Long Duration Persistent Photocurrent in 3 nm Thin Doped Indium Oxide for Integrated Light Sensing and In-Sensor Neuromorphic Computation

Miniaturization and energy consumption by computational systems remain major challenges to address. Optoelectronics based synaptic and light sensing provide an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single-element image sensors that allow reception of information and execution of in-memory computing processes while maintaining memory for much longer durations without the need for frequent electrical or optical rehearsals. In this work, ultra-thin (<3 nm) doped indium oxide (In₂O₃) layers are engineered to demonstrate a monolithic two-terminal ultraviolet (UV) sensing and processing system with long optical state retention operating at 50 mV. This endows features of several conductance states within the persistent photocurrent window that are harnessed to show learning capabilities and significantly reduce the number of rehearsals. The atomically thin sheets are implemented as a focal plane array (FPA) for UV spectrum based proof-of-concept vision system capable of pattern recognition and memorization required for imaging and detection applications. This integrated light sensing and memory system is deployed to illustrate capabilities for real-time, in-sensor memorization, and recognition tasks. This study provides an important template to engineer miniaturized and low operating voltage neuromorphic platforms across the light spectrum based on application demand.     

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall.     

Unital Design Based Location Service for Subterranean Network Using Long Range Topology

Wireless Personal Communications - Wireless Underground Networks comprise the ability to constantly monitor several physical parameters such as ground temperature, water level and soil condition,...     

Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

Journal of Signal Processing Systems - Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful...     

The Double Edge Sword Based Distributed Executor Service

Scalability is one of the most important quality attribute of software-intensive systems, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) (which is also known as executor service) has been used extensively as a middleware service in software-intensive systems. TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime. In case of distributed-TPS (DTPS), another issue is the load balancing b/w available set of TPSs running at backend servers. Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload. Consequently, the performance of software-intensive system is suffered. Thus, in this paper, we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword. On the one hand, it effectively performs the load balancing (in case of overload situation) among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs. And on the other hand, its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size, because the pool size in each TPS is kept equal to the request rate on it, hence dynamically optimizes TPS. We evaluated the results of the proposed system against state of the art DTPSs by a client-server based simulator and found that our system outperformed by sustaining smaller response times.     

Local calibration of rigid pavement performance models using resampling methods

The performance prediction models in the Pavement-ME design software are nationally calibrated using in-service pavement material properties, pavement structure, climate and truck loadings, and performance data obtained from the Long-Term Pavement Performance programme. The nationally calibrated models may not perform well if the inputs and performance data used to calibrate those do not represent the local design and construction practices. Therefore, before implementing the new M-E design procedure, each state highway agency (SHA) should evaluate how well the nationally calibrated performance models predict the measured field performance. The local calibrations of the Pavement-ME performance models are recommended to improve the performance prediction capabilities to reflect the unique conditions and design practices. During the local calibration process, the traditional calibration techniques (split sampling) may not necessarily provide adequate results when limited number of pavement sections are available. Consequently, there is a need to employ statistical and resampling methodologies that are more efficient and robust for model calibrations given the data related challenges encountered by SHAs. The main objectives of the paper are to demonstrate the local calibration of rigid pavement performance models and compare the calibration results based on different resampling techniques. The bootstrap is a non-parametric and robust resampling technique for estimating standard errors and confidence intervals of a statistic. The main advantage of bootstrapping is that model parameters estimation is possible without making distribution assumptions. This paper presents the use of bootstrapping and jackknifing to locally calibrate the transverse cracking and IRI performance models for newly constructed and rehabilitated rigid pavements. The results of the calibration show that the standard error of estimate and bias are lower compared to the traditional sampling methods. In addition, the validation statistics are similar to that of the locally calibrated model, especially for the IRI model, which indicates robustness of the local model coefficients.     

Lateral Magnetically Modulated Multilayers by Combining Ion Implantation and Lithography

The combination of lithography and ion implantation is demonstrated to be a suitable method to prepare lateral multilayers. A laterally, compositionally, and magnetically modulated microscale pattern consisting of alternating Co (1.6 µm wide) and Co‐CoO (2.4 µm wide) lines has been obtained by oxygen ion implantation into a lithographically masked Au‐sandwiched Co thin film. Magnetoresistance along the lines (i.e., current and applied magnetic field are parallel to the lines) reveals an effective positive giant magnetoresistance (GMR) behavior at room temperature. Conversely, anisotropic magnetoresistance and GMR contributions are distinguished at low temperature (i.e., 10 K) since the O‐implanted areas become exchange coupled. This planar GMR is principally ascribed to the spatial modulation of coercivity in a spring‐magnet‐type configuration, which results in 180° Néel extrinsic domain walls at the Co/Co‐CoO interfaces. The versatility, in terms of pattern size, morphology, and composition adjustment, of this method offers a unique route to fabricate planar systems for, among others, spintronic research and applications.     

Trimetallic PtCoFe Alloy Monolayer Superlattices as Bifunctional Oxygen‐Reduction and Ethanol‐Oxidation Electrocatalysts

A synthesis strategy for the preparation of trimetallic PtCoFe alloy nanoparticle superlattices is reported. Trimetallic PtCoFe alloy monolayer array of nanoparticle superlattices with a large density of high index facets and platinum‐rich surface are successfully prepared by coreduction of metal precursors in formamide solvent. The concentration of cetyl trimethyl ammonium bromide plays a vital role for the formation of a monolayer array of nanoparticle superlattices, while the size of nanoparticles is determined by NaI. The prepared monolayer array of nanoparticle superlattices is the superior catalyst for oxygen reduction reaction as well as for ethanol oxidation owing to their specific structural and compositional characteristics.     

Biosynthesis and characterisation of nano‐silica as potential system for carrying streptomycin at nano‐scale drug delivery

A growing trend within nanomedicine has been the fabrication of self‐delivering supramolecular nanomedicines containing a high and fixed drug content ensuring eco‐friendly conditions. This study reports on green synthesis of silica nanoparticles (Si‐NPs) using Azadirachta indica leaves extract as an effective chelating agent. X‐ray diffraction analysis and Fourier transform‐infra‐red spectroscopic examination were studied. Scanning electron microscopy analysis revealed that the average size of particles formed via plant extract as reducing agent without any surfactant is in the range of 100–170 nm while addition of cetyltrimethyl ammonium bromide were more uniform with 200 nm in size. Streptomycin as model drug was successfully loaded to green synthesised Si‐NPs, sustain release of the drug from this conjugate unit were examined. Prolong release pattern of the adsorbed drug ensure that Si‐NPs have great potential in nano‐drug delivery keeping the environment preferably biocompatible, future cytotoxic studies in this connection is helpful in achieving safe mode for nano‐drug delivery.Inspec keywords: silicon compounds, nanofabrication, nanomedicine, drug delivery systems, nanoparticles, X‐ray diffraction, Fourier transform infrared spectra, scanning electron microscopyOther keywords: nanosilica, streptomycin, nanoscale drug delivery, nanomedicine, silica nanoparticles, Azadirachta indica leaves extract, X‐ray diffraction analysis, Fourier transform‐infrared spectroscopy, scanning electron microscopy, cetyltrimethyl ammonium bromide, SiO₂