Average Delay Research Articles

Impacts of connected autonomous vehicle platoon breakdown on highway

Connected Autonomous Vehicles (CAVs) are seen as an opportunity to improve traffic efficiency and safety. However, further studies are needed to prove such positive effects. Particularly, there is a lack of quantitative research on the impacts of CAV breakdown in mixed traffic flow conditions (i.e., CAVs and Human Driven Vehicles, HDVs). The aim of this research is to explore the impacts generated by the breakdown of the leading vehicle of a CAV platoon in mixed traffic conditions, which in turn generates CAV platoon breakdown (i.e., platoon dispersion), by considering several CAV market penetration rates (MPRs) and platoon size. In this perspective, a control algorithm (“avoidance algorithm”) for modelling CAVs and HDVs behaviour to avoid obstacles (i.e., breakdown CAV platoon) has been developed and tested on some simulation scenarios in order to derive key traffic flow parameters. The dynamic characteristics of traffic flow with CAV platoon breakdown have been explored for both low and high traffic flow volumes. Finally, potential conflicts, congestions, as well as energy consumption and CO2 emissions resulting from the breakdown of CAV platoons in mixed traffic streams have been assessed and discussed. Results suggest that (1) CAV platoon breakdown can reduce the traffic capacity by about 20 %; (2) higher CAV MPRs are more suitable for enhancing highway safety even in breakdown conditions, decreasing energy consumption, and reducing CO2 emissions; (3) platoon size should be limited to 4, since larger sizes affect traffic safety, increases vehicle average delay time as well as energy consumption. The obtained results provide useful insights for transportation planners and transport infrastructure management companies to design and apply policies aimed at improving driving conditions, traffic quality, and safety on highways.

Monitoring System for Traffic Light Lamp Damage using BLYNK Application Based on IOT ESP32

The performance of traffic lights often encounters issues such as lights being out or other malfunctions, which can result in erratic traffic patterns and even total gridlock at the same intersection, potentially leading to accidents among drivers. Therefore, it is crucial to design a monitoring system for traffic light malfunctions using the Blynk application based on IoT ESP32. This system aims to facilitate technicians in monitoring the condition of traffic lights in case of malfunctions across all lanes. It utilizes an ESP32 microcontroller, LED lights or traffic lights, resistors as converters, and the Blynk application as a monitoring tool. From the testing of the prototype device, it was found that the average values for red light were ADC 1.367, voltage 1.10 volts, and current 5.01 mA. For yellow light, the values were ADC 1.265, voltage 1.02 volts, and current 4.63 mA. As for green light, the values were ADC 1.283, voltage 1.03 volts, and current 4.70 mA. The average delays for red, yellow, and green lights were 2 seconds, 2 seconds, and 5 seconds respectively, with the green light flashing three times for 0.5 seconds each time for all lanes.

IoT-Based Vehicle Monitoring System on LoRa Network: Addressing Community Needs in Indonesia

Our innovative IoT-based vehicle monitoring system emerges from a comprehensive survey among vehicle owners. The device circuit is designed using ESP32, GPS to detect location and speed, MPU6050 to detect vibrations from the vehicle engine, and a relay that is used to disconnect the vehicle socket if needed. The data collected from each component is sent through the LoRa Antares network. Access to the data can be done using a cell phone through the MOTRAV application that has been integrated with the Antares server. The designed system is able to display the location, speed, and condition of the vehicle consistently with an average delay of 7 seconds and can receive 65% of the control signals to start and stop the vehicle engine. The system can also send a warning notification if the vehicle that is not being used is detected at a speed of more than 5.5 km/h with engine vibrations detected reaching 28 vibrations/5 seconds.

Regional traffic congestion coordination control based on critical links

Critical links have the characteristics of carrying large traffic flow and quickly affecting the operational performance of the road network. Existing regional traffic congestion coordination control methods do not consider the impact of critical links. To solve this issue, this paper proposes a regional traffic congestion coordination control method based on critical links. First, the median, gravitational index, and traffic sharing ratio are selected as the identification indexes of critical links in urban networks. Second, the road network vulnerability is assessed, and road link criticality is calculated based on joint entropy theory and multi-scale factor synthesis of multi-metric identification results. Then, the regional signal control range is determined, and a multi-stage signal control optimization model is developed. Finally, the optimal traffic signals for intersections are obtained by minimizing the pressure at the maximum phase of the road link and maximizing the capacity of critical links. To demonstrate the effectiveness of the proposed methods, two benchmarks (i.e., the MAXBAND method and existing traffic signals) are compared. The results show that (1) in terms of boundary control, the average vehicle delay at the boundary intersection of the road network is 34.62 s, an increase of 7.49 s and 5.03 s compared to the benchmark methods, respectively. (2) In terms of internal control, the average delay of the road network based on the proposed method is 87.54 s, which is reduced by 16.38 % and 28.73 % compared with the benchmark methods, and the queue lengths of the critical links are reduced by 21.47 % and 34.15 %, respectively. (3) The control process of the proposed method enables the number of vehicles within the road network to be maintained near the optimal carrying capacity, which gives the road network an efficient operating efficiency.

Multi-objective task offloading for highly dynamic heterogeneous Vehicular Edge Computing: An efficient reinforcement learning approach

Vehicular Edge Computing (VEC) provides a flexible distributed computing paradigm for offloading computations to the vehicular network, which can effectively solve the problem of limited vehicle computing resources and meet the on-vehicle computing requests of users. However, the conflict of interest between vehicle users and service providers leads to the need to consider a variety of conflict optimization goals for computing offloading, and the dynamic nature of vehicle networks, such as vehicle mobility and time-varying network conditions, make the offloading effectiveness of vehicle computing requests and the adaptability to complex VEC scenarios challenging. To address these challenges, this paper proposes a multi-objective optimization model suitable for computational offloading of dynamic heterogeneous VEC networks. By formulating the dynamic multi-objective computational offloading problem as a multi-objective Markov Decision Process (MOMDP), this paper designs a novel multi-objective reinforcement learning algorithm EMOTO, which aims to minimize the average task execution delay and average vehicle energy consumption, and maximize the revenue of service providers. In this paper, a preference priority sampling module is proposed, and a model-augmented environment estimator is introduced to learn the environmental model for multi-objective optimization, so as to solve the problem that the agent is difficult to learn steadily caused by the highly dynamic change of VEC environment, thus to effectively realize the joint optimization of multiple objectives and improve the decision-making accuracy and efficiency of the algorithm. Experiments show that EMOTO has superior performance on multiple optimization objectives compared with advanced multi-objective reinforcement learning algorithms. In addition, the algorithm shows robustness when applied to different environmental settings and better adapting to highly dynamic environments, and balancing the conflict of interest between vehicle users and service providers.

Handling Efficient VNF Placement with Graph-Based Reinforcement Learning for SFC Fault Tolerance

Network functions virtualization (NFV) has become the platform for decomposing the sequence of virtual network functions (VNFs), which can be grouped as a forwarding graph of service function chaining (SFC) to serve multi-service slice requirements. NFV-enabled SFC consists of several challenges in reaching the reliability and efficiency of key performance indicators (KPIs) in management and orchestration (MANO) decision-making control. The problem of SFC fault tolerance is one of the most critical challenges for provisioning service requests, and it needs resource availability. In this article, we proposed graph neural network (GNN)-based deep reinforcement learning (DRL) to enhance SFC fault tolerance (GRL-SFT), which targets the chain graph representation, long-term approximation, and self-organizing service orchestration for future massive Internet of Everything applications. We formulate the problem as the Markov decision process (MDP). DRL seeks to maximize the cumulative rewards by maximizing the service request acceptance ratios and minimizing the average completion delays. The proposed model solves the VNF management problem in a short time and configures the node allocation reliably for real-time restoration. Our simulation result demonstrates the effectiveness of the proposed scheme and indicates better performance in terms of total rewards, delays, acceptances, failures, and restoration ratios in different network topologies compared to reference schemes.

Deep reinforcement learning based controller placement and optimal edge selection in SDN-based multi-access edge computing environments

Multi-Access Edge Computing (MEC) can provide computility close to the clients to decrease response time and enhance Quality of Service (QoS). However, the complex wireless network consists of various network hardware facilities with different communication protocols and Application Programming Interface (API), which result in the MEC system's high running costs and low running efficiency. To this end, Software-defined networking (SDN) is applied to MEC, which can support access to massive network devices and provide flexible and efficient management. The reasonable SDN controller scheme is crucial to enhance the performance of SDN-assisted MEC. At First, we used the Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM) model to predict the network traffic to calculate the load. Then, the optimization objective is formulated by ensuring the load balance and minimizing the system cost. Finally, the Deep Reinforcement Learning (DRL) algorithm is used to obtain the optimal value. Based on the controller placement algorithm ensuring the load balancing, the dynamical edge selection method based on the Channel State Information (CSI) is proposed to optimize the task offloading, and according to CSI, the strategy of task queue execution is designed. Then, the task offloading problem is modeled by using queuing theory. Finally, dynamical edge selection based on Lyapunov's optimization is introduced to get the model solution. In the experiment studies, the assessment method evaluated the performance of two sets of baseline algorithms, including SAPKM, the PSO, the K-means, the LADMA, the LATA, and the OAOP. Compared to the baseline algorithms, the proposed algorithms can effectively reduce the average communication delay and total system energy consumption and improve the utilization of the SDN controller.

Digital Twin-Enhanced Adaptive Traffic Signal Framework under Limited Synchronization Conditions

Unmanned traffic signal control is regarded as a sustainable intelligent management methodology. However, it faces the challenge of unpredictable traffic flow due to stochastic arrivals. The digital twin (DT) has emerged as a promising approach to address the challenges of time-varying traffic demand in urban transportation. Previous studies of DT-based adaptive traffic signal control (ATSC) methods all assume ideal synchronization conditions between the DT and the physical twin (PT). It means that the DT can immediately figure out the next neglecting limitation of realistic conditions, i.e., discrepancies between the DT and PT and computational ability. This paper proposes a DT-ATSC framework aimed at reducing the total delay at isolated intersections under limited synchronization conditions. The framework contains two parts: (1) a cell transmission model-based intersection simulation model featuring less computational consumption and the parameter self-calibration mechanism; and (2) scheme searching algorithms that can guide the DT to create optimization-oriented signal timing scheme candidates. Three options are provided for the scheme searching algorithms, i.e., grid search (GS), the genetic algorithm (GA), and Bayesian optimization (BO). A testing platform is created to validate the effectiveness of the proposed DT-ATSC. Experimental results indicate that the proposed DT-ATSC-BO outperforms the DT-ATSC-GA and DT-ATSC-GS. Meanwhile, the average vehicle delay of the DT-ATSC-BO is up to 53% lower than that of the current adaptive signal control method, which indicates that the proposed DT-ATSC has achieved the expected effect. Moreover, compared to the previous related work, the proposed DT-ATSC framework is more likely to be able to be applied in realistic situations because synchronization issues are incorporated in the design of the DT-ATSC by assuming a limited margin time for a decision.

The role of process and geometrical parameters of gate stack Inverted-T shape junction less FET at 20 nm technology node

In the present era, FET devices are seamlessly integrated with complex circuits that can be used to realize chips capable of operating at a significantly higher speed. These designed devices can meet the requisite of circuits used in designing mobile phones, tablets and laptops, thereby enhancing the overall performance. The present work focuses on designing of gate stack inverted-T junctionless FET at 20 nm technology node. Both static and low frequency characteristics of device such as threshold voltage (VTh), transconductance (gm), on (ION) & off (IOFF) state currents, as well as its high-frequency characteristics like total gate capacitance (Cgg), cut-off frequency (fT), maximum oscillation frequency (fmax) and gain bandwidth product (GBW) are methodically demonstrated with alterations in the device process and geometrical variations. The parameters such as height of fin, width of fin and work function are varied to study their impact. The SS, DIBL, ION/IOFF (current switching ratio) and VTh, for 20 nm gate length are observed as 67.94 mV/dec, 34.32 mV/V, 108, 0.32 V, respectively. The fmax is determined to be in the THz range and has early voltage of approximately 6.12V. Additionally, the effect of temperature is also studied by varying it in the range of 250 K–450 K. Analysis and virtual modelling of device is carried out using Visual TCAD. In addition, p-channel transistor is designed along with the n-channel configuration to investigate the device performance for CMOS based circuits. The noise margin and average delay of the inverter circuit are observed to be 0.37 V and 36.7 ps respectively.

A Flexible Topology Control Strategy for Mega-Constellations via Inter-Satellite Links Based on Dynamic Link Optimization

In large-scale satellite constellations, the efficiency of inter-satellite communication is paramount. Traditional topology control strategies, such as the Manhattan configuration, provide stable links but can result in indirect communication paths, affecting the efficiency of information transfer. This paper addresses this issue by proposing an innovative “3 + 1” dynamic topology control scheme. The scheme retains three static links determined by the relative angular velocity and acceleration while introducing a dynamic link based on distance and angular velocity constraints to optimize the link duration and overall network communication efficiency. To address the complexity of matching dynamic links, this paper introduces an elite strategy-based maximum weighted matching algorithm for general graphs. Compared to traditional greedy algorithms, our proposed algorithm significantly improves the link duration and topological stability. Through simulation experiments comparing communication delays between Xiamen and Los Angeles, our results show that the proposed dynamic link scheme substantially reduces the average delay, enhancing the efficiency and flexibility of inter-satellite communication. This research not only extends the duration of inter-satellite links but also provides new perspectives and methodologies for further studies on inter-satellite topology control strategies.

Living with axial spondyloarthritis: a cross-sectional survey of patient knowledge and perceptions

Diagnosis and effective treatment of axial spondyloarthritis (AxSpA) are often delayed due to inadequate awareness and poor patient-physician communication. Some AxSpA patients fail to maintain an active lifestyle by exercising regularly, further worsening their disease management. The evolving concept of patient-centred care necessitates better understanding of patient awareness and their needs. We aimed to survey AxSpA patients to reflect on healthcare planning and management perspectives. Our self-administered questionnaire focused on perceptions of AxSpA diagnosis and management, particularly exploring issues of physical activity and active lifestyle. Satisfaction with AxSpA medical care and its accessibility, diagnostic delays, patient-physician communication, and support for disease management were also explored. This offline survey was arranged at the Department of Rheumatology, Immunology, and Internal Medicine of Jagiellonian University Medical College and Krakow University Hospital. We surveyed patients with AxSpA attending outpatient clinics between December 1st, 2023 and April 22nd, 2024. The questionnaire included questions on types of physical activities, barriers to exercising, satisfaction with medical care, patient-physician interactions, diagnostic delays, and use of teleconsultations. A total of 117 patients with AxSpA were enrolled (mean age 41.62 years). The majority (n = 93, 79.5%) were employed. There was a male predominance (69, 59%). The average diagnostic delay was 5.5 years. Notably, 104 (88.9%) responders perceived physical activity as a factor influencing their disease course. However, only 32 (27.35%) managed to exercise regularly (≥ 30 min, 2–3 times a week). The majority (70, 59.83%) were irregularly engaged in some form of physical activity, with 15 (12.8%) not exercising at all, and nearly half (48%) reported at least one barrier to maintaining a physically active lifestyle. Pain (32, 27.35%), fatigue (27, 23.08%), lack of motivation (17, 14.53%), and lack of time (12, 10.26%) were noted as barriers to exercising. The respondents preferred to exercise at home. The survey identified critical areas where patient dissatisfaction or uncertainty were notably prevalent: 38 (32.5%) were uncertain and 35 (30%) were dissatisfied with rehabilitation access. For spa therapy, 63 (53.85%) reported uncertainty and 23 (19.7%) expressed dissatisfaction. Only 48 (41%) were treated by a rehabilitation specialist last year. Only 23% of AxSpA patients took part in teleconsultations last year, and 65% preferred in-person visits. While AxSpA patients recognize the importance of physical activity, significant barriers exist to engaging them regularly in exercising. Addressing these barriers through personalized, motivational, and educational strategies could improve patient outcomes. Improving patient satisfaction with healthcare services, particularly in areas of rehabilitation and physician-patient communication, is crucial for improving the overall care of AxSpA patients.

UAV‐aided distribution line inspection using double‐layer offloading mechanism

AbstractWith the continuous growth of electricity demand, the safe and stable operation of distribution lines is crucial for power transportation. Unmanned aerial vehicle (UAV) inspection has been widely used for the maintenance and repair of distribution lines. Due to the limitations of computational power and endurance, it is difficult for UAVs to independently complete data processing. Combined with mobile edge computing (MEC), this paper proposes a computing offloading strategy based on multi‐agent reinforcement learning and double‐layer offloading mechanism, which can further utilize the computing power of non‐task devices and edge servers. Firstly, three‐layer system architecture, named MEC‐U‐NTDC (MEC‐UAV‐Non‐task Device Cloud), is built. Secondly, double‐layer offloading mechanism is designed to comprehensively utilize the computing power of edge servers and neighbouring non‐task devices. Finally, a multi‐agent algorithm DLMQMIX is proposed to minimize the total cost for UAV inspection. Simulation experiments show that the proposed algorithm can effectively solve the task offloading problem of UAV‐aided distribution line inspection, and compared with algorithms such as PSO, GA, and QMIX, it performs better in terms of average delay, system cost, and load balancing, achieving a smaller total system cost.

Perancangan Prototipe Sistem Pemanggil Perawat Nirkabel menggunakan Keypad dan Transceiver NRF24L01

Hospital health services require effective communication tools between patients and nurses for patient comfort. Along with the development of wireless technology, many nurse call systems have been developed by researchers, application developers, and electronics activists to produce a more effective system and ease of use. The purpose of this study is to design a prototype of a wireless nurse caller system using NRF24L01 communication, and a keypad as a button that can be used by patients in the inpatient room to call patients, as well as an audio system in the nurse's room to receive calls from patients. By using the System Development Life Cycle (SDLC) method, the prototype of the nurse caller system was successfully realized and tested at the range and delay without obstacles and with obstacles. Based on the results of the distance and delay test in the unobstructed area, the caller node device and the call receiving node device can communicate at a distance of <100 meters with an average delay of 70.4 ms. Meanwhile, in the area where there is an obstacle (in the building), it shows the maximum communication ability between devices at a distance of 60 m with a delay of 74 ms and an average delay of 71 ms, Thus this prototype can be applied in hospitals.

Adaptive Traffic Management Model for Signalised Intersections

As population increases, one of the factors affecting life is traffic. Efficient traffic management has a direct positive impact on issues such as time, carbon dioxide emissions, and fuel consumption. Today, an important parameter under the heading of traffic is the signalling systems for intersections, which are operated with fixed-time, semi-actuated, fully actuated, and fully adaptive control methods. In this study, an adaptive traffic management model is developed for signalised intersections. The adaptive traffic management model developed includes phase extension with minimum and maximum time intervals dependent on density and phase skip features. Additionally, the most distinctive feature of the model is its flexible phase structure rather than a sequential phase. The Heybe intersection, located within the boundaries of Antalya province, is modelled one-to-one in the simulation of urban mobility (SUMO) simulation programme with real intersection data. The developed adaptive traffic management model is applied to the Heybe intersection, and the effects of the model are revealed. Improvements obtained from the SUMO simulation programme were verified through visual inspection, and high-accuracy results were determined. As a result of the studies, it was found that the application of the adaptive traffic management model developed at Heybe intersection, which has approximately 50,000 vehicles passing daily, resulted in a 27.2 % improvement in the average delay per vehicle parameter, a 32.4 % improvement in the average waiting time per vehicle parameter, and a 16.7 % improvement in the average speed per vehicle parameter.

UAV-Assisted Mobile Edge Computing: Dynamic Trajectory Design and Resource Allocation.

The recent advancements of mobile edge computing (MEC) technologies and unmanned aerial vehicles (UAVs) have provided resilient and flexible computation services for ground users beyond the coverage of terrestrial service. In this paper, we focus on a UAV-assisted MEC system in which the UAV equipped with MEC servers is used to assist user devices in computing their tasks. To minimize the weighted average energy consumption and delay in the UAV-assisted MEC system, a LQR-Lagrange-based DDPG (LLDDPG) algorithm, which jointly optimizes the user task offloading and the UAV trajectory design, is proposed. To be specific, the LLDDPG algorithm consists of three subproblems. The DDPG algorithm is used to address the issue of UAV desired trajectory planning, and subsequently, the LQR-based algorithm is employed to achieve the real-time tracking control of UAV desired trajectory. Finally, the Lagrange duality method is proposed to solve the optimization problem of computational resource allocation. Simulation results indicate that the proposed LLDDPG algorithm can effectively improve the system resource management and realize the real-time UAV trajectory design.

Implementation of the first respiratory syncytial (RSV) immunization campaign with nirsevimab in an autonomous community in Spain

ABSTRACT Respiratory syncytial virus (RSV) is the main cause of low respiratory tract infections in infants under one year of age. In the 2023/2024 season, the monoclonal antibody nirsevimab was available to protect children from RSV, and Spain has become one of the first countries worldwide to implement this strategy. It is essential to evaluate the results of this first campaign and different characteristics of the immunized population in order to plan next campaigns, especially for countries that are going to include this immunization. Our coverage was high (91.5% for those born during the season and 88.3% globally). For those born during the season, only 4.9% preferred not to immunize at the maternity hospital, which meant an average delay of 27.45 days. We observed a lower coverage in the population of immigrant origin. There was a rapid pace of immunization, since for those born before the beginning of the campaign the mean to be immunized was 15.63 days, without differences between healthy and at-risk children. This allows immunization before the RSV season (90% of the catch-up children had been immunized on November 3). The average age at which all the immunized children have received nirsevimab was lower in healthy children compared to those with risk conditions (49.65 versus 232.85 days). For those born during the campaign, the average age was also lower in healthy children (3.14 versus 14.58 days). In conclusion, we consider that the implementation of the immunization strategy with nirsevimab in the Region of Murcia, Spain, has been a success.

Design and Implementation of an Intensive Care Unit Command Center for Medical Data Fusion.

The rapid advancements in Artificial Intelligence of Things (AIoT) are pivotal for the healthcare sector, especially as the world approaches an aging society which will be reached by 2050. This paper presents an innovative AIoT-enabled data fusion system implemented at the CMUH Respiratory Intensive Care Unit (RICU) to address the high incidence of medical errors in ICUs, which are among the top three causes of mortality in healthcare facilities. ICU patients are particularly vulnerable to medical errors due to the complexity of their conditions and the critical nature of their care. We introduce a four-layer AIoT architecture designed to manage and deliver both real-time and non-real-time medical data within the CMUH-RICU. Our system demonstrates the capability to handle 22 TB of medical data annually with an average delay of 1.72 ms and a bandwidth of 65.66 Mbps. Additionally, we ensure the uninterrupted operation of the CMUH-RICU with a three-node streaming cluster (called Kafka), provided a failed node is repaired within 9 h, assuming a one-year node lifespan. A case study is presented where the AI application of acute respiratory distress syndrome (ARDS), leveraging our AIoT data fusion approach, significantly improved the medical diagnosis rate from 52.2% to 93.3% and reduced mortality from 56.5% to 39.5%. The results underscore the potential of AIoT in enhancing patient outcomes and operational efficiency in the ICU setting.

Accelerating the continuous community sharing of digital neuromorphology data.

The tree-like morphology of neurons and glia is a key cellular determinant of circuit connectivity and metabolic function in the nervous system of essentially all animals. To elucidate the contribution of specific cell types to both physiological and pathological brain states, it is important to access detailed neuroanatomy data for quantitative analysis and computational modeling. NeuroMorpho.Org is the largest online collection of freely available digital neural reconstructions and related metadata and is continuously updated with new uploads. Earlier in the project, we released multiple datasets together yearly, but this process caused an average delay of several months in making the data public. Moreover, in the past 5 years, >80% of invited authors agreed to share their data with the community via NeuroMorpho.Org, up from <20% in the first 5 years of the project. In the same period, the average number of reconstructions per publication increased 600%, creating the need for automatic processing to release more reconstructions in less time. The progressive automation of our pipeline enabled the transition to agile releases of individual datasets as soon as they are ready. The overall time from data identification to public sharing decreased by 63.7%; 78% of the datasets are now released in less than 3 months with an average workflow duration below 40 days. Furthermore, the mean processing time per reconstruction dropped from 3 h to 2 min. With these continuous improvements, NeuroMorpho.Org strives to forge a positive culture of open data. Most importantly, the new, original research enabled through reuse of datasets across the world has a multiplicative effect on science discovery, benefiting both authors and users.

Comparative Analysis of CMOS-based D-type Flip-Flop Architectures for High-Performance VLSI Applications Using 45-nm CMOS Technology

High performance VLSI (very large-scale integration) is an essential electronic technology required for space missions and scientific advancements. The only source of reliance for the VLSI designer is the data storage devices designated as flip-flops, which are widely used in semiconductor memory devices as well as for data processing and storage in the telecommunication fields. In this paper, the four D-type flip-flop architectures of CMOS-based D-type flip-flops, such as D-type flip-flops using traditional technology (DFF-T), D-type flip-flops with Demorgan’s law (DFF-DL), D-type flip-flops with transmission gates (DFF-TG), and D-type flip-flops with five transistors (DFF-F), have been designed and constructed. The analytical simulations of the schematics and equivalent layouts of these four architectures are implemented using 45-nm complementary metal-oxide-semiconductor (CMOS) technology. With cadence virtuoso design software, the performance investigation of the four D-type flip-flop architectures is compared in terms of layout area, transistor counts, average power consumption, rise time, fall time, and propagation delay. The DFF-F technologies have modestly compact design goals in order to reduce production costs and achieve faster processing speeds with fewer transistors.

Kajian Ulang Perencanaan Geometrik Simpang Simpang Tak Bersinyal Berdasarkan Highway Capacity Manual

Simpang Empat Jeuram Nagan Raya is often mentioned by local people as “Simpang Peut” is the main route of the city's arterial road serving traffic movement between districts or between provinces, which is located in Kuala District, Nagan Raya Regency. The intersection has four arms, serving the flow of 4 lanes of the main road with a median and 2 lanes of minor roads connecting the route for offices, residential residents, trade and as a route to the city center. The problem that is raised and also the purpose of research is how the performance of the intersection to traffic volume occurs in the four arms and distinguishes the difference between the current calculation and previous research. The limitations of the discussion are the research location at Simpang Empat Jeuram, the performance of the intersection based on the 1993 HCM method, side barriers and not predicting previous research. Based on the results, the current volume is 3,310 pcu/hour with a composition of 2,789 pcu/hour. The capacity of 4,009 pcu/hour is above the existing volume which is categorized as not jammed or not saturated. Side barriers 1,328 events are categorized as commercial areas and roadside market activity is very high. The degree of saturation is 0.70 &lt; DS (DS &lt; 0.80 - 0.90) with an average delay of 7.95 seconds/pcu and a queue length of 40% - 20% with a service level D where the current is unstable and sometimes has to slow down the speed of 43 km/hour. Previous research has shown significant results, because the increase in vehicles has continued to increase by 75% in the last 3 years.