Selection Logic Research Articles

Energy‐Efficient Routing Using Fuzzy Neural Network in Wireless Sensor Networks

In wireless sensor networks, energy is a precious resource that should be utilized wisely to improve its life. Uneven distribution of load over sensor devices is also the reason for the depletion of energy that can cause interruptions in network operations as well. For the next generation’s ubiquitous sensor networks, a single artificial intelligence methodology is not able to resolve the issue of energy and load. Therefore, this paper proposes an energy‐efficient routing using a fuzzy neural network (ERFN) to minimize the energy consumption while fairly equalizing energy consumption among sensors thus as to prolong the lifetime of the WSN. The algorithm utilizes fuzzy logic and neural network concepts for the intelligent selection of cluster head (CH) that will precisely consume equal energy of the sensors. In this work, fuzzy rules, sets, and membership functions are developed to make decisions regarding next‐hop selection based on the total residual energy, link quality, and forward progress towards the sink. The developed algorithm ERFN proofs its efficiency as compared to the state‐of‐the‐art algorithms concerning the number of alive nodes, percentage of dead nodes, average energy decay, and standard deviation of residual energy.

Signal conditioning circuits for low vibration signals using an array of piezoelectric sensors

Piezo-electric sensors have found widespread application today due to their ability to provide an interface between the mechanical and electronic world. For humans, in order to interact with these materials, we require an electronic interface. This paper describes one of the possible interfaces, which includes a charge amplifier cascaded with a filter circuit to obtain the frequency response. A novel circuitry consisting of a rectifier and comparator is used to identify the activated channels of the array of piezoelectric sensor. This signal conditioning circuit makes use of active devices such as op amps and nonlinear devices such as diodes along with passive components. The novel circuitry is designed for 2 channels, and demonstrates the logic of selection of these based on the signal parameters. This concept can be extended to any number of channels.

Logic of selection of pedagogical conditions of vocational training of future physical culture teachers for organizing individual work with pupils

Logic of selection of pedagogical conditions of vocational training of future physical culture teachers for organizing individual work with pupils

Pluggable Micronetwork for Layer Configuration Relay in a Dynamic Deep Neural Surface

The classical convolution neural network architecture adheres to static declaration procedures, which means that the shape of computation is usually predefined and the computation graph is fixed. In this research, the concept of a pluggable micronetwork, which relaxes the static declaration constraint by dynamic layer configuration relay, is proposed. The micronetwork consists of several parallel convolutional layer configurations and relays only the layer settings, incurring a minimum loss. The configuration selection logic is based on the conditional computation method, which is implemented as an output layer of the proposed micronetwork. The proposed micronetwork is implemented as an independent pluggable unit and can be used anywhere on the deep learning decision surface with no or minimal configuration changes. The MNIST, FMNIST, CIFAR-10 and STL-10 datasets have been used to validate the proposed research. The proposed technique is proven to be efficient and achieves appropriate validity of the research by obtaining state-of-the-art performance in fewer iterations with wider and compact convolution models. We also naively attempt to discuss the involved computational complexities in these advanced deep neural structures.

Punitive selectivity, racism and over-incarceration in Brazil

The purpose of this article is to analyze racism and social inequality as foundations of over-incarceration in Brazil. The first section discusses the relationship between punitive selectivity and structural racism from a historical perspective. This is followed by an analysis of the relationship between the downgrading of the population's living conditions through the process of neoliberal counter-reforms and the increase in incarceration and naturalization of dehumanizing practices in the prison environment. The third section presents notes on the increase in female incarceration in Brazil for the crime of drug trafficking. The final remarks highlight that, in the neoliberal phase of capitalism, the structural racism that permeates the criminal justice system finds renewed arguments in the “war on drugs”, to legitimize the over-imprisonment of women, poor and black women in particular. The results indicate that the selective criminal logic reiterates the conditions of oppression from the perspective of gender. Women are doubly punished: by the criminal justice system and by the conservative morality that underpin patriarchal oppression. The method used in the analysis is the materialist-dialectic. From the perspective of social totality, we aim to capture the complex and multiple determinations that constitute the object of study. The research methodology is based on literature and document reviews.

RSNN: A Software/Hardware Co-Optimized Framework for Sparse Convolutional Neural Networks on FPGAs

Convolutional Neural Networks (CNNs) have been shown to be very useful in image recognition and other Artificial Intelligence (AI) applications, however, at the expense of intensive computation requirement. To address the challenge of overwhelming calculation requirements, researchers have proposed various network pruning techniques. But, due to the irregular sparse patterns, unstructured sparse networks are difficult to compute efficiently on either Graphic processing units (GPUs) or Field Programmable Gate Arrays (FPGAs). In this paper, we propose a software/hardware co-optimized Reconfigurable Sparse convolutional Neural Network accelerator design (RSNN) on FPGAs. A novel sparse convolution dataflow is proposed with simpler control logic than existing mux-based selection logic. To balance the computation load on different Processing Units (PUs), we propose a software-based load-balance aware pruning technique as well as a kernel merging method. Experimental results show that RSNN is $2.41\times - 7.91\times $ better on Digital Signal Processor (DSP) efficiency than previous dense CNN FPGA accelerators, and $1.23\times - 2.93\times $ better than previous sparse CNN FPGA accelerators.

Active Processor Hardware Stack

The characteristic features of the reduced instruction set computer (RISC) architecture are used to develop high-performance microprocessors and microcontrollers. Complex operations, which include subroutine calls and interrupt service, are difficult to implement in hardware for the same time interval with all the other commands. Such operations, when executed, involve recording the value of the transition address in the register of the processor’s software counter while saving the return address from the subroutine. A specific design of the processor hardware stack is proposed in the work, which allows performing complex machine operations in one clock cycle. It is established that the required technical result can be achieved by introducing in the design of the processor N identical software counters, which are actually analogues of the stack registers. It is shown that a stack pointer (SP) with the logic of the register selection connected to a block of program counters (PCs) activates the next PC when a subroutine is called or an interrupt is serviced, while the previous counter stores the return address from the subroutine. This excludes the procedures of saving the return address from the PC in the registers of the stack or the RAM cells and restoring its state from the stack. This allows us to make a subroutine call or jump along the interrupt vector and return to the call point in one clock cycle of the master oscillator. As a result of this, it becomes possible to increase the processor’s speed during such operations by 30–50% and improve overall performance without increasing the clock frequency.

A comparison of fuzzy logic and TOPSIS methods for landfill site selection according to field visits, engineering geology approach and geotechnical experiments (case study: Rudbar County, Iran).

The present study evaluated and selected the best location among susceptible landfill sites in Rudbar County using 27 criteria, as the maximum effective criteria, in the decision-making process. The emergence and comparison between the two methods of fuzzy and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with an engineering-geological approach have been conducted to obtain the best results. In this regard, inappropriate regions for landfill construction have been restricted after the conflation of two maps based on the restriction criteria. Then, the direction of prevailing winds and proper area for landfill construction was investigated for a 20-year perspective, and the regions that had more than 35-hectare area and were not located in the direction of the prevailing winds have been identified as responsive sites for landfill construction. Ultimately, selected susceptible regions were prioritized separately based on the TOPSIS and fuzzy methods. To ascertain the accuracy of the results and the reality of the resulted models, complementary field studies, geotechnical digging, and engineering-geological experiments have been used as verification parameters. According to the results of the verification, the fuzzy model having maximum matches with the verified data has been suggested for prioritizing susceptible regions.

Application of fuzzy logic for IoT node elimination and selection in opportunistic networks: performance evaluation of two fuzzy-based systems

Opportunistic Networks (OppNets) are a sub-class of DTN, designed as a specialized ad hoc network suitable for applications such as emergency responses. Unlike traditional networks, in OppNets communication opportunities are intermittent, so an end-to-end path between the source and the destination may never exist. Existing networks have already brought connectivity to a broad range of devices, such as hand held devices, laptops, tablets, PC, etc. The Internet of Things (IoT) will extend the connectivity to devices beyond just mobile phones and laptops, but to buildings, wearable devices, cars, different things and objects. One of the issues for these networks is the selection of the IoT nodes to carry out a task in OppNets. In this work, we implement two Fuzzy-Based Systems: Node Elimination System (NES) and Node Selection System (NSS) for IoT node elimination and selection in OppNets. We use three input parameters for NES: Node’s Distance to Event (NDE), Node’s Battery Level (NBL), Node’s Free Buffer Space (NFBS) and four input parameters for NSS: Node’s Number of Past Encounters (NNPE), Node’s Unique Encounters (NUE), Node Inter Contact Time (NICT), Node Contact Duration (NCD). The output parameter is IoT Node Selection Possibility (NSP). The results show that the proposed systems make a proper elimination and selection decision for IoT nodes in OppNets.

The name to remember: Flexibility and contextuality of preliterate folk plant categorization from the 1830s, in Pernau, Livonia, historical region on the eastern coast of the Baltic Sea

Ethnopharmacological relevanceResearch on the folk categorization of nature in preliterate societies in Europe is complicated due to the fragmentation of the information available and is rarely undertaken. Yet the data is valuable and may provide, in certain circumstances, important insights, if not into novel medicines, then into the historical logic of selection and memorisation of plants useful from a medicinal perspective. Aims of the studyWe aim to understand the ethnobotany of a preliterate society by analysing the emic (derived from people) perspective on nature-related culture of one of Europe's smaller nations, whose written language and culture was shaped in the 18th-19th centuries by other, larger nations of Europe, and thus from the etic (academic) perspective. We attempt to identify how folk categorization is reflected in the relationships between plant names and uses and to map the structure of those relationships. Data and methodsWe base our analysis on one of the oldest ethnobotanical manuscripts and herbaria of the Baltic governorates, compiled in 1831 by an amateur botanist, Baltic German Pastor Johann Heinrich Rosenplänter (1782–1846), which was derived from conversations with his parishioners from the tiny Pärnu parish. The historical dataset was critically analysed from an ethnobotanical perspective in light of recent identifications of the herbarium specimens. ResultsAlthough the Rosenplänter collection is fragmentary, the logic of plant categorization by non-literate peasants at that time is clearly seen in the data. Plants preserved in the herbarium were predominantly used for ethno-medicinal, food or ethno-veterinary purposes, such as treating chronic skin and joint diseases as well as severe acute diseases in humans and animals. Among 129 folk taxa analysed, more than one third had apparent purpose-related plant names providing clear links to their use, whereas a few multifunctional plants had several names reflecting diverse uses. For example, Hypericum spp., which was used in three different ways, had three semantically distinct names. However, among the plants that Rosenplänter collected, there were also some that were simply named and described by people but lacked any usability data (e.g., Trollius europaeus), meaning that use as such was not the primary criterion for recognising a plant. The web-like structure of preliterate thinking in plant-related knowledge reveals a deep relationship with the environment and the interpretation of new elements through familiar natural objects. ConclusionOur findings demonstrate that historical ethnobotanical data, if thoughtfully analysed, can be used not only for comparative purposes, but also for understanding the logic of preliterate thinking. We encourage future in-depth studies of historical ethnobotanical data in Europe in order to understand the relationship between nature and culture of native European populations.

Integration of GIS and fuzzy logic for land fill site selection

Integration of GIS and fuzzy logic for land fill site selection

Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions

Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions

NEEF: A Novel Energy Efficient Fuzzy Logic based Clustering Protocol for Wireless Sensor Network

The uttermost requirement of the wireless sensor network is prolonged lifetime. Unequal energy degeneration in clustered sensor nodes lead to the premature death of sensor nodes resulting in a lessened lifetime. Most of the proposed protocols primarily choose cluster head on the basis of a random number, which is somewhat discriminating as some nodes which are eligible candidates for cluster head role may be skipped because of this randomness. To rule out this issue, we propose a deterministic novel energy efficient fuzzy logic based clustering protocol (NEEF) which considers primary and secondary factors in fuzzy logic system while selecting cluster heads. After selection of cluster heads, non-cluster head nodes use fuzzy logic for prudent selection of their cluster head for cluster formation. NEEF is simulated and compared with two recent state of the art protocols, namely SCHFTL and DFCR under two scenarios. Simulation results unveil better performance by balancing the load and improvement in terms of stability period, packets forwarded to the base station, improved average energy and extended lifetime.

SPATIAL AND FREQUENCY DOMAIN COMBINATION BASED IN SCHEMES TOSTEGANOGRAPHY IN RGB DIGITAL IMAGES USING CANTOR SET

This paper describes a proposal based on the combination of based methods on the spatial domain and the frequency for the hiding of data in RGB digital images, by substituting bits in pixels less important and modifying coefficients DWT to increase data security. The main idea of this research is to take advantage of the selection logic of the Cantor set in defined sections of the cover images, where the pixels are regrouped, and in these sections the pixels that will be used to hide data are selected. This proposal has the advantage that the application of the logic of the Cantor set allows selecting at different levels a variable number of pixels to be modified and increasing the security of the steganography system. The results obtained are high loads of data embedding, as well as the approval of quality metrics for digital images how PSNR, MSE, SSIM and others. The dataset image used in the experiments is UCID due to the large number of images available for embedding messages in images.

A highly selective luminescent logic gates probe based on Cd-LMOF for pH detection

Abstract pH value is an important parameter to reflect water quality, reaction process and health condition. Thus, it is of great significance to develop a rapid and sensitive method for pH detection. Herein, a 3D porous framework {[Cd1.5(TPTA)(Me2NH)]·0.5 Methanol·0.5 H2O}n (LMOF 1) based on an amphoteric ligand of 2′-amino-[1,1′,4′,1″-terphenyl]-3,3″,5,5″- tetracarboxylic acid (H4TPTA) has been synthesized by solvothermal method. Fluorescence properties show that LMOF 1 can rapidly and sensitively recognize pH without being affected by other ions. In addition, to quickly recover LMOF 1, the magnetic composite γ-Fe2O3@LMOF 1 was successfully prepared by loading the magnetic γ-Fe2O3 nanoparticles onto LMOF 1. The composite is easily separated from the suspension by the magnet. After washing, the recognition performance of γ-Fe2O3@LMOF 1 for pH is almost the same as the original sample after three cycles. Meanwhile, the PXRD patterns and the TG curve of γ-Fe2O3@LMOF 1 after luminescence experiments indicate that the composite has good chemical and acid-base stability. Based on the relationship between the luminescence change of LMOF 1 and pH ranging from 3 to 7, an IMPLICATION-type logic gate in luminescence mode is constructed with pH = 3 and pH = 7 as chemical input.

A logic gate for fluoride anion detection based on carbon dots/gold nanoparticles

A selective logic gate sensor for fluoride anion is first fabricated based on carbon dots/gold nanoparticles hybrid material using 3-mercapto-l,2-propanediol (MP) as the bridge. First, 3-aminophenylboronic acid is chosen as the precursor to fabricate boronic acid decorated carbon dots (b-CDs). Then, b-CDs@MP-gold nanoparticles aggregation is formed by taking advantage of MP as a bridge. As for the occurrence of FRET behavior from b-CDs to gold nanoparticles, the fluorescent signal of b-CDs@MP-Au NPs aggregation based sensor quenches remarkably, called as OFF state. However, fluoride anion binds completely with synthesized b-CDs and induces b-CDs to break away from the surface of gold nanoparticles, activating fluorescent signal, named as ON state. As the concentration of fluoride anion increases, the fluorescence signal of the sensor gradually enhances. The fluorescent response for fluoride anion detection is in the linear range from 9 μM to 117 μM with the detection limit of 1.5 μM. Moreover, the b-CDs@MP-Au NPs based sensor is operated as a logic gate at the molecular level. The experimental results indicate that our present approach can be adapted for establishing the chemical logic gate for the fluoride anion. Furthermore, our proposed strategy holds great potential application in environmental monitoring.

On the logic of Fisherian sexual selection.

In Fisher's model of sexual selection, a female preference for a male trait spreads together with the trait because their genetic bases become correlated. This can be interpreted as a "greenbeard" system: a preference gene, by inducing a female to mate with a trait-bearing male, favors itself because the male is disproportionately likely also to carry the preference gene. Here, we use this logic to argue that Fisherian sexual selection in diploids proceeds via two channels: (i) trait-bearing males are disproportionately the product of matings between preference-bearing mothers and trait-bearing fathers, and thus trait and preference genes are correlated "in trans"; (ii) trait and preference genes come into gametic phase disequilibrium, and thus are correlated "in cis." Gametic phase disequilibrium is generated by three distinct mechanisms that we identify. The trans channel does not operate when sexual selection is restricted to the haploid phase, and therefore represents a fundamental difference between haploid and diploid models of sexual selection. We show that the cis and trans channels contribute equally to the spread of the preference when recombination between the preference and trait loci is free, but that the trans channel is substantially more important when linkage is tight.

High Performance Four Segment Error Tolerant Adder for 8-bit Pixel Depth Image Processing Applications

This research proposes a high-performance Carry Select Approximate Full Adder (CSAFA) with one error out of the eight possible output cases for high accuracy 8-bit pixel depth image processing applications. The logic optimization of the proposed CSAFA module is based on a structural hierarchy of AND-OR logic and multiplexer based pre-computation selection logic which reduces the critical path switching activity. The proposed method has the advantage of higher speed, lower power consumption and improved area efficiency. Simulation results show that the proposed CSAFA reduces the critical path delay, power consumption, area, Power-Delay Product (PDP) and Area-Delay Product (ADP) by 26.81%, 44.99%, 23.53%, 59.74%, 44.03% respectively, compared to the existing Conventional Full Adder (CFA). Further, the proposed structure incorporates the 8-bit Error Tolerant Adder (ETA-CSAFA and ETA-CSAFA1) designs. When comparing with 99.5992% Computational Accuracy (CA), the proposed ETA-CSAFA1 design exhibits 0.26% less CA and it offers a savings of 27.82% PDP and 34.39% ADP with respect to the existing ETA-2LOA architecture. The results can be substantiated with an example, a 4-bit accurate part based ETA-CSAFA1 implemented with the proposed approach almost achieves the same CA, while simultaneously reducing the power consumption by 18.14% with respect to the existing best 6-bit accurate part based ETA-2LOA architecture for 8-bit image processing applications.

Group Selection and Opportunities for Gender Diversity in the Judiciary

ABSTRACTIn the legislative context, there is a persistent empirical relationship between party-list proportional electoral systems and higher levels of gender diversity in office. I apply the logic of party-list selection to the judicial context and argue that the selection of judges as pairs or in groups may facilitate the process of gender diversification on courts by making it easier – or at least more likely – for observers to notice and be critical of gender disparity in judicial selection. Evidence from a survey experiment fielded in the United States demonstrates that observers are more likely to notice and are more critical of gender homogeneity when judges are selected as a group rather than one-by-one. These micro-foundations demonstrate that the logic of party-list PR in the legislative context may also apply to the judicial context, suggesting that one way to improve prospects of judicial diversity could be re-structuring the timing of judicial turnover.

Efficient Diminished-1 Modulo (2n+1) Adder Using Parallel Prefix Adder

Parallel prefix adder (PPA) is the core component of diminished-1 modulo ([Formula: see text]) adder structure. In this paper, group-carry selection logic based PPA design is proposed and it is free from redundant logic operations which otherwise present in the existing PPA design based on group sum selection logic. Further, the logic expression of pre-processing unit of PPA is also presented in a simplified form to save some logic resources. The proposed PPA design for bit-width 32-bit involves 26.1% less area, consumes 28.4% less power and marginally higher critical-path delay than the existing PPA design. An efficient diminished-1 modulo ([Formula: see text]) adder structure is presented using proposed PPA design and modified carry computation algorithm of existing design. The proposed diminished-1 modulo ([Formula: see text]) adder structure for bit-width 32-bit offers a saving of 25.5% in area-delay-product (ADP) and 24.1% in energy-delay-product (EDP) than the best of the existing modulo adder structure.