Sena Yakut, Ăzlem Ćeker, Ertugrul Batur, Gökhan Dalkılıç
Blockchain is a public ledger without a central authority. Immutable and inalterable data blocks prove reliability and security which are the deficiencies of the current Internet of things (IoT) devices. IoT devices are hardware constrained devices which have specific functions but create large network to gather different data. Because of the security issues of the IoT devices, industrial use is limited. Generally, secure data transaction and storage are the main concerns over IoT devices and in order to solve the problem various solutions are being tested. However, decentralized and secure structure like the blockchain could be a solution. In this study, challenges of blockchain implementation on hardware constraint devices are discussed. Studies regarding blockchain implementation methods and blockchain implemented projects are mentioned. Experiments on frameworks used by blockchain are conducted and with the test results of the experiments, feasibility of implementing the blockchain on IoT devices are discussed.
The emerging blockchain technology shows promising potential to enhance industrial systems and the Internet of things (IoT) by providing applications with redundancy, immutable storage, and encryption. In the past few years, many more applications in industrial IoT (IIoT) have emerged and the blockchain technologies have attracted huge amounts of attention from both industrial and academic researchers. In this paper, we address the integration of blockchain and IIoT from the industrial prospective. A blockchain-enabled IIoT framework is introduced and involved fundamental techniques are presented. Moreover, the main applications and key challenges are addressed. A comprehensive analysis for the most recent research trends and open issues is provided associated with the blockchain-enabled IIoT.
Industrial Internet of Things (IIoT) plays an indispensable role for Industry 4.0, where people are committed to implement a general, scalable, and secure IIoT system to be adopted across various industries. However, existing IIoT systems are vulnerable to single point of failure and malicious attacks, which cannot provide stable services. Due to the resilience and security promise of blockchain, the idea of combining blockchain and Internet of Things (IoT) gains considerable interest. However, blockchains are power-intensive and low-throughput, which are not suitable for power-constrained IoT devices. To tackle these challenges, we present a blockchain system with credit-based consensus mechanism for IIoT. We propose a credit-based proof-of-work (PoW) mechanism for IoT devices, which can guarantee system security and transaction efficiency simultaneously. In order to protect sensitive data confidentiality, we design a data authority management method to regulate the access to sensor data. In addition, our system is built based on directed acyclic graph -structured blockchains, which is more efficient than the Satoshi-style blockchain in performance. We implement the system on Raspberry Pi, and conduct a case study for the smart factory. Extensive evaluation and analysis results demonstrate that credit-based PoW mechanism and data access control are secure and efficient in IIoT.
There are many blockchain frameworks have been introduced to user, developer, and customer and they have to make a decision which one is most suitable for them in order for developing their applications. This paper describes an empirical research to benchmark the properties of frameworks in Hyperledger family. The purpose is to show the advantage & disadvantage of each framework. We apply the deeply study inside the framework and do the experiment to measure the properties as well as give literature and numerical review of each framework. Our results show the status of tested frameworks and do not demonstrate how to employ the framework as well as apply to the detail application. The main contribution of this paper is a statistical analysis of the effectiveness of Hyperledger frameworks as a tool for developer to develop their applications.
Mengting Liu, F. Richard Yu, Yinglei Teng, Victor C. M. Leung · 5 authors
Recent advances in the industrial Internet of things (IIoT) provide plenty of opportunities for various industries. To address the security and efficiency issues of the massive IIoT data, blockchain is widely considered as a promising solution to enable data storing/processing/sharing in a secure and efficient way. To meet the high throughput requirement, this paper proposes a novel deep reinforcement learning (DRL)-based performance optimization framework for blockchain-enabled IIoT systems, the goals of which are threefold: 1) providing a methodology for evaluating the system from the aspects of scalability, decentralization, latency, and security; 2) improving the scalability of the underlying blockchain without affecting the system's decentralization, latency, and security; and 3) designing a modulable blockchain for IIoT systems, where the block producers, consensus algorithm, block size, and block interval can be selected/adjusted using the DRL technique. Simulations results show that our proposed framework can effectively improve the performance of blockchain-enabled IIoT systems and well adapt to the dynamics of the IIoT.
Brain-computer interface can be currently accelerated to develop the exoskeleton for healthy people as well as patients who are unable to move muscles around the world. In this situation, the communication between the electroencephalogram (EEG) and prosthesis discovers the vulnerabilities to taking personal information. However, previous researches only focus on the analysis of attack pattern rather than fixing the vulnerability. In order to complement the vulnerability, we propose a blockchain platform in which try to identify the modulated data when server is attacked. Also, we find out potential risks in EEG data with non-blockchain environments after attack in our study. As a result, the proposed system can guarantee the integrity of EEG data by knowing the change of hash, and can prevent attacks such as hijacking, sniffing, and eavesdropping.
The consensus algorithm played a significant role in successful adoption of Blockchain technology. The security of bitcoin blockchain hugely depends on the complexity and reliability of the Proof-of-Work (PoW) consensus. However, a non-financial application, whose security requirements are less demanding, requires faster operation and so PoW is not suitable for many of the commercial applications. In this paper, we propose a lightweight Trust-dependent Consensus scheme (TCON) for a blockchain based IoT access control framework. TCON ensures that the blocks are added at a faster rate, spending minimal energy compared to PoW based systems. To the best of our knowledge, this is the first time the notion of trust is quantified and used as a means to simplify the consensus problem in blockchain. A comparison of the various metrics to understand the differences in overhead involved suggest that the proposed scheme outperforms other non-trust-based consensus schemes.
Md. Abdur Rahman, M. Shamim Hossain, Md. Mamunur Rashid, Stuart J. Barnes · 6 authors
Although ElectroEncephaloGram (EEG) signals allow subjects suffering from neuromuscular disorders to interface their brains with the cyber-physical world, occupational therapy can be enhanced with the introduction of further modalities better assist the disabled person. In this paper, we propose an in-home occupational therapy environment, which leverages a rich set of occupational therapy-related activity recognition modalities, namely, EEG signals to understand brain activity, ElectroMyoGram (EMG) signals for muscle activity, gesture-tracking sensors for forward and inverse kinematics activities, and smart home appliance control sensors. To support a wide variety of disabled people's in-home occupational therapy, we have incorporated both selective attention and motor imagery processes for mapping a mental command with that of an occupational therapy-related command within a serious game environment. To attain higher accuracy and to avoid a higher number of false positives, a subject is first recommended to use a selective attention-based serious game in which a digital avatar of the subject acting as a model therapist will guide the therapy session. Once familiar with the generation of proper motor imagery, an advanced user can use self-paced motor imagery signals to perform occupational therapy activities within the serious game environment. The occupational therapy consists of a serious game environment in which smart home appliances are mapped with therapeutic activities through forward and inverse kinematics. The therapy data has been secured through blockchain and off-chain-based distributed repositories. The test results show the viability of using the framework in a clinical environment.
Khaled Salah, Muhammad Habib ur Rehman, Nishara Nizamuddin, Ala AlâFuqaha
Recently, artificial intelligence (AI) and blockchain have become two of the most trending and disruptive technologies. Blockchain technology has the ability to automate payment in cryptocurrency and to provide access to a shared ledger of data, transactions, and logs in a decentralized, secure, and trusted manner. Also with smart contracts, blockchain has the ability to govern interactions among participants with no intermediary or a trusted third party. AI, on the other hand, offers intelligence and decision-making capabilities for machines similar to humans. In this paper, we present a detailed survey on blockchain applications for AI. We review the literature, tabulate, and summarize the emerging blockchain applications, platforms, and protocols specifically targeting AI area. We also identify and discuss open research challenges of utilizing blockchain technologies for AI.
Blockchain is a distributed ledger that gained a prevalent attention in many areas. Many industries have started to implement blockchain solutions for their application and services. It is important to know the key components, functional characteristics, and architecture of blockchain to understand its impact and applicability to various applications. The most well-known use case of blockchain is bitcoin: a cryptocurrency. Being a distributed ledger, consensus mechanism is needed among peer nodes of a blockchain network to ensure its proper working. Many consensus algorithms have been proposed in literature each having its own performance and security characteristics. One consensus algorithm cannot serve the requirements of every application. It is vital to technically compare the available consensus algorithms to highlight their strengths, weaknesses, and use cases. We have identified and discussed parameters related to performance and security of consensus in blockchain. The consensus algorithms are analyzed and compared with respect to these parameters. Research gap regarding designing an efficient consensus algorithm and evaluating existing algorithms is presented. This paper will act as a guide for developers and researchers to evaluate and design a consensus algorithm.
Jul 1, 2018·2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
Kai Fan, Yanhui Ren, Zheng Yan, Shangyang Wang · 6 authors
The era of information has arrived. As an important part of new generation of information technology., Internet of Things (IoT)., which is developing rapidly., requires higher and higher time accuracy. However, the malicious nodes located in network can influence the time synchronization. The security issue of time transfer and consistency is critical and challenging. In this paper, we propose a secure scheme based on blockchain to solve the problem of time announcement in IoT. In this distributed network, a closed blockchian to record and broadcast time is utilized, which minimize attacks from external environments. Moreover, this scheme has the advantage of adapting the changes of network topology. By employing POS consensus mechanism, time synchronization can be implemented efficiently. At last, the analysis results show that this secure scheme can be achieved with high efficiency and less communication cost.
Seunghyun Yoo, Seungbae Kim, Joshua Joy, MĂĄrio Gerla
A proof-of-work blockchain adopts an incentive-driven design to encourage people to participate in the network. Miners provide computing resources and services in exchange for incentives such as static block rewards and transaction fees collected from blockchain users. However, our findings suggest that the current reward scheme may not encourage miners to process user transactions. A non-cooperative strategy that submits a block with no transaction can be more rewarded than a cooperative strategy. As a consequence, the non-cooperative strategy can prevail over the cooperative strategy, decrease the system throughput, and distort credit distribution. We particularly choose Ethereum project as a subject since it is a general-purpose smart contract platform. By investigating the past two years of its ledger history, we find network propagation and block processing delays are the most significant factors that cause miners to choose the non-cooperative strategy. From this finding, we develop a more accurate statistical model for a block discovery time, as well as a reward matrix. We then derive the condition that either strategy has no additional gain, which also helps to estimate whether the transaction fee is underpriced or not. Simulation results show that the non-cooperative strategy is no longer dominant under the revised reward scheme.
Stacked Denoising Autoencoders (SDA) are deep networks which have gained popularity owing to their superior performance in image classification applications, but they haven't been used much in healthcare applications. SDA can be efficiently retrained to adapt to large streams of data, and this property is used in this work to develop a technique for classification of arrhythmias in a patient-specific manner. This approach is particularly useful in continuous remote systems because they gather large amounts of data for longer periods of time. Blockchain is a decentralized distributed ledger which secures transactions with cryptography. It is proposed as an access control manager to securely store and access data required by the classifier during retraining in real-time from an external data storage. This work uses MIT-BIH Arrhythmia database and the results show an increased accuracy for Ventricular Ectopic Beats (VEB) (99.15%) and Supraventricular Ectopic Beats (SVEB) (98.55%), which is higher than the published results of deep networks that are not retrained.
Blockchain technologies is one of the most popular issue in recent years, it has already changed people's lifestyle in some area due to its great influence on many business or industry, and what it can do will still continue cause impact in many places. Although the feature of blockchain technologies may bring us more reliable and convenient services, the security issues and challenges behind this innovative technique is also an important topic that we need to concern.
Timothy H. Lucas, Xilin Liu, Milin Zhang, Sri Sritharan · 10 authors
BCI: brainâcomputer interface DCN: dorsal column nuclei ICMS: intracortical microstimulation LED: light-emitting diode PDMS: polydimethylsiloxane RF: radiofrequency The dexterous hand is a defining feature of human existence. Evolved over tens of millions of years, modern humans are able to perform remarkable tasks with their hands. From typing hundreds of words per minute to playing Rachmaninoff's Piano Concerto No. 2, the dexterous hand defines us. Unfortunately, a number of maladies rob us of this defining human characteristic. In the most extreme case, paralyzed individuals lose communication between the brain and the periphery. This condition affects an estimated 5.4 million people, or 2% of the US population.1 At present, no effective treatment restores function to these individuals. Regaining hand function is a principal concern for paralyzed patients. Toward this aim, significant advances in motorâor efferentâbrainâcomputer interface (BCI) systems have occurred in recent years. Efferent BCI systems extract movement-relevant information from electrocorticography (ECoG) or electroencephalography (EEG). These analogue signals are transformed into control commands to drive robotic arms2 or evoke muscle contractions in paralyzed limbs.3-8 In the later example, compound wrist flexion may be evoked by brain-controlled functional electrical stimulation of forearm flexors. Planned clinical trials aim to capitalize upon these scientific advances to test efferent BCI across a range of conditions and control routines. While these proof-of-principal systems are encouraging, a number of substantial hurdles remain. Perhaps the most pressing barrier to restoring dexterous hand movements is the lack of systems to restore somatosensory feedback. Even in the presence of intact descending motor systems, precise hand movements are abolished when somatosensation is missing.9-16 Indeed, the majority of efferent BCI systems currently in testing rely solely upon visual guidance. This constraint is unnatural and unlikely to be useful if deployed clinically. Visual guidance requires constant vigilance and introduces substantial time-lags to error correct each movement. To restore naturalistic movements, bi-directional BCI systems that link movements and real-time sensory feedback must be developed. The feedback loop of bi-directional BCI is closed with sensory feedback. Unfortunately, the field of sensoryâor afferentâbrainâcomputer interface has not kept pace with the maturation of efferent systems. This is due, in part, to the challenges concerning sensory research in animals. Sensory perception is a uniquely subjective experience that does not lend itself readily to the quantitative metrics. For decades, experimentalists have attempted to characterize the perceptual experiences associated with stimulation of the sensory cortices, including primary somatosensory cortex (S1), secondary somatosensory cortex (S2), and parietal association areas in animal models. From this body of literature, we know that intracortical microstimulation (ICMS) of S1 yields sufficient percepts to permit limited binary decisions, such as differentiating between 2 stimulation frequencies or amplitudes.17-21 Despite exhaustive investigation, no study has convincingly reproduced the complex sensory phenomena that are fundamental to our routine encounters with the physical world. Compounding the problem, very limited human data are available to assess the efficacy of S1 stimulation. Animal studies do not answer the question of how stimulation feels. To answer these qualitative questions, we need human data. Most human data have been obtained during brief testing sessions in awake craniotomies or during stimulation in patients with implanted ECoG electrodes.22-24 Invariably, these patients reported that S1 stimulation yielded only vague âtinglingâ sensations with modest regional localization. Flesher and colleagues recently reported the first human data using ICMS encoding in S1 with chronic penetrating arrays.25 In this experiment, a 28-yr-old male with a spinal cord injury underwent implantation of 2 32-channel multi-electrode arrays into primary somatosensory cortex (S1). Over the course of several months, the investigators mapped perceptual responses to ICMS up to 100 ÎŒA. The majority of responses (93%) were categorized as âpossibly natural,â âpressureâ sensations. The perceptual intensity was modulated by stimulation amplitude with increased pressure corresponding to increase stimulus amplitude. This finding mirrors that of ICMS in primary visual cortex where phosphine brightness is modulated by stimulus amplitude.26 These data constitute a substantial step toward clinical sensory BCI. However, there were a number of findings that tempered enthusiasm for immediately clinical implementation. For instance, none of the S1 electrodes activated sensory representations of the distal fingers where feedback is most needed. Instead, the majority of responses were localized to the palmar crease region of the hand proximal to the fingers. Also, the detection thresholds of a many electrode sites rose significantly over the short course of the study, raising the concern that the effect of S1 encoding will fade over time. Finally, few of the stimuli evoked properly ânaturalisticâ percepts. These limitations and the disappointing results from similar work in visual cortex raise the question of whether cortical ICMS encoding is the optimal solution for sensory restoration. These unanswered questions motivate our research program. Our work aims to bridge the divide between current state-of-the-art and the clinical needs of our patients. Our overarching strategy is to develop closed-loop, autonomous bidirectional brainâmachine interface systems. These systems, as conceived, provide real-time communication between the brain and body. Because the field of efferent BCI has vastly outpaced that of afferent BCI, our work primarily focuses on developing sensory-brain interfaces to couple with existing BCIs (see Bouton et al27 for example). Our strategy focuses on 3 critical intersections of engineering and neuroscience. The first is development of a suite of sensors that serve as mechanoreceptors for the paralyzed, insensate hand. The second is development of a chronic neural interface for artificial sensory encoding. The third is a body area network that links peripheral sensors with novel neural interfaces. The integration of these components is illustrated in Figure 1.FIGURE 1: Body area network. Fully integrated system with implantable force and flex sensors (1, 2), wearable analyzer (3), electrogoniometer (4), and neural interface (5).In this brief overview, we outline our approach, preliminary data, and future directions. This work collectively represents a fruitful collaboration between neurosurgery and electrical engineering. We are grateful to the National Science Foundation for funding our work. RESEARCH APPROACH Our research strategy follows 3 central aims: development of novel sensors, characterization of novel neural interfaces, and development of an autonomous body-area network. Novel Sensors Hand somatosensation can be characterized by a multidimensional space with axes defined by sensory modality (eg, light touch, proprioception), somatotopy, temporal dynamics, the influence of descending central inputs, and brain state. Restoring native somatosensation is perhaps too lofty a goal for a first-generation sensorâbrain interface. Instead, we reduce the dimensionality of the problem to a single sensory modality at a single somatotopic location. We have developed a number of force sensors and a proprioceptive sensor as our first aim. The design of our force sensors is constrained by the form and function of the human hand. Relevant design features include: sensor sensitivity, range, power, form-factor, and complexity. Sensitivity is defined as a sensor's accuracy to convert mechanical force into voltage changes on the sensor. Dynamic range captures the extremes of mechanical force spanning interactions between the hand and the physical environment. The feature of power concerns both the requirements of the sensor (active or passive) as well as the sensor's efficiency to convert physical energy into electrical energy. For wireless sensors, the power feature also includes power harvesting and wireless transmission of data. Form-factor is defined as the mechanical properties of the sensor (size, shape) as well as the flexibility and elasticity of the substrate. Finally, the complexity of the sensor constrains fabrication and durability. These competing design constraints inevitably require engineering trade-offs. In the interest of brevity, we focus on 2 prototype force sensors and a proprioceptive sensor to illustrate these engineering trade-offs in the context of sensorâbrain interface. First we consider scattering force sensors and optical force sensors before moving toward proprioceptive electrogoniometers. Scattering force sensors operate under the principle of radiofrequency (RF) back scatter. RF identification is a common technique used to track tags, like those attached to garments at a department store to prevent theft or those implanted subdermally in house pets to identify them when they are lost. The central concept is that RF energy polarizes conductive elements, such as the linear segments of an antenna, and scatter energy back in a measurable way. Deformations of the segment length or shape cause a shift in the back-scatter pattern as the polarization of each segment is related to its orientation in a pulsed electromagnetic field. By calibrating the back-scatter patterns induced by force-induced deformations of RF antenna segments, one may indirectly measure forces applied to a flexible antenna implanted under the skin. In the first series of experiments, our group characterized the back-scatter signatures of a number of antenna designs serving as passive sensor nodes. An advantage of passive sensors is that they do not require active power supplies. Therefore, flexible antennas can be implanted under the skin without the need of wires or batteries. Initial antennas were made with copper tape for rapid prototyping. Antenna shapes were constructed into space filling curves (eg, Hilbert, Peano curves) that varied in the number and length of conductive segments (Figure 2). Changes in size and shape of copper RF antennas were associated with reproducible batter scatter properties.FIGURE 2: Passive scattering force sensor design. A, Antenna shapes with different linear segments in second order Hilbert and Peano curves. B and C, Polarization of antenna segments within electromagnetic field. D, Radiofrequency response curves as a function of area of RF tag (left), and shifts in curves with ±2% change in area (right). E, Prototype indiumâgallium tags in PDMS substrate. Central reservoir visible in series with antenna segments. F, RF tuning curve of indiumâgallium tags in response to forces applied to central reservoir. Rapid shift noted in low end of force axes indicates appropriate sensitivity for precise finger grip.To build force sensitivity, our second series of experiments examined the flexibility of antennas across a range of forces routinely encountered by the human hand. Liquid metal indiumâgallium antennas were designed within a flexible, skin-like polydimethylsiloxane (PDMS) substrate. Indiumâgallium is a highly conductive eutectic alloy whose melting point is sufficiently low (⌠â2°F) to allow the alloy to remain in liquid phase at room temperature. Channels were laser-etched into the PDMS in the shape of space filling curves to house the alloy (Figure 2). Force sensitivity was amplified by creating a central compressible metal reservoir in series with the channels. When force was applied, the liquid metal filled the channel segments proportionally. As each successful segment of the antenna was filled with conductive metal, the RF back-scatter properties shifted (Figure 2). As can be seen in the RF response curve, the antenna was sufficiently sensitive to capture force changes within 5 N of fingertip pressure, appropriate for precision grip activities. These experiments verified the feasibility of force sensing RF tags. However, limitations to this technique include the need for sensitive detecting antennas to measure back scatter. For this reason, we examined force sensor designs that were independent of RF signal. Optical force sensing is a method to detect fingertip pressure without electromagnetic interference. An optical force sensor layers PDMS membrane on SiO2 within an implantable chip (Figure 3) that could be implanted subdermally. At one end of the floor of the sensor, an internal 80 ÎŒm2 light-emitting diode (LED) emits light. The light is reflected by the internal ceiling of the chip that is constructed of PDMS in an inverse lenticular structure. Reflected light is detected by a photodiode at the opposite end of the sensor. The intervening SiO2 acts as an optical waveguide. In the absence of force (or compressing pressure), the waveguide allows reflected light to excite the photodiode with an efficient electric-to-optical conversion, a high sensitivity (0.02 kPaâ1) and a pressure sensing resolution (38 mPa). When force is applied, the PDMS ceiling bows downward, opening light channels in the membrane. This allows light to escape, which in turn decreases the voltage at the photodiode monotonically, and yields a scaled readout.FIGURE 3: Optical force sensor design. A, Side view of optical sensor in absence of load. Directional path of light shown in yellow reflected from internal surface of PDMS ceiling. LED emitter located in lower left of sensor; photodiode (PD) located in lower right. B, Applied forces reduce light received by photodiode end. C, Diagram of optical force sensor circuit. D, Idealized relationship between applied force and photodiode voltageBoth scatter sensors and optical sensors achieved their desired engineering goals of converting force into measurable data. Neither system represented optimal solutions. In the case of scatter sensors, environmental noise may obscure the back-scatter energy detected by a horn antenna. In the case of optical sensors, an active circuit is required. On-going experiments aim to address these limitations by increasing the signal-to-noise ratio (RF sensors) and integrating rechargeable power (optical sensors). Beyond touch sensation, proprioception is a fundamental sensory modality that informs us about limb position. To restore proprioception across large joints, we developed a wireless electrogoniometer.28 Unlike other electrogoniometers that require strain gauges or power-hungry potentiometers, our system was designed to have very low power requirements (âŒ20 ÎŒW) both in terms of sensing and wireless data transmission. This was achieved using a pair of impulse-radio ultrawide band wireless smart sensor nodes interfacing with low-power 3-axis accelerometers through event-driven analog-to-digital converters. Electrogoniometers are designed to operate across large joints, such as the elbow, which are too large for strain sensors or other position sensors. On-going experiments aim to combine multiple sensor modalities in the same organism. Novel Central Nervous System Targets Our second aim is to identify optimal sensory encoding nodes along the neuraxis. Cortical encoding has been attempted for decades in animals, and recently in humans, with mixed results. It remains to be seen how well S1 ICMS will faithfully reproduce naturalistic perception. ICMS in other sensory areas, like primary visual cortex, generates phosphenes but not complex visual images.26 This may be due to the fact that cortical representations are distributed. Complex experiential phenomena, like rich somatosensory percepts, are therefore unlikely to be reproduced with focal stimulation without activation of a larger network. Upstream sensory circuits have To to this we developed the first chronic neural interface of the dorsal column nuclei to and stimulation in awake The a for sensory encoding. These nuclei on the dorsal surface of the and proprioceptive signals from primary (Figure from the high information to the for sensory the descending from that may sensory column nuclei interface. A, between and nuclei and in are readily with of the B, implanted in the of a at of in for several C, of of electrode in to studies of the were limited to or In our first of in were implanted with multi-electrode arrays to the feasibility of a chronic interface. Over several months, we that these arrays are and well in without data from implanted yielded a number of Over were The most was that over that are frequencies occurred with a in the we that could be over multiple in with chronic by the results of we designed a series of stimulation In experiments, we the at sensory encoding through at in a highly precise stimulation of the evoked responses in primary sensory stimulation evoked and field in the S1 (Figure which is to from sensory The induced for up to This finding may the of perceptual experiences primary that circuits between and S1 have a function for sensory To test perceptual thresholds of were on a detection and When stimuli were with to detect the electrical stimuli over rose to thresholds for are to cortical thresholds This that encoding experiments to characterize the efficacy of evoked Cortical responses to encoding. A, evoked responses to stimulation. to the which B, of frequencies stimulation. the stimulus at a well the stimulus feasibility of and encoding testing not from these experiments have for somatosensory currently will characterize responses and their to nodes including the and sensory Novel BCI Novel systems are to link peripheral sensor nodes and sensory encoding We developed a bidirectional brainâmachine the as our third aim. This when links a suite of implantable and wearable peripheral sensor nodes with neural and electrodes (Figure the system and its nodes are to as a body area network. At the of the are wireless including a neural a neural a sensor and a The of a neural neural feature neural and associated The neural feature are for or field the system includes an neural energy and a detection with control is in the form of a that sensor data from peripheral sensors to desired patterns related to somatosensory cortex (Figure of brainâcomputer interface A, intersections between BCI systems and in the case of paralyzed or feedback control from nodes within in B, control loop integrating neural and stimulation in the flexibility to paralyzed or neural may be to or or stimulation with a voltage of Our the to current the that that neural and current current stimulation with a to a phase that neural However, changes due to during the Over millions of develop and in that the interface and To properly for this we a feedback that the phase when a point is detected (Figure The of this circuit is an error that error by the during stimulus the are the range, stimulation are as error that this method over that the system will have in experiments are to test this principle A, and of between phase and phase shown Idealized shown in shown in B, the on of point body area network requires real-time communication between the is with an impulse-radio band The and components to and between For clinical communication between nodes must be and operate within an of data The features an data of 2 in The error was over a of 3 of these are well within the desired for human moving toward human a number of must be of the system must be in must also be to To whether of the system was and effective at percepts, we designed in experiments on the the were to a to the by visual a pattern the is were to ICMS by the as a on the (Figure As the animal the stimulation As significantly in the presence of perception. When the was during was are able to systems to perception in a and effective In feasibility testing of novel systems. A, B, design is to in by ICMS as Idealized illustrated from with and optimal in presence of In our strategy to develop a sensorâbrain interface system focuses on 3 aims: development of novel sensors, characterization of novel neural and development of autonomous body-area network. We have made in each of these areas, but substantial work We to our systems up to channel peripheral sensors and our In we our systems to animal models. It is our goal to this sensor brain interface with existing efferent systems to a bidirectional BCI to paralyzed This work was in by the National Science The have no or interest in of the or in this
Blockchain, which is the backbone of Bitcoin, has recently received a lot of attention. Blockchain functions as an immutable ledger that enables decentralized transactions. Numerous fields, such as the Internet of Things (IoT), reputation systems, and financial services, are being covered by blockchain-based applications. However, blockchain technology still faces numerous difficulties, such as scalability and security issues, that need to be resolved. A comprehensive overview of blockchain technology is provided in this paper. First, we compare some common consensus algorithms utilized by various blockchains and provide an overview of the architecture of blockchains. In addition, a brief list of recent advancements and technical difficulties is provided. In addition, we outline potential blockchain trends for the future.
Reports on the concept of blockchains, a new form of information technology that could have several important future applications. One is blockchain thinking, formulating thinking as a blockchain process. This could have benefits for both artificial intelligence and human enhancement, and their potential integration. Blockchain thinking is outlined here as an input-processing-output computational system.