Biblio

Deep learning has been used in a wide range of applications like computer vision, natural language processing and image detection. The advancement in deep learning algorithms in image detection and manipulation has led to the creation of deepfakes, deepfakes use deep learning algorithms to create fake images that are at times very hard to distinguish from real images. With the rising concern around personal privacy and security, Many methods to detect deepfake images have emerged, in this paper the use of deep learning for creating as well as detecting deepfakes is explored, this paper also propose the use of deep learning image enhancement method to improve the quality of deepfakes created.

Security cameras and video surveillance systems have become important infrastructures for ensuring safety and security of the general public. However, the detection of high-risk situations through these systems are still performed manually in many cities. The lack of manpower in the security sector and limited performance of human may result in undetected dangers or delay in detecting threats, posing risks for the public. In response, various parties have developed real-time and automated solutions for identifying risks based on surveillance videos. The aim of this work is to develop a low-cost, efficient, and artificial intelligence-based solution for the real-time detection and recognition of weapons in surveillance videos under different scenarios. The system was developed based on Tensorflow and preliminarily tested with a 294-second video which showed 7 weapons within 5 categories, including handgun, shotgun, automatic rifle, sniper rifle, and submachine gun. At the intersection over union (IoU) value of 0.50 and 0.75, the system achieved a precision of 0.8524 and 0.7006, respectively.

In this paper, a generic model for a differential stripline is created using machine learning (ML) based regression analysis. A recursive approach of creating various inputs is adapted instead of traditional design of experiments (DoE) approach. This leads to reduction of number of simulations as well as control the data points required for performing simulations. The generic model is developed using 48 simulations. It is comparable to the linear regression model, which is obtained using 1152 simulations. Additionally, a tabular W-element model of a differential stripline is used to take into consideration the frequency-dependent dielectric loss. In order to demonstrate the expandability of this approach, the methodology was applied to two differential pairs of striplines in the frequency range of 10 MHz to 20 GHz.

Object recognition with the help of outdoor video surveillance cameras is an important task in the context of ensuring the security at enterprises, public places and even private premises. There have long existed systems that allow detecting moving objects in the image sequence from a video surveillance system. Such a system is partially considered in this research. It detects moving objects using a background model, which has certain problems. Due to this some objects are missed or detected falsely. We propose to combine the moving objects detection results with the classification, using a deep neural network. This will allow determining whether a detected object belongs to a certain class, sorting out false detections, discarding the unnecessary ones (sometimes individual classes are unwanted), to divide detected people into the employees in the uniform and all others, etc. The authors perform a network training in the Keras developer-friendly environment that provides for quick building, changing and training of network architectures. The performance of the Keras integration into a video analysis system, using direct Python script execution techniques, is between 6 and 52 ms, while the precision is between 59.1% and 97.2% for different architectures. The integration, made by freezing a selected network architecture with weights, is selected after testing. After that, frozen architecture can be imported into video analysis using the TensorFlow interface for C++. The performance of such type of integration is between 3 and 49 ms. The precision is between 63.4% and 97.8% for different architectures.

To manage and handle the issues of physical security in the modern world, there is a dire need for a multilevel security system to ensure the safety of precious belongings that could be money, military equipment or medical life-saving drugs. Security locker solution is proposed which is a multiple layer security system consisting of various levels of authentication. In most cases, only relevant persons should have access to their precious belongings. The unlocking of the box is only possible when all of the security levels are successfully cleared. The five levels of security include entering of password on interactive GUI, thumbprint, facial recognition, speech pattern recognition, and vein pattern recognition. This project is unique and effective in a sense that it incorporates five levels of security in a single prototype with the use of cost-effective equipment. Assessing our security system, it is seen that security is increased many a fold as it is near to impossible to breach all these five levels of security. The Raspberry Pi microcomputers, handling all the traits efficiently and smartly makes it easy for performing all the verification tasks. The traits used involves checking, training and verifying processes with application of machine learning operations.

Phasor measurement units (PMUs) provide high-fidelity situational awareness of electric power grid operations. PMU data are used in real-time to inform wide area state estimation, monitor area control error, and event detection. As PMU data becomes more reliable, these devices are finding roles within control systems such as demand response programs and early fault detection systems. As with other cyber physical systems, maintaining data integrity and security are significant challenges for power system operators. In this paper, we present a comprehensive study of multiple machine learning techniques for detecting malicious data injection within PMU data streams. The two datasets used in this study are from the Bonneville Power Administration's PMU network and an inter-university PMU network among three universities, located in the U.S. Pacific Northwest. These datasets contain data from both the transmission level and the distribution level. Our results show that both SVM and ANN are generally effective in detecting spoofed data, and TensorFlow, the newly released tool, demonstrates potential for distributing the training workload and achieving higher performance. We expect these results to shed light on future work of adopting machine learning and data analytics techniques in the electric power industry.

Summary form only given. Strong light-matter coupling has been recently successfully explored in the GHz and THz [1] range with on-chip platforms. New and intriguing quantum optical phenomena have been predicted in the ultrastrong coupling regime [2], when the coupling strength Ω becomes comparable to the unperturbed frequency of the system ω. We recently proposed a new experimental platform where we couple the inter-Landau level transition of an high-mobility 2DEG to the highly subwavelength photonic mode of an LC meta-atom [3] showing very large Ω/ωc = 0.87. Our system benefits from the collective enhancement of the light-matter coupling which comes from the scaling of the coupling Ω ∝ √n, were n is the number of optically active electrons. In our previous experiments [3] and in literature [4] this number varies from 104-103 electrons per meta-atom. We now engineer a new cavity, resonant at 290 GHz, with an extremely reduced effective mode surface Seff = 4 × 10-14 m2 (FE simulations, CST), yielding large field enhancements above 1500 and allowing to enter the few (\textbackslashtextless;100) electron regime. It consist of a complementary metasurface with two very sharp metallic tips separated by a 60 nm gap (Fig.1(a, b)) on top of a single triangular quantum well. THz-TDS transmission experiments as a function of the applied magnetic field reveal strong anticrossing of the cavity mode with linear cyclotron dispersion. Measurements for arrays of only 12 cavities are reported in Fig.1(c). On the top horizontal axis we report the number of electrons occupying the topmost Landau level as a function of the magnetic field. At the anticrossing field of B=0.73 T we measure approximately 60 electrons ultra strongly coupled (Ω/ω- \textbackslashtextbar\textbackslashtextbar