 2 and a 2×2 window.The classification layer comprises a Fully-Connected(FC)layer and a soft-max activation function used to take the classification decision.A novel saliencybased region detection algorithm and an active contour segmentation strategy are applied to segment COVID-19 and pneumonia CT images.The acquired findings substantiate the efficacy of the proposed framework for helping the specialists in automated diagnosis applications.展开更多
Objectives:Advertising messages can affect the public as a risk or protective factor for socially disadvantaged groups,and they may reflect how characters reflect perceptions are perceived in a society.This study aime...Objectives:Advertising messages can affect the public as a risk or protective factor for socially disadvantaged groups,and they may reflect how characters reflect perceptions are perceived in a society.This study aimed to investigate how older people are portrayed in televised food commercials from the approach of a healthy aging perspective in contemporary Chinese society.Methods:All televised advertising in the Ad Topic archive were screened against inclusion and exclusion criteria,and a total of 164 commercials from the years of 2016-2019 that portrayed Chinese older people were sampled.The association between the main older characters with the product categories,healthy vs.unhealthy foods,use of health claims,sex,type of spokesperson,companions,and tones and manners were included in the analysis.Results:Older people more frequently appeared in unhealthy food products than in healthy food products.Health claims involving older adults were portrayed adequately,whereas nursing professions as companions of older adults were overlooked.Positive advertising that delivered happy,caring,or warm tones was overwhelmingly represented.Thus,the advertising messages circulated in China represent a binary stereotype model of images of older adults'characteristics that reflect ageist and the so-called agelessism,referring to the new application of the look from the approach of social psychology and marketing field.Conclusions:This study examined aging discrimination reflected in advertisements.Studies exploring the impact of a crisis remain limited.Research is needed to improve the accuracy of advertised healthy older adults and normal aging.展开更多
This work presents the use of extended Modified Lambert Beer(MLB)model for accurate andcontinuous monitoring of percent blood carboxyhemoglobin(COHb)(SCO)and oxyhemoglobin(OxyHb)saturation(SO,)via a fitting procedure....This work presents the use of extended Modified Lambert Beer(MLB)model for accurate andcontinuous monitoring of percent blood carboxyhemoglobin(COHb)(SCO)and oxyhemoglobin(OxyHb)saturation(SO,)via a fitting procedure.This quantification technique is based on theabsorption characteristics of hemoglobin derivatives in the wavelength range of 520-600 nm togive the best estimates of the required parameters.A comparison of the performance of the developed model and MILB law is made using attenuation data from Monte Carlo simulations for a two-layered skin model.The results revealed a lower mean absolute error of 0.4%in the valuesestimated by the developed model as compared to 10%that is given by the MILB law.This studyshowed that the discussed approach is able to provide consistent and accurate measurement ofblood SO,and SCO across diferent skin pigmen tations suggesting that it may potentially be usedas an alternative means for clinical diagnosis of carbon monoxide(CO)poisoning.展开更多
This paper presents a collaborative beamforming （CB） technique to organize the sensor node＇s location in a linear array for green wireless sensor network （WSN） applications. In this method, only selected clusters...This paper presents a collaborative beamforming （CB） technique to organize the sensor node＇s location in a linear array for green wireless sensor network （WSN） applications. In this method, only selected clusters and active CB nodes are needed each time to perform CB in WSNs. The proposed least-square linear array （LSLA） manages to select nodes to perform as a linear antenna array （LAA）, which is similar to and as outstanding as the conventional uniform linear array （ULA）. The LSLA technique is also able to solve positioning error problems that exist in the random nodes deployment. The beampattern fluctuations have been analyzed due to the random positions of sensor nodes. Performances in terms of normalized power gains are given. It is demonstrated by a simulation that the proposed technique gives similar performances to the conventional ULA and at the same time exhibits lower complexity.展开更多
For military warfare purposes,it is necessary to identify the type of a certain weapon through video stream tracking based on infrared(IR)video frames.Computer vision is a visual search trend that is used to identify ...For military warfare purposes,it is necessary to identify the type of a certain weapon through video stream tracking based on infrared(IR)video frames.Computer vision is a visual search trend that is used to identify objects in images or video frames.For military applications,drones take a main role in surveillance tasks,but they cannot be confident for longtime missions.So,there is a need for such a system,which provides a continuous surveillance task to support the drone mission.Such a system can be called a Hybrid Surveillance System(HSS).This system is based on a distributed network of wireless sensors for continuous surveillance.In addition,it includes one or more drones to make short-time missions,if the sensors detect a suspicious event.This paper presents a digital solution to identify certain types of concealed weapons in surveillance applications based on Convolutional Neural Networks(CNNs)and Convolutional Long Short-Term Memory(ConvLSTM).Based on initial results,the importance of video frame enhancement is obvious to improve the visibility of objects in video streams.The accuracy of the proposed methods reach 99%,which reflects the effectiveness of the presented solution.In addition,the experimental results prove that the proposed methods provide superior performance compared to traditional ones.展开更多
Objective:This current study pursued an exploration of the psychological mechanism that determines college students'continuance intention to use fitness apps.Methods:This current study adopted a mixed methods rese...Objective:This current study pursued an exploration of the psychological mechanism that determines college students'continuance intention to use fitness apps.Methods:This current study adopted a mixed methods research that composed two distinct phases.Study 1 was quantitative research that helped to identify determinants of Chinese college students'continuance intention to use.A self-reported questionnaire was completed by 379 college students to ascertain their user experience.Study 2 was qualitative research.A semi-structured interview was conducted with a sample of 10 college students.Study 2 can be seen as a follow-up study and it pursued an in-depth understanding on how college students use fitness apps in the everyday life and their views towards study 1's major findings.Results:The results revealed that five factors(confirmed usefulness,confirmed ease of use,satisfaction,fitness achievement and social connection)were found to significantly and positively affect college students'continuous intention to use fitness apps.Entertainment did not show obvious impact.In the interview,college students reported that even if they don't obtain entertainment from fitness apps,they will still push themselves to use them,because they have a very specific goal when using fitness apps,which is to achieve health and fitness.Conclusion:These findings indicated that successful fitness apps should make users feel convenient to use and indeed improves the fitness user's efficiency.Besides,people are more eager to get the information with strong credibility with the negligible effort.This implies more efforts should be made to design apps that can provide high-quality services.Moreover,if apps designers can pay more attention to protecting the personal information and data,it will inspire more people to use social connection functions.展开更多
COVID-19 remains to proliferate precipitously in the world.It has significantly influenced public health,the world economy,and the persons’lives.Hence,there is a need to speed up the diagnosis and precautions to deal...COVID-19 remains to proliferate precipitously in the world.It has significantly influenced public health,the world economy,and the persons’lives.Hence,there is a need to speed up the diagnosis and precautions to deal with COVID-19 patients.With this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical images.This paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)images.The proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT images.The classification stage is firstly applied to detect and classify the input medical CT images.Then,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT images.The classification stage is implemented based on a simple and efficient CNN deep learning model.This model comprises four Rectified Linear Units(ReLUs),four batch normalization layers,and four convolutional(Conv)layers.TheConv layer depends on filters with sizes of 64,32,16,and 8.A2×2windowand a stride of 2 are employed in the utilized four max-pooling layers.A soft-max activation function and a Fully-Connected(FC)layer are utilized in the classification stage to perform the detection process.For the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid approach.The proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,accurately.To summarize the contributions of the paper,we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis system.Once the images are accepted by the system,it is possible to perform further processing through a segmentation process to isolate the regions of interest in the images.The region of interest can be assesses both automatically and through experts.This strategy helps somuch in saving the time and efforts of specialists with the explosion of COVID-19 pandemic in the world.The proposed classification approach is applied for different scenarios of 80%,70%,or 60%of the data for training and 20%,30,or 40%of the data for testing,respectively.In these scenarios,the proposed approach achieves classification accuracies of 100%,99.45%,and 98.55%,respectively.Thus,the obtained results demonstrate and prove the efficacy of the proposed approach for assisting the specialists in automated medical diagnosis services.展开更多
Breast cancer is themost common type of cancer,and it is the reason for cancer death toll in women in recent years.Early diagnosis is essential to handle breast cancer patients for treatment at the right time.Screenin...Breast cancer is themost common type of cancer,and it is the reason for cancer death toll in women in recent years.Early diagnosis is essential to handle breast cancer patients for treatment at the right time.Screening with mammography is the preferred examination for breast cancer,as it is available worldwide and inexpensive.Computer-Aided Detection(CAD)systems are used to analyze medical images to detect breast cancer,early.The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations.Processing of mammogram images has four main steps:pre-processing,segmentation of the region of interest,feature extraction and classification of the images into normal or abnormal classes.This paper presents an efficient framework for processing of mammogram images and introduces an algorithm for segmentation of the images to detect masses.The pre-processing step of mammogram images includes removal of digitization noise using a 2D median filter,removal of artifacts using morphological operations,and contrast enhancement using a fuzzy enhancement technique.The proposed fuzzy image enhancement technique is analyzed and compared with conventional techniques based on an Enhancement Measure(EME)and local contrast metrics.The comparison shows an outstanding performance of the proposed technique from the visual and numerical perspectives.The segmentation process is performed using Otsu’smultiple thresholding method.This method segments the image regions into five classes with variable intensities using four thresholds.Its effectiveness is measured based on visual quality of the segmentation output,as it gives details about the image and positions of masses.The performance of the proposed framework is measured using Dice coefficient,Hausdorff,and Peak Signal-to-Noise Ratio(PSNR)metrics.The segmented tumor region with the proposed segmentation method is 81%of the ground truth region provided by an expert.Hence,the proposed framework achieves promising results for aiding radiologists in screening of mammograms,accurately.展开更多
Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in China.World Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death rate.Chest X-Ray(CXR)and C...Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in China.World Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death rate.Chest X-Ray(CXR)and Computerized Tomography(CT)screening of infected persons are essential in diagnosis applications.There are numerous ways to identify positive COVID-19 cases.One of the fundamental ways is radiology imaging through CXR,or CT images.The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality.Hence,automated classification techniques are required to facilitate the diagnosis process.Deep Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical images.The deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image datasets.In this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is offered.Three activation functions as well as three optimizers are tested and compared for this task.The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it.The performance is tested and investigated on the CT and CXR datasets.Three activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch sizes.Different optimizers are studied with different batch sizes and a constant learning rate.Finally,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is determined.Hence,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage.The proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR and CT datasets,respectively.The best results are obtained using the ReLU activation function combined with the SGDM optimizer at a learning rate of 10−5 and a minibatch size of 16.展开更多
In developing countries,medical diagnosis is expensive and time consuming.Hence,automatic diagnosis can be a good cheap alternative.This task can be performed with artificial intelligence tools such as deep Convolutio...In developing countries,medical diagnosis is expensive and time consuming.Hence,automatic diagnosis can be a good cheap alternative.This task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks(CNNs).These tools can be used on medical images to speed up the diagnosis process and save the efforts of specialists.The deep CNNs allow direct learning from the medical images.However,the accessibility of classified data is still the largest challenge,particularly in the field of medical imaging.Transfer learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs to medical image classification.However,because of the inhomogeneity and enormous overlap in intensity between medical images in terms of features in the diagnosis of Pneumonia and COVID-19,transfer learning is not usually a robust solution.Single-Image Super-Resolution(SISR)can facilitate learning to enhance computer vision functions,apart from enhancing perceptual image consistency.Consequently,it helps in showing the main features of images.Motivated by the challenging dilemma of Pneumonia and COVID-19 diagnosis,this paper introduces a hybrid CNN model,namely SIGTra,to generate super-resolution versions of X-ray and CT images.It depends on aGenerative Adversarial Network(GAN)for the super-resolution reconstruction problem.Besides,Transfer learning with CNN(TCNN)is adopted for the classification of images.Three different categories of chest X-ray and CT images can be classified with the proposed model.A comparison study is presented between the proposed SIGTra model and the other relatedCNNmodels for COVID-19 detection in terms of precision,sensitivity,and accuracy.展开更多
Due to the rapid growth of telemedicine and healthcare services,color medical image security applications have been expanded precipitously.In this paper,an asymmetric PTFrFT(Phase Truncated Fractional Fourier Transfor...Due to the rapid growth of telemedicine and healthcare services,color medical image security applications have been expanded precipitously.In this paper,an asymmetric PTFrFT(Phase Truncated Fractional Fourier Transform)-based color medical image cryptosystem is suggested.Two different phases in the fractional Fourier and output planes are provided as deciphering keys.Accordingly,the ciphering keys will not be employed for the deciphering procedure.Thus,the introduced PTFrFT algorithm comprises asymmetric ciphering and deciphering processes in contrast to the traditional optical symmetric OSH(Optical Scanning Holography)and DRPE(Double Random Phase Encoding)algorithms.One of the principal impacts of the introduced asymmetric cryptosystem is that it eliminates the onedimensionality aspects of the related symmetric cryptosystems due to its remarkable feature of phase nonlinear truncation components.More comparisons on various colormedical images are examined and analyzed to substantiate the cryptosystem efficacy.The achieved experimental outcomes ensure that the introduced cryptosystem is robust and secure.It has terrific cryptography performance compared to conventional cryptography algorithms,even in the presence of noise and severe channel attacks.展开更多
The next wave of communication and applications will rely on new services provided by the Internet of Things which is becoming an important aspect in human and machines future. IoT services are a key solution for prov...The next wave of communication and applications will rely on new services provided by the Internet of Things which is becoming an important aspect in human and machines future. IoT services are a key solution for providing smart environments in homes, buildings, and cities. In the era of massive number of connected things and objects with high growth rate, several challenges have been raised, such as management, aggregation, and storage for big produced data. To address some of these issues, cloud computing emerged to the IoT as Cloud of Things （COT）, which provides virtually unlimited cloud services to enhance the large-scale IoT platforms. There are several factors to be considered in the design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying a suitable ＂middleware＂ which sits between things and applications as a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next, we study different architecture styles and service domains. Then, we present several middlewares that are suitable for CoT-based platforms and finally, a list of current challenges and issues in the design of CoT-based middlewares is discussed.展开更多
文摘In this paper, an enhanced greedy bit and power allocation algorithms for orthogonal frequency division multiplexing (OFDM) communication systems are introduced. These algorithms combine low complexity greedy power allocation algorithms with a simplified maximum ratio combining (MRC) precoding technique at the transmitter for maximizing the average data throughput of OFDM communication systems. Results of computer simulations show that precoding is an effective technique for improving the throughput performance of the proposed bit and power allocation algorithms.
基金the Deanship of Scientific Research,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,Grant No.(44-PRFA-P-131).
文摘The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia technology.High-quality images require well-tuned communication standards.The Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and integrity.The data need a space away fromunauthorized access,modification,or deletion.These requirements are to be fulfilled by digital image watermarking and encryption.This paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication standard.It introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver side.The proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD watermarking.In addition,image encryption is implemented based on chaos and DNA encoding.The encrypted watermarked images are then transmitted through the wireless SC-FDMA system.The linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted images.Two subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this paper.The study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked images.In addition,different variants of SC-FDMA based on the Discrete Wavelet Transform(DWT),Discrete Cosine Transform(DCT),and Fast Fourier Transform(FFT)are considered and compared for the image communication task.The simulation results and comparison demonstrate clearly that DWT-SC-FDMAis better suited to the transmission of the digital images in the case of PedestrianAchannels,while the DCT-SC-FDMA is better suited to the transmission of the digital images in the case of Vehicular A channels.
基金supported by the Institute for Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.2019-0-01343,Training Key Talents in Industrial Convergence Security).
文摘With the emergence of the COVID-19 pandemic,the World Health Organization(WHO)has urged scientists and industrialists to exploremodern information and communication technology(ICT)as a means to reduce or even eliminate it.The World Health Organization recently reported that the virus may infect the organism through any organ in the living body,such as the respiratory,the immunity,the nervous,the digestive,or the cardiovascular system.Targeting the abovementioned goal,we envision an implanted nanosystem embedded in the intra living-body network.The main function of the nanosystem is either to perform diagnosis and mitigation of infectious diseases or to implement a targeted drug delivery system(i.e.,delivery of the therapeutic drug to the diseased tissue or targeted cell).The communication among the nanomachines is accomplished via communication-based molecular diffusion.The control/interconnection of the nanosystem is accomplished through the utilization of Internet of bio-nano things(IoBNT).The proposed nanosystem is designed to employ a coded relay nanomachine disciplined by the decode and forward(DF)principle to ensure reliable drug delivery to the targeted cell.Notably,both the sensitivity of the drug dose and the phenomenon of drug molecules loss before delivery to the target cell site in long-distance due to the molecules diffusion process are taken into account.In this paper,a coded relay NM with conventional coding techniques such as RS and Turbo codes is selected to achieve minimum bit error rate(BER)performance and high signal-to-noise ratio(SNR),while the detection process is based on maximum likelihood(ML)probability and minimum error probability(MEP).The performance analysis of the proposed scheme is evaluated in terms of channel capacity and bit error rate by varying system parameters such as relay position,number of released molecules,relay and receiver size.Analysis results are validated through simulation and demonstrate that the proposed scheme can significantly improve delivery performance of the desirable drugs in the molecular communication system.
文摘The role of religious groups in the political field has arised in Africa in 1990s and despite being less visible it is still active. In fact, this is the climax of a very old process in Africa. Religion always participates in politics and vice versa. It is therefore not surprising that religious groups take into account political preoccupations in their communication processes. Sometimes they get involved in political debates, particularly when the questions reveal very important choices for the Nation, as it was the case in 1960s and 1990s. But mostly, they get involved in the social field which is, for them, the key to have one foot in political field. However, beyond these considerations, this paper intends to show how the use of media by religious institutions is closely related to their understanding of the relationship between the religion as an institution, and the society.
文摘This paper presents results from investigation of cultural transformations exhibited by Wasukuma youth in regard to getting fiancées in Ngasamo ward, Busega district, Simiyu region, Tanzania. The Main Objective was to assess the manner former Wasukuma young men used mega-stone objects in selecting prospective fiancées and compare with the current trend of using media in some areas of Bariadi district, Simiyu region. Specific Objectives included the following: to relocate tangible cultural heritage resources (mega-stones) used by Wasukuma young men in former times for getting fiancées in Simiyu region;to identify electronic media used by Wasukuma young men of today to communicate in a bid to get fiancées in Simiyu region;and to provide suggestions for pertinent protection, conservation as well as presentation of cultural heritage resources. Such investigation was carried out through surveys that included field observation, documentation together with records for Global Positioning System (GPS) coordinates per surveyed locality and key informant interviews. Results from the study identified granite rock boulders that were used as grinding stones for cereals such that they formed grinding hollows. Besides production of flour for making food like stiff porridge or soft porridge, such grinding hollows were used by youth of former times to identify hard working young ladies who could be useful for becoming life partners. Such cultural heritage assets need sustainable preservation as well as conservation plans in line with Antiqui-ties Act, Antiquities Rules and Monuments of 1980, Cultural Policy of 1997 together with Antiquities Policy of 2008. On the other hand, today’s youth in Bariadi area, Simiyu region and elsewhere in Usukuma areas, for instance, Kwimba district in Mwanza region used such mega-stones with the same purposes. However, currently, youth are using Information Communication Technology (ICT), for example, electronic media through television, mobile phones and the like to communicate with young ladies so as to build a permanent bond that could culminate to marriage.
基金Princess Nourah bint Abdulrahman University Researchers Supporting ProjectNumber (PNURSP2022R66), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
文摘Authentication of the digital image has much attention for the digital revolution.Digital image authentication can be verified with image watermarking and image encryption schemes.These schemes are widely used to protect images against forgery attacks,and they are useful for protecting copyright and rightful ownership.Depending on the desirable applications,several image encryption and watermarking schemes have been proposed to moderate this attention.This framework presents a new scheme that combines a Walsh Hadamard Transform(WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding(DRPE).First,on the sender side,the secret medical image is encrypted using DRPE.Then the encrypted image is watermarking based on WHT.The combination between watermarking and encryption increases the security and robustness of transmitting an image.The performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index(SSIM),Peak Signal-to-Noise Ratio(PSNR),Normalized cross-correlation(NC),and Feature Similarity Index(FSIM).
基金This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Fast-track Research Funding Program.
文摘Effective medical diagnosis is dramatically expensive,especially in third-world countries.One of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant challenge.Hence,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific tasks.Unfortunately,experimentation and utilization of different models of transfer learning do not achieve satisfactory results.In this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related diseases.This model is based on classification,denoising autoencoder,and transfer learning.Firstly,pre-processing is employed to prepare the medical images.It depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis process.Then,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect pneumonia.The proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray images.Finally,a comparative study is introduced for the classification performance with and without the denoising process.The proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and testing.To demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN models.The achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the existing diagnosis models.
基金This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Fast-track Research Funding Program.
文摘Corona Virus Disease-2019(COVID-19)continues to spread rapidly in the world.It has dramatically affected daily lives,public health,and the world economy.This paper presents a segmentation and classification framework of COVID-19 images based on deep learning.Firstly,the classification process is employed to discriminate between COVID-19,non-COVID,and pneumonia by Convolutional Neural Network(CNN).Then,the segmentation process is applied for COVID-19 and pneumonia CT images.Finally,the resulting segmented images are used to identify the infected region,whether COVID-19 or pneumonia.The proposed CNN consists of four Convolutional(Conv)layers,four batch normalization layers,and four Rectified Linear Units(ReLUs).The sizes of Conv layer used filters are 8,16,32,and 64.Four maxpooling layers are employed with a stride of 2 and a 2×2 window.The classification layer comprises a Fully-Connected(FC)layer and a soft-max activation function used to take the classification decision.A novel saliencybased region detection algorithm and an active contour segmentation strategy are applied to segment COVID-19 and pneumonia CT images.The acquired findings substantiate the efficacy of the proposed framework for helping the specialists in automated diagnosis applications.
基金the University of Macao(Grant No.:MYRG2019-00079-FSS).
文摘Objectives:Advertising messages can affect the public as a risk or protective factor for socially disadvantaged groups,and they may reflect how characters reflect perceptions are perceived in a society.This study aimed to investigate how older people are portrayed in televised food commercials from the approach of a healthy aging perspective in contemporary Chinese society.Methods:All televised advertising in the Ad Topic archive were screened against inclusion and exclusion criteria,and a total of 164 commercials from the years of 2016-2019 that portrayed Chinese older people were sampled.The association between the main older characters with the product categories,healthy vs.unhealthy foods,use of health claims,sex,type of spokesperson,companions,and tones and manners were included in the analysis.Results:Older people more frequently appeared in unhealthy food products than in healthy food products.Health claims involving older adults were portrayed adequately,whereas nursing professions as companions of older adults were overlooked.Positive advertising that delivered happy,caring,or warm tones was overwhelmingly represented.Thus,the advertising messages circulated in China represent a binary stereotype model of images of older adults'characteristics that reflect ageist and the so-called agelessism,referring to the new application of the look from the approach of social psychology and marketing field.Conclusions:This study examined aging discrimination reflected in advertisements.Studies exploring the impact of a crisis remain limited.Research is needed to improve the accuracy of advertised healthy older adults and normal aging.
文摘This work presents the use of extended Modified Lambert Beer(MLB)model for accurate andcontinuous monitoring of percent blood carboxyhemoglobin(COHb)(SCO)and oxyhemoglobin(OxyHb)saturation(SO,)via a fitting procedure.This quantification technique is based on theabsorption characteristics of hemoglobin derivatives in the wavelength range of 520-600 nm togive the best estimates of the required parameters.A comparison of the performance of the developed model and MILB law is made using attenuation data from Monte Carlo simulations for a two-layered skin model.The results revealed a lower mean absolute error of 0.4%in the valuesestimated by the developed model as compared to 10%that is given by the MILB law.This studyshowed that the discussed approach is able to provide consistent and accurate measurement ofblood SO,and SCO across diferent skin pigmen tations suggesting that it may potentially be usedas an alternative means for clinical diagnosis of carbon monoxide(CO)poisoning.
基金supported by the Ministry of Education Malaysia,Universiti Teknologi Malaysia and RUG vote 11H60
文摘This paper presents a collaborative beamforming （CB） technique to organize the sensor node＇s location in a linear array for green wireless sensor network （WSN） applications. In this method, only selected clusters and active CB nodes are needed each time to perform CB in WSNs. The proposed least-square linear array （LSLA） manages to select nodes to perform as a linear antenna array （LAA）, which is similar to and as outstanding as the conventional uniform linear array （ULA）. The LSLA technique is also able to solve positioning error problems that exist in the random nodes deployment. The beampattern fluctuations have been analyzed due to the random positions of sensor nodes. Performances in terms of normalized power gains are given. It is demonstrated by a simulation that the proposed technique gives similar performances to the conventional ULA and at the same time exhibits lower complexity.
基金This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Research Funding Program(Grant No#FRP-1440-23).
文摘For military warfare purposes,it is necessary to identify the type of a certain weapon through video stream tracking based on infrared(IR)video frames.Computer vision is a visual search trend that is used to identify objects in images or video frames.For military applications,drones take a main role in surveillance tasks,but they cannot be confident for longtime missions.So,there is a need for such a system,which provides a continuous surveillance task to support the drone mission.Such a system can be called a Hybrid Surveillance System(HSS).This system is based on a distributed network of wireless sensors for continuous surveillance.In addition,it includes one or more drones to make short-time missions,if the sensors detect a suspicious event.This paper presents a digital solution to identify certain types of concealed weapons in surveillance applications based on Convolutional Neural Networks(CNNs)and Convolutional Long Short-Term Memory(ConvLSTM).Based on initial results,the importance of video frame enhancement is obvious to improve the visibility of objects in video streams.The accuracy of the proposed methods reach 99%,which reflects the effectiveness of the presented solution.In addition,the experimental results prove that the proposed methods provide superior performance compared to traditional ones.
文摘Objective:This current study pursued an exploration of the psychological mechanism that determines college students'continuance intention to use fitness apps.Methods:This current study adopted a mixed methods research that composed two distinct phases.Study 1 was quantitative research that helped to identify determinants of Chinese college students'continuance intention to use.A self-reported questionnaire was completed by 379 college students to ascertain their user experience.Study 2 was qualitative research.A semi-structured interview was conducted with a sample of 10 college students.Study 2 can be seen as a follow-up study and it pursued an in-depth understanding on how college students use fitness apps in the everyday life and their views towards study 1's major findings.Results:The results revealed that five factors(confirmed usefulness,confirmed ease of use,satisfaction,fitness achievement and social connection)were found to significantly and positively affect college students'continuous intention to use fitness apps.Entertainment did not show obvious impact.In the interview,college students reported that even if they don't obtain entertainment from fitness apps,they will still push themselves to use them,because they have a very specific goal when using fitness apps,which is to achieve health and fitness.Conclusion:These findings indicated that successful fitness apps should make users feel convenient to use and indeed improves the fitness user's efficiency.Besides,people are more eager to get the information with strong credibility with the negligible effort.This implies more efforts should be made to design apps that can provide high-quality services.Moreover,if apps designers can pay more attention to protecting the personal information and data,it will inspire more people to use social connection functions.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project Number(PNU-DRI-Targeted-20-027).
文摘COVID-19 remains to proliferate precipitously in the world.It has significantly influenced public health,the world economy,and the persons’lives.Hence,there is a need to speed up the diagnosis and precautions to deal with COVID-19 patients.With this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical images.This paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)images.The proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT images.The classification stage is firstly applied to detect and classify the input medical CT images.Then,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT images.The classification stage is implemented based on a simple and efficient CNN deep learning model.This model comprises four Rectified Linear Units(ReLUs),four batch normalization layers,and four convolutional(Conv)layers.TheConv layer depends on filters with sizes of 64,32,16,and 8.A2×2windowand a stride of 2 are employed in the utilized four max-pooling layers.A soft-max activation function and a Fully-Connected(FC)layer are utilized in the classification stage to perform the detection process.For the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid approach.The proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,accurately.To summarize the contributions of the paper,we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis system.Once the images are accepted by the system,it is possible to perform further processing through a segmentation process to isolate the regions of interest in the images.The region of interest can be assesses both automatically and through experts.This strategy helps somuch in saving the time and efforts of specialists with the explosion of COVID-19 pandemic in the world.The proposed classification approach is applied for different scenarios of 80%,70%,or 60%of the data for training and 20%,30,or 40%of the data for testing,respectively.In these scenarios,the proposed approach achieves classification accuracies of 100%,99.45%,and 98.55%,respectively.Thus,the obtained results demonstrate and prove the efficacy of the proposed approach for assisting the specialists in automated medical diagnosis services.
基金This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Fast-track Research Funding Program.
文摘Breast cancer is themost common type of cancer,and it is the reason for cancer death toll in women in recent years.Early diagnosis is essential to handle breast cancer patients for treatment at the right time.Screening with mammography is the preferred examination for breast cancer,as it is available worldwide and inexpensive.Computer-Aided Detection(CAD)systems are used to analyze medical images to detect breast cancer,early.The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations.Processing of mammogram images has four main steps:pre-processing,segmentation of the region of interest,feature extraction and classification of the images into normal or abnormal classes.This paper presents an efficient framework for processing of mammogram images and introduces an algorithm for segmentation of the images to detect masses.The pre-processing step of mammogram images includes removal of digitization noise using a 2D median filter,removal of artifacts using morphological operations,and contrast enhancement using a fuzzy enhancement technique.The proposed fuzzy image enhancement technique is analyzed and compared with conventional techniques based on an Enhancement Measure(EME)and local contrast metrics.The comparison shows an outstanding performance of the proposed technique from the visual and numerical perspectives.The segmentation process is performed using Otsu’smultiple thresholding method.This method segments the image regions into five classes with variable intensities using four thresholds.Its effectiveness is measured based on visual quality of the segmentation output,as it gives details about the image and positions of masses.The performance of the proposed framework is measured using Dice coefficient,Hausdorff,and Peak Signal-to-Noise Ratio(PSNR)metrics.The segmented tumor region with the proposed segmentation method is 81%of the ground truth region provided by an expert.Hence,the proposed framework achieves promising results for aiding radiologists in screening of mammograms,accurately.
基金This work was funded and supported by the Taif University Researchers Supporting Project Number(TURSP-2020/147),Taif University,Taif,Saudi Arabia.
文摘Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in China.World Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death rate.Chest X-Ray(CXR)and Computerized Tomography(CT)screening of infected persons are essential in diagnosis applications.There are numerous ways to identify positive COVID-19 cases.One of the fundamental ways is radiology imaging through CXR,or CT images.The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality.Hence,automated classification techniques are required to facilitate the diagnosis process.Deep Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical images.The deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image datasets.In this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is offered.Three activation functions as well as three optimizers are tested and compared for this task.The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it.The performance is tested and investigated on the CT and CXR datasets.Three activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch sizes.Different optimizers are studied with different batch sizes and a constant learning rate.Finally,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is determined.Hence,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage.The proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR and CT datasets,respectively.The best results are obtained using the ReLU activation function combined with the SGDM optimizer at a learning rate of 10−5 and a minibatch size of 16.
基金This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Fast-track Research Funding Program.
文摘In developing countries,medical diagnosis is expensive and time consuming.Hence,automatic diagnosis can be a good cheap alternative.This task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks(CNNs).These tools can be used on medical images to speed up the diagnosis process and save the efforts of specialists.The deep CNNs allow direct learning from the medical images.However,the accessibility of classified data is still the largest challenge,particularly in the field of medical imaging.Transfer learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs to medical image classification.However,because of the inhomogeneity and enormous overlap in intensity between medical images in terms of features in the diagnosis of Pneumonia and COVID-19,transfer learning is not usually a robust solution.Single-Image Super-Resolution(SISR)can facilitate learning to enhance computer vision functions,apart from enhancing perceptual image consistency.Consequently,it helps in showing the main features of images.Motivated by the challenging dilemma of Pneumonia and COVID-19 diagnosis,this paper introduces a hybrid CNN model,namely SIGTra,to generate super-resolution versions of X-ray and CT images.It depends on aGenerative Adversarial Network(GAN)for the super-resolution reconstruction problem.Besides,Transfer learning with CNN(TCNN)is adopted for the classification of images.Three different categories of chest X-ray and CT images can be classified with the proposed model.A comparison study is presented between the proposed SIGTra model and the other relatedCNNmodels for COVID-19 detection in terms of precision,sensitivity,and accuracy.
基金This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program to support publication in the top journal(Grant no.42-FTTJ-12).
文摘Due to the rapid growth of telemedicine and healthcare services,color medical image security applications have been expanded precipitously.In this paper,an asymmetric PTFrFT(Phase Truncated Fractional Fourier Transform)-based color medical image cryptosystem is suggested.Two different phases in the fractional Fourier and output planes are provided as deciphering keys.Accordingly,the ciphering keys will not be employed for the deciphering procedure.Thus,the introduced PTFrFT algorithm comprises asymmetric ciphering and deciphering processes in contrast to the traditional optical symmetric OSH(Optical Scanning Holography)and DRPE(Double Random Phase Encoding)algorithms.One of the principal impacts of the introduced asymmetric cryptosystem is that it eliminates the onedimensionality aspects of the related symmetric cryptosystems due to its remarkable feature of phase nonlinear truncation components.More comparisons on various colormedical images are examined and analyzed to substantiate the cryptosystem efficacy.The achieved experimental outcomes ensure that the introduced cryptosystem is robust and secure.It has terrific cryptography performance compared to conventional cryptography algorithms,even in the presence of noise and severe channel attacks.
文摘The next wave of communication and applications will rely on new services provided by the Internet of Things which is becoming an important aspect in human and machines future. IoT services are a key solution for providing smart environments in homes, buildings, and cities. In the era of massive number of connected things and objects with high growth rate, several challenges have been raised, such as management, aggregation, and storage for big produced data. To address some of these issues, cloud computing emerged to the IoT as Cloud of Things （COT）, which provides virtually unlimited cloud services to enhance the large-scale IoT platforms. There are several factors to be considered in the design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying a suitable ＂middleware＂ which sits between things and applications as a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next, we study different architecture styles and service domains. Then, we present several middlewares that are suitable for CoT-based platforms and finally, a list of current challenges and issues in the design of CoT-based middlewares is discussed.
