Accurate disease diagnosis is becoming a critical challenge owing to the limited success of classical machine-learning approaches, especially in classification problems. Accurate and rapid brain tumor classification in medical diagnosis could aid in early detection and treatment planning. This paper presents a novel quantum-enhanced machine learning approach using a Quantum Support Vector Machine (QSVM) and Variational Quantum Classifier (VQC) models for improved tumor classification. ZFeatureMap is deployed for the conversion of classical data points to quantum space, as it is found to be most suitable for the characteristics of the brain tumor dataset and provides a balance between accuracy, efficiency, and computational feasibility. The QSVM and VQC models are implemented on a publicly available brain tumor dataset, demonstrating significant improvement over classical algorithms. Both models were implemented on a 32-qubit quantum simulator and a seven-qubit quantum computer. The simulation results indicate that the QSVM-based model is 3.62% more accurate and 60.4 times faster, while the VQC-based model is 2.49% more accurate and twice as fast as classical SVM. The findings suggest that quantum-enhanced machine learning has the scope to revolutionize medical diagnosis with more precise and faster results.

This research unveils an improved indoor positioning method by utilizing inbuilt Inertial Measuring Unit (IMU) and pressure sensor of smartphone and enhancing accuracy in Global Positioning System (GPS)-challenged environments. Initially, Ground Truth (GT) trajectories in 3 cases were obtained by measuring different points with measuring-tape. Each GT trajectory represents actual Positional Data (PD) which is compared with the calculated PD trajectories generated from PD 1, 2, 3, 4, and 5 obtained with 5 different methods. Applying Rotational Transformation (RT), it amalgamates accelerometer and gyroscope data to derive earth linear accelerations (ELA) for PD 1. Applying a Fast Fourier Transform (FFT)-based High Pass Filter (HPF) enhances ELA, refining it into PD 2. Incorporating the magnetic field through the Kalman Filter produces PD 3. Linear Regression (LR) models on IMU and pressure sensor data generate PD 4. Through LR model training with past Positional Data 3, IMU, pressure sensor data, and Positional Data 1, the algorithm achieves superior accuracy in PD 5. Demonstrating a substantial 19.49–78.57

This paper introduces a refined indoor position estimation algorithm leveraging sensors’ data from smartphone. Initially, ground truth data is determined through precise physical measurements using a measuring tape, establishing an accurate trajectory. Subsequently, utilizing the smartphone’s built-in inertial measuring unit (IMU) and pressure sensor data along this trajectory, the proposed algorithm estimates respective positions. The methodology integrates Sensor Fusion (SF) techniques, including Rotational Transformation (RT) and Kalman Filter (KF), alongside a Fast Fourier Transform (FFT) based High Pass Filter (HPF), and employs a Multi-Layer Perceptron (MLP) for data training and validation. The Root Mean Square Error (RMSE) between actual and calculated trajectories is reported at 0.017 m, significantly outperforming GPS data comparison with an RMSE of 2.22 m. Notably, an improvement of 72.4% to 98.91% in RMSE is observed compared to existing literature.

This review article presents the key fundamentals of indoor positioning system (IPS) and its progressing footprints. The need of IPS and work done with methodology adopted to implement IPS for various applications have been discussed. The evolution from conventional to deep learning (DL) has been presented, addressing various challenges existing in conventional IPS like poor localization, improper accuracy, non -line -of -sight problems, instability of signal due to fading, requirements of large infrastructure, data -set and labour, high cost, and their existing solutions have been disclosed. Further in order to compute the indoor positioning with acute precision various advanced positioning technologies including sensor fusion, artificial Intelligence (AI), and hybrid technologies have been explored. The issues and challenges existing in current IPS technology have been presented and future insights to work in this direction have also been provided.

This paper presents new finite element method (FEM) based approach for radio frequency (RF) and crosstalk (X talk) characterization of chip interconnects. Being based on scattering parameters (S-parameters), this approach truly and accurately demonstrates the transmission line behavior of chip interconnects over a wideband of frequencies. To demonstrate FEM based method, a single-line and a 3-line interconnect test structures on SiO2-Si substrate have been designed and simulated in High Frequency Structure Simulator (HFSS). The RF and crosstalk characterization of chip interconnect materials Copper (Cu), doped multilayer Graphene Nanoribbon (DMLGNR), and neutral multilayer Graphene Nanoribbon (NMLGNR) have been demonstrated in terms of transmission coefficient (S-ij &S-mn) from 1 to 1000 GHz. The single-line three-dimensional (3D) structures comprising of Cu, DMLGNR, and NMLGNR have maximum transmission loss values of-15.93 dB, -22.03 dB, and -13.73 dB at frequencies of 643 GHz, 402 GHz, and 643 GHz, respectively whereas three-line bus structure exhibit maximum victim-line transmission loss values of -15.28 dB, -17.47 dB, and -15.98 dB at frequencies of 247 GHz, 829 GHz, and 377 GHz, respectively. Further, the crosstalk results have demonstrated that as frequency increases significant crosstalk is observed between nearby lines due to electromagnetic interference and coupling (EMI/EMC) issues.

The evolution of quantum computers and quantum machine learning (QML) algorithms have started demonstrating exponential speed-ups. In machine learning problems, the efficient handling and manipulation of linear algebra subroutines defines the complexity of the task to be performed. Quantum computers handle big datasets in the form of vectors and matrix operations very efficiently. In this paper, quantum support vector machine (QSVM) algorithm is used to solve a classification problem using a benchmarking MNIST dataset of handwritten images of digits. Quantum SVM variational and kernel matrix algorithms are implemented to analyze quantum speedup on quantum simulator and physical quantum processor back-ends. The study compared classical and quantum SVM algorithms in terms of execution time and accuracy. The results explicitly prove quantum speed-up achieved by quantum classifiers on quantum back-ends for machine learning applications.

This paper presents RF and crosstalk analysis of Copper (Cu) and multi-layer Graphene nanoribbon (MLGNR) based interconnects using Fin field-effect transistor (FinFET) and virtual-source carbon nanotube field effect transistor (CNFET) based repeater insertions in sub-10 nm regime. The SPICE based analysis utilizes an accurate π-type equivalent single conductor (ESC) model for mutually coupled interconnects at 7 nm technology node. The transfer function and 3-dB bandwidth results of lithium-doped MLGNRs offer many fold improved RF performance than Cu. The out-of-phase crosstalk induced (OPXT) delay results with FinFET repeaters demonstrate 27.54 and 67.6 % reductions for pristine and lithium-doped MLGNRs as compared to Cu, whereas CNFET repeaters demonstrate 20.48 and 81.88 % reductions at interconnect length of 1000 µm. The peak far-end crosstalk (FEXT) noise voltage results demonstrate 86.03 and 62.5 % using FinFET repeaters and 88.14 and 69.9 % reductions using CNFET repeaters for pristine and Li-doped MLGNRs than Cu at 1000 µm length. Further, the energy-delay-product (EDP) results demonstrate 59.7 and 97 % reductions using FinFET repeaters for pristine and Li-doped MLGNRs than Cu at length of 1000 µm. The EDP results using CNFET repeaters exhibit 34 % degradations for pristine-MLGNRs than Cu while Li-MLGNR exhibit 98.61% reductions than Cu at length of 1000 µm.

A comparative radio-frequency (RF) and crosstalk analysis is performed on carbon nano-interconnects based on an efficient pi-type equivalent single-conductor model of bundled multiwall carbon nanotubes (MWCNTs) and stacked multilayer graphene nanoribbons (MLGNRs). Simulation results are extracted using HSPICE for global-level nano-interconnects at the 14-nm node. RF performance is evaluated in terms of skin depth and a 3-dB bandwidth, while crosstalk performance is analysed in terms of crosstalk-induced delay and average power consumption. The skin-depth results indicate significant improvements in skin-depth degradation at higher frequencies for AsF5-doped zig-zag MLGNRs compared with that of Cu, nanotubes and MWCNTs. The transfer gain results explicitly demonstrate that AsF5-doped MLGNRs exhibit excellent RF behaviour, showing 10- and 20-fold improvements over MWCNTs and copper (Cu), respectively. Further, the 3-dB bandwidth calculations for AsF5-doped MLGNRs suggest 18.6- and 9.7-fold enhancement compared with Cu and MWCNTs at 1000 mu m. Significant reductions are obtained in crosstalk-induced out-of-phase delays for AsF5-doped MLGNRs-their delay values were 84.7% and 60.24% less than those for Cu and MWCNTs. Further, AsF5-doped MLGNRs present the most optimal energy-delay product results, with values representing 98.6% and 99.6% improvements over their Cu and MWCNT counterparts at a global length of 1000 mu m.

The novel characteristics of CNTFET have eliminated many technological and fundamental hindrances being faced by CMOS transistors. CNTFET is emerging as prospective replacement for CMOS transistors in digital circuits and systems. This chapter introduces design of CNTFET-based basic logic gates. The basic logic gates analyzed are inverter, NAND, and NOR gates. The designed gates are evaluated in terms of delay, power consumption, and figure-of-merit power-delay-product (PDP). The standard H-SPICE CNTFET model of Stanford University has been used for all simulations. The impact of dielectric material variations on performance parameters of carbon nanotube field effect transistor based universal gates has been analyzed. Comparison between CMOS and CNTFET-based logic circuits is carried out for different dielectric material at 16 nm technology node.

Intercalation doping is emerging as a prospective solution to enhance the performance of graphene nanoribbon interconnects. In this paper, the radio frequency (RF) analysis of stage-2 arsenic pentafluoride- and lithium-doped multilayer graphene nanoribbons (MLGNRs) has been carried out for global-level interconnects in terms of skin depth, surface impedance, critical ratio ( C R ), transfer gain, and 3-dB bandwidth. The skin-depth results demonstrate that doped MLGNRs exhibit minimum performance degradation primarily due to their higher conductivity, mean free path, and momentum relaxation time as compared to neutral MLGNR. An equivalent second-order accurate RLC model of an intercalation-doped MLGNR has been used to extract the transfer gain and 3-dB bandwidth results at 14-nm technology node for global-level interconnects. The results are further evaluated by implementing an advanced π-type equivalent single conductor derived from multi-conductor transmission line model. The doped MLGNR interconnects demonstrate 11-fold enhancement of 3-dB bandwidth as compared to copper (Cu). Also, the delay and energy-delay-product (EDP) computations in time domain for doped MLGNR interconnects exhibit nearly 10 times lesser delay and significant reduction in EDP than Cu counterparts. It is also observed that optimum values for 3-dB bandwidth and EDP parameters for intercalated MLGNRs could be achieved through width optimization. The RF and transient results validate intercalated MLGNRs as a potential candidate to replace Cu for next-generation global-level interconnects.

Quantum computing is proving to be very beneficial for solving complex machine learning problems. Quantum computers are inherently excellent in handling and manipulating vectors and matrix operations. The ever increasing size of data has started creating bottlenecks for classical machine learning systems. Quantum computers are emerging as potential solutions to tackle big data related problems. This paper presents a quantum machine learning model based on quantum support vector machine (QSVM) algorithm to solve a classification problem. The quantum machine learning model is practically implemented on quantum simulators and real-time superconducting quantum processors. The performance of quantum machine learning model is computed in terms of processing speed and accuracy and compared against its classical counterpart. The breast cancer dataset is used for the classification problem. The results are indicative that quantum computers offer quantum speed-up.

This paper investigates crosstalk (XT) analysis of emerging multi-layer graphene nanoribbons (MLGNR) as chip interconnect material with different types of repeater insertions. The performance of repeaters comprising of FinFET and carbon nanotube field effect transistor (CNFET) have been evaluated based on the equivalent single conductor (ESC) model. Using three-line bus architecture, the worst-case XT delay on the victim line for armchair (ac) and zig-zag (zz) based MLGNR interconnects have been computed for local/ intermediate and global levels. The detailed crosstalk investigations include in-phase (IP), out-of-phase (OP), and net XT induced delay. Also, the peak crosstalk noise voltage and average power consumption have been computed for MLGNR interconnects. The SPICE simulation results further indicate reduced crosstalk delay at local/intermediate levels for MLGNR based interconnects with degradation at global level. The reductions observed in worst-case XT induced delay for MLGNR interconnects with FinFET repeaters are 41.54, 34.57, and 19.71% and with CNFET repeaters are 47.47, 30.91, and 20.65% as compared to Copper (Cu) interconnects for interconnect lengths of 100, 500, and 1000 µm, respectively. Significant reductions in peak crosstalk noise voltage amplitude have been observed for local/intermediate level MLGNR interconnects as compared to Copper.

The carbon nanotube field-effect transistor (CNFET) is emerging as one of the most promising alternatives to complementary metal–oxide–semiconductor (CMOS) transistors due to its one-dimensional (1-D) band structure, low off-current capability, near-ballistic transport operation, high stability, and low power consumption. This paper presents the design of a CNFET-based ternary content-addressable memory (TCAM) cell and rigorously analyzes its performance in terms of power–delay product (PDP) and static noise margin (SNM). The effect of variations of the chiral vector on the performance of the TCAM cell is also comprehensively investigated. While selecting the chirality, SNM, PDP, and search time are considered as figures of merit. In this TCAM cell design, we apply the same chirality for all CNFETs of the same type. Extensive HSPICE simulations have been performed for computation of performance parameters using the Stanford University CNFET model. Comparison of CNFET- and CMOS-based TCAM cells has been carried out at the 16-nm technology node. The results show that the CNFET-based TCAM cell exhibits significant improvements of PDP, i.e., by 38 % during write operation and 98 % during search operation, and 53 % in SNM, compared with its CMOS counterpart. It is also observed that the best chirality for the TCAM cell design is (22, 19, 0) or (10, 19, 0) from the point of view of SNM and PDP, respectively.

Multilayer Graphene nanoribbon (MLGNR) are emerging as prominent future material for chip interconnects owing to their simple fabrication process and equivalent performance to CNTs. This paper presents performance analysis of MLGNR based interconnects on equivalent single-conductor (ESC) model for local/intermediate and global level of chip interconnects at 13.4 and 21 nm technology nodes. The number of conducting channel variations with Fermi energy and interconnect width are analyzed first. The performance of metallic armchair (AC) and Zig-Zag (ZZ) based MLGNR interconnects is computed employing equivalent single conductor (ESC) model in terms of delay, average power consumption, and power-delay product (PDP). The computed results for metallic armchair and Zig-Zag MLGNR are compared against Copper based interconnects. It is observed that MLGNRs offer significantly improved delay ratio, average power reduction, and lower PDP values than copper based interconnects.

Processors are widely used in embedded systems to deliver high speed computation, high power efficiency at a very low cost. 8085 processor is a general purpose processor widely used in embedded design. However, in nanoscale regime these designs are becoming more sensible to transient faults caused by radiations. Thus temperance of soft error is major concern. Nowadays, a new technique Software Implemented Hardware Fault Tolerance (SIHFT) is used to safeguard devices against soft error at very low cost, on the other hand this technique has certain disadvantages like overheads in design like area, memory, and power consumption. In this work, we implement set of techniques on which we use different set of error correction and detection rules. Furthermore, low overhead technique is also implemented. An elaborated discussion of results specifying execution time and area overheads is presented.

Carbon nanotubes (CNTs) owing to their novel electro-thermal properties have been emerging as possible solutions to existing chip interconnect materials. Several fabrication attempts demonstrated that multiwall carbon nanotubes (MWCNTs) are generally preferred than single wall carbon nanotubes (SWCNTs) due to reduced concern regarding density and chirality control. Till date, lot of work is required in analyzing the impact of geometrical parameters of MWCNT like shell count, diameter and length on electrical parameters like R, L, and C. This paper comprehensively evaluates the impact of geometrical tube parameter variations on quantum/scattering resistances, kinetic/magnetic inductances, and quantum/electrostatic capacitances for isolated and bundles of MWCNTs. The results reveal that as the shell count increases, the resistivity decreases drastically due to increase in conducting channels, the kinetic/magnetic inductances decreases, and shell to shell capacitances of MWCNT based interconnects increases. The length variation results predicted that MWCNTs are not optimal solution for local level interconnects (<100 μm) due to their higher resistivity, but they offer significant improvements in resistivity for intermediate (>100 μm) and global level interconnects (>500 μm) than Copper and SWCNT based interconnects. Also the effect of variations in aspect ratio of bundles of MWCNTs on performance has been analyzed and compared against bundles of SWCNTs and Copper interconnects. MWCNT based interconnects have been compared evaluating power consumption, delay and power-delay-product (PDP) for 32, 22, and 16 nm technology nodes.

A miniaturized multiband slotted Microstrip antenna for wireless applications is designed and its characteristics have been investigated. The proposed antenna consists of a substrate, one side of substrate is having rectangular radiating patch containing C-shaped slots and other side is having a partial ground plane. The dimensions for proposed antenna are kept small to 25 × 25 × 1.6 mm. The number, length/width, and positions of the C-shaped slots have been selected suitably so as to achieve results for return loss, VSWR in frequency bands of interest. The proposed antenna characterizes four bands at resonant frequencies of 3.39, 4.29, 5.46, and 5.77 GHz with return loss values of −16.45, −19.24, −11.85, and −12.35 dB and VSWR values of 1.37, 1.24, 1.69, and 1.64 respectively. The designed antenna can serve IEEE 802.11a radio WLAN applications in frequency band 5.180–5.825 GHz, IEEE 802.16-2004 fixed WiMAX applications in frequency band 2–11 GHz, and IEEE 802.16e mobile WiMAX applications in frequency band 2–6 GHz. The proposed antenna has nearly omnidirectional radiation pattern exhibiting reasonable gains across the desired frequency bands.

Nowadays, embedded linux is widely used operating system due to its compatibility with variety of systems like super computer, work station, personal computer, system on chip (SoC), server and custom computer. The major reason of using linux is high portability and open source code. Also, it supports different type of architectures like- x86, PowerPC, MIPS, H8, SPARC, or ARM and many more. This paper deals with design, implementation and testing of character device driver for the GPIO pins of ARM Cortex based platform. Device driver is a piece of code written in C language which is responsible for controlling the hardware and part of the kernel. ARM based platform Raspberry Pi is used in this work, supports the Linux OS. Due to missing of dependencies and patches, the option of cross compilation is available such as tool chain which contains GCC compiler, assembler, linker and debugger etc.

Thin film photovoltaic solar cells have been identified as an alternative solution for the fabrication of cost effective solar cells in future. Highly efficient thin-film solar cells can be realized using heterostructure semiconductor materials. As epitaxial CdTe has high carrier concentration, a radiative recombination rate approaching unity, and low defect density, which makes it an attractive candidate for highly efficient solar cells perhaps, becoming competitive with GaAs. This paper presents, design of a thin-film CdTe-CdS heterojunction based solar cell using different substrate materials. Also, a comprehensive performance analysis of designed solar cell has been carried out by computing various performance parameters. Epitaxial CdTe layer has been employed as an absorber region for the designed solar cell. This heterojunction combination yields conversion efficiency approaching 28.12% and open circuit voltage (VOC) of more than 920 mV. This increased efficiency and open-circuit voltage leads to enhanced short-circuit current (ISC). Further the performance of designed thin-film epitaxial CdTe-CdS heterojunction solar cell has been investigated for GaAs, CdTe and Si substrate materials.