Memory Storage Research Articles

Compression of electrocardiogram signals using compressive sensing technique based on curvelet transform toward medical applications

AbstractElectrocardiogram (ECG) signals can be monitored from many patients based on healthcare systems. To enhance these systems, the ECG signals should be collected and then stored in a cloud platform for later analysis. Hence, ECG signals can be utilized to diagnose heart diseases. However, the ECG signals require great internet capacity. So, compression techniques can be implemented to reduce a memory storage capacity for these signals. One of the potential compression techniques is the compressive sensing (CS). This paper proposes a CS technique to compress ECG signals. This technique is used to reduce sampling rates of the ECG signals to be less than the Nyquist rate. Moreover, a framework is suggested for the compression of maternal and fetal ECG signals. The compression of these signals is based on the curvelet transform (CT) to produce sparsity in ECG signals. The MIT-BIH database are utilized for testing the ECG signals. This database includes several ECG signals with various sampling rates, such as aberrant and normal signals. The proposed CS technique achieved a compression ratio (CR) of 15.7 with an accuracy of 98.2%. Also, a percentage root mean difference (PRD) is utilized to calculate the performance of the reconstructed ECG signals. The achieved value of the PRD is 2.0.

Recognizing the opponent: The consolidation of long-term social memory in zebrafish males

Recognizing and remembering another individual in a social context could be beneficial for individual fitness. Especially in agonistic encounters, remembering an opponent and the previous fight could allow for avoiding new conflicts. Considering this, we hypothesized that this type of social interaction forms a long-term recognition memory lasting several days. It has been shown that a second encounter 24 h later between the same pair of zebrafish males is resolved with lower levels of aggression. Here, we evaluated if this behavioral change could last for longer intervals and a putative mechanism associated with memory storage: the recruitment of NMDA receptors. We found that if a pair of zebrafish males fight and fight again 48 or 72 h later, they resolve the second encounter with lower levels of aggression. However, if opponents were exposed to MK-801 (NMDA receptor antagonist) immediately after the first encounter, they solved the second one with the same levels of aggression: that is, no reduction in aggressive behaviors was observed. These amnesic effect suggest the formation of a long-term social memory related to recognizing a particular opponent and/or the outcome and features of a previous fight.

Joint dual-teacher distillation and unsupervised fusion for unpaired real-world image dehazing

Existing learning-based dehazing algorithms struggle to deal with real world hazy images for lack of paired clean data. Moreover, most dehazing methods require significant computation and memory. To address the above problems, we propose a joint dual-teacher knowledge distillation and unsupervised fusion framework for single image dehazing in this paper. First, considering the complex degradation factors in real-world hazy images, two synthetic-to-real dehazing networks are explored to generate two preliminary dehazing results with the heterogeneous distillation strategy. Second, to get more qualified ground truth, an unsupervised adversarial fusion network is proposed to refine the preliminary outputs of teachers with unpaired clean images. In particular, the unpaired clean images are enhanced to deal with the dim artifacts. Furthermore, to alleviate the structure distortion in the unsupervised adversarial training, we constructed an intermediate image to constrain the output of the fusion network. Finally, considering the memory storage and computation overhead, an end-to-end lightweight student network is trained to learn the mapping from the original hazy image to the output of the fusion network. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on real-world hazy images in terms of no-reference image quality assessment and the parameters.

Mobile Fog AI for Internet of BattleField Things (IoBT)

New mechanisms provide soldiers automation capabilities that include interactive, mobile, fast, scalable, secured, powerful computational communication in contested environments such as battle fields. Internet of BattleField Things (IoBT) is a technology that develops integrated Internet of Things (IoT) solutions for the war of the future. The IoBT connects soldiers with smart technology in armour, radios, weapons, munitions, other objects such as health sensors, GPS, and various IoT devices. These ``things'', objects, or constrained devices can communicate with each other or across existing network infrastructure such as the Internet or network meshes. They most of the time come with limited capacity in terms of memory storage, computational power and energy. IoT, networking (Device Communication (D2D), Peer to Peer Communication (P2P)), machine intelligence, multimodal sensing and fusion are some areas involved in developing interdisciplinary IoBT networks. Our aim is to equip soldiers with AI powered IoBT devices. Existing D2D/P2P/LPWAN based IoBT technology focuses on edge computing devices which are very limited, being slow, insecure, and lack sufficient scalability. We present a novel concept, i.e., Mobile Fog AI for IoBT to overcome existing IoBT limitations.

Synthesis and multifunctional characterizations of Gd2 O3 and Sm2 O3 modified ZnO nanoparticles

In the present communication multifunctional characterizations suitable for spintronics and memory storage devices are described. Double doped (Gd3+, Sm3+) ZnO nanoparticles are prepared from Sol-Gel method using poly vinyl alcohol (PVA) as chelating agent. The as prepared powders are annealed at 500 °C–1000 °C with a step size of 100 °C. The genesis of single phase hexagonal wurtzite structure, morphological and topographical changes, optical, magnetic and mechanical properties were studied with XRD (X-ray diffraction), SEM (scanning electron microscopy), TEM (transmission electron microscopy), EDS (energy dispersive spectroscopy), UV- DRS (ultraviolet diffuse reflective spectroscopy), PL (photoluminescence), VSM (vibrating sample magnetometer) and pin on disc tribometer. XRD study untangled the crystallite size and found to be in the range23-50nm. Polycrystalline nanoparticles of spherical geometry with induced porosity is observed from SEM study. The particle size associated with the samples annealed at 500 °C and 900 °C is 24 nm and 27 nm respectively from TEM study. Analyzed the proximity of functional groups, molecular vibrations in the range of 400-4000 cm−1 employing FTIR (Fourier transform infrared spectroscopy). EDS results revealed the stoichiometry of the produced samples. The pronounced optoelectronic applications of Zn1-x Gdx Smx O (ZGS) can be attributed to the large band gap (3.50–3.02 eV) associated with the materials with rise in annealing temperature. The room temperature ferromagnetism was evidenced with double doped ZnO nanomaterials. The wear coefficient, and friction coefficient were evaluated using pin-on-disc tribometer. The utility of materials as solid state lubricants was confiscated from the range of frictional coefficient values (0.067–0.893).

An Assessment of the Role of the Timex Sampling Strategy on the Precision of Shoreline Detection Analysis

Remote video imagery using shoreline edge detection is widely used in coastal monitoring in order to acquire measurements of nearshore and swash features. Some of these systems are constrained by their long setup time, positioning requirements and considerable hardware costs. As such, there is a need for an autonomous low-cost system (~EUR 500), such as Timex cameras, that can be rapidly deployed in the field, while still producing the outcomes required for coastal monitoring. This research presents an assessment of the effect of the sampling strategy (time-lapse intervals) on the precision of shoreline detection for two low-cost cameras located in a remote coastal area in western Ireland, overlooking a dissipative beach–dune system. The analysis shows that RMSD in the detected shoreline is similar to other studies for sampling intervals ranging between 1 s and 30 s (i.e., RMSDmean for Camera 1 = 1.4 m and Camera 2 = 0.9 m), and an increase in the sampling interval from 1 s to 30 s had no significant adverse effect on the precision of shoreline detection. The research shows that depending on the intended use of the detected shorelines, the current standard of 1 s image sampling interval when using Timex cameras can be increased up to 30 s without any significant loss of accuracy. This positively impacts battery life and memory storage, making the systems more autonomous; for example, the battery life increased from ~10 days to ~100 days when the sampling interval was increased from 1 to 5 s.

Cycle equivalence classes, orthogonal Weingarten calculus, and the mean field theory of memristive systems

It has been recently noted that for a class of dynamical systems with explicit conservation laws represented via projector operators, the dynamics can be understood in terms of lower dimensional equations. This is the case, for instance, of memristive circuits. Memristive systems are important classes of devices with wide-ranging applications in electronic circuits, artificial neural networks, and memory storage. We show that such mean-field theories can emerge from averages over the group of orthogonal matrices, interpreted as cycle-preserving transformations applied to the projector operator describing Kirchhoff’s laws. Our results provide insights into the fundamental principles underlying the behavior of resistive and memristive circuits and highlight the importance of conservation laws for their mean-field theories. In addition, we argue that our results shed light on the nature of the critical avalanches observed in quasi-two-dimensional nanowires as boundary phenomena.

Tuneable quantised conductance memory states in TiO2 based resistive switching devices in crossbar geometry for high density memory applications

The tunability and controllability of conductance quantization mediated multilevel resistive switching (RS) memory devices, fabricated in crossbar geometry can be a promising alternative for boosting storage density. Here, we report fabrication of Cu/TiO2/Pt based RS devices in 8 × 8 crossbar geometry, which showed reliable bipolar RS operations. The crossbar devices showed excellent spatial and temporal variability, time retention and low switching voltage (<1 V) and current (∼100 μA). Furthermore, during the reset switching, highly repeatable and reliable integral and half-integral quantized conductance (QC) was observed. The observed QC phenomenon was attributed to the two dimensional confinement of electrons as lateral width of the conducting filament (CF) matches the fermi wavelength. The magnitude and number of the QC steps were found to increase from ∼2.5 to 12.5 and from 5 to 18, respectively by increasing the compliance current (I C) from 50 to 800 μA which also increased the diameter of the CF from ∼1.2 to 3.3 nm. The enhancement in both number and magnitude of QC states was explained using electrochemical dissolution mechanism of CF of varying diameter. A thicker CF, formed at higher I C, undergoes a gradual rupture during reset process yielding a greater number of QC steps compared to a thinner CF. The realisation of QC states in the crossbar Cu/TiO2/Pt device as well as I C mediated tunability of their magnitude and number may find applications in high-density resistive memory storage devices and neuromorphic computing.

Complete Stability of Delayed Recurrent Neural Networks With New Wave-Type Activation Functions.

Activation functions have a significant effect on the dynamics of neural networks (NNs). This study proposes new nonmonotonic wave-type activation functions and examines the complete stability of delayed recurrent NNs (DRNNs) with these activation functions. Using the geometrical properties of the wave-type activation function and subsequent iteration scheme, sufficient conditions are provided to ensure that a DRNN with n neurons has exactly (2m + 3)n equilibria, where (m + 2)n equilibria are locally exponentially stable, the remainder (2m + 3)n - (m + 2)n equilibria are unstable, and a positive integer m is related to wave-type activation functions. Furthermore, the DRNN with the proposed activation function is completely stable. Compared with the previous literature, the total number of equilibria and the stable equilibria significantly increase, thereby enhancing the memory storage capacity of DRNN. Finally, several examples are presented to demonstrate our proposed results.

Advancements And Applications of Finfet Technology in Modern Semiconductor Engineering

This comprehensive study presents an in-depth analysis of Fin Field-Effect Transistor (FinFET) technology, a significant innovation in semiconductor technology, focusing on its structural attributes, applications, and future prospects. FinFETs, characterized by their distinctive fin-like channels, are pivotal in overcoming the limitations of traditional MOSFETs, particularly in the context of device miniaturization. The paper begins by exploring the basic theory and structural characteristics of FinFETs, emphasizing their enhanced control over short channel effects and reduced leakage currents in scaled-down devices. Attention is then directed towards the integration of FinFET technology in Static Random-Access Memory (SRAM), detailing how FinFETs contribute to improved stability and efficiency in memory storage. Another crucial application discussed is in the biomedical field, where FinFETs have facilitated the development of advanced instrumentation amplifiers and sensitive sensors, marking a leap in medical electronics. The paper concludes by addressing the challenges encountered by FinFETs as fabrication approaches the 5-7nm scale, including their physical and structural limitations. It also highlights ongoing research in structural modifications and material innovations like Gate-All-Around FETs and alternative materials, underscoring the continued evolution and relevance of FinFET technology in the ever-advancing semiconductor industry.

A numerical method for two-dimensional transient nonlinear convection-diffusion equations

In this research, we have developed the half-boundary method (HBM) for nonlinear convection-diffusion equations (CDEs), which hold significant importance in nuclear power engineering. The HBM employs a variable relationship between the nodes within the computational domain and the nodes located on half of the boundaries. This approach offers notable benefits, includingthe reduction ofthe maximum matrix order and the optimization the maximum memory storage for calculations. Moreover, the HBM is an efficient and streamlined approach to directly handle Neumann boundary conditions, thanks to the utilization of mixed variables. We primarily investigate the memory usage and accuracy of the proposed algorithm in the unsteady-state CDEs, in context of material nonlinear CDEs, the Burgers’ equation and the system of Burgers' equations. The numerical results obtained demonstrate the method’s potential in simulating flow and heat transfer phenomena, particularly in situations where convection is dominant.

The meso-connectomes of mouse, marmoset, and macaque: network organization and the emergence of higher cognition.

The recent publications of the inter-areal connectomes for mouse, marmoset, and macaque cortex have allowed deeper comparisons across rodent vs. primate cortical organization. In general, these show that the mouse has very widespread, "all-to-all" inter-areal connectivity (i.e. a "highly dense" connectome in a graph theoretical framework), while primates have a more modular organization. In this review, we highlight the relevance of these differences to function, including the example of primary visual cortex (V1) which, in the mouse, is interconnected with all other areas, therefore including other primary sensory and frontal areas. We argue that this dense inter-areal connectivity benefits multimodal associations, at the cost of reduced functional segregation. Conversely, primates have expanded cortices with a modular connectivity structure, where V1 is almost exclusively interconnected with other visual cortices, themselves organized in relatively segregated streams, and hierarchically higher cortical areas such as prefrontal cortex provide top-down regulation for specifying precise information for working memory storage and manipulation. Increased complexity in cytoarchitecture, connectivity, dendritic spine density, and receptor expression additionally reveal a sharper hierarchical organization in primate cortex. Together, we argue that these primate specializations permit separable deconstruction and selective reconstruction of representations, which is essential to higher cognition.

Skewing information flow through pre- and postsynaptic plasticity in the mushroom bodies of Drosophila.

Animal brains need to store information to construct a representation of their environment. Knowledge of what happened in the past allows both vertebrates and invertebrates to predict future outcomes by recalling previous experience. Although invertebrate and vertebrate brains share common principles at the molecular, cellular, and circuit-architectural levels, there are also obvious differences as exemplified by the use of acetylcholine versus glutamate as the considered main excitatory neurotransmitters in the respective central nervous systems. Nonetheless, across central nervous systems, synaptic plasticity is thought to be a main substrate for memory storage. Therefore, how brain circuits and synaptic contacts change following learning is of fundamental interest for understanding brain computations tied to behavior in any animal. Recent progress has been made in understanding such plastic changes following olfactory associative learning in the mushroom bodies (MBs) of Drosophila A current framework of memory-guided behavioral selection is based on the MB skew model, in which antagonistic synaptic pathways are selectively changed in strength. Here, we review insights into plasticity at dedicated Drosophila MB output pathways and update what is known about the plasticity of both pre- and postsynaptic compartments of Drosophila MB neurons.

Self-powered photodetector based on 1D TiO2-3D CdS mixed dimensional heterostructure fabricated at low temperature

Solution processed photodetectors have garnered great attention in applications such as, machine vision perception, neuromorphic computing and opto-electronic memory storage. Though, such photodetectors offer several advantages such as ease of fabrication, high scalability, low thermal budget and low-cost processing, multi-modal functionality etc. however, they suffer from the major drawback of inferior device performance −as low responsivity and slow rise time, particularly due to the intrinsic poor crystallinity of the photoactive material. In this work, we demonstrate a solution processed photodetector with impressive performance at comparatively low processing temperatures (<150 °C) based on the mixed dimensional heterostructure configuration of 1D TiO2 nanorods and 3D CdS nanoflowers. TiO2 nanorods have been synthesized by hydrothermal technique, whereas their CdS sensitization is done by chemical bath deposition. Low cost carbon paste is used as electrode instead of conventional non-economic noble metal electrodes. X-ray diffraction studies validated excellent crystallinity of the photoactive material even under low temperature processing condition. The type-II Heterojunction (TiO2 and CdS) configuration photodetector shows efficient response at zero bias, thus yielding a self-powered device. The detector shows response in UV and visible region, with excellent responsivity of 110 mA/W (5 V), 563 µA/W (0 V) and a quicker rise time of 81 ms. Albeit the simple fabrication scheme and low processing temperatures, the detector exhibited promising figures-of-merit, which aids in fabrication of novel solution processed photodetectors.

DESIGN AND ANALYSIS ON IMAGE COMPRESSION USING NEURAL NETWORKS

Due to the explosion of video based information proliferating in the world due to the ubiquitous usage of video cameras the amount of video based information that is currently being generated around the world is huge. And due to security purposes it is becoming imperative that these video data needs to be stored in computer memory for an extended period of time for referrals by security agencies. Because of the advancement of imaging technologies that is being used nowadays it is possible to capture extremely detailed high definition images. But it is not physically possible to store all these high-definition images in computer memories for a long time because infrastructure providers will run out of memory. Image compression is a technology which assists us in this regard. Nowadays this technology has moved from image compression to video compression to compression of 3- dimensional videos which is now becoming more and more popular due to the ever increasing usage of Augmented Reality, Virtual Reality and Ec The quantity of image data produced in modern surveillance networks is rising exponentially year on year which necessitated development of novel schemes for reducing the sizes of the images captured by CCTV cameras while not compromising on the image quality increases when the images are decompressed. This paper proposed a novel Deep Neural Network based method of compressing images in which the image accuracy is not lost but the space it occupies in the memory storage of the computes is reduced greatly compared to other image compression schemes, this proposed scheme is best suited for usage in CCTVs and other networks using Internet of Things which record images and videos continuously.

Processing wh-filler-gap dependencies.

We present experimental evidence showing that different wh-filler-gap dependencies are processed differently, depending on their syntactic licensors. Our studies compared the active storage profiles for why, how, and who (serving as subject or object of the verb). The results of offline and online experiments revealed that these wh-fillers are stored in memory for different durations, and predictably so based on the hypothesised structural distance between each wh-filler and the licensor which determines its grammatical and interpretive functions. Furthermore, the results showed that once the wh-filler is licenced, it is integrated to the current structure, and no longer engenders additional memory costs. Based on these findings, we argue that the mechanism of online sentence processing may employ both storage and integration components in memory.

Beyond the Pump: A Narrative Study Exploring Heart Memory.

The field of organ transplantation, particularly heart transplantation, has brought to light interesting phenomena challenging traditional understandings of memory, identity, and consciousness. Studies indicate that heart transplant recipients may exhibit preferences, emotions, and memories resembling those of the donors, suggesting a form of memory storage within the transplanted organ. Mechanisms proposed for this memory transfer include cellular memory, epigenetic modifications, and energetic interactions. Moreover, the heart's intricate neural network, often referred to as the "heart brain," communicates bidirectionally with the brain and other organs, supporting the concept of heart-brain connection and its role in memory and personality. Additionally, observations from hemispherectomy procedures highlight the brain's remarkable plasticity and functional preservation beyond expectations, further underscoring the complex interplay between the brain, body, and identity. However, ethical and philosophical questions regarding the implications of these findings, including the definition of death and the nature of personal identity, remain unresolved. Further interdisciplinary research is needed to unravel the intricacies of memory transfer, neuroplasticity, and organ integration, offering insights into both organ transplantation and broader aspects of neuroscience and human identity. Understanding these complexities holds promise for enhancing patient care in organ transplantation and deepens our understanding of fundamental aspects of human experience and existence.

Versatile polyamides with new triphenylamine monomer based on dinaphtho furan as core for electrochromic/electrofluorochromic device, TNP detection and memory storage

In order to obtain a versatile electrochromic (EC) and electrofluorochromic (EFC) material with good processability, stable redox properties, high optical contrast and fast response, a novel diamine monomer, N1, N1'-(dinaphtho[2,1-b:1',2'-d] furan-5,9-diyl) bis(N1-(4-methoxyphenyl) benzene- 1,4-diamine) (DNFTPA-NH2), which introducing an oxa[5]helicene core into two electron-rich triphenylamine (TPA) blocks is designed and synthesized. Subsequently, four polyamides (PAs) were obtained via polycondensation of diamine with dicarboxylic acids, denoted as DNFTPA-CA, DNFTPA-OA, DNFTPA-NA and DNFTPA-SA, respectively. The four PAs show excellent solution processability and thermal stability, and cyclic voltammograms (CV) with excellent redox reversibility. Among them, DNFTPA-SA shows marked optical contrast of 34 % at 831 nm and rapid switching time (color/bleach, 3.23s/0.39s) between 0 V and 1.0 V, with no significant decay after 5000 s cycle. DNFTPA-CA exhibits high fluorescence (PL) quantum efficiency of 27 %, which is sensitive to explosive trinitrophenol (TNP) with a low detection limit of 7.24 × 10−2 μM. Given the practical application, it is deemed necessary to prepare EC/EFC device. The findings demonstrate the value of PAs for practical applications in EC/EFC device. Moreover, PAs used in memory devices with sandwich structure of ITO/PAs/Al demonstrate non-volatile flash-type behavior with excellent stability within 10000 s and current switching ratios up to 103. Thus, it provides a facile strategy for the design of polymers in the devices of EC/EFC, PL sensing detection and memristor.

A post-quantum secure PUF based cross-domain authentication mechanism for Internet of drones

With the increasing prevalence of drones, guaranteeing their authentication and secure communication has become paramount in drone networks to mitigate unauthorized access and malicious attacks. Cross-domain authentication is crucial in the context of the Internet of Drones (IoD) for safely verifying and establishing trust between diverse drones and their respective control stations, which may belong to different regions or organizations. Effectively accessing resources or services in another domain while maintaining security and efficiency poses a significant challenge. Conventional authentication mechanisms relying on challenging problems like discrete logarithm and integer factorization might not be sufficient to guarantee the security and effectiveness of drone-based systems in the post-quantum era. To address this, we propose a distributed post-quantum cryptography and Physical Unclonable Function (PUF) based cross-domain authentication protocol for IoD. Key contributions of this work include the elimination of secret key storage on drones, mutual authentication, emphasis on hardware security, incorporation of post-quantum security measures, efficient cross-domain authentication and resilience against cyber attacks such as eavesdropping, impersonation, replay attack, untraceability, and PUF-modeling attack. The performance of the proposed protocol is assessed utilizing metrics like processing time, communication cost and storage utilization. In operations associated to the blockchain ledger, variables such as latency, throughput, CPU utilization, and memory utilization are also examined. The protocol shows a reduced computation time and zero sensitive data storage in drone memory, despite a slightly higher communication cost that is manageable with 5G-enabled drones. Comparative analysis against existing solutions in the domain highlights the superior security of the proposed protocol, positioning it as a promising solution for the evolving quantum landscape.

Confining single Er3+ ions in sub-3 nm NaYF4 nanoparticles to induce slow relaxation of the magnetisation

Molecular systems known as single-molecule magnets (SMMs) exhibit magnet-like behaviour of slow relaxation of the magnetisation and magnetic hysteresis and have potential application in high-density memory storage or quantum computing. Often, their intrinsic magnetic properties are plagued by low-energy molecular vibrations that lead to phonon-induced relaxation processes, however, there is no straightforward synthetic approach for molecular systems that would lead to a small amount of low-energy vibrations and low phonon density of states at the spin-resonance energies. In this work, we apply knowledge accumulated over the last decade in molecular magnetism to nanoparticles, incorporating Er3+ ions in an ultrasmall sub-3 nm diamagnetic NaYF4 nanoparticle (NP) and probing the slow relaxation dynamics intrinsic to the Er3+ ion. Furthermore, by increasing the doping concentration, we also investigate the role of intraparticle interactions within the NP. The knowledge gained from this study is anticipated to enable better design of magnetically high-performance molecular and bulk magnets for a wide variety of applications, such as molecular electronics.