Miracle-dz/UniFAR-FTUs huggingface dataset:
A Karnaugh Map Approximate Adder With Intrinsic Error Compensation

Approximate computing emerges as a new design paradigm to fill the gap between rigid design specifications and limited on-chip hardware resources. In this paper, an approximate Karnaugh Map-based Error Compensation Adder (KMECA) with intrinsic error compensation characteristics for loop accumulations is proposed for power and area savings while providing minimal computation errors. The proposed adder is based on a smart modification of the Karnaugh map to acquire zero mean error and generate an internal error compensation effect for loop accumulations. KMECA achieves the smallest Normalized Mean Error Distance (NMED) and Mean Square Error (MSE) in loop accumulation compared with the previously published approximate adders. An error model is also proposed to portray the error characteristics and explain the error compensation effect of KMECA.
Towards green data centers: Energy efficiency and performance evaluation

The Internet of Things (IoT) is considered a new paradigm that aims to connect a large number of devices. IoT is increasingly present in domains such as healthcare, transport, agriculture, and other industrial branches. An increasing number of IoT devices, as well as the amount of data, leads to increased energy consumption and a negative impact on the environment. Therefore, researchers are focusing on the concept of Green IoT that aims to increase energy efficiency and create a safe environment. The focus of this paper is on energy-efficient techniques within green data centers. Also, the performance evaluation of data centers was performed in the GreenCloud simulator for the optimal load of data centers in terms of energy efficiency and sustainability.
Forestry 4.0 – An Overview of Authentication in the Internet of Things

Today’s Internet of Things, a federation of emerging Internet-based technologies, enables connectivity, security, scalability, and functionality of a shared network. Its primary mission is to interconnect multifarious Things to enable service and information identification as well as exchange between each stakeholder at eye level. Numerous practical and academic examples have confirmed that the Internet of Things can be applied to various fields and specific areas of knowledge or expertise, such as smart home, smart city, and health care. Forestry 4.0 refers to a proposed framework to promote intelligence in current forestry activities, such as forest management, production and logistics, as well as the use of the Internet of Things to digitalize value chains. As connected IoT devices increase in numbers and complexity, considering security evolves inevitably. This paper provides an overview of the status of IoT security, especially regarding authentication. We also present the vision of challenges (a) and issues related to authentication in today’s Forestry 4.0.
Deep Transfer Learning For Improving Alzheimer Disease Diagnosis

Alzheimer disease (AD) is a neurological disorder which shrinks the brain and causes dementia. In the past, this disease was more prevalent in American countries. However, it is now common in other countries as well. When compared to youth, older people are more affected by this disease. The number of people affected by this disease is gradually increasing each year, and according to one study, this number may reach around 15 million in the near future. People who are affected will experience symptoms such as memory loss and confusion. Early detection of Alzheimer disease is essential for providing appropriate treatments. Neuroimaging based Machine Learning methods are commonly utilized for the detection and diagnosis of Alzheimer’s, but they are time-consuming. The time consumption can be reduced, and the detection accuracy can be increased further with the help of Deep Learning and Transfer Learning algorithms. This proposed work compares 4 different Transfer Learning Models. VGG-16 has the highest accuracy of 97.2 percent out of the four models tested.
Positional Error Estimation of the Parallel Manipulator for Control Application

Parallel manipulator (PM) become popular in industrial application over serial manipulator owing to the higher accuracy, fast response and rigidity. In the present study electrohydraulic two degree of freedom (2DOF) PM is considered for study the control performance. Apart from measurement and sensor error parallel manipulator may experienced structural error, which may cause of joint clearance. Lab. scale 2DOF PM has been considered structural error analysis due to joint clearance. Further, a model free self-tuning fuzzy-PID-bias (F-PID-B) controller has been developed for control of the orientation of the top platform of PM. The controller performance has been studied for combined sinusoidal heave and pitch demand. The robustness of the controller has been tested under various demand and different external load. F-PID-B controller has shown the ability to handle external load disturbances with measurement uncertainty and system nonlinearity using a low cost proportional valve controlled hydraulically actuated PM.
A Practical Framework for Multi Agent Experiments in Aerial Robotics

A framework is presented for distributed control of rotary wing aerial robots using open-source flight controller firmware, commercial communication and computing hardware in an outdoor visual line of sight test environment. The work builds upon existing documented frameworks that focus mainly on laboratory based experiments. The research context is evaluation of novel rules for safe intersection of high-density aerial traffic flows. The framework is based on the use of a PX4 flight controller, Linux onboard computer running bespoke flight management code, MAVlink WiFi Bridge and WiFi module on each flight vehicle, and a ground control station running QGroundControl and a bespoke graphical user interface for interaction with vehicles and control of the overall experiment. SITL functionality is provided using Gazebo for physical simulation. Communication is via WiFi, with an individual radio control transmitter for each vehicle provided as backup. The developed framework was evaluated using a fleet of 10 multirotor vehicles based on 1kg class racing drones operating in a test volume approximately 100x100x100m. Engineering evaluation results are presented in the form of an technical issue register which highlights the engineering challenges that had to be solved to achieve a practical working system. Results are then presented for an example drone traffic intersection experiment in which two helical traffic flows are joined without conflict using flight rules running local to each drone. The proposed framework has been shown to be scalable to up to 10 vehicles, with addition of further vehicles being limited by WiFi router performance. The framework is hardware agnostic as far as possible and is extensible to use for a range of different distribute control experiments.
Performance Evaluation of Predictive Models for Breast Cancer Classification

Breast cancer (BC) is the second most typical carcinoma in women after most of the tumors. With the development of contemporary diagnostic technologies, globalization, and commercialization, higher incidences of this cancer have been documented. BC accounts for 16% of all cancer-related deaths worldwide, making it the most prevalent cause of cancer-related death among women. BC is lethal in just 50% of cases. The purpose of this study is to investigate a report on BC in which the possibility that a patient would survive their illness was predicted using the most recent technological advancements. To create the prediction models utilizing a big dataset, we employed five well-known machine learning (ML) methods LR, DT, NB, KNN and SVM.
A partial view of the energy crisis in the Europe

The last energy crisis manifested itself as a rise in electricity prices and an increase in the profits of energy companies. Profits in the energy sector last year were expected to be 150 percent higher than in 2021. In 2021, electricity prices increased by 170% compared to the previous year. In 2022, however 150% against the previous year. Therefore, the end users felt the energy crisis as an increase in their energy costs. The state can only protect small consumers from usury by regulating the permitted prices. Medium and large consumers must understand the operation of the energy exchange and conclude contracts with traders that take advantage of the futures contracts and daily spot trading. To intelligently buy energy on the power exchange, which means buying at the lowest possible prices, customers must be able to predict energy prices on the power exchange and predict their energy consumption. In the article, we present medium-term price forecast on the HUPX exchange for 2023. We also explain the tools for short-term price forecasting for 14 days.
Deep Learning and Image Processing Techniques applied in Panoramic X-Ray Images for Teeth Detection and Dental Problem Classification

Due to its remarkable achievements in detection,prediction, and classification, deep convolutional neural network has gotten huge attention into clinical analysis. Specialists can use panoramic dental radiograph analysis to uncover issues in low-light locations, inside the buccal cavities, or even narrow regions. Weak picture resolution or weariness, on the other hand, can lead the diagnosis to differ, making therapy more difficult. With the help of comprehensive x-ray pictures, the automated tooth detection and dental condition classification method we present in this study, medical practitioners can make more accurate diagnosis decisions. In order to conduct this study, panoramic radiographics from three dental offices were gathered and interpreted, showcasing 14 different potential tooth problems. An annotated dataset was used to train CNN and acquire semantic segmentation data. Then, different image processing methods were applied to segment and fine-tune the bounding boxes corresponding to the teeth defections. The final step involved labelling each tooth sample inside the identified area of interest and using histogram-based majority voting to determine the issues that affected it. Some of the criteria used to assess the developed method included specificity, memory, result for derived bounded area detection, and intersection over union in supervised classification. The outcomes were contrasted with information from different approaches and found to be superior, demonstrating the proposed solutions’ superiority. In addition, tasks such as tooth categorization, identification of illnesses and severe gum disease like periodontitis, and how much cavity cleaning should be done are completed.
Time Skew Calibration for Time-Interleaved Analog-to-Digital Converters

This paper presents an all-digital extraction method for time skew in the time interleaved analog-to-digital converters. Firstly, the fractional delay filter is used to construct the reference of extraction time skew. Secondly, a time-varying step and sign-sign gradient descent least mean squares algorithm is applied to extract time skew. Thirdly, an appropriate window function is designed to make the bandwidth of fractional delay filter larger. Finally, time-varying steps and iterations-varying samples reduce the power consumption of estimation in the LMS algorithm. The advantage of the proposed technique is that it doesn't require additional hardware reference and has low power consumption, fast convergence rate, strong portability.
A 16-Bit 2 MS/s Cyclic-pipelined ADC with Calibration for Inter-stage Amplification

The inaccuracy of residue amplification has become the major bottleneck when it comes to design pipelined analogue-to-digital converters (ADCs). High-gain and high-speed operational amplifiers (Op-Amps) usually consume too much power for a decent ADC. Therefore, we proposed a foreground calibration technique, which can correct amplification errors in cyclic-pipelined ADCs and consequently alleviate the DC gain requirement for internal amplifiers. The proposed calibration scheme is implemented in an area-efficient 16-bit, 2 MS/s cyclic-pipelined ADC, fabricated in 180 nm CMOS technology. The ADC is designed and realized by cycling a 5-bit sub-ADC four times, and each time 1-bit redundancy is exploited to suppress the errors due to sub-ADCs. Actual gain of each amplification can be feasibly calculated by the Fix-Point Iteration algorithm. Simulation results show the signal-to-noise-and-distortion-ratio (SINAD) to be 100.6 dB even with a 57dB-DC-Gain amplifier. The total power consumption of ADC is 30.43 mW and it occupies an active area of 1.8 mm square.
Electron momentum spectroscopy of crystals: From dream to reality

37/661 (2), Fort P.O. , Trivandrum-695 023, Kerala, India Nanoscale Interactions and their applications: Essays in honour of Ian McCarthy, (2006): 000-000 ISBN: 00-0000-000-0 Electron Momentum Spectroscopy of Crystals: from dream to reality Maarten Vos Atomic and Molecular Physics Laboratories, Research School of Physical Sciences and Engineering, Australian National University, Canberra, ACT, 0200 Australia Abstract The use of Electron Momentum Spectroscopy for the study of thin, single-crystal films is sketched. These films have an anisotropic electronic structure. Resolving this anisotropy in (e,2e) experiments has been a dream that has become a reality in recent years. Here I describe some results for graphite, silicon and noble metals. The experimental results are used to illustrate the concepts of Brillouin zone boundaries and Bloch functions, both central to condensed matter physics.
High-Resolution Image Reconstruction Considering the Inaccurate Sub-Pixel Motion Information

The demand for high-resolution images is gradually increasing, whereas many imaging systems have been designed to allow a certain level of aliasing during image acquisition. Thus, digital image processing approaches have recently been investigated to reconstruct a high-resolution image from aliased low-resolution images. However, since the sub-pixel motion information is assumed to be accurate in most conventional approaches, the satisfactory high-resolution image cannot be obtained when the sub-pixel motion information is inaccurate. Therefore, in this paper we propose a new algorithm to reduce the distortion in the reconstructed high-resolution image due to the inaccuracy of sub-pixel motion information. For this purpose, we analyze the effect of inaccurate sub-pixel motion information on a high-resolution image reconstruction, and model it as zero-mean additive Gaussian errors added respectively to each low-resolution image. To reduce the distortion we apply the modified multi-channel image deconvolution approach to the problem. The validity of the proposed algorithm is both theoretically and experimentally demonstrated in this paper.
3D Thermal Detection of Humans in Smoky Indoor Environments

Human victim detection during search and rescue missions is a challenging task for firefighters. Especially in smoky indoor environments, firefighters are limited in their vision and orientation. Mobile robots have gained a significant attention in recent years, as they support firefighters during their mission. These robots are equipped with several sensors and operate autonomously or remote controlled by an operator outside the hazardous environment. In this paper, a new approach to detect human bodies in a light smoky indoor environment is proposed. The approach consists of a monocular RGB camera and a low resolution thermal camera $(8\times 8\ \text{px})$ mounted on a mobile robot. The mobile robot is controlled remotely by an operator outside the scene. A Large-Scale Direct Simultaneous Localization and Mapping method (LSD-SLAM) is used to generate a 3D thermal map of a human body in the smoky environment by superimposing thermal information onto a 3D surface reconstructed by a monocular RGB camera. Finally, the approach is approved by an experiment in a light smoky indoor environment.
Contribution of Wind Power Plants to Transmission System Transient Stability

The increase in the number of wind farms and their share in the total electrical energy generation leads to the need for a different approach to this source in cases where the stability of the power system is potentially impaired. With the development of different types of wind power plants, equipped with power electronics devices, there is the possibility of quick power management and injection, in conditions when it is needed, where a huge amount of accumulated kinetic energy can also be used. This paper presents the influence of a wind power plant equipped with a full-scale converter on the transient stability in cases of close and distant short circuits, during the outage of a heavily loaded line. Special attention was paid to the Rate of Change of Frequency (RoCoF) in the power system in cases with and without a wind farm where fast power injections were possible.
Hierarchical and Recursive Floorplanning Algorithm for NoC-Bascd Scalable Multi-Die FPGAs

Emerging applications are calling for significantly larger FPGAs with multi-dies. However, these multi-die FPGAs with a traditional substrate-based interconnection are not scalable enough, because the execution time and probability of failure of their floorplanning algorithm will increase dramatically with the growth of design or the number of ides. Therefore, future multi-die FPGAs will require a scalable interconnection architecture and its associated floorplanning algorithm. To address this issue, we propose both a new NoC-based scalable multi-die FPGA architecture and a corresponding floorplanning algorithm, namely Hierarchical and Recursive Floorplanning Algorithm(HRFA). First, we introduce the interconnection architecture with a class of scalable hierarchical topologies. Second, we formulate the floorplanning problem for the proposed NoC architecture as an ILP (Integer Linear Programming). Third, we develop a novel recursive method to solve the ILP formulation by taking advantage of the parallelization opportunities exploited from the hierarchical interconnection architectures. The experiments on a Convolutional Neural Network (CNN) benchmark show that the scalability of our proposed technique is at least $3\times$ as that of the state-of-the-art solutions measured by the size of the feasible benchmark, with no loss of design performance.
Evaluation of BLER and throughput during the coexistence of both 4G LTE and 5G NR

The progress of development tends to share the equal spectrum for both 4G LTE and 5G NR. If the eMBB of 5G NR will coexist with the MBB of 4G LTE, there will be the requirement of the process of cognition to reduce the interference. When the spectrum of 5G NR will share the same spectrum with the spectrum of 4G LTE, then there will be possibility for new advantageous innovations which provide a huge scope in future. As the spectrum two signals will share equally with each other, there will be possibility of interference and leads to the reduction of reliability. For the purpose of the reduction of interference, 4G LTE should be kept fixed and the oscillator frequency for 5G NR should be variable for spectrum sharing. Realization indicates that when the parameters for both 4G LTE and 5G NR are equal during same spectrum sharing, there will be BLER but no throughput So in order to increase the throughput and decrease the BLER, the oscillator frequencies for 5G NR must be increased or decreased in comparison to the frequency for 4G LTE. Increase in throughput indicates the increase in reliability.
The Curious Case of Effective Isotropic Sensitivity: Polarization mismatch in over-the-air testing of wireless user equipment

This article provides the theory behind the two-step over-the-air (OTA) test method widely used in 3rd Generation Partnership Project (3GPP) test specifications and, in particular, the testing and characterization of 5G millimeter-wave user equipment (UE) in compact antenna test ranges (CATRs). Special attention is paid to receiver (Rx) characteristics and the polarization mismatch problem, which is encountered in spherical coverage tests. The received power of a UE with a dual-polarized antenna system is derived, and the output signal-to-noise ratio (SNR) is calculated using the maximal ratio combining (MRC) principle. And finally, flaws in the dominant interpretation of this test method in the development of 5G millimeter-wave standards and their consequences are addressed.
Potential of Mass Customization using Product Configurators

Widespread demand for product customization has led to the expansion of utilizing product configurators. Greater flexibility in product design and manufacturing are one of the main parts of the production process that could be impacted with this approach. Mass customization represent a manufacturing approach that combines the adaptability and customization of products with the rational costs of mass production. The aim of the paper is to demonstrate potential mass customization applications using product configurators while outlining benefits and limitations that could occur. Product configurators enable organizations to produce numerous customized items while simultaneously maintaining control over the design and manufacturing processes. On the other hand, customers have an opportunity to select from a variety of features to design a product that is adjusted to their requirements.
Frequency Reconfigurable Antenna Using SSRR Metamaterial for Smart Applications

In order to use an antenna at different frequencies in a particular frequency band, a Frequency Reconfigurable Antenna design is proposed using Single Split Ring Resonators (SSRR). It uses a FR4(2.5×2.5mm2) as a substrate with a thickness of 0.25 mm. It uses an inset feed and a metal surface at the back of substrate of same size which act as a reflector. This reflector is used to increase the gain and other characteristics of the antenna. Single PIN diode is used as switch for connecting the Split Ring Resonators. By ON/OFF the switch we get the two different modes and different resonating frequencies at those modes. It exhibits the VSWR <2 and simulated on CSToMWSo. It resonates at two different frequencies, 10.9 GHz, 9.2 GHz, with a maximum gain of 8.45 dBi, 8.54dBi respectively.
Logic Locking for Hardware Security Using Voltage-Gated Spin-orbit Torque Magnetic Tunnel Junction

With the rapid interest in exploiting the advantages of beyond CMOS devices in various applications, we explore, in this work, voltage-gated spin-orbit torque-assisted magnetic tunnel junction (VGSOT-MTJ) based on the Verilog-A behavioral model to design a possible logic locking system for hardware security. The VGSOT MTJ can switch without needing a magnetic field, and the antiferromagnetic (AFM) strip provides SOT and an exchange bias, thus paving the way for more practical applications. Compared to spin transfer torque (STT) MTJs, these AFM-based SOT-MTJs do not require passing high write current through the thin layer of the MTJ stack, thus increasing their endurance significantly. Compared with Heavy metal (HM) based SOT-MTJ, the VGSOT-MTJ utilizes the voltage-controlled magnetic anisotropy (VCMA) effect to significantly reduce the $J_{\mathbf{SOT},\mathbf{critical}}$. We perform a Monte-Carlo analysis to account for the effect of Process Variation on critical MTJ parameters for designing the logic locking block. Eye Diagram test, transient performance, and the effect of thermal noise are analyzed for High-Speed Integrated Circuits systems, and the results are compared with HM-based SOT-assisted MTJ as both are three-terminal (3T) MTJ structures.