Set Of Switches Research Articles

Mimicking somatic behavior of neurons using the integrate and fire model of 2D SnS memristive switching characteristics

This Letter presents the fabrication and characterization of a 2D SnS memristor, proposing its integrate and fire (I&F) model as a potential hardware implementation of neuronal somatic behavior. The memristor comprises a thin layer of tin (II) sulfide (SnS) sandwiched between copper (Cu) electrodes on a silicon (Si) substrate. This structure exhibits an impressive Roff:Ron ratio of 103 at a read voltage Vrd of 0.25 V with exceptionally low switching Vsw and set Vset voltages of 0.3 and 0.35 V, respectively, with ∼3 order variation between the maximum Rmax and Rmin resistances offered during single voltage sweep cycle. We have explained the memristive behavior using the dual ionic conduction mechanism in the SnS active layer. We extracted the real-time band diagram of SnS using ultraviolet photoelectron spectroscopy, explaining the low Vsw observed. We propose that the emulation of the I&F artificial neuron model exhibited by the fabricated device could serve as a promising application in the field of artificial neuron spiking.

Design of high performances electronically reconfigurable antenna arrays by means of parasitic structures and RF-MEMS switches

AbstractThis work explores the development of smart array antennas, able to electronically change their beam patterns through reconfigurable linear, planar, or conformal parasitic structures controlled by mean of a set of electronic switches namely the radio frequency micro-electromechanical switches (RF-MEMS). The motivation of this study is to extend and make competitive the use of reconfigurable antenna arrays in areas such as the Internet of Things (IoT) and wireless sensor networks. These hot research areas require a capillary distribution of sensors characterized by compact dimensions and limited cost. The radiating systems play a key role in such applications since the improvement of the radiating properties can dramatically reduce the power consumption of such sensors and extend their operative range. Standard smart antenna arrays such as phased, or fully adaptive arrays are extremely expensive, expensive and difficult to control. In this work, the use of smart reconfigurable antenna arrays that do not require expensive digital variable phase shifters and attenuators has been considered. The proposed antenna structures are based on reconfigurable parasitic structures, that thanks to suitable RF-MEMs switches are able to obtain a desired beam pattern, by steering the main beam in a given direction, removing eventually interfering signals and maximizing the signal-to-noise ratio SNR. The versatility, simplicity, and low cost of such antennas make them particularly suitable for the emergent wireless sensor networks and IoT applications. Theoretical background, design guidelines, and suggestions for their fabrication are detailed in this work. Some antenna models for different practical applications are designed, optimized, numerically assessed with commercial electromagnetic simulators, and commented to provide a valid alternative to researchers involved in antenna design for IoT applications.

Grounded and floating memristor emulator employing ICCTA: decremental or incremental operation

ABSTRACT This article describes floating and grounded memristor emulator built with an Inverting Current Conveyor Transconductance Amplifier (ICCTA). One ICCTA, one capacitor, and three resistors are used to implement the charge controlled floating memristor emulator. In contrast, a flux controlled grounded memristor emulator consists of one ICCTA, one resistor, and one capacitor. Both presented circuits do not incorporate complex circuits such as analog multiplier circuits, passive inductors, and analog to digital converter circuits in their implementation, which is advantageous in terms of circuit realisation. The designed circuits may be operated in incremental as well as decremental configurations by appropriate setting of MOS switches. In addition, PVT (process, voltage, and temperature) analysis of the proposed memristor is also included. Furthermore, the non-ideal explanation of the proposed circuits is mathematically examined. The proposed emulators are simulated in SPICE simulator using 180 nm technology. Meminductor circuit and memristor-based low pass filter are used to validate the effectiveness of the designed circuits.

Physics-informed Graph Neural Network for Dynamic Reconfiguration of power systems

To maintain a reliable grid we need fast decision-making algorithms for complex problems like Dynamic Reconfiguration (DyR). DyR optimizes distribution grid switch settings in real-time to minimize grid losses and dispatches resources to supply loads with available generation. DyR is a mixed-integer problem and can be computationally intractable to solve for large grids and at fast timescales. We propose GraPhyR, a Physics-Informed Graph Neural Network (GNNs) framework tailored for DyR. We incorporate essential operational and connectivity constraints directly within the GNN framework and train it end-to-end. Our results show that GraPhyR is able to learn to optimize the DyR task.

Efficient handling of ACL policy change in SDN using reactive and proactive flow rule installation

Software-defined networking (SDN) is a pioneering network paradigm that strategically decouples the control plane from the data and management planes, thereby streamlining network administration. SDN's centralized network management makes configuring access control list (ACL) policies easier, which is important as these policies frequently change due to network application needs and topology modifications. Consequently, this action may trigger modifications at the SDN controller. In response, the controller performs computational tasks to generate updated flow rules in accordance with modified ACL policies and installs flow rules at the data plane. Existing research has investigated reactive flow rules installation that changes in ACL policies result in packet violations and network inefficiencies. Network management becomes difficult due to deleting inconsistent flow rules and computing new flow rules per modified ACL policies. The proposed solution efficiently handles ACL policy change phenomena by automatically detecting ACL policy change and accordingly detecting and deleting inconsistent flow rules along with the caching at the controller and adding new flow rules at the data plane. A comprehensive analysis of both proactive and reactive mechanisms in SDN is carried out to achieve this. To facilitate the evaluation of these mechanisms, the ACL policies are modeled using a 5-tuple structure comprising Source, Destination, Protocol, Ports, and Action. The resulting policies are then translated into a policy implementation file and transmitted to the controller. Subsequently, the controller utilizes the network topology and the ACL policies to calculate the necessary flow rules and caches these flow rules in hash table in addition to installing them at the switches. The proposed solution is simulated in Mininet Emulator using a set of ACL policies, hosts, and switches. The results are presented by varying the ACL policy at different time instances, inter-packet delay and flow timeout value. The simulation results show that the reactive flow rule installation performs better than the proactive mechanism with respect to network throughput, packet violations, successful packet delivery, normalized overhead, policy change detection time and end-to-end delay. The proposed solution, designed to be directly used on SDN controllers that support the Pyretic language, provides a flexible and efficient approach for flow rule installation. The proposed mechanism can be employed to facilitate network administrators in implementing ACL policies. It may also be integrated with network monitoring and debugging tools to analyze the effectiveness of the policy change mechanism.

Underground Cable Fault Detection Using IOT

Abstract: The objective of this project is to determine the distance of underground cable fault from base station in kilo meters using an Arduino. The underground cable system is a common practice followed in many urban areas. An issue may happen in an underground cable just because of earth tremors or any burrowing procedure. Since the area of the issue happened is obscure it is very hard for the fixing procedure. This hindrance is handled with the assistance of optical fibre framework. A lot of optical strands is put alongside the force cables. While a fault occurs for some reason, at that time the repairing process related to that particular cable is difficult due to not knowing the exact location of the cable fault. The proposed system is to find the exact location of the fault. The project uses the standard concept of Ohms law i.e., when a low DC voltage is applied at the feeder end through a series resistor (Cable lines), then current would vary depending upon the location of fault in the cable. In case there is a short circuit (Line to Ground), the voltage across series resistors changes accordingly, which is then fed to inbuilt ADC of Arduino to develop precise digital data for display in kilometres. The project is assembled with a set of resistors representing the cable length in km and the fault creation is made by a set of switches at every known km to cross check the accuracy of the same. The fault occurring at a particular distance, the respective phase along with the distance is displayed on the LCD. The same information is also sent over IOT, interfaced to the Arduino. Further this project can be enhanced by using capacitor in an ac circuit to measure the impedance which can even locate the open circuited cable, unlike the short circuited fault only using resistors in DC circuit as followed in the above proposed project.

Disentangling cognitive flexibility: a model-based assessment of women with anorexia nervosa

ABSTRACT Cognitive flexibility (CF) has been proposed as a potential trait marker in anorexia nervosa (AN), although findings have been inconsistent. To address this inconsistency, we applied a model that distinguishes between three subtypes of CF: task switching, switching sets, and stimulus-response mapping, which we then assessed using a paradigm-based task battery. The aim of the study was to investigate how AN is associated with these three CF subtypes. Thirty-three women with AN and 37 age- and education-matched controls performed a battery of computerized cognitive tasks to assess the three CF subtypes. Compared to the control group, individuals with AN exhibited poorer performance on the task switching and switching sets subtypes, as measured by response time switch cost, but not on the stimulus-response mapping subtype. No differences were found between the groups in response accuracy. Furthermore, switching sets as compared to the task switching and stimulus-response mapping subtypes was found to better explain the differences between the groups. These findings indicate a domain-specific impairment in CF among patients with AN, reflecting deficits observed in subtypes related to the disorder’s characteristics, particularly that associated with visual perception. Therefore, CF impairment in AN should not be viewed dichotomously, but rather as a relative impairment that varies depending on the specific CF subtype.

Multiple heating loads participate in the system power regulation control strategy

With the addition of a high proportion of new energy and power electronic equipment, the power system has shown characteristics such as low inertia and significant energy output fluctuation, which has brought great challenges to power/frequency regulation and control of the power system. Therefore, this article uses the inertia characteristics of heat energy on the load side and exerts reasonable start-stop control to play a positive and beneficial role in the power regulation of the power system. Firstly, the room temperature variation process was transformed into a lumped parameter equivalent model. A lumped parameter heat transfer model of a single typical load was built in Simulink, and its dynamic characteristics were analyzed by symbolic calculation. Based on this, a time-domain simulation was carried out to explore the influence of the law of system parameters and external input on the change of system state quantity. Based on time domain simulation, the behavior of the heating load is analyzed. Then, the effects of typical and multiple loads on system power regulation at different outdoor temperatures are studied. It is found that when the average outdoor temperature decreases, the sustainable time of system power upturn will increase, and the sustainable time of downturn will decrease. The power curve of multiple loads simultaneously is more stable, and the adjustment ability is stronger. The reasonable and flexible control method of load switch setting, namely the coherent characteristic load power regulation strategy, realizes the sustainable power optimization of the load to the system in the specified period.

Distributed Adaptive Tracking Control of Hidden Leader-Follower Multi-Agent Systems with Unknown Parameters

The distributed leader-follower control of multi-agent systems is discussed. Each agent is expressed in a discrete-time and non-linear dynamic model with an unknown parameter and can be affected by its neighbors’ history information. For each agent, to identify the parameter, one switching set of the parameter estimates is constructed and the optimal parameter estimate is chosen based on the index switching function. Using the given desired reference signal, the leader agent’s control law is designed, and relying on the neighbors’ history information, each follower agent’s local control law is designed. With the designed distributed tracking adaptive control laws, the whole system tracks the given desired reference signal, and in the face of strong couplings the closed-loop system ultimately reaches an agreement. Finally, by comparing simulations of the control strategy with a normal projection algorithm, the results indicate that the adaptive control method with a switching set of the parameter estimates is effective in improving the control performance.

Design model of a twisted and folded Clos network with multi-step grouped intermediate switches guaranteeing admissible blocking probability

A future data center network is expected to be constructed by a Clos network consisting of optical-circuit switches to deal with traffic growth. A previous model addressed a Clos-network design problem that divides the set of intermediate switches by each role for request routing to guarantee an admissible blocking probability to maximize the switching capacity. However, the previous model divides the set of intermediate switches into at most only two groups, and there is room for a more flexible design by increasing the number of divisions. This paper proposes a design model that generalizes the number of divisions to increase the switching capacity while guaranteeing an admissible blocking probability. We formulate the design model as an optimization problem. We introduce two algorithms to obtain a feasible solution that satisfies the constraints of the optimization problem. Numerical results showed that the proposed model can increase the switching capacity as the number of divisions increases.

Cognitive flexibility in women who recovered from anorexia nervosa – a model-based approach

Research findings on cognitive flexibility (CF) functioning in women who recovered from anorexia nervosa (RAN) were found to be inconsistent. This was attributed to the multiple definitions of CF and the diverse measuring tools used to assess it. Applying a deductive approach to explore CF function may address these inconsistencies; thus, we used a model that divides CF into three subtypes, namely, stimulus-response mapping, switching sets and task switching. Additionally, we explored the association between CF subtypes and the disorder’s clinical measures to assess the relation of CF to recovery. Forty-three RAN and 54 healthy controls performed tasks designed to assess CF subtypes based on the model’s division, and the RAN group completed the Eating Disorder Examination Questionnaire. The results showed that the RAN group performed significantly worse than controls only in the stimulus-response mapping subtype. Additionally, there were no correlations between CF subtypes and clinical symptoms or the disorder measures – current and nadir body mass index, age of onset, time since recovery, and disorder duration. In conclusion, the study revealed CF impairment after recovery from AN, specifically in stimulus-response mapping. The variability in performance of the CF subtypes supports the application of a theory-driven perspective viewing CF as a modular ability in RAN. Additionally, CF is unrelated to clinical measures post-recovery and thus may not be used as a criterion for evaluating recovery.

A proactive controller failure recovery mechanism in SD-WAN with multiple controllers

The separation of data and control plane is a key feature of Softwaredefined networking (SDN), which makes network administration more intelligent. The control plane is realized using a logically centralized controller. In SD-WAN, as the network becomes larger, multiple controllers are needed to manage the network. In this scenario, there is a chance for controller failure due to overload. Once the controller fails, the switches lose connection with the controller. The load of the failed controller is to be re-distributed among other controllers. Sometimes this load transfer may cause many other problems like controller chain failure. It may consistently undermine the trustworthiness of the network. In the proposed technique, the multiple controller deployment based on affinity propagation clustering in SD-WAN is extended to include a proactive controller failure recovery mechanism. Whenever the controller load exceeds a pre-defined threshold, a set of switches under the bottleneck controller will be reassigned to a neighbouring controller without exceeding its capacity. The simulation results show that when network traffic increased, the proposed proactive controller failure recovery method balanced the controller load, resolved a cascading controller failure, improved the average throughput, and reduced the average end-to-end delay and packet loss effectively.

AALLA: Attack-Aware Logical Link Assignment Cost-Minimization Model for Protecting Software-Defined Networks against DDoS Attacks.

Software-Defined Networking (SDN), which is used in Industrial Internet of Things, uses a controller as its "network brain" located at the control plane. This uniquely distinguishes it from the traditional networking paradigms because it provides a global view of the entire network. In SDN, the controller can become a single point of failure, which may cause the whole network service to be compromised. Also, data packet transmission between controllers and switches could be impaired by natural disasters, causing hardware malfunctioning or Distributed Denial of Service (DDoS) attacks. Thus, SDN controllers are vulnerable to both hardware and software failures. To overcome this single point of failure in SDN, this paper proposes an attack-aware logical link assignment (AALLA) mathematical model with the ultimate aim of restoring the SDN network by using logical link assignment from switches to the cluster (backup) controllers. We formulate the AALLA model in integer linear programming (ILP), which restores the disrupted SDN network availability by assigning the logical links to the cluster (backup) controllers. More precisely, given a set of switches that are managed by the controller(s), this model simultaneously determines the optimal cost for controllers, links, and switches.

Canary: Congestion-aware in-network allreduce using dynamic trees

The allreduce operation is an essential building block for many distributed applications, ranging from the training of deep learning models to scientific computing. In an allreduce operation, data from multiple hosts is aggregated together and then broadcasted to each host participating in the operation. Allreduce performance can be improved by a factor of two by aggregating the data directly in the network. Switches aggregate data coming from multiple ports before forwarding the partially aggregated result to the next hop. In all existing solutions, each switch needs to know the ports from which it will receive the data to aggregate. However, this forces packets to traverse a predefined set of switches, making these solutions prone to congestion. For this reason, we design Canary, the first congestion-aware in-network allreduce algorithm. Canary uses load balancing algorithms to forward packets on the least congested paths. Because switches do not know from which ports they will receive the data to aggregate, they use timeouts to aggregate the data in a best-effort way. We develop a P4 Canary prototype and evaluate it on a Tofino switch. We then validate Canary through simulations on large networks, showing performance improvements up to 40% compared to the state-of-the-art.

Design and analysis of a passive exoskeleton with its hip joint energy-storage.

A novel passive hip exoskeleton has been designed and built with the aim of reducing metabolic consumption during walking by a passive way of storing the negative mechanical energy in the deceleration phase and releasing it in the acceleration phase. A ratchet spiral spring mechanism with a set of double stable switches is designed inside the exoskeleton for the above purpose. An analysis is conducted on the mechanism and the switching timing for the energy management to automatically store or release the energy according to the biomechanics of walking. In addition, a gravity-balance mechanism embedded inside the exoskeleton is designed as well to minimize the influence of the lower limb weight on muscle work. Human-exoskeleton interaction has been studied using the Opensim software, and simulation results demonstrated the effectiveness of the exoskeleton in reducing metabolic consumption during walking. An exoskeleton prototype has been built and tested with experiments measuring assistive torque and surface electromyography signal, confirming the effectiveness of the gravity-balance mechanism and energy-storage method, as well as the exoskeleton's actual assistive effect.

Scaled consensus for MASs with compound noises over Markovian switched topologies

AbstractThis article aims to address the stochastic scaled consensus issue in almost surely and mean square sense of stochastic multiagent systems (SMASs) with compound noises in a Markovian switching setting. At first, based on stochastic approximation technique and nearest‐neighbor interaction rules, the stochastic scaled consensus controller is built for SMASs in presence of multiplicative and additive noise by designing a time‐varying gain. Meanwhile, some consensus criterions of SMASs are addressed if the union of the graph and time‐varying gain fulfill some mild requirements. Furthermore, the scaled consensus means that the agents' state variables tend to a prescribed proportion rather than the common value, so this behavior can be seen as non‐convergent behavior. The difference with most of excellent literatures is that the coexistence of noncooperative behavior and multiplicative noise generates that it is not easy to transform the multiplicative noise term into the form of an error equation, which means that the Lyapunov methods cannot be directly utilized in our article. To cope with this, we first demonstrate the boundedness for each agent's state, and hence the convergence can be achieved. Finally, in order to reveal the efficiency of our proposed protocol, the corresponding example is given in the simulation part.

A novel hybrid arc suppression device for single line-to-ground faults

Traditional passive arc suppression devices (ASDs) do not provide complete compensation for single line-to-ground (SLG) fault currents. Meanwhile, active ASDs are expensive. This study proposes a novel hybrid ASD to balance compensation and cost. The hybrid ASD consists of a passive part and an active part. To reduce the fault voltage to near zero, the passive part uses a single-phase isolating transformer and two sets of phase-selection switches. The active part, which compensates for the residual voltage caused by the transformer's leakage impedance, is connected to the secondary side of the transformer and the ground using a small-capacity inverter. A model predictive control (MPC) method is employed as the controller. We conducted simulations and experiments for various SLG fault cases, which demonstrated the effectiveness and robustness of the hybrid ASD in arc suppression. The hybrid ASD that we propose is independent of the neutral point and has several advantages, including low cost, strong arc suppression capabilities, and high robustness.

Programmable photovoltaic submodules for hotspot mitigation

ABSTRACT This paper introduces a novel approach to conventional Photovoltaic (PV) modules that involves regulating the internal connections of the PV strings (or submodules) using a set of switches to isolate shaded submodules while leaving unshaded submodules unaffected. By extending the connection types of the PV submodules to include parallel or series via an external circuit, along with a set of switches, the output voltage and current can be adjusted without requiring additional PV modules to increase the output current. A microcontroller is employed to control the connections of the PV submodules to achieve three modes: series connection, parallel connection, or mixed connection. As a result, there is no need for a buck-boost converter, and the effects of partial shading are minimized, while the hotspot effect is mitigated without bypass diodes. A Matlab/Simulink model is utilized to validate the proposed configurations, and two customized hardware PV modules are fabricated to represent the conventional module and the proposed module. The results obtained from the hardware implementation support the concept of separating shaded PV submodules and regulating the internal connections of the submodules in different configurations. This feature of separating the unshaded submodules is implemented to enhance the efficiency of the PV modules.

Solution-processed small-molecular organic memristor with a very low resistive switching set voltage of 0.38 V

Organic memristors are considered to be the next-generation storage element due to their unique advantages of flexibility, transparency, and good solution processability. In this Letter, a Zn-porphyrin based small-molecular organic memristor is prepared by spin-coating with an ultralow resistive switching set voltage of 0.38 V. It is found that the zinc atom in the porphyrin molecule plays a very important role in improving the resistance switching characteristics of organic memristors. By tracking the change in oxygen valence in the vertical dimension, we demonstrate that Zn atom located in the core of porphyrin helps to enhance the oxygen ion migration across the active layer, clearly revealing the memory mechanism of low-cost solution-processed Zn-porphyrin based small-molecular organic memristors. This organic memristor shows excellent memristive performance resulting from rational material design and appropriate device structure engineering.

An enhanced reconfiguration approach for mitigating the shading effect on photovoltaic array using honey badger algorithm

The series–parallel configuration is one of the widely utilized schemes in the photovoltaic arrays. The existence of shaded or failed modules in the string of the array has a significant impact on the array-produced power. Therefore, this work introduce an innovative objective function and a novel approach of honey badger algorithm (HBA) as a first time to dynamically reconfigure the series–parallel interconnected photovoltaic array under the partial shading and random failure situations to boost the harvested array’s power. The reconfiguration process is performed via electrically reconnecting the modules among the strings via a set of switches. The performance of the proposed approach is examined with two series of analyses. The first one focuses on handling the impact of partial shading phenomena while the second concerns optimizing the array output in case of existing random failed modules inside the array besides partial shading conditions. Two arrays are considered, including a symmetric 9 × 9 array and an asymmetric 10 × 8 photovoltaic one. The proposed optimizer is compared with a set of recently proposed meta-heuristic approaches, including wild horse optimizer, artificial gorilla Troops optimizer, and particle swarm optimizer to clarify the reason for selecting the HBA optimizer. The harvested global maximum power, the mismatch power loss, and enhancement are quality measures performed to affirm the proposed approach’s ability to enhance the array output power. Moreover, energy-saving and income payback are investigated over a day and a year for two systems using symmetric 9 × 9 array and asymmetric 10 × 8 array. The comparisons and analyses showed that, the reconfigured arrays based on HBA provide higher values of maximum global power with smoother photovoltaic characteristics. Moreover, the 9 × 9 and 10 × 8 PV systems-based HBA save income by the percentage of 18.12%, 24.24% compared with series–parallel systems without reconfiguration per year, respectively.