Blockchain has revolutionized numerous fields, which include financial services, health care, the Internet of things, academia and supply chain management. Blockchain technology enables us to have an immutable, distributed ledger for managing the transactions of untrusted users. However, the technology has many open challenges, such as privacy leaks, scalability, and energy consumption. User identity can be easily tracked using network analysis, as transactions are accessible to everyone, which is a serious concern of blockchain. In this paper, we propose a new efficient, privacy-preserving, and quantum-resistant key generation algorithm, namely, lattice-based hierarchical deterministic key generation (LB-HDKG), for maintaining user privacy in the public blockchain. The LB-HDKG scheme generates many cryptographic keys in a tree-like structure from a single seed to hide the links between transactions of the same user. Our proposal uses the lattice NTRU cryptosystem, the security of which relies on the shortest vector problem (SVP) and closest vector problem (CVP) over the polynomial ring. Operations on the lattice NTRU cryptosystem are efficient and secure against classical computers and quantum computers. Security and performance analyses of our scheme show that the model is more secure and efficient and should replace current models to safeguard data from quantum computers.

Privacy-preserving hierarchical deterministic key generation based on a lattice of rings in public blockchain

Implementation of Sobel operator based image edge detection on FPGA

The article discusses the problem of detecting sick or mechanically damaged potatoes using machine learning methods. We proposed an algorithm and developed a system for the rapid detection of damaged tubers. The system can be installed on a conveyor belt in a vegetable store, and it consists of a laptop computer and an action camera, synchronized with a flashlight system. The algorithm consists of two phases. The first phase uses the Viola-Jones algorithm, applied to the filtered action camera image, so it aims to detect separate potato tubers on the conveyor belt. The second phase is the application of a method that we choose based on video capturing conditions. To isolate potatoes infected with certain types of diseases (dry rot, for example), we use the Scale Invariant Feature Transform (SIFT)—Support Vector Machine (SVM) method. In case of inconsistent or weak lighting, the histogram of oriented gradients (HOG)—Bag-of-Visual-Words (BOVW)—neural network (BPNN) method is used. Otherwise, Otsu’s threshold binarization—a convolutional neural network (CNN) method is used. The first phase’s result depends on the conveyor’s speed, the density of tubers on the conveyor, and the accuracy of the video system. With the optimal setting, the result reaches 97%. The second phase’s outcome depends on the method and varies from 80% to 97%. When evaluating the performance of the system, it was found that it allows to detect and classify up to 100 tubers in one second, which significantly exceeds the performance of most similar systems.

Development of an Optimal Algorithm for Detecting Damaged and Diseased Potato Tubers Moving along a Conveyor Belt Using Computer Vision Systems

An image quality matrix provides a significant principle for objectively observing an image based on an alteration between the original and distorted images. During the past two decades, a novel universal image quality assessment has been developed with the ability of adaptation with human visual perception for measuring the difference of a degraded image from the reference image, namely a structural similarity index. Structural similarity has since been widely used in various sectors, including medical image evaluation. Although numerous studies have reported the use of structural similarity as an evaluation strategy for computer-based medical images, reviews on the prospects of using structural similarity for medical imaging applications have been rare. This paper presents previous studies implementing structural similarity in analyzing medical images from various imaging modalities. In addition, this review describes structural similarity from the perspective of a family’s historical background, as well as progress made from the original to the recent structural similarity, and its strengths and drawbacks. Additionally, potential research directions in applying such similarities related to medical image analyses are described. This review will be beneficial in guiding researchers toward the discovery of potential medical image examination methods that can be improved through structural similarity index.

/pdf/prospects-of-structural-similarity-index-for-medical-image-25zad8js.pdf

Prospects of Structural Similarity Index for Medical Image Analysis

The sustainable functioning of the transport system requires solving the problems of identifying and classifying road users in order to predict the likelihood of accidents and prevent abnormal or emergency situations. The emergence of unmanned vehicles on urban highways significantly increases the risks of such events. To improve road safety, intelligent transport systems, embedded computer vision systems, video surveillance systems, and photo radar systems are used. The main problem is the recognition and classification of objects and critical events in difficult weather conditions. For example, water drops, snow, dust, and dirt on camera lenses make images less accurate in object identification, license plate recognition, vehicle trajectory detection, etc. Part of the image is overlapped, distorted, or blurred. The article proposes a way to improve the accuracy of object identification by using the Canny operator to exclude the damaged areas of the image from consideration by capturing the clear parts of objects and ignoring the blurry ones. Only those parts of the image where this operator has detected the boundaries of the objects are subjected to further processing. To classify images by the remaining whole parts, we propose using a combined approach that includes the histogram-oriented gradient (HOG) method, a bag-of-visual-words (BoVW), and a back propagation neural network (BPNN). For the binary classification of the images of the damaged objects, this method showed a significant advantage over the classical method of convolutional neural networks (CNNs) (79 and 65% accuracies, respectively). The article also presents the results of a multiclass classification of the recognition objects on the basis of the damaged images, with an accuracy spread of 71 to 86%. 

https://www.mdpi.com/2071-1050/14/4/2420/pdf?version=1645346524

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

Primary user emulation attack mitigation using neural network

The Low Noise Amplifier (LNA) is the first stage in RF CMOS receivers. The Common Gate (CG) LNA and Inductively Degenerated Common Source (CS) LNA are one of the widely used topologies for realizing RF CMOS receivers. The present work emphasizes a simple and exhaustive search procedure for the synthesis and analysis of CMOS CG and Inductively Degenerated CS LNA circuits. The width (W), gate source voltage (
 $$ V_{gs} $$
 ) and drain source voltage (
 $$ V_{ds} $$
 ) of the transistors constitute the design space in the circuit design. The design first involves the use of a circuit simulator (HSPICE) to obtain the small signal parameters of the circuit for various W, $$ V_{gs}$$
 , and $$ V_{ds} $$
 of the transistors and then to generate a Look-Up Table (LUT) for all design points using the obtained values. This LUT is used to meet the target performance specifications along with appropriate analytical expressions derived from the circuit in a numerical simulator (MATLAB). This will enable one to explore the whole design space quickly and fastly for arriving at the optimal values for the device dimensions, bias voltages and bias currents of the two LNA circuits. The design methodology is demonstrated by designing CG and Inductively Degenerated CS LNA circuits using 90 nm CMOS technology library in which Inductively Degenerated CS LNA gets high gain and low noise figure than CG LNA.

/pdf/design-of-inductively-degenerated-common-source-rf-cmos-low-cqdf5myio0.pdf

Design of inductively degenerated common source RF CMOS Low Noise Amplifier

To minimize the consumption of power, chip area and to enhance the battery life and performance of the system, the low power VLSI circuit is designed. Scaling design or counter is used as a key element for increasing or decreasing the values of an operator depending on its previous state. During the counting process frequency and time can be measured. The major problem in scaling circuit is the power consumption due to the power dissipation in the clock during standby mode. One-third of the total power is consumed by the clock signal in a counter. In this paper, power consumption is reduced by minimizing the number of switching activities. The power consumption in counter further reduced by reducing the power consumption in flip-flops. This can be achieved by combining True Single Phase Clock Logic (TSPCL) with Self-controllable Voltage Level (SVL) technique. TSPCL performs the Flip-Flop operation at high speed with low power. SVL technique suppresses the power due to leakage current and also uses less number of transistors thus the system complexity also gets reduced. The proposed design consumes 27% less power than the existing design. The proposed methodology reveals promising avenues for low power modern electronics items.

Low Power Design of 4-bit Simultaneous Counter using Digital Switching Circuits for Low Range Counting Applications

https://www.techscience.com/iasc/v32n1/45304/pdf

D. Malathi

Papers

Primary user emulation attack mitigation using neural network

Implementation of Sobel operator based image edge detection on FPGA

Design of inductively degenerated common source RF CMOS Low Noise Amplifier

Low Power Design of 4-bit Simultaneous Counter using Digital Switching Circuits for Low Range Counting Applications

A Design Framework for Smart Ration Shop Using Blockchain and IoT Technologies