Detection Techniques
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Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hao Wang ◽  
Xilin Yang ◽  
Zeqi Liu ◽  
Jing Pan ◽  
Yuan Meng ◽  
...  

Abstract Structured light with customized topological patterns inspires diverse classical and quantum investigations underpinned by accurate detection techniques. However, the current detection schemes are limited to vortex beams with a simple phase singularity. The precise recognition of general structured light with multiple singularities remains elusive. Here, we report deep learning (DL) framework that can unveil multi-singularity phase structures in an end-to-end manner, after feeding only two intensity patterns upon beam propagation. By outputting the phase directly, rich and intuitive information of twisted photons is unleashed. The DL toolbox can also acquire phases of Laguerre–Gaussian (LG) modes with a single singularity and other general phase objects likewise. Enabled by this DL platform, a phase-based optical secret sharing (OSS) protocol is proposed, which is based on a more general class of multi-singularity modes than conventional LG beams. The OSS protocol features strong security, wealthy state space, and convenient intensity-based measurements. This study opens new avenues for large-capacity communications, laser mode analysis, microscopy, Bose–Einstein condensates characterization, etc.


Author(s):  
Rekha P. M. ◽  
Nagamani H. Shahapure ◽  
Punitha M. ◽  
Sudha P. R.

The economic growth and information technology leads to the development of Internet of Things (IoT) industry and has become the emerging field of research. Several intrusion detection techniques are introduced but the detection of intrusion and malicious activities poses a challenging task. This paper devises a novel method, namely the Water Moth Search algorithm (WMSA) algorithm, for training Deep Recurrent Neural Network (Deep RNN) to detect malicious network activities. The WMSA algorithm is newly devised by combining Water Wave optimization (WWO) and the Moth Search Optimization (MSO). The pre-processing is employed for the removal of redundant data. Then, the feature selection is devised using the Wrapper approach, then using the selected features; the Deep RNN classifier effectively detects the intrusion using the selected features. The proposed WMSA-based Deep RNN showed improved results with maximal accuracy, specificity, and sensitivity of 0.96, 0.973 and 0.960.


Author(s):  
Victor Blanco ◽  
Alberto Japón ◽  
Justo Puerto

AbstractIn this paper we propose a novel methodology to construct Optimal Classification Trees that takes into account that noisy labels may occur in the training sample. The motivation of this new methodology is based on the superaditive effect of combining together margin based classifiers and outlier detection techniques. Our approach rests on two main elements: (1) the splitting rules for the classification trees are designed to maximize the separation margin between classes applying the paradigm of SVM; and (2) some of the labels of the training sample are allowed to be changed during the construction of the tree trying to detect the label noise. Both features are considered and integrated together to design the resulting Optimal Classification Tree. We present a Mixed Integer Non Linear Programming formulation for the problem, suitable to be solved using any of the available off-the-shelf solvers. The model is analyzed and tested on a battery of standard datasets taken from UCI Machine Learning repository, showing the effectiveness of our approach. Our computational results show that in most cases the new methodology outperforms both in accuracy and AUC the results of the benchmarks provided by OCT and OCT-H.


2021 ◽  
Author(s):  
Mehdi Safarpour ◽  
Tommy Z. Deng ◽  
John Massingham ◽  
Lei Xun ◽  
Mohammad Sabokrou ◽  
...  

Operating at reduced voltages offers substantial energy efficiency improvement but at the expense of increasing the probability of computational errors due to hardware faults. In this context, we targeted Deep Neural Networks (DNN) as emerging energy hungry building blocks in embedded applications. Without an error feedback mechanism, blind voltage down-scaling will result in degraded accuracy or total system failure. To enable safe voltage down-scaling, in this paper two solutions based on Self-Supervised Learning (SSL) and Algorithm Based Fault Tolerance (ABFT) were developed. A DNN model trained on MNIST data-set was deployed on a Field Programmable Gate Array (FPGA) that operated at reduced voltages and employed the proposed schemes. The SSL approach provides extremely low-overhead (≈0.2%) fault detection at the cost of lower error coverage and extra training, while ABFT incurs less than 8%overheads at run-time with close to 100% error detection rate. By using the solutions, substantial energy savings, i.e., up to 40.3%,without compromising the accuracy of the model was achieved


2021 ◽  
Author(s):  
Mehdi Safarpour ◽  
Tommy Z. Deng ◽  
John Massingham ◽  
Lei Xun ◽  
Mohammad Sabokrou ◽  
...  

Operating at reduced voltages offers substantial energy efficiency improvement but at the expense of increasing the probability of computational errors due to hardware faults. In this context, we targeted Deep Neural Networks (DNN) as emerging energy hungry building blocks in embedded applications. Without an error feedback mechanism, blind voltage down-scaling will result in degraded accuracy or total system failure. To enable safe voltage down-scaling, in this paper two solutions based on Self-Supervised Learning (SSL) and Algorithm Based Fault Tolerance (ABFT) were developed. A DNN model trained on MNIST data-set was deployed on a Field Programmable Gate Array (FPGA) that operated at reduced voltages and employed the proposed schemes. The SSL approach provides extremely low-overhead (≈0.2%) fault detection at the cost of lower error coverage and extra training, while ABFT incurs less than 8%overheads at run-time with close to 100% error detection rate. By using the solutions, substantial energy savings, i.e., up to 40.3%,without compromising the accuracy of the model was achieved


QJM ◽  
2021 ◽  
Vol 114 (Supplement_1) ◽  
Author(s):  
Ibrahim H Aboughaleb ◽  
M Matboli ◽  
Sherif M Shawky ◽  
Mohamed Hisham Aref ◽  
Yasser H El-Sharkawy

Abstract Hepatocellular carcinoma (HCC) is a noteworthy health problem with a poor diagnosis due to limited detection techniques. Transcriptome studies can be used to classify cancer further away from anatomical location and histopathology. Recent studies demonstrated the novelty of numerous types of specific RNA biomarkers that differentially expressed both the normal liver and the HCC tissues, but those specific types overlapped with the detection of other types of cancers. In this study, total RNA was used to ensure the existence of differences between different cancer types. A multispectral light source (340-1000 nm) interacted with the sample. Multi-wavelengths images were captured using a hyperspectral camera (wavelength 380-1000 nm). The optimum wavelength to discriminate between the normal and HCC samples was selected by calculating the optical properties (transmission, absorption and scattered light). Results showed specific spectral signatures for total RNA within the red-band (633-700 nm) that discriminate HCC from control. The amount of light scattering, transmission and absorption relatively changed due to the variations of size, shape, and concentration of total RNA. The spectral RNA signature that is dependent on the shape and size of total RNA may be utilized as the gold standard for HCC detection.


2021 ◽  
Vol 2 (2) ◽  
pp. 25-32
Author(s):  
Ashutosh Kumara ◽  
Neha Janu

Digital images are important part of our life. Copy and Move forgery detection techniques are designed to detect edited part of the image. The copy and move forgery techniques are based on the feature detection and matching. The techniques which are designed so far use the Euclidean distance concept for feature matching. The feature detection techniques which are much popular like Haar transformation are used for feature extraction. In this research, the PCA algorithm is used for the simplification of features which are extracted with Haar transformation. The GLCM algorithm is used for texture feature analysis of input image. In the end, Euclidean distance is used for feature matching and mismatched features are marked as forgery. The proposed approach is implemented in MALTAB and results are analyzed in terms of accuracy.


2021 ◽  
Vol 7 (1) ◽  
pp. 4
Author(s):  
Clara Lebrato-Vázquez ◽  
Alberto J. Molina-Cantero ◽  
Juan A. Castro-García ◽  
Manuel Merino-Monge ◽  
Isabel M. Gómez-González

This paper describes several computer access methods tested by Eva, a woman with choreoathetosic cerebral palsy. This disease prevents her from controlling the peripherals and configurations that normally give access to information and communication technologies, further limiting her independence. To make Eva access a computer, we focused our efforts on the methodologies that Eva could control by just moving her neck and head. These sensors were: Kinect, inertial measurement units (IMU), and video. Kinect, composed of a system of cameras and sensors, gives the option to interact and control the devices contactlessly. The IMU is a device consisting of an accelerometer and a gyroscope that measure velocity, orientation, and gravitational forces. For live image processing, a common webcam was used. During the development of the experiment, Eva must follow a sequence shown on the computer screen that alternates movement of the head with rest. These movements involved moving the head up, down, right, or left. Our results showed that the Kinect system could not be used effectively, while the image-processing algorithm obtained the best performance.


Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2354
Author(s):  
Md Khaled Hasan ◽  
Md. Shamim Ahsan ◽  
Abdullah-Al-Mamun ◽  
S. H. Shah Newaz ◽  
Gyu Myoung Lee

Face detection, which is an effortless task for humans, is complex to perform on machines. The recent veer proliferation of computational resources is paving the way for frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is little attention paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. First, we explore a wide variety of the available face detection algorithms in five steps, including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all-inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of the neural network. We present detailed comparisons among the algorithms in all-inclusive and under sub-branches. We provide the strengths and limitations of these algorithms and a novel literature survey that includes their use besides face detection.


2021 ◽  
Vol 70 (9) ◽  
pp. 1295-1303
Author(s):  
Chang-Ju Park ◽  
Jae-chang Kim ◽  
Jae-Yoon Jeong ◽  
Sangshin Kwak

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