Biometric traits are unique characteristics of an individual’s body or behavior that can be used for identification and authentication. Biometric authentication uses unique physiological and behavioral traits for secure identity verification. Traditional unimodal biometric authentication systems often suffer from spoofing attacks, sensor noise, forgery, and environmental dependencies. To overcome these limitations, our work presents multimodal biometric authentication integrated with the characteristics of electroencephalograph (EEG) signals and handwritten signatures to enhance security, efficiency, and robustness. EEG-based authentication uses the brainwave patterns’ intrinsic and unforgeable nature, while signature recognition demonstrates an additional behavioral trait for effectiveness. Our system processes EEG data of an individual with 14-channel readings, and the signature with the images ensures a seamless fusion of both modalities.Combining physiological and behavioral biometrics, our approach will significantly decrease the risk of unimodal authentication, including forgery, spoofing, and sensor failures. Our system, evaluated on a dataset of 30 subjects with genuine and forged data, demonstrates a 97% accuracy. Designed for small organizations, the modular structure, low computation algorithms, and simplicity of the hardware promote deployment scalability.

Biometric systems aim to identify or verify individuals using physiological or behavioral traits reliably, but traditional modalities such as fingerprints, faces, and voices are vulnerable to spoofing and replay attacks. EEG has therefore emerged as a promising biometric alternative due to its inherent security, subject specificity, and resistance to forgery. However, a key limitation of existing EEG based biometric systems is poor cross session generalization, resulting in significant performance drops when models are tested on data from unseen recording sessions, which restricts real world deployment. In this work, we present a cross session EEG biometric identification framework evaluated on the publicly available BED dataset under eyes closed resting state conditions, using a strict protocol in which sessions one and two are used for training and validation, and session three is reserved exclusively for testing. We have proposed two hybrid CNN, LSTM, and encoder architectures, one with a four layer CNN paired with two layer stacked LSTMs and an encoder, and a deeper 8 layer CNN paired with four layer stacked LSTMs and an encoder. The cross session test performance for the four layer CNN and two layer LSTM pipeline achieved the highest test accuracy of 93.88% for six seconds, and the eight layer CNN and four layer LSTM pipeline reached a peak accuracy of 97.89% at two seconds. Finally, when compared with prior BED based cross session work that typically reports only a mid 80% cross session accuracies for CNN and classical pipelines, the proposed CNN LSTM and encoder design set a much higher accuracy mark on eyes closed BED Dataset while following to a strict cross session split and transparent extraction procedure with the highest accuracy of 97.89% for two seconds time segment the shortest time window we have experimented on in our work.

This paper presents a fully integrated solar energy harvesting system and power management unit (SEHS-PMU) for ultra-low-power Internet of Things (IoT) applications. The proposed system is implemented in 45 nm CMOS technology and employs a reconfigurable charge pump (RCP) architecture to achieve efficient energy conversion without using bulky inductors. A maximum power point tracking (MPPT) algorithm enables optimal energy extraction from the solar cell under varying illumination conditions. In addition, capacitor value modulation (CVM) is used for impedance matching, supporting compact monolithic integration. The proposed RCP-based DC–DC converter achieves a peak power conversion efficiency of 95%. Furthermore, on-chip low-dropout regulators (LDOs) generate regulated output voltages of 2.8 V, 1.3 V, and 0.7 V to support different subsystems of IoT, smart city, and IoMT sensor nodes. The proposed SEHS-PMU provides a compact, energy-efficient power management solution that enables sustainable operation of next-generation self-powered IoT devices.

Ensuring on-shelf availability (OSA) is vital for retail success, directly impacting profitability and customer satisfaction. However, traditional methods like manual stock checks and RFID sensors often lack accuracy and efficiency, leading to delayed restocking and poor inventory management. This research introduces an innovative semi-supervised learning framework integrated with the YOLO object detection model to automate empty-shelf detection. By minimizing manual annotation efforts, the system delivers robust performance across diverse retail environments. The optimized YOLOv5s variant achieves exceptional accuracy, boasting a mean Average Precision (mAP) of 99.2% at an Intersection over Union (IoU) threshold of 0.5, a precision of 95.6%, and a recall of 98.8%. Designed for real-time operation on edge devices, it provides a scalable, cost-effective solution for dynamic shelf management. Explainable AI (XAI) enhances transparency by offering visual insights into model decisions and detected shelves with empty spaces. The system also predicts restocking times, reducing manual effort and processing time. Validated across multiple datasets, this approach outperforms existing methods, setting a new benchmark for automated inventory management.

Malaria remains a major global health challenge, necessitating the development of highly sensitive and reliable diagnostic technologies capable of detecting subtle variations in blood properties. In this work, a novel photonic crystal fiber (PCF)-based surface plasmon resonance (SPR) sensor incorporating rectangular and circular air-slot features is proposed for broad-range refractive index (RI) sensing and malaria detection in human body. A 30 nm gold layer is employed as the plasmonic medium, and the sensing characteristics are numerically investigated using the finite element method over an analyte RI range of 1.26 to 1.40. The proposed sensor achieves a maximum wavelength sensitivity of 15,000 nm/RIU, amplitude sensitivity of 2197.4 RIU-1, a sensor resolution of 6× 10-6 RIU, and a figure of merit of 357 RIU-1. To assess the potential of the proposed sensor for malaria detection, RI variations corresponding to different stages of infection are analyzed. A maximum wavelength sensitivity of 11,578 nm/RIU for the Trophozoite phase, 10000 nm/RIU for the Schizont phase, and 5714 nm/RIU for the Ring phase was achieved. Validation through comparison with previously reported experimental and simulation - based SPR structures demonstrates close agreement in resonance characteristics, supporting the reliability of the proposed model. Fabrication tolerance analysis also confirms the robustness of the sensor under practical structural variations. Compared with malaria sensors reported in the literature, the proposed sensor achieves a maximum sensitivity while maintaining a broad operating wavelength range of 530 to 1050 nm. These results demonstrate the capability of the proposed sensor to resolve RI variations associated with malaria-infected blood and highlight its potential for future biomedical and refractive index sensing applications.

Epilepsy is a neurological disorder that affects millions worldwide, which features seizures. Though the Electroencephalogram (EEG) is the standard test for manual processing, it is expensive, time-consuming, and liable to error because of humanity's limited endurance compared to machines' capacity to process data continually. Even though there is "class imbalance,"where rare seizure occurrences result in poor sensitivity in clinical models. This research proposes a robust "Seizure detection framework using the TUH EEG Seizure Corpus."(v2.0.3). In this research, we propose a Hybrid that integrates Convolutional Neural Networks to extract spatial features and Long Short-Term Memory networks to capture temporal dependencies. A significant contribution of this research is a data balance approach through under-sampling for handling the lack of seizure data. The developed model performed well, achieving 91.0% Recall and 94.7% accuracy on the validation set, considerably better than the baseline of around 30% from previous works. This shows that dealing with data imbalance is a crucial step in the process of developing effective screening devices that are of a clinical grade to help neurologists.

Crystalline silicon solar cells have reached technological maturity, with efficiency gains slowing as performance approaches the intrinsic Auger recombination limit. The record efficiency of 26.7% highlights the inherent constraints of single-junction silicon devices and motivates the development of advanced architectures. Monolithic perovskite-silicon tandem solar cells offer a promising pathway for enhanced spectral utilization and higher conversion efficiency. Owing to their tunable band gap and strong optical absorption, metal-halide perovskites are well suited for integration with silicon bottom cells to reduce thermalization and transmission losses. In this work, a simulation-based optimization of a monolithic perovskite-silicon tandem solar cell is presented. The optimized structure employs a 1.68 eV perovskite top absorber integrated with a 1.12 eV crystalline silicon heterojunction (HIT) bottom cell. Interface engineering using Me-4PACz and an indium tin oxide (ITO)-based tunnel recombination junction enables improved hole extraction and efficient current matching while limiting parasitic optical losses. Systematic analysis of absorber thickness, interface defect density, spectral filtering, energy band alignment, operating temparature and illuminated J-V characteristics yields a power conversion efficiency of 34% and an open-circuit voltage of 2.02 V. These results provide practical design guidelines for high-efficiency perovskite-silicon tandem solar cells.

The increasing deployment of self-powered Internet of Things (IoT) devices has created a demand for efficient photovoltaic (PV) energy harvesting and power management systems. This paper presents a Physics-Informed Regression-Based Maximum Power Point Tracking (PIR-MPPT) approach integrated with a low-power power management unit for photovoltaic energy harvesting applications. The proposed PIR-MPPT model directly predicts the maximum power point voltage from irradiance and temperature conditions, reducing the computational complexity associated with conventional iterative MPPT techniques. The complete energy harvesting system consists of a voltage-controlled oscillator (VCO), non-overlapping clock generator, ramp charge pump, Banba bandgap reference (BGR), and programmable low-dropout regulator (LDO). The system is modeled using Verilog-A and Cadence Virtuoso and implemented using 45-nm GPDK technology. Simulation results demonstrate accurate maximum power point prediction with an R2 value of 0.9082, RMSE of 20.56 mV, and MAE of 9.71 mV. The proposed ramp charge pump boosts the PV input voltage from 1.0–1.35 V to an output voltage range of 3.3–3.5 V with high conversion efficiency. The programmable LDO subsequently generates regulated output voltages of 1.2 V, 1.8 V, and 2.5 V to support diverse low-power IoT loads. Furthermore, the Banba BGR provides a stable 0.8 V reference voltage with excellent temperature stability, while the integrated system achieves reliable end-to-end operation, low output ripple, and efficient power conversion. These results validate the proposed architecture as a promising solution for self-powered IoT sensor nodes and low-power embedded systems.

Federated learning (FL) enables collaborative model training without sharing any raw data. However, real-world deployments in domains such as finance are limited by factors such as statistical heterogeneity, adversarial manipulation, privacy leakage, and low interpretability. Existing work address these challenges but are largely studied in isolation, with assessment usually limited to predictive performance. This paper proposes TAP-FL (Trust-Aware Privacy-Preserving Federated Learning), a unified federated learning framework designed to perform an extensive assessment in realistic conditions. TAP-FL uses non-IID data distributions with multiple adversarial attack settings, local differential privacy and a trust-aware aggregation mechanism which works based on update's behavioral consistency and deviation. A multi-dimensional evaluation setup is added to collectively measure predictive performance, attack success, privacy leakage, and as well as SHAP-based analysis to measure explanation stability and attribution drift. The framework is further applied to cross-domain credit risk assessment task, combining heterogeneous models via a meta-learning formulation. Experimental results demonstrate that training stability is mostly influenced by adversarial updates, and privacy mechanisms create regulated uncertainty with minimal effect on inference risk. TAP-FL achieves consistent resilience against different types of attacks and has a consistent balance between utility and privacy, while maintaining interpretability.

Decentralized IoT (DIoT) and wireless sensor networks face critical challenges in energy efficiency, resource management, and reliable data transmission. This paper proposes two novel routing techniques that integrate Dynamic Physically Unclonable Function (DPUF) and Grey Wolf Coot Optimization (GWCO) to address these issues. A dynamic fitness function is introduced, incorporating residual energy, traffic load, buffer capacity, hop count, and transmission distance to identify optimal routing paths. Unlike conventional approaches, the proposed methods achieve balanced resource utilization, enhanced fault tolerance, and improved scalability in DIoT environments. Extensive simulations validate their effectiveness, showing significant improvements: communication delay reduced by up to 333 s, energy consumption decreased by 2.97 mJ, packet delivery ratio (PDR) increased to 99.98%, and throughput enhanced to 312 Mbps. These results demonstrate that the GWCO-DPUF framework outperforms traditional routing protocols, offering an energy-efficient, secure, and resilient solution for next-generation decentralized IoT systems.

A smart city is an improved, managed, and secure way of living using information and communication technologies (ICT). The application domain of smart cities is vast, where security plays an important role, and biometrics is the intelligent solution for every security need. Different biometric traits can be used effectively to address various issues in smart cities. Recently, the EEG-based biometric system using additional machine learning and deep learning approaches proved its efficiency for authentication due to its inherited uniqueness and cannot be stolen or copied. This chapter emphasizes security measures of personal identification for smart cities using EEG brain signal resources with proposed unimodal and multimodal models.

Breast cancer continues to be one of the most prevalent and life-threatening diseases affecting women worldwide. Early and accurate diagnosis significantly improves survival rates, and in recent years, machine learning (ML) has emerged as a transformative tool in enhancing diagnostic precision. This paper presents a comprehensive state-of-the-art review of publicly available machine learning datasets specifically designed for breast cancer detection. The review categorizes and analyzes a wide range of datasets including tabular, imaging, and genomic types such as the Wisconsin Breast Cancer Dataset (WBCD/WDBC), BreakHis, Digital Database for Screening Mammography (DDSM), MIAS, CBIS-DDSM, and TCGA-BRCA. Each dataset is evaluated based on key parameters such as data type, feature richness, class balance, sample size, and its suitability for various ML tasks like classification, segmentation, and multimodal learning. Additionally, the paper outlines the strengths, limitations, and real-world applicability of each dataset, providing critical insights for researchers in selecting appropriate benchmarks for model development. The study also highlights current challenges and suggests future directions for constructing more diverse, annotated, and standardized datasets to support robust and generalizable breast cancer detection systems.

Brain tumor detection through machine learning has gained significant traction due to its potential for early diagnosis, accurate classification, and automated segmentation in clinical settings. The success of such models is closely tied to the quality and availability of annotated datasets. This survey presents a comprehensive review of major publicly available datasets - including BraTS, Figshare Brain MRI, TCGAGBM/LGG, REMBRANDT, CQ500, and IBSR - highlighting their imaging modalities, annotation protocols, dataset sizes, and clinical relevance. Special emphasis is placed on BraTS for segmentation and Figshare for multi-class classification. Genomics-integrated datasets like TCGA and REMBRANDT support multi-modal learning, while IXI and CQ500 are valuable for pretraining and emergency diagnostic models. The survey identifies key limitations, such as inter-observer variability, class imbalance, and inconsistent annotation formats. It also underscores the need for more diverse, standardized, and richly labeled datasets. By evaluating the strengths and weaknesses of existing resources, this work provides guidance for selecting suitable benchmarks and suggests future directions such as federated learning and synthetic data augmentation to improve clinical robustness.

This research introduces a Reconfigurable Charge Pump (RCP) architecture developed to Harvest energy efficiently from low-voltage sources, particularly solar cells. The design includes four charge pumping stages-voltage Doubler, Tripler, Quadrupler, and Quintupler-which are activated based on the incoming supply voltage. A control logic block selects the appropriate multiplier depending on the input, which ranges from 0.6 V to 2.0 V. Regardless of the variation in input, the system maintains a stable output of 3.3 V. An impedance matching technique enhances energy transfer, significantly improving performance. The design was implemented using Cadence Virtuoso in a 45 nm CMOS technology node. Simulation results show a power conversion efficiency (PCE) between 74% and 84%, demonstrating this design's suitability for compact, low-power applications such as IoT devices and wearable electronics.

In the field of MEMS, there is a wide use of capacitive sensors for several applications like pressure sensor, accelerometers etc. in internet of things (IoT) applications. Due to this we also need analog to digital converters which are more compact and can provide better gain. Hence, in this research, we are designing a Capacitance to Digital converter using MASH Sigma-Delta Architecture in the ADC part which can provide better SNR than the existing methods. MASH Sigma-Delta Architecture provides excellent Noise-Shaping which helps in increasing the circuit efficiency. A switched capacitor circuit is used for capacitance-to-voltage conversion, and the voltage output is fed to the MASH Sigma Delta ADC. Switched capacitance circuits provide better frequency response, and the ADC part provides better noise rejection and gain. This design is implemented in the Cadence Virtuoso environment using 90nm technology. We designed the building blocks and integrated them to form the whole CDC circuit and the simulation results and performance metrics validate the proposed concept. In this implementation, we calculated the metrics of the circuits by the parameters and achieved an SNR of 77.62 dB with a capacitive resolution of 78.13 aF.

This research presents a simulated AES encryption and decryption system designed to protect against side-channel attacks using advanced masking and redundancy techniques. Two power trace generators were developed: one simulates power consumption with adjustable noise and scaling, and the other enhances security through masking and least significant bit (LSB) randomization. A Differential Power Analysis (DPA) attack model was used to test the system's resistance by analyzing differences in power trace patterns. Implemented in Python, the simulation results show that the proposed system effectively hides power consumption behavior and significantly lowers the success rate of DPA attacks. By shuffling bits during each encryption round and spreading power usage across multiple paths, the system makes it harder for attackers to identify keyrelated activity. These countermeasures provide a strong defense compared to standard AES and show promise for future realworld and hardware-based cryptographic applications.

The power requirement in IoT is essential to fulfill the energy demand of the power-hungry sensors at end nodes. The use of fixed batteries restricts sustainability and makes the system costly. This work presents a battery-less solar energy harvesting system (EHS). Designing a state-of-the-art EHS needs a lot of exercise. Proper modeling of each unit makes the system robust and can be tuned at every stage to get an optimum result. The proposed EHS comprises a clock generator, DC-DC converters, analog-to-digital converters (ADCs), a maximum power point tracking (MPPT) unit, and a digital controller. The DC-DC converter and ADCs are designed in Verilog-A. The MPPT module digital controller is designed using Verilog HDL. The digital controller decides the mode of operation of the EHS based on power availability. Verilog-AMS allows us to do the mixed-mode simulation very early, so errors can only be eliminated in the initial stages at the circuit level. The proposed EHS is simulated in the Cadence Virtuoso AMS Designer Simulator (using the Incisive Run tool). The input solar voltage is 1 V to 1.5 V, and the output is 3 V to 3.5 V. The EHS provides supply voltages of 3.3 V, 1.8 V, and 1 V to the end node devices in IoT. The EHS is further designed with the parameters obtained from modeling in Cadence using virtuoso (for analog circuits) and genus (for digital circuits) and finally combined in Innovous (mixed-mode tool) for tape-out.

The ring oscillator (RO) is widely used to address different hardware security issues. For example, the RO-based physical unclonable function (PUF) generates a secure and reliable key for the cryptographic application, and the RO-based aging sensor is used for the efficient detection of recycled ICs. In this paper, a CMOS inverter with two voltage control signals is used to design a configurable RO (CRO). With its control signals, the proposed CRO can both accelerate and lower the impact of aging on the oscillation frequency. This vital feature of the proposed CRO makes it suitable for use in PUFs and RO-based sensors. The performance of both the proposed modified architecture, i.e., CRO PUF and CRO sensor, is evaluated in 90 nm CMOS technology. The aging tolerant feature of the proposed CRO enhances the reliability of CRO PUF. Similarly, the aging acceleration property of CRO improves the rate of detection of recycled ICs. Finally, both the proposed architectures are area and power-efficient compared to standard architectures.

In recent years, the rapid growth in urbanization and smart cities has been empowered by efficient and intelligent solutions in areas such as transportation, governance, and smart banking. These applications feature large-scale Internet of Things (IoT) deployment of wirelessly connected smart embedded devices with sensors and actuators. Providing adequate energy to power this large number of IoT devices remains a crucial challenge. In this regard, energy harvesting (EH) emerges as a promising approach that converts ambient energy into usable electrical energy, allowing IoT devices to function autonomously and sustainably, thereby reducing maintenance efforts while enhancing the overall system reliability. Although EH systems offer significant advantages, they are also vulnerable to various threats and attacks that underscore the need to design secure and reliable EH solutions. In this article, we comprehensively review the state-of-the-art EH techniques and associated security aspects. We discuss current research on EM techniques, optimization algorithms, and the challenges involved in energy-efficient routing within IoT. Next, we analyze the existing EH methods in two directions - energy extraction and energy storage. In terms of energy extraction from renewable sources, we review the maximum power point tracking (MPPT) algorithms used in IoT devices. Then, we explore the energy storage capabilities of sensor nodes, which are crucial for consistent operation. Furthermore, we discuss the security and reliability mechanisms within IoT EH frameworks, identifying the threats and countermeasures.We conclude the survey by discussing the future research directions and listing a few open problems.

This research paper investigates a hardware-type attack on System-on-Chips (SoCs) involving a trigger and a payload. A stealthy and controllable fabrication time attack, A2, is demonstrated, and a circuit is developed based on charge accumulation from rare events within the system. When voltage gets buildup due to charge coupling, the payload has been activated, leading to a privilege-escalation attack. In this research, a specific analog hardware Trojan (A2) detection and mitigation circuit is designed. This Trojan affects the circuit performance by targeting sensitive wires (like reset) in SoCs. This paper presents a method for detecting the Trojan and implementing proper mitigation techniques to safeguard SoCs from malicious attacks.

In this paper, a new promising hardware security primitive physically unclonable Function (PUF) is implemented to generate a unique secret key for each SOC Board. Especially FPGA-based, IoT is most widely used for different applications. Several types of PUFs are designed and implemented due to their remarkable performance for hardware security applications. In most of the PUFs ring oscillators are mostly preferred, but these are for limited input. In this context, we proposed a new PUF without increasing the size of the hardware implementation, and power. In this research, we used simple XNOR and XOR gates to increase the number of inputs. Even though it is a weak PUF, generally, weak PUFs is the most preferable for implementation, and by increasing CRPs, one can make a weak PUF as strong. This Arbitrary PUF is implemented on the Artix-7 AC701 Evaluation platform using Xilinx Vivado 2019.1.

Biometric authentication is pivotal in identifying individuals with unique physiological or behavioral characteristics. General recognition methods, such as fingerprint, voice, iris, and face recognition, are widely used but have significant flaws. These can be sensitive to spoofing, raise privacy concerns, and often struggle in certain environments. To fix these shortcomings, we proposed a novel biometric method: Electroencephalogram (EEG) authentication. Electroencephalogram (EEG) technology measures brainwave activity through electrodes and is known for its reliability, resistance to forgery, and inherent uniqueness, similar to fingerprints. EEG is particularly significant for liveness detection, making it a strong candidate for robust biometric authentication in high-security applications. This study utilizes a publicly available dataset consisting of EEG data from 109 subjects. The raw data is first scaled and then analyzed using various classifiers, such as κ-nearest neighbors (κ-NN), Auto-Encoder with κ-NN, and Convolutional Neural Networks (CNN). The model’s performance is evaluated under four different conditions based on the subjects’ activities, with the CNN achieving an authentication accuracy of 92%.

In this research paper, we propose an unsupervised framework for feature learning based on an autoencoder to learn sparse feature representations for EEG-based person identification. Autoencoder and CNN do the person identification task for signal reconstruction and recognition. Electroencephalography (EEG) based biometric system is vesting humans to recognize, identify and communicate with the outer world using brain signals for interactions. EEG-based biometrics are putting forward solutions because of their high-safety capabilities and handy transportable instruments. Motor imagery EEG (MI-EEG) is a maximum broadly centered EEG signal that exhibits a subject’s motion intentions without real actions. The Proposed framework proved to be a practical approach to managing the massive volume of EEG data and identifying the person based on their different task with resting states. The experiments have been conducted on the standard publicly available motor imagery EEG dataset with 109 subjects. The highest recognition rate of 87.60% for task-based identification and 99.89% recognition rate for resting-state has been recorded using the Autoencoder-CNN model. The outcomes imply that the overall performance of our proposed framework is similar or advanced to that of the state-of-the-art method. The shape is a realistic technique to control the full-size extent of EEG data and to pick out the individual based totally on their specific task.

Recently, harvesting natural energy is gaining more attention than other conventional approaches for sustainable IoT. System on chip power requirement for the internet of things (IoT) and generating higher voltages on chip is a massive challenge for on-chip peripherals and systems. In this article, an on-chip reliable energy-harvesting system (EHS) is designed for IoT with an inductor-free methodology. The control section monitors the computational load and the recharging of the battery/super-capacitor. An efficient maximum power point tracking algorithm is also used to avoid quiescent power consumption. The reliability of the proposed EHS is improved by using an aging tolerant ring oscillator. The effect of Trojan on the performance of energy-harvesting system is analyzed, and proper detection and mitigation mechanism is proposed. Finally, the proposed ripple mitigation techniques further improves the performance of the aging sensor. The proposed EHS is designed and simulated in CMOS 90-nm technology. The output voltage is in the range of 3-3.55 V with an input 1-1.5 V with a power throughput of 0-22 μW. The EHS consumes power under the ultra-low-power requirements of IoT smart nodes.

Biometric person identification is getting more effective and popular because of Electroencephalography (EEG). EEG signals can be captured from human scalp invasively or non-invasively with the help of electrodes. EEG-based biometric system is more secure and unique for person identification. In this paper, we have used two different states to explore the adaptive and uniqueness of the EEG-based biometric system. We have used Eyes Open (EO) state as well as Eyes Closed (EC) state of a EEG motor imagery publicly available dataset of 109 users.The model is trained and tested with EO and EC states alternatively to prove the reliability and robustness of the model. The biometric person identification model has been designed using Support Vector machine (SVM) for classification. We achieved a notable person identification rate of 96% (EO) and 91.78% (EC) using SVM with Radial Basis Function (RBF) kernel. We have also used Ensemble Support Vector Machine (ESVM) to enhance the performance of person identification and observed the average performance accuracy of 96.16% with n number of classifier.

The Internet-of-Things (IoT) consists of a large number of different and heterogeneous devicesunder one umbrella. Building a typical architecture for the devices used in IoT is a challenge due to the involvement of a vast number of devices, layers, protocols, middleware, and software. The sensors in IoT have to monitor the activities regularly, making the end node devices energy hungry. Traditional fixed batteries get drained out in limited time, requiring continuous replacement, thereby increasing the budget. This article focuses on consumer technologies in the perspective of IoT computing targeted for smart villages and smart cities. The article discusses security-by-design (SbD) concepts in the energy harvester technologies for sustainable and secured IoT with uninterruptedenergy resource smart villages and smart cities.

Security and energy-consumptiont are two conflicting challenges in the design and operation of the smart cities that use Internet-of-Things (IoT). Providing power to IoT things (i.e., sensors and their communications) is a challenge as battery have a limited lifetime, and their maintenance and disposal are costly and hazardous. System on chip (SoC) power requirements for IoT ultra-low-power realm is different and is a challenge for the design engineers to provide uninterrupted power. In this paper, a paradigm shift research that addresses a secure self-sustainable solar-energy harvesting system (EHS) with a security mechanism is proposed. This design incorporates Physically Unclonable Functions (PUFs) for the security of EHS along with an aging sensor for recycled IC detection. The control unit monitors the computational load, recharging of the battery, and security mechanism. Capacitor value modulation (CVM) is used for impedance matching between solar cell and converter during maximum power point tracking (MPPT) to avoid quiescent power consumption. The existing resources of EHS used for designing the PUFs and aging sensor. The secure EHS is designed and fabricated in CMOS 90nm technology. The resulting output is in the range of 3-3.55 V with an input 1-1.5 V. The proposed EHS is consuming 22 μW of power, that satisfies the ultra-low-power requirements of IoT smart nodes.

In IoT (internet of things) realm, sensors need an uninterrupted power supply for continuous operation. This paper presents an on-chip solar EHS (SEHS) design for IoT edge node devices. For matching the impedance between the solar cell and converter, CVM technique is adopted. The energy efficient hill-climbing algorithm (HCA) is adopted to follow the maximum power point (MPP). The control section takes care of the MPPT procedure, and computational load along with the charging of the supercapacitor. LDO (low dropout regulators) is used to generate various voltages needed by the sensors. The proposed system is capable of providing power supply to various edge node devices in IoT. The SEHS is designed in CMOS 180 nm technology. The output voltage is in the range of 1.2–3.55 V with an input of 1–1.5 V. The SEHS is consuming 25 W of power, which is within the ultra-low-power range in IoT.

This paper presents an ultra-low-power solar energy harvesting system with a power management module (SEHS-PMU) for the Internet-of-Things (IoT) applications. The voltages generated are suitable for the IoT sensors used for smart cities as well as Internet-of-Medical-Things (IoMT) based biomedical applications. The SEHS-PMU is harvesting energy from the solar cell, and it can provide regulated voltages of 3.3 V, 1.8 V, 1 V, and 0.5 V. The harvesting mechanism leads to battery-less IoT, and is safe for the humanity and environment. Charge pumps (CP) are used as a boost and buck converter, which is suitable for monolithic integration. Low drop-out regulators (LDOs) are used for regulating the loads. A digital controller controls the selection of loads as per requirement for saving the power. The SEHS-PMU is designed in CMOS 180 nm technology library.

An Ultra-low power solar energy harvesting system (EHS) for IoT end node devices is presented in this paper. To provide an uninterrupted power supply to IoT nodes is a challenge. The solar cell is used as an input source and this low input voltage is boosted by using the DC-DC converter. The charge pump is used as a voltage booster and the impedance matching between the solar cell and the converter is achieved through frequency tuning and capacitor value modulation. A hill-climbing technique is used for maximum power point (MPP) achievement. The EHS designed is self-sustainable and the output is in the range of 3-3.55 V with an input of 1-1.5 V. The EHS is designed in CMOS 90 nm technology library. The simulation results validate the proposed concept and the EHS is consuming a power of 22 μW, which is within the Ultra-low power range of IoT smart nodes.

Biometric representation of humans often requires the tasks such as identification and verification. This can be done using various modalities such as fingerprint, face, retina, voice, etc. However, existing biometric systems are vulnerable to various security attacks. Recently, Electroencephalography (EEG) is considered to be one of the alternatives to develop a robust biometric system. Brain activities represented by EEG signals are more sensitive, secure, and difficult to copy and steal. In this paper, we propose a spatio-temporal dense architecture for EEG-based person identification. Firstly, raw EEG are processed to extract robust and informative spatial features using Convolutional Neural Networks (CNN) as they are known for automatic feature extraction from the raw data. Then, a Long short-term memory (LSTM) network is utilized to process temporal data and person identification is carried out. The experiment has been carried out on a publicly available dataset consisting of EEG of 109 subjects. The architecture is tested on two baseline situations, i.e., eye closed (EC) and the eye opened (EO). Person identification rates of 99.95% and 98% have been recorded for EC and EO states using the proposed scheme. Experimental results demonstrate the robustness of the proposed scheme in terms of person identification and outstrip existing works.

Open source software (OSS) can be used for the development of geospatial database in cloud computing environment. Open source GIS (OSGIS) yields strong spatial solutions in a cost-effective manner and used for work connected to geospatial database creation, geospatial Web services, geospatial database storage, etc. The present work critically analyzes two popular OSGIS software i.e., Quantum GIS and Map Window GIS for geospatial database creation to give better comprehension of procedure and execution in education sector with cloud SDI. Presently, geospatial database provides detailed information about the entire technical institute of Odisha in test case. The objective is to deliver spatial statistics at reasonable costs and anticipated to be advantageous for stake holders such as students, parents, faculty members, government organizations. The same may be scaled up in future for technical institutes of other states in India.

The harmonics and the reactive power is a major concern in power distribution system. The extensive use of the non-linear loads in the present context as computers, SMPS, Rectifiers etc. give rise to harmonics and reactive power in the power system. These will cause a large undesirable effects like equipment heating, damaging devices etc. To compensate harmonics and reactive power, active power filter (APF) is widely used. This paper presents a three-phase APF for power line conditioning (PLC) system to improve the power quality in the point of common coupling (PCC). It also consists of a controller to control the APF. The hysteresis current controller (HCC) is used to generate the switching pulses for the voltage source inverter. VHDL is used to design a digital controller as it is independent of process technology. To generate the reference current synchronous reference frame (SRF) theory is used. The PI current algorithm (using FSM+DATAPATH) and the HCC are designed using VHDL codes and implemented on FPGA for hardware. The three phase APF is modeled, tested and investigated under different unbalanced non linear conditions using MATLAB/SIMULINK environment. The simulation results justify that the APF is compensating the harmonics and reactive power at the PCC. The APF system is in compliance with IEEE 519 and IEC 61000-3 recommended harmonic standards.

Now a days, Wireless Sensor Networks (WSN) are emerging as a challenging and interesting area. Wireless Sensor Network consists of a large number of heterogeneous or homogeneous sensor nodes which communicates through wireless medium and works together to sense or monitor the environment. The number of sensor nodes in a network can vary from hundreds to thousands. The node senses data from environment and sends these data to the gateway node. Mostly WSNs are used for applications such as military surveillance and disaster monitoring. WSNs are multi-hop networks, which depend on the intermediate nodes to relay the data packet to the destination. These nodes are equipped with lesser memory, limited battery power, little computation capability, small range of communication and need a secured and efficient routing path to forward the incoming packet delay by data aggregation) and multipath sensor networks to increase the resilience and reliability of the network. The localization information is crucial for the operation of WSN. There are mainly two types of Localization algorithms. The Range-based localization algorithm has requirements on hardware, thus is expensive to be implemented in practice. The Range-free localization algorithm reduces the hardware cost. In this paper we locate known nodes by placement (DV Hop) in various locations for best result. Our algorithm improves the localization accuracy compared with previous algorithms, which has been demonstrated by the simulating results.

The non-linear devices used in the present environment like computers, printers, lasers, smps, rectifiers etc. pollutes the power system by harmonics and reactive power. Due to the presence of harmonics in the power system a number of undesirable effects caused in the distribution network. To find the solution for this the active power filter (APF) is one of the suitable solutions, which compensates Harmonics as well as reactive power simultaneously. This paper presents a active power line conditioning system to improve the power quality in the distribution network and implementation of a digital controller using 8051 microcontroller. The active power line conditioner is implemented with Pulse Width Modulation (PWM) based current controlled voltage source inverter (VSI). The Digital Controller is designed using MCS 8051 microcontroller which becomes independent of process technology. Synchronous reference frame (SRF) is used for generation of reference current. The PI current algorithm is implemented using MATLAB platform. The shunt APF is modeled and investigated under different unbalanced non-linear load conditions in MATLAB/SIMULINK. The simulation results reveals that the active power filter is efficiently compensating the current harmonics and reactive power in the distribution network. The APF system is in compliance with the IEEE 519 and IEC 61000-3 recommended standards.

Various electronic circuits such as inverters, choppers, cyclo-converters, SMPS used by industrial and domestic purposes are non-linear in nature, which makes the load current to be distorted, causing undesirable effects like heating, equipment damages, EMI related problems in power system. The active power filter (APF) is the best solution for eliminating the harmonics caused by the non-linear loads. This paper presents about a three-phase four-wire active power filter for power line conditioning to improve power quality in the distribution network and implementation of a digitally controlled APF. The active power filter is implemented with PWM based current controlled voltage source inverter (VSI).The switching signals for APF are generated through proposed three-level hysteresis current controller (HCC). The shunt APF system is modeled and investigated under different unbalanced non-linear load conditions using MATLAB program. The Controller Design using Hardware Description Language (VHDL or VERILOG) becomes independent of process technology. Synchronous reference frame (SRF) is used for generation of reference current. Both PI current algorithm and HCC are written in VHDL code and implemented using FPGA platform. © 2013 IEEE.