Proposed System Research Articles

An Elixir for Blockchain Scalability with Channel based Clustered Sharding

Blockchain refers to distributed ledger technology which stores records without the help of a central authority. Born with bitcoin, this brainstorming technology finds its applications in healthcare, land registry, education, pharmaceutical industry, digital records, manufacturing companies and so on. The properties of blockchain such as immutability, distributed nature, tamper-resistant made it a disruptive technology in many applications. The highlighting feature of this pioneering technology is the distributed storage of ledger on all the nodes of the network. This helps to achieve decentralization without the trust for third party. The transactions are proposed, executed, validated and are then added as blocks to the blockchain. The problems with all the blockchain framework is scalability with respect to storage space and throughput. Scalability is the most significant factor to be considered in this big data era. This article proposes a solution called Channel Based Clustered Sharding (CBCS) approach for Hyperledger fabric blockchain framework. In this work, a lookup table is maintained which helps in forwarding the transactions to the clustered shards for validation. The CBCS approach helps in parallel transaction processing which in turn improves scalability and throughput of the system. The performance of the proposed work is measured with the help of Hyperledger caliper, a benchmarking tool for the performance analysis of Hyperledger fabric. The results show that the performance of the proposed system is increased from 3000 tps to 30,000 tps.

SecureSense: Enhancing Person Verification through Multimodal Biometrics for Robust Authentication

Biometrics provide enhanced security and convenience compared to conventional methods of individual authentication. A more robust and effective method of individual authentication has emerged due to recent advancements in multimodal biometrics. Unimodal systems offer lower security and lack the robustness found in multimodal biometric systems. The research paper introduces a novel approach, employing multiple biometric modalities, including face, fingerprint, and iris, to authenticate users in a multimodal biometric system. The paper proposes the ”Secure Sense” framework, which combines multiple biometric modalities to improve person verification accuracy. The proposed system utilizes both web-based and real-time datasets. For the web-based dataset, we employed the Chicago Face dataset for facial data, the MMU1 dataset for iris data, and the SOCOfing dataset for fingerprint data. In real-time data collection, facial data is captured using a Zebronics Zeb-Gem webcam, fingerprint data is obtained using the Mantra MFS scanner, and iris data is collected using the Mantra MIS scanner. In the envisioned system, we introduce an innovative approach that employs a decision-level fusion technique across three unique biometric modalities, resulting in an impressive accuracy rate of approximately 93% across all modalities.

Online Virtual Reality Networked Control Laboratory Applied in Control Engineering Education.

Online laboratories play an important role in engineering education. This work discusses a WebVR-based virtual laboratory system. The user enters the simulated laboratory environment through a virtual reality (VR) device and interacts with the experimental equipment, similar to hands-on experiments in a physical laboratory. In addition, the proposed system allows users to design their own control algorithms and observe the effects of different control parameters to enhance their understanding of the experiment. To illustrate the features of the proposed virtual laboratory, an example is provided in this paper, which is an experiment on a double inverted pendulum system. The experimental results show that the proposed system allows users to conduct experiments in an immersive and interactive manner and provides users with a complete experimental process from principal design to experimental operation. A solution is also provided to change any virtual laboratory into a WebVR-based virtual laboratory for education and training.

NeuroIGN: Explainable Multimodal Image-Guided System for Precise Brain Tumor Surgery.

Precise neurosurgical guidance is critical for successful brain surgeries and plays a vital role in all phases of image-guided neurosurgery (IGN). Neuronavigation software enables real-time tracking of surgical tools, ensuring their presentation with high precision in relation to a virtual patient model. Therefore, this work focuses on the development of a novel multimodal IGN system, leveraging deep learning and explainable AI to enhance brain tumor surgery outcomes. The study establishes the clinical and technical requirements of the system for brain tumor surgeries. NeuroIGN adopts a modular architecture, including brain tumor segmentation, patient registration, and explainable output prediction, and integrates open-source packages into an interactive neuronavigational display. The NeuroIGN system components underwent validation and evaluation in both laboratory and simulated operating room (OR) settings. Experimental results demonstrated its accuracy in tumor segmentation and the success of ExplainAI in increasing the trust of medical professionals in deep learning. The proposed system was successfully assembled and set up within 11min in a pre-clinical OR setting with a tracking accuracy of 0.5 (± 0.1) mm. NeuroIGN was also evaluated as highly useful, with a high frame rate (19 FPS) and real-time ultrasound imaging capabilities. In conclusion, this paper describes not only the development of an open-source multimodal IGN system but also demonstrates the innovative application of deep learning and explainable AI algorithms in enhancing neuronavigation for brain tumor surgeries. By seamlessly integrating pre- and intra-operative patient image data with cutting-edge interventional devices, our experiments underscore the potential for deep learning models to improve the surgical treatment of brain tumors and long-term post-operative outcomes.

Developing an AI-empowered head-only ultra-high-performance gradient MRI system for high spatiotemporal neuroimaging

Recent advances in neuroscience requires high-resolution MRI to decipher the structural and functional details of the brain. Developing a high-performance gradient system is an ongoing effort in the field to facilitate high spatial and temporal encoding. Here, we proposed a head-only gradient system NeuroFrontier, dedicated for neuroimaging with an ultra-high gradient strength of 650 mT/m and 600 T/m/s. The proposed system features in 1) ultra-high power of 7MW achieved by running two gradient power amplifiers using a novel paralleling method; 2) a force/torque balanced gradient coil design with a two-step mechanical structure that allows high-efficiency and flexible optimization of the peripheral nerve stimulation; 3) a high-density integrated RF system that is miniaturized and customized for the head-only system; 4) an AI-empowered compressed sensing technique that enables ultra-fast acquisition of high-resolution images and AI-based acceleration in q-t space for diffusion MRI (dMRI); and 5) a prospective head motion correction technique that effectively corrects motion artifacts in real-time with 3D optical tracking. We demonstrated the potential advantages of the proposed system in imaging resolution, speed, and signal-to-noise ratio for 3D structural MRI (sMRI), functional MRI (fMRI) and dMRI in neuroscience applications of submillimeter layer-specific fMRI and dMRI. We also illustrated the unique strength of this system for dMRI-based microstructural mapping, e.g., enhanced lesion contrast at short diffusion-times or high b-values, and improved estimation accuracy for cellular microstructures using diffusion-time-dependent dMRI or for neurite microstructures using q-space approaches.

Development and assessment of a novel isobaric compressed hydrogen energy storage system integrated with pumped hydro storage and high-pressure proton exchange membrane water electrolyzer

In the work, a novel isobaric compressed hydrogen energy storage system integrated with pumped hydro storage and high-pressure proton exchange membrane water electrolyzer is proposed to improve system performance. By integrating pumped hydro storage, the gas storage chamber can operate with a constant pressure at a counter-hydraulic pressure, enhancing the effectiveness and energy storage density of the system. Utilizing high-pressure proton exchange membrane water electrolyzer technology directly to produce high pressure hydrogen and oxygen simplifies the system structure by eliminating the need for gas compressors and cooling unit, thereby improving system stability. Additionally, insulated storage reservoirs are employed in the proposed system to store working fluids, maximizing system performance by minimizing potential waste heat losses. Furthermore, the electrical, thermal, and exergy efficiencies, as well as energy storage density of the proposed system, are assessed using thermodynamic techniques. The results indicate that the round-trip electrical efficiency, round-trip thermal efficiency, round-trip exergy efficiency, and energy storage density of the system can reach 35.39%, 81.03%, 37.89% and 53.46 kWh/m3, respectively. Lastly, parametric studies are conducted to further evaluate system performance under various working conditions.

Experimental research and energy effectiveness analysis on chip-level two-stage loop thermosyphon system for data centre free cooling

The development of 5G technology has put forward new requirements for computing the abilities of chips, and chip power has also significantly increased. Consequently, the heat load of the rack will increase rapidly in the future. Traditional computer room air conditioning (CRAC) systems cannot satisfy the rack’s high heat flux dissipation requirements, and new cooling forms such as chip-level cooling should be adopted. In this study, a two-stage chip-level cooling system, which is pumpless and waterless, offers flexible and detachable installation based on practical requirements, was proposed. The cooling system removes heat from the chips to the outdoors via two thermosiphon loops, where the heat is first transferred to cold plates tightly attached to the chip surfaces, then transferred through heat exchangers attached to the rack side, and finally dissipated outdoors through air cooling. In this study, a two-stage chip-cooling system was constructed and tested in an enthalpy-difference laboratory. The effects of the liquid filling ratio on the thermal resistance, total temperature difference, and fluid flow pattern under different heat loads were investigated. Second, a heat transfer model for analysing thermal resistance networks was constructed, and the proportions of different types of thermal resistance were determined. Third, an energy-saving analysis of the system is established. The experimental results showed that at single chip heat load 200 W and total heat load 2 kW, the heat transfer temperature difference between chips and outdoors is less than 20 °C at extremely high energy efficiency (cooling load factor reaches 0.05 at outdoor temperature 40 °C), which makes it feasible for free cooling throughout the year in most Chinese areas. The annual PUE in Beijing using the proposed system is 1.15, which indicates a 30 % reduction of the total energy consumption of data centres.

Re-envisioning the Park-and-Ride concept for the automated vehicle (AV) era with Private-to-Shared AV transfer stations

Cities implemented park-and-ride (PNR) systems to decrease congestion in dense urban areas while providing transit options to travelers who live in a city's low- to medium-density regions. The success of PNR systems is mixed, as they suffer from several disadvantages, namely, the uncertainty of parking locations and infrequent and/or unreliable transit services, and the fact that travelers still need to walk to their destination. Motivated by the premise of PNR systems and the potential of automated vehicles (AVs), to address each of the shortcomings of PNR systems, this study proposes a future system with near-ubiquitous AVs where travelers transfer from privately owned AVs (PAVs) to shared-use, shared-ride AVs (SAVs), called a PAV-SAV transfer system. This study proposes a modeling framework to assess the potential market share of the PAV-SAV transfer system and the network impacts (e.g., congestion, vehicle miles traveled) of the proposed system. Finally, the study identifies good designs for the PAV-SAV transfer system using scenario analysis. The critical design variables are the location of transfer stations, the capacity of SAVs, and the transfer station connector links. For the Greater Los Angeles area, the computational results show a market share for PAV-SAV of almost 18% for person trips terminating in downtown Los Angeles. In all scenarios, the proposed PAV-SAV system decreases vehicle hours traveled (VHT) across the whole network with significant decreases in the urban core. For all designs, the PAV-SAV system decreases vehicle miles traveled (VMT) compared to a network without PAV-SAV transfer stations, albeit only slightly. Locating transfer stations closer to the urban core, increasing vehicle capacities, and connecting transfer stations to both arterial links and highway links improves network performance (i.e., VMT and VHT) and increases the market share of the PAV-SAV system.

A validated multi-physic model for the optimization of an innovative Trombe Wall for winter use

Trombe walls are passive systems integrated into the building envelopes that contribute to limiting heating demands. Usually, to rationally use the absorbed solar radiation, traditional configurations are not equipped with an insulating layer in the massive structure, and this feature could be in contrast with rules that impose the achievement of limited thermal transmittances. Alternative modifications such as the Thermo-Diode Trombe Wall, partially transparent and with appreciable insulation properties, can overcome this issue. This study proposed the validation of a multi-physics model of such an innovative solution through data provided by an experimental set-up properly monitored. A parametric study was then conducted to investigate how important geometrical and physical properties affect the thermal performances of the proposed solution. Results confirm that air temperatures over 35 °C can be achieved in the upper part of the solar space maintaining air velocity below 0.2 m/s and allowing for transferring significant thermal loads to the adjacent room. Simulations identified 24 cm as the optimum solar space thickness, whereas insulation layers over 12 cm do not improve thermal performances significantly but increase the system encumbrance. If the thermal conductivity of the separating wall is doubled, a percentage increase of 24% of the transferred peak heating load was detected reaching a value of 28 W/m2. Results confirm the proposed system as a feasible solution to meet energy-saving purposes and regulation constraints in the building design.

Harnessing stochasticity for superconductive multi-layer spike-rate-coded neuromorphic networks

Conventional semiconductor-based integrated circuits are gradually approaching fundamental scaling limits. Many prospective solutions have recently emerged to supplement or replace both the technology on which basic devices are built and the architecture of data processing. Neuromorphic circuits are a promising approach to computing where techniques used by the brain to achieve high efficiency are exploited. Many existing neuromorphic circuits rely on unconventional and useful properties of novel technologies to better mimic the operation of the brain. One such technology is single flux quantum (SFQ) logic—a cryogenic superconductive technology in which the data are represented by quanta of magnetic flux (fluxons) produced and processed by Josephson junctions embedded within inductive loops. The movement of a fluxon within a circuit produces a quantized voltage pulse (SFQ pulse), resembling a neuronal spiking event. These circuits routinely operate at clock frequencies of tens to hundreds of gigahertz, making SFQ a natural technology for processing high frequency pulse trains. This work harnesses thermal stochasticity in superconducting synapses to emulate stochasticity in biological synapses in which the synapse probabilistically propagates or blocks incoming spikes. The authors also present neuronal, fan-in, and fan-out circuitry inspired by the literature that seamlessly cascade with the synapses for deep neural network construction. Synapse weights and neuron biases are set with bias current, and the authors propose multiple mechanisms for training the network and storing weights. The network primitives are successfully demonstrated in simulation in the context of a rate-coded multi-layer XOR neural network which achieves a wide classification margin. The proposed methodology is based solely on existing SFQ technology and does not employ unconventional superconductive devices or semiconductor transistors, making this proposed system an effective approach for scalable cryogenic neuromorphic computing.

Enhancing Kitchen Independence: Deep Learning-Based Object Detection for Visually Impaired Assistance

Visually impaired individuals face substantial challenges in kitchens, where identifying objects accurately is crucial yet difficult due to the complexity and variability of the environment. Traditional object detection1 methods fall short in these settings, struggling with the assortment of items. This research highlights the need for advanced, kitchen-specific solutions that leverage deep learning to improve detection accuracy and offer real-time, interactive guidance through speech technologies. By focusing on the unique demands of kitchen environments, the proposed system aims to significantly enhance the autonomy and safety of visually impaired users, presenting a notable advancement in assistive technology. The effectiveness of this approach is assessed by its ability to accurately identify kitchen items for visually impaired individuals.

Machines, mathematics, and modules: the potential to provide real-time metrics for pain under anesthesia.

The brain-based assessments under anesthesia have provided the ability to evaluate pain/nociception during surgery and the potential to prevent long-term evolution of chronic pain. Prior studies have shown that the functional near-infrared spectroscopy (fNIRS)-measured changes in cortical regions such as the primary somatosensory and the polar frontal cortices show consistent response to evoked and ongoing pain in awake, sedated, and anesthetized patients. We take this basic approach and integrate it into a potential framework that could provide real-time measures of pain/nociception during the peri-surgical period. This application could have significant implications for providing analgesia during surgery, a practice that currently lacks quantitative evidence to guide patient tailored pain management. Through a simple readout of "pain" or "no pain," the proposed system could diminish or eliminate levels of intraoperative, early post-operative, and potentially, the transition to chronic post-surgical pain. The system, when validated, could also be applied to measures of analgesic efficacy in the clinic.

Intent Identification by Semantically Analyzing the Search Query

Understanding and analyzing the search intent of a user semantically based on their input query has emerged as an intriguing challenge in recent years. It suffers from small-scale human-labeled training data that produce a very poor hypothesis of rare words. The majority of data portals employ keyword-driven search functionality to explore content within their repositories. However, the keyword-based search cannot identify the users’ search intent accurately. Integrating a query-understandable framework into keyword search engines has the potential to enhance their performance, bridging the gap in interpreting the user’s search intent more effectively. In this study, we have proposed a novel approach that focuses on spatial and temporal information, phrase detection, and semantic similarity recognition to detect the user’s intent from the search query. We have used the n-gram probabilistic language model for phrase detection. Furthermore, we propose a probability-aware gated mechanism for RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Approach) embeddings to semantically detect the user’s intent. We analyze and compare the performance of the proposed scheme with the existing state-of-the-art schemes. Furthermore, a detailed case study has been conducted to validate the model’s proficiency in semantic analysis, emphasizing its adaptability and potential for real-world applications where nuanced intent understanding is crucial. The experimental result demonstrates that our proposed system can significantly improve the accuracy for detecting the users’ search intent as well as the quality of classification during search.

Experimental investigation of electromagnetic interference impact on phase change material coated solar photovoltaic panel

ABSTRACT High Voltage Transmission Line Electromagnetic Field’s (HVTL-EMF) impact on solar PV performance is analyzed in the proposed work. Depending on HV-EMF, the electrical characteristics and conversion process of PhotoVoltaic (PV) panels are analyzed. Electromagnetic (EM) field from HV lines has a partially positive and somewhat negative impact on solar PV. When absorption probability is low, one photon and one phonon are required. Phonons are vibrational energy units. A solar PV cell made of Silicon (Si) semiconductor material requires phonons to have high conversion efficiency and electric field from HV transmission line induces phonon. In addition, electric field from HV line increases the solar cell temperature (10 to 15°C) to the ambient temperature. Because of high solar PV cell temperature and high EM field, solar PV power generation is affected by increasing panel temperature. The proposed system uses unnatural Phase Change Material (PCM-Glauber salt (Na2SO4.10 H2O)) coated to the backside of solar PV to normalize the temperature. The impact of electromagnetic fields produced by HV lines on solar cells coated with PCM is validated with experimental testing results. The experiment considers a HVTL of 230kV and solar PV rating of 20 W. The experimental results are measured at various distance ranging from 12 to 22 meters between solar PV and HV transmission lines. Conversion efficiency of solar PV with PCM coating under the impact of HVTL- EMF has increased by 3%.

Performance analysis of electromagnetic vibration energy harvester under square excitation

Harvesting energy from surrounding vibrations is an effective method for powering various low-power electronic devices. The energy output performance of vibration energy harvesters under some special excitations remains to be investigated. In this paper, a vibration energy harvester (VEH) system based on a rack and pinion mechanism and a bevel gear set is proposed, and a detailed electromechanical coupling modeling of the system under general excitation and square wave excitation is carried out. The energy output characteristics of the proposed system under square wave excitation are explored through simulation and experiment. When the energy output is the target, the priority of adjusting the constituent parameters in the equivalent mass parameter, equivalent damping parameter and equivalent stiffness parameter of the system can be obtained based on the simulation results. The experimental results under periodic square wave excitation force show that the maximum output power of the proposed system is 2.7 W at an amplitude of 4 kN and a frequency of 3 Hz. This is a 55 % increase in the maximum power compared to the sinusoidal excitation under the same conditions. It is demonstrated that the proposed system has great potential to power various low-power electronic devices.

Multimodal Robot Programming Interface Based On RGB-D Perception and Neural Scene Understanding Modules

In this paper, we propose a system for natural and intuitive interaction with the robot. Its purpose is to allow a person with no specialized knowledge and no training in robot programming to program a robotic arm.We utilize data from the RGB-D camera to segment the scene and detect objects. We also estimate the configuration of the operator's hand and the position of the visual marker to determine the intentions of the operator and the actions of the robot. To this end, we utilize trained neural networks and operations on the input point clouds. Also, voice commands are used to define or trigger the execution of the motion. Finally, we performed a set of experiments to show the properties of the proposed system.

Multimodal Sensors Enabled Autonomous Soft Robotic System with Self-Adaptive Manipulation.

Human hands are amazingly skilled at recognizing and handling objects of different sizes and shapes. To date, soft robots rarely demonstrate autonomy equivalent to that of humans for fine perception and dexterous operation. Here, an intelligent soft robotic system with autonomous operation and multimodal perception ability is developed by integrating capacitive sensors with triboelectric sensor. With distributed multiple sensors, our robot system can not only sense and memorize multimodal information but also enable an adaptive grasping method for robotic positioning and grasp control, during which the multimodal sensory information can be captured sensitively and fused at feature level for crossmodally recognizing objects, leading to a highly enhanced recognition capability. The proposed system, combining the performance and physical intelligence of biological systems (i.e., self-adaptive behavior and multimodal perception), will greatly advance the integration of soft actuators and robotics in many fields.

Remote Emotion Recognition Using Continuous-Wave Bio-Radar System.

The Bio-Radar is herein presented as a non-contact radar system able to capture vital signs remotely without requiring any physical contact with the subject. In this work, the ability to use the proposed system for emotion recognition is verified by comparing its performance on identifying fear, happiness and a neutral condition, with certified measuring equipment. For this purpose, machine learning algorithms were applied to the respiratory and cardiac signals captured simultaneously by the radar and the referenced contact-based system. Following a multiclass identification strategy, one could conclude that both systems present a comparable performance, where the radar might even outperform under specific conditions. Emotion recognition is possible using a radar system, with an accuracy equal to 99.7% and an F1-score of 99.9%. Thus, we demonstrated that it is perfectly possible to use the Bio-Radar system for this purpose, which is able to be operated remotely, avoiding the subject awareness of being monitored and thus providing more authentic reactions.

NOMA-based retrodirective frequency diverse array for multi-user communication

Frequency Diverse Array (FDA) and retrodirective array have found applications in wireless communication networks, offering cost-effective and efficient beamforming capabilities. In this paper, a novel concept of the retrodirective frequency diverse array (RFDA) is introduced, which empowers each array element to handle multiple communication channels, thereby enabling support for multiple users. This is made possible by utilizing the non-orthogonal multiple access (NOMA) scheme. The antenna elements are organized in a uniform circular array configuration. The system supports the multitude of users by assigning them into distinct clusters based on the serving antenna and then allocating transmit power to each user based on their respective channel conditions and distances from the base station. Consequently, efficient schemes for user clustering and power allocation tailored to the NOMA-RFDA system are developed. As a result, multi-user connectivity is achieved while maintaining low design costs. The simulation outcomes affirm that the proposed system attains superior performance in terms of both data rate and energy efficiency when compared to similar reference schemes.

Towards ultrafast quantitative phase imaging via differentiable microscopy [Invited

With applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. Despite significant advances in fast multiplexed imaging sensors and deep-learning-based inverse solvers, the throughput of QPM is currently limited by the pixel-rate of the image sensors. Complementarily, to improve throughput further, here we propose to acquire images in a compressed form so that more information can be transferred beyond the existing hardware bottleneck of the image sensor. To this end, we present a numerical simulation of a learnable optical compression-decompression framework that learns content-specific features. The proposed differentiable quantitative phase microscopy (∂-QPM) first uses learnable optical processors as image compressors. The intensity representations produced by these optical processors are then captured by the imaging sensor. Finally, a reconstruction network running on a computer decompresses the QPM images post aquisition. In numerical experiments, the proposed system achieves compression of × 64 while maintaining the SSIM of ∼0.90 and PSNR of ∼30 dB on cells. The results demonstrated by our experiments open up a new pathway to QPM systems that may provide unprecedented throughput improvements.