Consumer Devices Research Articles

Reconfigurable Resistive Switching in VO2/La0.7Sr0.3MnO3/Al2O3 (0001) Memristive Devices for Neuromorphic Computing.

The coexistence of nonvolatile and volatile switching modes in a single memristive device provides flexibility to emulate both neuronal and synaptic functions in the brain. Furthermore, such a device structure may eliminate the need for additional circuit elements such as transistor-based selectors, enabling low-power consumption and high-density device integration in fully memristive spiking neural networks. In this work, we report dual resistive switching (RS) modes in VO2/La0.7Sr0.3MnO3 (LSMO) bilayer memristive devices. Specifically, the nonvolatile RS is driven by the movement of oxygen vacancies (Vo) at the VO2/LSMO interface and requires a higher biasing voltage, whereas the volatile RS is controlled by the metal-insulator transition (MIT) of VO2 under a lower biasing voltage. The simple device structure is electrically driven between the two RS modes and thus can operate as a one selector-one resistor (1S1R) cell, which is a desirable feature in memristive crossbar arrays to avoid the sneak-path current issue. The RS modes are found to be stable and repeatable and can be reconfigured by exploiting the interfacial and phase transition properties, and thus, they hold great promise for applications in memristive neural networks and neuromorphic computing.

Optimal scheduling and demand response implementation for home energy management

The optimal scheduling of the loads based on dynamic tariffs and implementation of a direct load control (DLC) based demand response program for the domestic consumer is proposed in this work. The load scheduling is carried out using binary particle swarm optimization and a newly prefaced nature-inspired discrete elephant herd optimization technique, and their effectiveness in minimization of cost and the peak-to-average ratio is analyzed. The discrete elephant herd optimization algorithm has acceptable characteristics compared to the conventional algorithms and has determined better exploring properties for multi-objective problems. A prototype hardware model for a home energy management system is developed to demonstrate and analyze the optimal load scheduling and DLC-based demand response program. The controller effectively schedules and implements DLC on consumer devices. The load scheduling optimization helps to improve PAR by a value of 2.504 and results in energy cost savings of ₹ 12.05 on the scheduled day. Implementation of DLC by 15% results in monthly savings of ₹ 204.18. The novelty of the work is the implementation of discrete elephant herd optimization for load scheduling and the development of the prototype hardware model to show effects of both optimal load scheduling and the DLC-based demand response program implementation.

Wireless-based portable device heart rate measurement as biomedical devices for stress detection

Stress can increase the heart rate, causing dangerous conditions that cause significant harm and even death. Therefore, managing stress well to control the heart rate is vital. Monitoring stress levels using biomedical devices is essential to prevent further damage to the individual. Regular monitoring facilitates the acquisition of individual stress levels through heart rate information. In this research, a MAX30100 sensor can produce Beats Per Minute (BPM) data, which show the stress experienced by a person during four daily activities (sitting, walking, running, and sleeping) and when watching a horror movie. This research underscores the potential of cost-effective wireless solutions to increase portability and facilitate real-time monitoring and remote surveillance. The researchers introduced an innovative methodology by reusing everyday consumer devices, initially intended for routine activities, to measure stress levels in a unique context—precisely in a controlled experiment involving a movie-watching scenario. The ultimate achievement of this research was creating a stationary heart rate monitoring device that is easy to access, easy to use, space-saving, and adept at effectively tracking stress indicators. BPM readings obtained from the MAX30100 sensor have scores similar to those obtained from the heart rate app. The researchers proposed the inclusion of other sensors, such as a photoplethysmograph, an electrocardiogram, and galvanic skin response, to provide a more holistic assessment of stress. Leveraging cloud technology for data storage enables long-term monitoring and in-depth analysis of stress patterns over time.

Dynamics of a piezoelectric vibration energy harvester with a pseudoelastic SMA spring

The development of small-scale and low power consumption devices has been motivating the design of intelligent mechanisms, exhibiting a wide power density spectrum across various external sources. Smart materials come up as an attractive alternative due to their intrinsic multi-coupling between different physical domains. In this context, piezoelectric materials allow conversion of mechanical energy of movement into electrical power through the direct piezoelectric effect. This work investigates a piezoelectric energy harvester combined with a shape memory alloy (SMA) spring to explore the combination of both the smart materials for energy harvesting. The pseudoelastic hysteretic effect of SMA is explored in order to passively change the internal system properties such as stiffness and damping during the harvesting process. Numerical analyses are performed considering the normalized power converted by the harvester, focusing on the influence of the SMA martensite phase transformation under different scenarios. The results exhibit an increment in the harvester bandwidth when compared with a traditional linear piezoelectric energy harvester.

Framework for Agent-Based Multistage Application Partitioning Algorithm in Mobile Cloud Computing

The idea of computational offloading is quickly catching on in the world of mobile cloud computing (MCC). Today’s applications have heavy demands on power and computing resources, creating issues with energy consumption, storage capacity, and mobile device performance. Mobile devices may efficiently offload their calculations to cloud servers using the offloading paradigm and then get the processed results back onto the device. The investigation relates to identifying the specific application components that should be offloaded and run remotely and which parts are supposed to be treated locally. The applications need to be partitioned to differentiate between remote and local codes. In this paper, an agent-based multistage graph partitioning (ABMP) scheme is proposed. The framework of the scheme is based on three-tier architecture that includes mobile, cloudlet, and cloud for the execution of application tasks. The main goal is to provide an efficient partitioning and offloading scheme in the mobile cloud computing area. The results show that incorporating both agent-based multistage graph partitioning and offloading algorithms yields superior performance as compared to previous methods in terms of reducing execution costs and conserving battery life for mobile devices.

Development of the 3D printing market and its application in medicine


 
 
 According to forecasts and predictions by leading analyst firms, several million 3D printers were expected to be sold annually by 2020, with consumer devices in virtually every home being the driving force.
 This article aims to conduct a systematic literature review (SLR) of various applications of 3D printing in medicine. The author based his considerations on Polish and foreign literature, studying scientific articles and electronic sources. The analysis included a review of literature and online sources from 2012–2023. The author used the following professional, academic databases to gather scientific literature: Library of Science, Google Scholar, ScienceDirect, Emerald, Emis Intelligence, Passport, and Statista.
 This article defines the concept of 3D printing, presents the development of the 3D printing market, and indicates market trends and applications of 3D printing in medicine.
 The analysis made it possible to determine the extent of the phenomenon and to identify the main trends that are observed in the field and environment studied.
 
 

Area-selective atomic layer deposition on 2D monolayer lateral superlattices

The advanced patterning process is the basis of integration technology to realize the development of next-generation high-speed, low-power consumption devices. Recently, area-selective atomic layer deposition (AS-ALD), which allows the direct deposition of target materials on the desired area using a deposition barrier, has emerged as an alternative patterning process. However, the AS-ALD process remains challenging to use for the improvement of patterning resolution and selectivity. In this study, we report a superlattice-based AS-ALD (SAS-ALD) process using a two-dimensional (2D) MoS2-MoSe2 lateral superlattice as a pre-defining template. We achieved a minimum half pitch size of a sub-10 nm scale for the resulting AS-ALD on the 2D superlattice template by controlling the duration time of chemical vapor deposition (CVD) precursors. SAS-ALD introduces a mechanism that enables selectivity through the adsorption and diffusion processes of ALD precursors, distinctly different from conventional AS-ALD method. This technique facilitates selective deposition even on small pattern sizes and is compatible with the use of highly reactive precursors like trimethyl aluminum. Moreover, it allows for the selective deposition of a variety of materials, including Al2O3, HfO2, Ru, Te, and Sb2Se3.

Biohybrid Energy Storage Circuits Based on Electronically Functionalized Plant Roots.

Biohybrid systems based on plants integrate plant structures and processes into technological components targeting more sustainable solutions. Plants' biocatalytic machinery, for example, has been leveraged for the organization of electronic materials directly in the vasculature and roots of living plants, resulting in biohybrid electrochemical devices. Among other applications, energy storage devices were demonstrated where the charge storage electrodes were seamlessly integrated into the plant tissue. However, the capacitance and the voltage output of a single biohybrid supercapacitor are limited. Here, we developed biohybrid circuits based on functionalized conducting roots, extending the performance of plant based biohybrid energy storage systems. We show that root-supercapacitors can be combined in series and in parallel configuration, achieving up to 1.5 V voltage output or up to 11 mF capacitance, respectively. We further demonstrate that the supercapacitors circuit can be charged with an organic photovoltaic cell, and that the stored charge can be used to power an electrochromic display or a bioelectronic device. Furthermore, the functionalized roots degrade in composting similarly to native roots. The proof-of-concept demonstrations illustrate the potential of this technology to achieve more sustainable solutions for powering low consumption devices such as bioelectronics for agriculture or IoT applications.

Intelligent Electrocardiogram Acquisition Via Ubiquitous Photoplethysmography Monitoring.

Recent advances in machine learning, particularly deep neural network architectures, have shown substantial promise in classifying and predicting cardiac abnormalities from electrocardiogram (ECG) data. Such data are rich in information content, typically in morphology and timing, due to the close correlation between cardiac function and the ECG. However, the ECG is usually not measured ubiquitously in a passive manner from consumer devices, and generally requires 'active' sampling whereby the user prompts a device to take an ECG measurement. Conversely, photoplethysmography (PPG) data are typically measured passively by consumer devices, and therefore available for long-period monitoring and suitable in duration for identifying transient cardiac events. However, classifying or predicting cardiac abnormalities from the PPG is very difficult, because it is a peripherally-measured signal. Hence, the use of the PPG for predictive inference is often limited to deriving physiological parameters (heart rate, breathing rate, etc.) or for obvious abnormalities in cardiac timing, such as atrial fibrillation/flutter ("palpitations"). This work aims to combine the best of both worlds: using continuously-monitored, near-ubiquitous PPG to identify periods of sufficient abnormality in the PPG such that prompting the user to take an ECG would be informative of cardiac risk. We propose a dual-convolutional-attention network (DCA-Net) to achieve this ECG-based PPG classification. With DCA-Net, we prove the plausibility of this concept on MIMIC Waveform Database with high performance level (AUROC 0.9 and AUPRC 0.7) and receive satisfactory result when testing the model on an independent dataset (AUROC 0.7 and AUPRC 0.6) which it is not perfectly-matched to the MIMIC dataset.

Optimising building heat load prediction using advanced control strategies and Artificial Intelligence for HVAC system

Amid the dynamic challenges posed by the COVID-19 era, this study offers a nuanced exploration, delving into the complexities of optimising building heat load prediction. This study addresses the imperative challenge of optimising building heat load prediction by implementing advanced control strategies and integrating Artificial Intelligence (AI) into air conditioning systems. The study emphasises the need to design HVAC devices for minimal energy consumption without compromising comfort conditions. The study introduces an intelligent predictive adaptive neuro-fuzzy inference system (ANFIS) model that proves highly capable of accurately predicting HVAC performance outcomes while contributing to the development of a comprehensive dataset. The uncertainty percentage is evaluated across various membership functions, with the trapezoidal membership exhibiting the lowest error rate, followed by the Gaussian membership function. Weather parameters crucial to ventilation efficiency and heat load are examined, leading to the identification of an optimal combination involving 45 % relative humidity, 13 °C dry bulb temperature, and 5 km/h wind speed. The current system demonstrates significant efficiency improvements, with ventilation and heat load rates reaching 93 % and 97 %, respectively, compared to pre-COVID-19 conditions. The findings underscore the importance of considering these parameters in future HVAC designs, particularly in the context of COVID-19 guidelines (75 % and 79 %, respectively).

Reducing Waste in Healthcare: A Systemic Design Approach for Sustainable Disposables Manufacturers

Efficient waste management is crucial in the healthcare system due to the complex composition and associated risks to workers, patients, and the environment. While there is growing awareness of the need to change the system, there is still an urgent need for a sustainable healthcare system. Sustainability concepts can guide designers in taking action, from considering waste reduction in the choice of materials to optimizing management systems through information and staff training. Systemic design methods can help involve business teams in the development of sustainable practices and strategies for production, use, and disposal. Research aimed to identify the types of disposables produced according to their polymers and weight, and to detect associated problems. The quantification of results led to the development of strategies aimed at reducing the number of polymer types used in product manufacturing with A case study on the Neonatal Nasal Mask, a consumable device was conducted to apply sustainable design strategies.

Forward Learning of Large Language Models by Consumer Devices

Large Language Models achieve state of art performances on a broad variety of Natural Language Processing tasks. In the pervasive IoT era, their deployment on edge devices is more compelling than ever. However, their gigantic model footprint has hindered on-device learning applications which enable AI models to continuously learn and adapt to changes over time. Back-propagation, in use by the majority of deep learning frameworks, is computationally intensive and requires storing intermediate activations into memory to cope with the model’s weights update. Recently, “Forward-only algorithms” have been proposed since they are biologically plausible alternatives. By applying more “forward” passes, this class of algorithms can achieve memory reductions with respect to more naive forward-only approaches and by removing the need to store intermediate activations. This comes at the expense of increased computational complexity. This paper considered three Large Language Model: DistilBERT, GPT-3 Small and AlexaTM. It investigated quantitatively any improvements about memory usage and computational complexity brought by known approaches named PEPITA and MEMPEPITA with respect to backpropagation. For low number of tokens in context, and depending on the model, PEPITA increases marginally or reduces substantially arithmetic operations. On the other hand, for large number of tokens in context, PEPITA reduces computational complexity by 30% to 50%. MEMPEPITA increases PEPITA’s complexity by one third. About memory, PEPITA and backpropagation, require a comparable amount of memory to store activations, while MEMPEPITA reduces it by 50% to 94% with the benefits being more evident for architectures with a long sequence of blocks. In various real case scenarios, MEMPEPITA’s memory reduction was essential for meeting the tight memory requirements of 128 MB equipped edge consumer devices, which are commonly available as smartphone and industrial application multi processors.

Metamorphosis of India’s electronic consumer device landscape: Case study of Asia’s most successful brand – boAt

boAt is an Indian-based consumer electronics business that sells earbuds, headphones stereos, portable chargers and luxury tough cables. It was founded in 2015. The co-founders Sameer Ashok Mehta and Aman Gupta founded Imagine Marketing Services Private Limited, in November 2013 which does business as boAt. The company has emerged as one of the prominent home-grown D2C brands that challenged market leaders in the earphones and wearable space. Overall, the company’s total annual profits surged nearly 61% to Rs 78.6 crore (INR 786 million) during FY21 from Rs 48.8 crore (INR 488 million) during FY20. The company’s success continued into FY22, where its revenue soared to 28.86 billion, accompanied by a profit (after tax) of ₹79 crore (INR 790 million). boAt’s remarkable performance in the consumer electronics market made it a notable player in the industry. In the fiscal year 2021–2022, boAt had set a revenue target of 500 crore (INR 5 billion). It was then the responsibility of the managing director, Aman Gupta, and his team to devise a strategic plan to achieve this goal. They faced the challenge of addressing potential issues anticipated by the team. Aman and his staff were particularly concerned with the corporate plan to offset the declining return on equity. They also had to formulate marketing strategies aimed at ensuring future sustainable growth for the company. This case study offered valuable insights into the changing landscape of India’s consumer device market and the strategies implemented by boAt’s management for successful navigation in the industry.

Parallel-pipelined-memory Blowfish FPGA-based radio system with improved power-throughput for secured IoT network

The Internet of Things (IoT) has emerged as a disruptive force, transforming industries, cities, and daily life in a time of unprecedented connection. This outcome has called for robust security, reliability, and efficient power management for the IoT network. The security of small mobile devices within the IoT network has become a pressing concern due to the multitude of wireless vulnerabilities that threaten data transmission, the harsh wireless environment for data transmission, and the need for low-power consumption devices. This study proposed the development of a new parallel-pipelined-memory (P2M) Blowfish field programmable gate array (FPGA)-based radio system as a prototype of a secured mobile device for IoT application. The FPGA platform verified the proposed system, indicating a minimum of 64 % power-throughput improvement. In this case, the hardware utilisation was reduced by 3.5 % compared to the recently published works. Meanwhile, the real-time transmission analysis of the suggested P2M Blowfish radio system in a non-line-of-sight (NLOS) indoor environment indicated that the transmitted data over 2.4 GHz ZigBee standard at 10 dBm radio frequency (RF) power level demonstrated the optimum signal quality with received signal strength and signal-to-noise ratio (SNR) of −34.58 dBm and 39.06 dB, respectively. This feature also included a 6.25 × 10−3 minimum bit error rate (BER) at 61 m as the maximum communication range. Thus, the P2M Blowfish FPGA-based radio system could improve the security, reliability, and power efficiency of IoT networks.

Ultralow Voltage Resistive Switching in Hafnium–Zirconium Oxide

AbstractUltralow SET and RESET voltage are essential for high‐density, low‐power, and small heat dissipation nonvolatile random‐access memory (NVRAM) elements. A nanoscale polycrystalline Hf0.75Zr0.25O2 (HZO) thin films on Pt/Si substrate are fabricated and investigated for suitability for bipolar resistive switching. The device illustrates monoclinic and tetragonal/orthorhombic phases with weak ferroelectricity and robust resistive switching. Small remanent polarization (≈0.1 μC cm−2) may assist in the height reduction of barrier height and ease the electron for transport. Remarkably, the Al/HZO/Pt/Si device, consisting of thin films with 10 and 5 nm thicknesses, exhibits a switching voltage below −30 mV from a low‐resistance state (LRS) to a high‐resistance state (HRS). It shows a significant ROFF/RON ratio of 106, making it suitable for low power consumption and minimal heat dissipation devices. Moreover, the utilization of an ultrathin film (5 nm) results in an improved reduction (< 0.7 V) of the operating window at the positive voltage.  Direct tunneling and the Fowler–Nordheim tunneling model are performed in current–voltage (I–V) data to study the charge transportation behavior over a trapezoidal and triangular potential barrier. These results of the HZO candidate may stimulate the futuristic nonvolatile resistive random‐access memory (ReRAM) in the optoelectronic industry.

ЦИФРОВІ РІШЕННЯ В УПРАВЛІННІ ЕЛЕКТРОННИМИ ВІДХОДАМИ

The article is devoted to the actual problem of substantiating directions for the use of innovative digital solutions in the management of waste electrical and electronic equipment. The structure of e-waste in developed countries in 2022, which includes IT and telecommunications devices, consumer devices and solar panels, power tools and medical devices, was analyzed and it was found that the largest share is household large-sized appliances (52.7%). Analysis of the dynamics of electronic waste generation per capita in industrialized countries in 2013–2022 showed that in 2022, each resident of Australia – 22.42 kg, Canada – 20.58 kg, Israel – 14.56 kg of electronic waste was generated, Japan – 20.55 kg, Korea – 16.80 kg, Great Britain – 24.63 kg, USA – 21.50 kg, EU – 28.54 kg. Considered the practice of waste management in industrially developed countries. For example, Japan (18–25%) is most actively involved in the processing of electronic waste. The EU has developed a strategy - an action plan for the closed cycle economy, in which the reduction of electrical and electronic waste is a key priority, and a number of proposals have been submitted to promote the repair of goods. The well-founded role of digitalization in the management of electronic waste, which consists in increasing the speed of waste processing and increasing its economic efficiency. The areas of use of innovative digital solutions in electronic waste management are defined, such as: robotics, artificial intelligence and neural networks, the Internet of Things, cloud solutions and data analytics. The advantages of digitization in waste management are disclosed, which includes: automation in waste disposal; improvement of the recycling process; ensuring waste minimization and saving natural resources; creating opportunities to identify problem areas and assess the efficiency of electronic waste disposal; helps to save money, energy and time for waste collection and disposal. The directions for handling electrical and electronic equipment waste have been defined, which consist in preventing the generation of waste, its collection, separation, processing, extraction of secondary raw materials through reuse, recycling and other forms of recovery, which will contribute to the realization of the goals of sustainable production and consumption and the improvement of environmental indicators.

Providing Skin Stretch On The Lower Back - Design And Psychophysical Evaluation With A Stepping Task.

Haptic devices are becoming popular in many applications, including medical, gaming, and consumer devices. Yet, the majority of studies focus on the use of haptics for the upper limbs, with much less attention to the stimulation of other regions of the body such as the lower back. In this study, we designed three types of skin stretch stimulation devices that can be placed on a belt and apply tactile stimulation on the lower back. We present these devices that apply lateral, longitudinal, and rotational skin stretch stimulation on the lower back, and evaluate their effectiveness in providing haptic commands for the lower limbs of healthy participants. We designed psychophysical experiments that quantify the discrimination accuracy of participants with a stepping task. The results demonstrate the ability of participants to discriminate two out of three features of stimulation provided on the lower back. These results demonstrate that skin stretch on the lower back can effectively transmit haptic signals and elicit responses in the lower limb for various applications. Future studies are needed to optimize providing skin stretch on the lower back to benefit various applications such as training, rehabilitation, gaming, and assistive devices.

A twin learning framework for traveling salesman problem based on autoencoder, graph filter, and transfer learning

Efficient solvers for traveling salesman problem (TSP) have great significance in the field of consumer electronic systems and devices. Existing studies require independent and repetitive runs for similar TSPs. To utilize useful knowledge buried in the twin TSP which is similar to the target TSP, a twin learning framework based on task matching and mapping strategy is proposed. We use an autoencoder to extract the feature vectors of the historical tasks and the target task to find the twin task. If not found, construct a twin task based on a graph-filter. Further, we get the solution of the target task with the optimized twin task by learning a mapping matrix from the twin task to the target task. Finally, we incorporate local search algorithms into the twin learning framework, which called strategy mapping solver (SMS) to further improve the quality of the solution. The efficacy of the SMS is evaluated through comprehensive empirical studies with commonly used TSP benchmarks, against meta-heuristic algorithms and a learning improvement heuristics algorithm, demonstrating its effectiveness for TSP. Moreover, a real-world combinatorial optimization application, laser engraving path planning, is presented to further confirm the efficacy of SMS.

MEDICO-SOCIAL PORTRAIT OF A SMOKER OF TOBACCO PRODUCTS FOR ELECTRONIC HEATING

Aim. To describe the medico-social portrait of a smoker of tobacco products for electronic heating (TPEH) among the young population of the Lviv region.
 Materials and methods.  A single anonymous survey of 817 young people (from 18 to 45 years of age) living in the Lviv region was conducted. From among them, we singled out a focus group of 93 respondents who indicated that they use tobacco products for electronic heating
 Results. The average age of a consumer of TPEHs at which a smoker tried this tobacco product for the first time amounted to 16.00 (14.00; 18.00) years of age. More than half of the participants of the formed focus group, namely 56.99±5.13% smoke regularly. The largest percentage among the respondents, 61.29±5.05% of TPEH smokers, work on a permanent basis, and almost half of the respondents, 48.39±5.18%, indicated their income as "average". Half of the respondents (51.61±5.18%) do not hide smoking from their human environment. More than half of TPEH users report that their parents smoke. Also, about a half of the respondents of the focus group have more than 10 acquaintances and relatives who use the latest devices for tobacco consumption. Also, TPEH smokers are not limited to this source of nicotine, but use 2.0 (1.0; 2.5) tobacco products on average.
 Conclusions. The information obtained will be used in the development of a personalized model of prevention of the latest methods of tobacco consumption among the young population in the Lviv region.

Magnetoelectric behaviour of Pb-free Na0.5Bi0.5TiO3-Ni0.5Co0.5Fe2O4 particulate novel ceramic composites

Magnetoelectric (ME) multiferroic composites are more promising candidates for low-power consumption devices, next-generation spintronic devices, and multistate memory devices since they exhibit significantly higher Magnetoelectric interaction over single-phase multiferroics. In this regard magnetic behaviour, dielectric, and magnetoelectric characteristics of a novel Pb-free (1-x) Na0.5Bi0.5TiO3-(x) Ni0.5Co0.5Fe2O4, (x = 0, 0.2, 0.4, 0.6, 0.8 & 1) particulate composites were explored. Solid-state synthesis was adopted to prepare the single-phase Na0.5Bi0.5TiO3 (NBT), Ni0.5Co0.5Fe2O4 (NCFO), and their composites. The presence of rhombohedral NBT and cubic inverse spinel NCFO in the composites were confirmed with X-ray diffraction (XRD) patterns. Microstrain (ε) induced in the prepared samples was estimated by using the Williamson-Hall (W-H) method. The piezoelectric NBT phase shows the tensile strain and the ferromagnetic NCFO shows the comprehensive strain. Among all the composites 0.8NBT-0.2NCFO shows a higher strain value of 0.012%, and the decrement in the strain values was observed upon increases in ferrite concentration. Backscattered scanning electron microscope (BS-SEM) images revealed the existence of highly dense uniformly distributed elements and Energy dispersive spectroscopy (EDS) confirmed the existing elements in the composites. UV–vis absorbance spectra disclosed a rise in energy gap values from 1.14 eV to 3.0 eV with increasing NBT percentage. Variations of dielectric permittivity and dielectric loss with respect to AC frequency were studied at room temperature (RT), and the 0.2NBT-0.8NCFO sample shows a high dielectric permittivity of 8500 at ∼100Hz. M − H (Magnetization-Magnetic field) hysteresis loops demonstrate the pure ferrite phase has a coercive field (Hc) = 578 Oe, remnant magnetization (Mr) = 21.43 emu/g and saturation magnetization (Ms) = 71.29 emu/g. The Ms and Mr values in the composites are enhanced with increasing ferrite percentage. Studies on ME behaviour of (1-x) NBT-(x) NCFO, (x = 0.2,0.4,0.6 & 0.8) show the coupling effect between both the phases at room temperature and the highest coupling coefficient value 3.16mV/Cm. Oe was observed for the 0.8NBT-0.2NCFO further the reasons and mechanisms are discussed.