Delay Performance Research Articles

The GluN2C/D-specific positive allosteric modulator CIQ rescues delay-induced working memory deficits in mice

Working memory is of short duration and is, therefore, particularly sensitive to time delays. Moreover, NMDA receptors are significantly involved in working memory. In the present study, we tested whether a commonly used measure of working memory, spontaneous alternation in the Y-maze, is sensitive to time delays and, if so, whether impairments due to time-delay can be rescued by treatment with CIQ, a positive allosteric modulator of the GluN2C/D subunits of NMDA receptor. Our results indicate that the effects of time delay do depend on the performance of the individual mice under basal condition. Those mice that performed well under basal conditions showed impaired spontaneous alternations when tested with a 45-s delay. Treatment with CIQ resulted in an improvement of spontaneous alternations, regardless of delay, sex, or basal performance. On the one hand, our study shows that repeated measures of individual behavior can better control the effects of confounding factors such as time delays. On the other hand, our study also highlights the potential of GluN2C/D-specific positive allosteric modulators in the treatment of human disorders associated with working memory deficits, such as schizophrenia.

Cooperative optimization techniques in distributed MAC protocols – a survey

Purpose Cross-layer approach in media access control (MAC) layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in wireless sensor network (WSN) and Internet of Things (IoT) applications. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Game theory optimization for distributed may increase the network performance. The purpose of this study is to survey the various operations that can be carried out using distributive and adaptive MAC protocol. Hill climbing distributed MAC does not need a central coordination system and location-based transmission with neighbor awareness reduces transmission power. Design/methodology/approach Distributed MAC in wireless networks is used to address the challenges like network lifetime, reduced energy consumption and for improving delay performance. In this paper, a survey is made on various cooperative communications in MAC protocols, optimization techniques used to improve MAC performance in various applications and mathematical approaches involved in game theory optimization for MAC protocol. Findings Spatial reuse of channel improved by 3%–29%, and multichannel improves throughput by 8% using distributed MAC protocol. Nash equilibrium is found to perform well, which focuses on energy utility in the network by individual players. Fuzzy logic improves channel selection by 17% and secondary users’ involvement by 8%. Cross-layer approach in MAC layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in WSN and IoT applications. Cross-layer and cooperative communication give energy savings of 27% and reduces hop distance by 4.7%. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Research limitations/implications Other optimization techniques can be applied for WSN to analyze the performance. Practical implications Game theory optimization for distributed may increase the network performance. Optimal cuckoo search improves throughput by 90% and reduces delay by 91%. Stochastic approaches detect 80% attacks even in 90% malicious nodes. Social implications Channel allocations in centralized or static manner must be based on traffic demands whether dynamic traffic or fluctuated traffic. Usage of multimedia devices also increased which in turn increased the demand for high throughput. Cochannel interference keep on changing or mitigations occur which can be handled by proper resource allocations. Network survival is by efficient usage of valid patis in the network by avoiding transmission failures and time slots’ effective usage. Originality/value Literature survey is carried out to find the methods which give better performance.

A - 171 Neuropsychological Performance Relates to Observed Risky Driving in Healthy Adults.

This study uses video telematics to examine relationships between neuropsychological performance and directly observed naturalistic driving-as-usual in healthy adult drivers. We hypothesized that visual attention and executive function measures would have the strongest associations with unsafe driving behaviors. Twenty-five healthy drivers (ages 23-61, 62% women) were recruited from the general community into this cross-sectional study. They completed neuropsychological testing and 28days of naturalistic driving with an in-vehicle video telematics platform that detected unsafe driving behaviors. The neuropsychological battery measured driving-relevant domains, namely visual attention, processing speed, executive function, and visuospatial memory. We examined correlations between neuropsychological measures and unsafe behaviors. Unsafe following distance correlated with better performance on Useful Field of View Selective Attention (r = 0.55, p = 0.004, 95% CI = 0.19-0.77), Symbol Digit Modalities Test Written (r = 0.51, p = 0.009, CI = 0.13-0.76) and Oral (r = 0.51, p = 0.010, CI = 0.13-0.75), Trails B (r = 0.42, p = 0.035, CI = 0.02-0.70), and Stroop Color (r = 0.46, p = 0.022, CI = 0.06-0.72). Speeding correlated with better Spatial Recall Test Immediate (r = 0.48, p = 0.015, CI = 0.10-0.73) and Delay performance (r = 0.42, p = 0.038, CI = 0.02-0.69). In healthy adult drivers, better performance in the domains of visuospatial memory, processing speed, and attention is associated with greater engagement in unsafe driving behaviors. In the absence of concern about cognitive compromise, individuals may feel more comfortable making risky behaviors. We plan to expand this work to broader samples and clinical populations to increase generalizability and applicability to clinical driving evaluations.

Secure WDM communication based on second order differential feedback electro-optical chaotic mutual injection synchronization

An optical phase chaos and mutual injection chaos synchronization scheme based on second order differential feedback electro-optical loop is proposed for chaotic communication. The filtered chaotic waves for mutual injection synchronization and multi-channel signals are transmitted through wavelength division multiplexing (WDM) to realize large-capacity confidential communication. The results show that the bandwidth of the optical chaos is 41.38 GHz, the normalized permutation entropy is 0.999, and the chaos has the performance of time delay suppressing (TDS). This scheme achieves up to 7-channel 25 GBaud 16QAM with the multi-modulus algorithm (MMA) algorithm at OSNR of 25 dB after 100 km transmission. The bit-rate distance of 280,000 Gb/s km is successfully realized by 500 km transmission of 4-channel 35 GBaud 16QAM signals at 25 dB OSNR. The analysis proves that the performance of the second order digital differential electro-optical phase feedback (2nd order DEOP) chaos is better than that of the 1st order DEOP chaos and 3rd order DEOP chaos. The 2nd order DEOP chaos can be generated at a smaller feedback coefficient, the bandwidth of radio frequency chaos is wider, and the TDS suppression is better. This scheme realizes high-speed and large-capacity secure optical chaos communication, which can be widely used in WDM networks and has practical feasibility and application value.

Attack-detection and multi-clock source cooperation-based accurate time synchronization for PLC-AIoT in smart parks

Power Line Communications-Artificial Intelligence of Things (PLC-AIoT) combines the low cost and high coverage of PLC with the learning ability of AI to provide data collection and transmission capabilities for PLC-AIoT devices in smart parks. With the development of smart parks, their emerging services require secure and accurate time synchronization of PLC-AIoT devices. However, the impact of attackers on the accuracy of time synchronization cannot be ignored. To solve the aforementioned problems, we propose a tampering attack-aware Deep Q-Network (DQN)-based time synchronization algorithm. First, we construct an abnormal clock source detection model. Then, the abnormal clock source is detected and excluded by comparing the time synchronization information between the device and the gateway. Finally, the proposed algorithm realizes the joint guarantee of high accuracy and low delay for PLC-AIoT in smart parks by intelligently selecting the multi-clock source cooperation strategy and timing weights. Simulation results show that the proposed algorithm has better time synchronization delay and accuracy performance.

Satellite Return Link Resource Allocation Strategy Based on DVB-RCS2

This article improves the priority of the request queue packets for the Return Channel Satellite Terminal (RCST) of the Geosynchronous Earth Orbit (GEO) satellite system. It designs an inverted numbered slot tree resource allocation algorithm based on the Request Queue Maximum Weighted Delay Priority Algorithm (RQM-LWDF) in conjunction with existing slot allocation algorithms. It builds a simulation model of the DVB-RCS2 return link using the satellite communication module (SNS3) on the NS3 (Network Simulator 3) software simulation platform. It studies the throughput, packet loss rate, and delay performance of the algorithm under specific conditions and evaluates the algorithm.

Modeling and performance analysis of coupled multilayer graphene nanoribbon (MLGNR) interconnects with intercalation doping

This paper investigated the temperature‐dependent performance of coupled top contact multilayer graphene nanoribbon interconnects (TC-MLGNRs) with different intercalation doping (viz., AsF5-, FeCl3-, and Li-doped) considering edge roughness and crosstalk effect, compared with SC-MLGNR, MWCNT, and copper (Cu) interconnects for 13.4 nm technology nodes at the global level. Based on the equivalent single conductor (ESC) modeling, a distributed transmission line model for coupled doped TC-MLGNRs is presented to obtain the crosstalk delay, step response, transfer gain, and 3-dB bandwidth, which considers in-phase and out-of-phase crosstalk modes and verified with the Hspice simulation. It is demonstrated that all interconnects at room temperature in the in-phase crosstalk mode outperform those in the out-of-phase crosstalk mode in terms of crosstalk delay, transfer gain, and 3-dB bandwidth, and Li-doped TC-MLGNR significantly reduces crosstalk delay and enhances transfer gain and 3-dB bandwidth compared to other interconnects. Moreover, it is found that the victim line of two-line coupling for doped TC-MLGNRs exhibits less crosstalk delay, greater transfer gain, and 3-dB bandwidth compared to the center line of three-line coupling for doped TC-MLGNRs in the same condition. Furthermore, we analyze the crosstalk delay and 3 dB-bandwidth for two- and three-line doped TC-MLGNRs at varying temperatures and compare them with SC-MLGNR, MWCNT, and Cu interconnects. Simulation results show that crosstalk delay and bandwidth performance are temperature dependent, and they both degrade with increasing temperature. Also, Li-doped TC-MLGNRs still exhibit the best crosstalk delay and bandwidth performance of all interconnects. The numerical calculation results show that decreasing interconnect temperature, increasing line spacing, and reducing edge roughness are efficient methods to reduce crosstalk delay and enhance 3-dB bandwidth for Li-doped TC-MLGNRs. In addition, the results obtained by the proposed model are well matched with the Hspice simulation. Hence, our performance analysis demonstrates that Li-doped TC-MLGNR can be a promising material for global interconnect applications in a thermally variable environment.

Forecasting glycaemia for type 1 diabetes mellitus patients by means of IoMT devices

The chronic metabolic condition, Type 1 diabetes mellitus (DM1), is marked by consistent hyperglycemia due to the body's inability to produce sufficient insulin. This necessitates the patient's daily monitoring of blood glucose fluctuations to discern a trend and predict future glycemia, subsequently dictating the amount of external insulin needed to regulate glycemia effectively. However, this technique often grapples with a degree of inaccuracy, presenting potential hazards. Nonetheless, contemporary advancements in information and communication technologies (ICT) coupled with novel biological signal sensors offer a refreshing perspective for DM1 management by enabling comprehensive, continual patient health evaluation. Herein, burgeoning technological disruptions such as Big Data, the internet of medical things (IoMT), cloud computing, and machine learning algorithms (ML) could serve pivotal roles in the effective control of DM1. This paper delves into the exploration of the latest IoMT-based methodologies for the unbroken surveillance of DM1 management, facilitating a profound characterization of diabetic patients. The fusion of wearable technologies with machine learning strategies has the potential to yield robust models for short-term blood glucose prediction. The ambition of this study is to develop precise, individual-centric prediction models harnessing an array of pertinent factors. The study applied modeling techniques to a comprehensive dataset comprising glycaemia-associated biological attributes, sourced from an expansive passive monitoring campaign involving 40 DM1 patients. Leveraging the Random Forest method, the resulting models can predict glucose levels over a 30-min time span with an average error as minimal as 18.60 mg/dL for six-hour data and 26.21 mg/dL for a 45-minute prediction horizon, offering also a good performance in the prediction delay.

Delay-Optimal Multi-User Wireless-Powered Relay Networks With Protection Against the Risk of Distributed Algorithm Manipulation

We consider simultaneous-wireless-information-and-power-transfer (SWIPT) in a relay network, where multiple users with stochastic traffic flows are serviced by a common wireless-powered relay. A two-phase forwarding protocol is deployed along with power splitting. In the first phase, the relay splits its received signal from sources into two parts: one for information decoding and the other for energy harvesting. In the second phase, the relay allocates the total harvested energy from the first phase to forward each user’s data. To optimize the system’s long-run delay performance, we formulate the relay’s power control problem as a Markov decision process (MDP) with per-stage (energy) budget constraints. We realize that the Bellman’s equations associated with the formulated MDP have a weakly-coupled structure, and assuming that users act as price-takers, the global optimum can be computed distributively via Lagrangian decomposition. We then abandon this assumption to face the more realistic case where the users are price-anticipating and may manipulate the rules of the distributed algorithm to favor themselves. To protect against the risk of unfaithful implementation, we design a dynamic economic mechanism that builds on the notions of transfer payments and periodic ex-post Nash equilibrium to disarm the users’ self-interest and ensure adherence to the algorithm.

URLLC-eMBB hierarchical network slicing for Internet of Vehicles: An AoI-sensitive approach

Network slicing has been considered as a promising candidate to meet the heterogeneous Quality of Service (QoS) requirements of various vehicular applications. In this paper, network slicing is utilized on the Road Side Unit (RSU) side for the heterogeneous data freshness requirements in Internet of Vehicles (IoV). The services in enhanced Mobile BroadBand (eMBB) slice are usually bandwidth-consuming, thus the performance can be characterized by throughput. Unlike eMBB slice, the services in Ultra-Reliable Low-Latency Communications (URLLC) slice are usually time-critical, calling for fresh content delivery within short delay, in this case, the trade-off can be made between Age of Information (AoI) and delay in URLLC slice. The resource limitation and coupling effect at both the inter-slice and the intra-slice level have brought challenges to the resource allocation of vehicular network slice. To this end, this paper models the inter- and intra-slice resource allocation problem as a Stackelberg game, in which the RSU acts as the leader and the two slices act as followers. To minimize the weighted sum of AoI and delay in URLLC slice while ensuring the throughput of eMBB slice, this paper proposes a multi-win Stackelberg game resource allocation (MWRA) algorithm. The proposed algorithm can achieve Stackelberg equilibrium (SE) and the SE point is taken as the optimal resource allocation strategy of inter- and intra-slice. Simulation shows the trade-off of inter- and intra-slice resource allocation, and further reveals the interaction of performance between different slices. Numerical results show that compared with the traditional hard slicing, the proposed algorithm can improve the AoI and delay performance of URLLC slice by about 30% under the premise of ensuring the throughput of eMBB slicing.

Novel MAC protocol for handling correlation and dynamics of medical traffic in WBANs

In Wireless Body Area Networks (WBANs), it is crucial to ensure reliable and timely delivery of gathered data, despite their intrinsic challenges related to limited resources at the sensor level. In fact, in the medical context, most real-time and life-critical applications are both delay-sensitive and loss-sensitive; the loss or corruption packets carrying critical data due to unreliable wireless networks could have severe consequences. In this regard, the concept of context awareness can be exploited; the more an application acquires additional information about users, sensors, and their context, the better it can adapt its functional behavior to improve its performance of delay, throughput and packet loss.Thus, acquiring some knowledge about the patient’s medical state over time may enhance the interpretation and intervention of the medical staff. In this context, we propose a dynamic context-aware MAC protocol (DMTM-MAC) that takes into account the dynamic and correlation aspects of medical traffic in order to adjust the channel access mechanisms and the general network behavior. We assign to each sensor a context that reflects the type of traffic generated and its QoS constraints. We aim to track specific biosensors, which are related to vital indicators relevant to a specific disease; the associated measurements may reflect the variation of the patient’s state over time. To such nodes, we assign a new node context, called dynamic context, which can help to predict the eventual health decline over time. The body node coordinator will have the ability to analyze the received data in order to detect abnormalities and disease evolution. Observed changes in collected measurements can be an early predictor of an advanced or complicated situation and can allow this BAN coordinator to handle dynamic context changes in these types of sensors by increasing their priority and transmission frequency to cope with the current situation. Performance evaluation showed that DMTM-MAC outperforms both MTM-MAC and IEEE 802.15.6 in terms of delay and packet delivery ratio. The proposed approach enhances the delay up to 80 % over IEEE 802.15.6.

An auction-based distributed network slicing scheme for resource allocation in satellite-UAV integrated networks

Network slicing is a promising solution to satisfy different service quality requirements in the satellite-UAV integrated network of B5G and 6G. To relieve the significant transmission delay and system overhead during network slicing, we propose an auction-based distributed network slicing scheme (ADNSS), in which the resource allocation is collaboratively performed by satellite heads using an auction algorithm without transmitting the messages to the ground station in a centralized way. The convergence and service delay performance of ADNSS is analyzed in this paper. Moreover, considering that the deviation of theoretical slice analysis models from actual ones may lead to the degradation of service delay performance of ADNSS, we design an online optimization method driven by the multi-arm bandit algorithm (ORLOM) to enhance the accuracy of slice models of ADNSS in the actual environment. Numerical results show that we can achieve optimal service delay performance by ADNSS applying ORLOM after finite slicing windows and realize 8% performance improvement compared with ADNSS without ORLOM when the service number is 9. In addition, ADNSS can reduce the system overhead by 8.5%–76% compared with the centralized method in the scenario where the hops count from satellite heads to the ground station exceeds that between satellite heads by 3. As the hops count difference and service number increase, ADNSS shows more significant advantages in reducing service delay and system overhead.

Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks

In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance.

A novel handover scheme for millimeter wave network: An approach of integrating reinforcement learning and optimization

The millimeter-Wave (mmWave) communication with the advantages of abundant bandwidth and immunity to interference has been deemed a promising technology to greatly improve network capacity. However, due to such characteristics of mmWave, as short transmission distance, high sensitivity to the blockage, and large propagation path loss, handover issues (including trigger condition and target beam selection) become much complicated. In this paper, we design a novel handover scheme to optimize the overall system throughput as well as the total system delay while guaranteeing the Quality of Service (QoS) of each User Equipment (UE). Specifically, the proposed handover scheme called O-MAPPO integrates the Reinforcement Learning (RL) algorithm and optimization theory. The RL algorithm known as Multi-Agent Proximal Policy Optimization (MAPPO) plays a role in determining handover trigger conditions. Further, we propose an optimization problem in conjunction with MAPPO to select the target base station. The aim is to evaluate and optimize the system performance of total throughput and delay while guaranteeing the QoS of each UE after the handover decision is made. The numerical results show the overall system throughput and delay with our method are slightly worse than that with the exhaustive search method but much better than that using another typical RL algorithm Deep Deterministic Policy Gradient (DDPG).

Spectrum Sharing Toward Delay Deterministic Wireless Network: Delay Performance Analysis

To accommodate Machine-type Communication (MTC) service, the wireless network needs to support low-delay and low-jitter data transmission, realizing delay deterministic wireless network. This paper analyzes the delay and jitter of the wireless network with and without spectrum sharing. When sharing the spectrum of the licensed network, the spectrum band of wireless network can be expanded, such that the delay and jitter of data transmission are reduced. The challenge of this research is to model the relation between the delay/jitter and the parameters such as node distribution, transmit power, and bandwidth, etc. To this end, this paper applies stochastic geometry and queueing theory to analyze the outage probability of the licensed network and the delay performance of the wireless network with and without spectrum sharing. By establishing the M/G/1 queueing model for the queueing of the Base Station (BS) in the wireless network, the downlink delay and jitter are derived. Monte Carlo simulation results show that the spectrum sharing reduces the delay and jitter without causing serious interference to the licensed network, which can lay a foundation for the application of spectrum sharing in delay deterministic wireless network supporting MTC service.

SABIC earns UL Verified for STAMAX™ 30YH570, offers bio-based NORYL, presents new data on low internal dissipation in ELCRES™ dielectric films and sets record with Genbeta racecar

On August 22, SABIC's STAMAX™ 30YH570 resin has earned the UL Verified Mark from Underwriters Laboratories. This 30 percent glass fiber-reinforced copolymer resin, a featured product offered under the company's BLUEHERO™ electrification initiative, reportedly is the first polymer used in electric vehicle (EV) battery systems to receive UL Verification for marketing claims of thermal and mechanical performance. UL Verification, reportedly based on an objective, scientific assessment by a respected third party, should give customers high confidence in the flame delay performance of this product.

High Performance, Low Power Wallace Tree Multiplier

An area-efficient high Wallace tree multiplier using adders is presented in this paper. The proposed Wallace tree multiplier is designed using logic gates and adders. The design is implemented in Cadence Virtuoso using a 45-nm technology library. The proposed design offers reduced delay and higher performance than conventional multipliers using carry-save adders with majority-based gate adder logic. The design also offers a reduced transistor count of 12, which is minimal compared to that of the conventional design. One of the fundamental building blocks of many VLSI applications is multipliers. To enhance the performance of circuits and systems, the design of multipliers is very important. The key feature of a high-performance Wallace tree multiplier lies in its efficient reduction of partial product additions. By utilising a combination of carry-save and carry-propagate adders, it minimises the critical path delay and maximises the speed of multiplication. Additionally, advanced optimisation techniques such as parallel prefix adders and parallel carry-save adders can be employed to further improve performance.

Real-time Monitoring Technology of Voltage Sag Disturbance in Distribution Network Based on TCN-Attention Neural Network and Flink Flow Computing

In the face of the challenges brought by the complexity of power grid, diversification of disturbance factors, isolation of monitoring points and other issues to the cause identification of voltage sag disturbance, this paper proposes a real-time monitoring technology for voltage sag disturbance in distribution network based on TCN-Attention neural network and Flink flow calculation, which has important practical significance for controlling voltage sag and reducing economic losses. This method uses Temporal Convolutional Network (TCN) to extract the cross time nonlinear characteristics of voltage sag time series data, which effectively solves the problems of long-term dependence on time series and low training output efficiency of existing time series models. In order to further improve the recognition accuracy of the model, Attention mechanism is introduced to mine the duration relationship in voltage sag data. At the same time, the method also constructs a parallel real-time monitoring platform based on Flink streaming computing framework, embeds the TCN-Attention voltage sag cause identification model generated by training, so as to realize real-time identification and monitoring analysis of voltage sag disturbances at each monitoring point of the distribution network. In this paper, various voltage sags are simulated on IEEE 14 bus system using PSCAD software, and the proposed method is verified and tested. The deep learning fusion model has high recognition accuracy for the cause of voltage sag, and the flow computing platform has excellent performance in time delay and throughput indicators, and can realize the parallel real-time monitoring and analysis of voltage sag causes in distribution network.

Two-Hop Cooperative Caching and UAVs Deployment Based on Potential Game

This paper explores the joint cache placement and 3D deployment of Unmanned Aerial Vehicle (UAV) groups, utilizing potential game theory and a two-hop UAV cooperative caching mechanism, which could create a tradeoff between latency and coverage. The proposed scheme consists of three parts: first, the initial 2D location of UAV groups is determined through K-means, with the optimal altitude based on the UAV coverage radius. Second, to balance the transmission delay and coverage, the MOS (Mean Opinion Score) and coverage are designed to evaluate the performance of UAV-assisted networks. Then, the potential game is modeled, which transfers the optimization problem into the maximization of the whole network utility. The locally coupling effect resulting from action changes among UAVs is considered in the design of the potential game utility function. Moreover, a log-linear learning scheme is applied to solve the problem. Finally, the simulation results verify the superiority of the proposed scheme in terms of the achievable transmission delay and coverage performance compared with two other tested schemes. The coverage ratio is close to 100% when the UAV number is 25, and the user number is 150; in addition, this game outperforms the benchmarks when it comes to maximizing MOS of users.

Asynchronous Sampling-Based Hybrid Equalizer

An equalizer architecture based on asynchronous sampling is described. The circuit implementation is a hybrid feed-forward equalizer (FFE) using three digital postcursor taps realized via level-crossing samplers (LCSs) and inverter-based delays. Utilizing a current mode logic (CML)-based summer stage, an analog main tap and digital postcursor taps are combined into an inductively tuned resistor load. With a 0.67-mV-rms simulated output-referred noise, the FFE has better noise performance than a conventional continuous-time linear equalizer (CTLE) and a slicer/latch-based decision feedback equalizer (DFE) circuit. Moreover, it exhibits very good linearity and group delay performance suitable for an equalizer. Using inverter-based delays enables area efficiency as well as the capability to tune the delay across process voltage and temperature (PVT) variations. Since the FFE is an event-driven combinational logic based on the incoming data, it does not require synchronous sampling coming from a high-speed clock and data recovery (CDR), and it is advantageous for high-speed and multirate operation as its power consumption scales with the data rate. Fabricated in 40-nm CMOS technology, the hybrid FFE achieves energy per bit of 2.7 pJ/bit with a 20-dB Nyquist insertion loss link at the 28.57-Gb/s nonreturn to zero (NRZ) data rate.