Image enhancement is better achieved when fractional-order masks are used rather than integer-order ones, this is due to the flexibility of fractional-order parameters control. This paper proposes a combination of fractional-order masks to be used in parallel as double filters system structure to improve image enhancement rather than using a single-stage filter. Various performance metrics are used in this work to evaluate the proposed system, such as Information Entropy (IE), Average Gradient (AG), Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR). Based on visual as well as numerical results, it is found that the combination of two double masks is superior to the single fractional-order system in terms of enhancing texture and edges. © 2021 IEEE.
Vulnerable Road Users Detection and Tracking using YOLOv4 and Deep SORT
Over the years, The detection and tracking of Vulnerable Road Users (VRUs) have become one of the most critical features of self-driving car components. Because of its processing efficiency and better detection algorithms, tracking-by-detection appears to be the best paradigm. In this paper, a detection-based tracking approach is presented for Multiple VRU Tracking of video from an inside-vehicle camera in real-time. YOLOv4 scans every frame to detect VRUs first, then Simple Online and Realtime Tracking with a Deep Association Metric (Deep SORT) algorithm, which is customized for multiple VRU tracking, is applied. The results of our experiments on both the Joint Attention in Autonomous Driving (JAAD) and Multiple Object Tracking (MOT) datasets exhibit competitive performance. © 2021 IEEE.
COVID-19 Diagnosis from CT-images Using Transfer Learning
In symptomatic patients, a positive COVID-19 test is critical for securing life-saving services such as ICU care and ventilator support; it may cause septic shock, septic pneumonia, respiratory failure, heart difficulties, liver issues, and even death. CAD systems help people in rural places and doctors in the early detection of COVID-19. A diagnostic and severity detection technique utilizing transfer learning and a backpropagation neural network has been developed with the aid of a computer for this purpose. This study aims to compare and analyze multiple deep learning-enhanced strategies for detecting COVID-19 in CT scan medical images. The COVID-19 CT scan binary classification challenge utilized two powerful pretrained CNN models: Inception ResNet V2 and ResNet50. To achieve higher accuracy in the diagnosis of COVID-19 using CT scan images, a new approach called Inception ResNet was employed, and it resulted in 97.3% accuracy and 97.38% specificity. Transfer learning techniques were employed to reduce the training time and get around the shortage of data. The proposed approaches outperformed more than other papers in the literature by 0.2%. © 2023 IEEE.
A Comparative Study of Different Chaotic Systems in Path Planning for Surveillance Applications
This paper compares the performance of four different chaotic systems in path planning for surveillance applications. The four investigated systems are Lorenz, Arneodo, Liu, and Chen. While the Lorenz system was employed in a similar application before, Arneodo, Liu, and Chen systems are newly introduced in this paper. A bounded-grid chaotic path planner is proposed based on the mirror mapping technique, which keeps the robot bounded in the terrain and prevents it from going outside. The effect of using different state variables of each chaotic system to control the motion angle of the robot is discussed and shown to have a significant impact on the robot’s performance. The obtained trajectory and several performance metrics show promising results of the chaotic path planner for the four systems. © 2021 IEEE.
Chaos-Based Image Encryption Using DNA Manipulation and a Modified Arnold Transform
Digital images, which we store and communicate everyday, may contain confidential information that must not be exposed to others. Numerous researches are interested in encryption, which protects the images from ending up in the hands of unauthorized third parties. This paper proposes an image encryption scheme using chaotic systems, DNA manipulation, and a modified Arnold transform. Both DNA manipulation and hyperchaotic Lorenz system are utilized in the substitution of the images’ pixel values. An additional role of hyperchaotic Lorenz system is that it generates the random numbers required within the DNA manipulation steps. DNA cycling is implemented based on simple DNA coding rules and DNA addition and subtraction rules with modulus operation. The modified Arnold transform alters the pixels’ positions, where it guarantees effective pixel permutation that never outputs the same input pixels arrangement again. The proposed design is simple and amenable for hardware realization. Several well established performance evaluation tests including statistical properties of the encrypted image, key space, and differential attack analysis were conducted for several images. The proposed scheme passed the tests and demonstrated good results compared to several recent chaos-based image encryption schemes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Built-in-current-sensor for testing short and open faults in CMOS digital circuits
In this paper, a novel built-in sensor (BIS) for digital CMOS circuit testing has been proposed. The proposed BIS has no voltage degradation and it is able to detect, identify and localize both open and short circuit faults. Moreover, it has a simple realization with very small area and detection time. A 4×4 multiplier cell is tested by the proposed BIS and all injected faults are detected. © 2009 IEEE.
Analog fault diagnosis by inverse problem technique
A novel algorithm for detecting soft faults in linear analog circuits based on the inverse problem concept is proposed. The proposed approach utilizes optimization techniques with the aid of sensitivity analysis. The main contribution of this work is to apply the inverse problem technique to estimate the actual parameter values of the tested circuit and so, to detect and diagnose single fault in analog circuits. The validation of the algorithm is illustrated through applying it to Sallen-Key second order band pass filter and the results show that the detecting percentage efficiency was 100% and also, the maximum error percentage of estimating the parameter values is 0.7%. This technique can be applied to any other linear circuit and it also can be extended to be applied to non-linear circuits. © 2011 IEEE.
Reconstruction of target properties for different distributions using transient adjoint technique
This paper discusses the sensitivity analysis and the inverse problem solution using the Adjoint Variable Method (AVM) integrated with Transmission Line Modeling (TLM) for many examples having different distributions. The sensitivity analyses of the Gaussian function relative to its parameters is introduced where, great discrimination is observed of the sensitivity magnitude which reflects on the electromagnetic sensitivity and the solution of the inverse problem. Different obstacles with properties (? r, ?) having Gaussian, Poisson and exponential distributions are investigated.
Image encryption based on double-humped and delayed logistic maps for biomedical applications
This paper presents a secured highly sensitive image encryption system suitable for biomedical applications. The pseudo random number generator of the presented system is based on two discrete logistic maps. The employed maps are: the one dimensional double humped logistic map as well as the two-dimensional delayed logistic map. Different analyses are introduced to measure the performance of the proposed encryption system such as: histogram analysis, correlation coefficients, MAE, NPCR as well as UACI measurements. The encryption system is proven to be highly sensitive to ±0.001% perturbation of the logistic maps parameters. The system is tested on medical images of palm print as well as Parkinson disease MRI images. © 2017 IEEE.
Mathematical analysis of gene regulation activator model
This paper presents a complete analysis of the mathematical model of the gene regulation process. The model describes the induced gene expression under the effect of activators. The model differential equations are solved analytically, and the exact solution of the gene model is introduced. Moreover, a study of the model dynamics, including the fixed points and stability conditions are presented. The parameters effects on the phase plane portraits and the transient responses of the mRNA as well as the protein concentrations are intensively detailed. This work serves as a brick stone towards a complete model for a complete gene regulation biological process for future prediction and control of diseases at the genetic level. © 2018 IEEE.