Electronics

Electronics is a scientific and engineering discipline that studies and applies the principles of physics to design, create, and operate devices that manipulate electrons and other electrically charged particles. It is a subfield of physics and electrical engineering which uses active devices such as transistors, diodes, and integrated circuits to control and amplify the flow of electric current and to convert it from one form to another, such as from alternating current (AC) to direct current (DC) or from analog signals to digital signals. Electronic devices have significantly influenced the development of many aspects of modern society, such as telecommunications, entertainment, education, health care, industry, and security. The main driving force behind the advancement of electronics is the semiconductor industry, which continually produces ever-more sophisticated electronic devices and circuits in response to global demand. The semiconductor industry is one of the global economy's largest and most profitable industries, with annual revenues exceeding $481 billion in 2018. The electronics industry also encompasses other branches that rely on electronic devices and systems, such as e-commerce, which generated over $29 trillion in online sales in 2017. == History and development == Karl Ferdinand Braun's development of the crystal detector, the first semiconductor device, in 1874 and the identification of the electron in 1897 by Sir Joseph John Thomson, along with the subsequent invention of the vacuum tube which could amplify and rectify small electrical signals, inaugurated the field of electronics and the electron age. Practical applications started with the invention of the diode by Ambrose Fleming and the triode by Lee De Forest in the early 1900s, which made the detection of small electrical voltages, such as radio signals from a radio antenna, practicable. Vacuum tubes (thermionic valves) were the first active electronic components which controlled current flow by influencing the flow of individual electrons, and enabled the construction of equipment that used current amplification and rectification to give us radio, television, radar, long-distance telephony and much more. The early growth of electronics was rapid, and by the 1920s, commercial radio broadcasting and telecommunications were becoming widespread and electronic amplifiers were being used in such diverse applications as long-distance telephony and the music recording industry. The next big technological step took several decades to appear, when the first working point-contact transistor was invented by John Bardeen and Walter Houser Brattain at Bell Labs in 1947. However, vacuum tubes continued to play a leading role in the field of microwave and high power transmission as well as television receivers until the middle of the 1980s. Since then, solid-state devices have all but completely taken over. Vacuum tubes are still used in some specialist applications such as high power RF amplifiers, cathode-ray tubes, specialist audio equipment, guitar amplifiers and some microwave devices. In April 1955, the IBM 608 was the first IBM product to use transistor circuits without any vacuum tubes and is believed to be the first all-transistorized calculator to be manufactured for the commercial market. The 608 contained more than 3,000 germanium transistors. Thomas J. Watson Jr. ordered all future IBM products to use transistors in their design. From that time on, transistors were almost exclusively used for computer logic circuits and peripheral devices. However, early junction transistors were relatively bulky devices that were difficult to manufacture on a mass-production basis, which limited them to a number of specialised applications. The MOSFET was invented at Bell Labs between 1955 and 1960. It was the first truly compact transistor that could be miniaturised and mass-produced for a wide range of uses. Its advantages include high scalability, affordability, low power consumption, and high density. It revolutionized the electronics industry, becoming the most widely used electronic device in the world. The MOSFET is the basic element in most modern electronic equipment. As the complexity of circuits grew, problems arose. One problem was the size of the circuit. A complex circuit like a computer was dependent on speed. If the components were large, the wires interconnecting them must be long. The electric signals took time to go through the circuit, thus slowing the computer. The invention of the integrated circuit by Jack Kilby and Robert Noyce solved this problem by making all the components and the chip out of the same block (monolith) of semiconductor material. The circuits could be made smaller, and the manufacturing process could be automated. This led to the idea of integrating all components on a single-crystal silicon wafer, which led to small-scale integration (SSI) in the early 1960s, and then medium-scale integration (MSI) in the late 1960s, followed by VLSI. In 2008, billion-transistor processors became commercially available. == Subfields == == Devices and components == An electronic component is any component, either active or passive, in an electronic system or electronic device. Components are connected together, usually by being soldered to a printed circuit board (PCB), to create an electronic circuit with a particular function. Components may be packaged singly or in more complex groups as integrated circuits. Passive electronic components are capacitors, inductors, resistors, whilst active components are such as semiconductor devices; transistors and thyristors, which control current flow at electron level. == Types of circuits == Electronic circuit functions can be divided into two function groups: analog and digital. A particular device may consist of circuitry that has either or a mix of the two types. Analog circuits are becoming less common, as many of their functions are being digitized. === Analog circuits === Analog circuits use a continuous range of voltage or current for signal processing, as opposed to the discrete levels used in digital circuits. Analog circuits were common throughout electronic devices in the early years, in devices such as radio receivers and transmitters. Analog electronic computers were valuable for solving problems with continuous variables until digital processing advanced. As semiconductor technology developed, many of the functions of analog circuits were taken over by digital circuits, and modern circuits that are entirely analog are less common; their functions being replaced by hybrid approach which, for instance, uses analog circuits at the front end of a device receiving an analog signal, and then use digital processing using microprocessor techniques thereafter. Sometimes it may be difficult to classify some circuits that have elements of both linear and non-linear operation. An example is the voltage comparator, which receives a continuous range of voltage but only outputs one of two levels, as in a digital circuit. Similarly, an overdriven transistor amplifier can take on the characteristics of a controlled switch, having essentially two levels of output. Analog circuits are still widely used for signal amplification, such as in the entertainment industry, and conditioning signals from analog sensors, such as in industrial measurement and control. === Digital circuits === Digital circuits are electric circuits based on discrete voltage levels. Digital circuits use Boolean algebra and are the basis of all digital computers and microprocessor devices. They range from simple logic gates to large integrated circuits, employing millions of such gates. Digital circuits use a binary system with two voltage levels labelled 0 and 1 to indicate logical status. Often logic 0 will be a lower voltage and referred to as Low while logic 1 is referred to as High. However, some systems use the reverse definition (0 is High) or are current based. Quite often, the logic designer may reverse these definitions from one circuit to the next as they see fit to facilitate their design. The definition of the levels as 0 or 1 is arbitrary. Ternary (with three states) logic has been studied, and some prototype computers made, but have not gained any significant practical acceptance. Universally, computers and digital signal processors are constructed with digital logic circuits using transistors such as MOSFETs in the electronic logic gates to generate binary states. Logic gates Adders Flip-flops Counters Registers Multiplexers Schmitt triggers Highly integrated devices: Memory chip Microprocessors Microcontrollers Application-specific integrated circuit (ASIC) Digital signal processor (DSP) Field-programmable gate array (FPGA) Field-programmable analog array (FPAA) System on chip (SOC) == Design == Electronic systems design deals with the multi-disciplinary design issues of complex electronic devices and systems, such as mob

Reasoning model

A reasoning model, also known as a reasoning language model (RLM) or large reasoning model (LRM), is a type of large language model (LLM) that has been specifically trained to solve complex tasks requiring multiple steps of logical reasoning. These models demonstrate superior performance on logic, mathematics, and programming tasks compared to standard LLMs. They possess the ability to revisit and revise earlier reasoning steps and utilize additional computation during inference as a method to scale performance, complementing traditional scaling approaches based on training data size, model parameters, and training compute. == Overview == Unlike traditional language models that generate responses immediately, reasoning models allocate additional compute, or thinking, time before producing an answer to solve multi-step problems. OpenAI introduced this terminology in September 2024 when it released the o1 series, describing the models as designed to "spend more time thinking" before responding. The company framed o1 as a reset in model naming that targets complex tasks in science, coding, and mathematics, and it contrasted o1's performance with GPT-4o on benchmarks such as AIME and Codeforces. Independent reporting the same week summarized the launch and highlighted OpenAI's claim that o1 automates chain-of-thought style reasoning to achieve large gains on difficult exams. In operation, reasoning models generate internal chains of intermediate steps, then select and refine a final answer. OpenAI reported that o1's accuracy improves as the model is given more reinforcement learning during training and more test-time compute at inference. The company initially chose to hide raw chains and instead return a model-written summary, stating that it "decided not to show" the underlying thoughts so researchers could monitor them without exposing unaligned content to end users. Commercial deployments document separate "reasoning tokens" that meter hidden thinking and a control for "reasoning effort" that tunes how much compute the model uses. These features make the models slower than ordinary chat systems while enabling stronger performance on difficult problems. == History == The research trajectory toward reasoning models combined advances in supervision, prompting, and search-style inference. Early alignment work on reinforcement learning from human feedback showed that models can be fine-tuned to follow instructions with "human feedback" and preference-based rewards. In 2022, Google Research scientists Jason Wei and Denny Zhou showed that chain-of-thought prompting "significantly improves the ability" of large models on complex reasoning tasks. Input → Step 1 → Step 2 → ⋯ → Step n ⏟ Reasoning chain → Answer {\displaystyle {\text{Input}}\rightarrow \underbrace {{\text{Step}}_{1}\rightarrow {\text{Step}}_{2}\rightarrow \cdots \rightarrow {\text{Step}}_{n}} _{\text{Reasoning chain}}\rightarrow {\text{Answer}}} A companion result demonstrated that the simple instruction "Let's think step by step" can elicit zero-shot reasoning. Follow-up work introduced self-consistency decoding, which "boosts the performance" of chain-of-thought by sampling diverse solution paths and choosing the consensus, and tool-augmented methods such as ReAct, a portmanteau of Reason and Act, that prompt models to "generate both reasoning traces" and actions. Research then generalized chain-of-thought into search over multiple candidate plans. The Tree-of-Thoughts framework from Princeton computer scientist Shunyu Yao proposes that models "perform deliberate decision making" by exploring and backtracking over a tree of intermediate thoughts. OpenAI's reported breakthrough focused on supervising reasoning processes rather than only outcomes, with Lightman et al.'s "Let's Verify Step by Step" reporting that rewarding each correct step "significantly outperforms outcome supervision" on challenging math problems and improves interpretability by aligning the chain-of-thought with human judgment. OpenAI's o1 announcement ties these strands together with a large-scale reinforcement learning algorithm that trains the model to refine its own chain of thought, and it reports that accuracy rises with more training compute and more time spent thinking at inference. Together, these developments define the core of reasoning models. They use supervision signals that evaluate the quality of intermediate steps, they exploit inference-time exploration such as consensus or tree search, and they expose controls for how much internal thinking compute to allocate. OpenAI's o1 family made this approach available at scale in September 2024 and popularized the label "reasoning model" for LLMs that deliberately think before they answer. The development of reasoning models illustrates Richard S. Sutton's "bitter lesson" that scaling compute typically outperforms methods based on human-designed insights. This principle was demonstrated by researchers at the Generative AI Research Lab (GAIR), who initially attempted to replicate o1's capabilities using sophisticated methods including tree search and reinforcement learning in late 2024. Their findings, published in the "o1 Replication Journey" series, revealed that knowledge distillation, a comparatively straightforward technique that trains a smaller model to mimic o1's outputs, produced unexpectedly strong performance. This outcome illustrated how direct scaling approaches can, at times, outperform more complex engineering solutions. === Drawbacks === Reasoning models require significantly more computational resources during inference compared to non-reasoning models. Research on the American Invitational Mathematics Examination (AIME) benchmark found that reasoning models were 10 to 74 times more expensive to operate than their non-reasoning counterparts. The extended inference time is attributed to the detailed, step-by-step reasoning outputs that these models generate, which are typically much longer than responses from standard large language models that provide direct answers without showing their reasoning process. One researcher in early 2025 argued that these models may face potential additional denial-of-service concerns with "overthinking attacks." === Releases === ==== 2024 ==== In September 2024, OpenAI released o1-preview, a large language model with enhanced reasoning capabilities. The full version, o1, was released in December 2024. OpenAI initially shared preliminary results on its successor model, o3, in December 2024, with the full o3 model becoming available in 2025. Alibaba released reasoning versions of its Qwen large language models in November 2024. In December 2024, the company introduced QvQ-72B-Preview, an experimental visual reasoning model. In December 2024, Google introduced Deep Research in Gemini, a feature designed to conduct multi-step research tasks. On December 16, 2024, researchers demonstrated that by scaling test-time compute, a relatively small Llama 3B model could outperform a much larger Llama 70B model on challenging reasoning tasks. This experiment suggested that improved inference strategies can unlock reasoning capabilities even in smaller models. ==== 2025 ==== In January 2025, DeepSeek released R1, a reasoning model that achieved performance comparable to OpenAI's o1 at significantly lower computational cost. The release demonstrated the effectiveness of Group Relative Policy Optimization (GRPO), a reinforcement learning technique used to train the model. On January 25, 2025, DeepSeek enhanced R1 with web search capabilities, allowing the model to retrieve information from the internet while performing reasoning tasks. Research during this period further validated the effectiveness of knowledge distillation for creating reasoning models. The s1-32B model achieved strong performance through budget forcing and scaling methods, reinforcing findings that simpler training approaches can be highly effective for reasoning capabilities. On February 2, 2025, OpenAI released Deep Research, a feature powered by their o3 model that enables users to conduct comprehensive research tasks. The system generates detailed reports by automatically gathering and synthesizing information from multiple web sources. OpenAI called GPT-4.5 its "last non-chain-of-thought model", and implemented with GPT-5 a router model that selects a model based on the difficulty of the task. ==== 2026 ==== In January 2026, Moonshot AI released Kimi K2.5, an open-source 1 trillion parameter MoE model with 32 billion active parameters. It uses an “Agent Swarm” system that dynamically decomposes tasks into sub-agents for reasoning and execution, enabling more scalable multi-step problem solving than a single sequential reasoning chain. == Training == Reasoning models follow the familiar large-scale pretraining used for frontier language models, then diverge in the post-training and optimization. OpenAI reports that o1 is trained with a large-

Wiki survey

Wiki surveys or wikisurveys are a software-based survey method that crowdsource discussions and help participants to find areas of agreement. Other names include bridging systems and collective response systems. The approach, inspired by Wikipedia, is to open up surveys where participants can shape the questions, instead of traditional 'closed' surveys where participants can only respond to the questions asked. Wiki surveys have been used for purposes including facilitating deliberative democracy, crowdsourcing opinions from experts and figuring out common beliefs on a given topic. A notable usage of wiki surveys is in Taiwan's government system, where citizens can participate in crowdsourced lawmaking through Pol.is wiki surveys. == Implementations == === All Our Ideas === All Our Ideas founders coined the term "wiki survey," explaining how they took inspiration from the organic evolution of Wikipedia and hoped to create something similar for surveys. They hosted 5000 surveys between 2010 and 2014. A 2020 survey using the tool found 3 of its top 10 findings were user-generated. === Decidim === Decidim has been used by governments throughout Spain and Europe to help with participatory budgeting and other public policy decisions. === Polis === Polis (also known as Pol.is) was developed in 2012. The focus of Polis is to project participants into an 'opinion space' where they can see how their voting behavior compares to other participants. The opinion space clusters participants into groups of similar opinion and is designed in a way to avoid tyranny of the majority by being able to include groups that have small numbers of participants. The questions participants are presented with are agree/disagree/pass on a single 'comment' submitted by a participant. The code for Polis is free and open-source software under the GNU AGPL. === Remesh === Remesh was founded in 2013 and has partnered with the United Nations and Alliance for Middle East Peace efforts to bring peaceful resolutions to conflicts. Participants are anonymous and the algorithm can be fine-tuned to better understand local dialects in specific regions. == Examples == PlaNYC used All Our Ideas to gather ideas on how to establish New York City's sustainability plan vTaiwan, a citizen-lead government process in Taiwan, uses Polis for enabling large amounts of citizens to deliberate and consequently provide input on Taiwan's legislative decisions OECD used All Our Ideas to gather ideas from the public prior to meeting for a forum and meeting on which skills are most important to invest in for the 21st century March On, an offshoot of the Women's March Movement, used Polis to understand the opinions of people wanting to support the movement Residents of Harrogate use Polis to debate issues in their community, with the results being released publicly to everyone == Characteristics == Wiki surveys often have these three characteristics: === Collaborativeness === Wiki surveys allow participants to contribute questions, as well as answer questions created by its participants. === Adaptivity === Wiki surveys adapt to elicit the most useful information from its participants. One example involves changing the ordering of questions based on the voting behavior of previous participants so as to maximize consensus. The heuristic determining the ordering of questions highly values showing the comments that have been voted on the least. === 'Greediness' === In the context of wiki surveys, 'greediness' simply means making full use of information that participants are willing to provide. Wiki surveys do not require participants to answer a fixed amount of questions, so participants can answer as little or as much as they want. This is intended to be more efficient in capturing participants' preferences by allowing more organic sharing of their perspectives. == Traditional survey methods vs. wiki surveys == Questions in traditional survey methods fall into two categories: Open and closed questions. Open questions ask the person taking the survey to write an open response while closed questions give a fixed set of responses to select from. Wiki surveys are like a hybrid of the two, enabling insightful consensus in certain situations where traditional survey methods may lack. Closed questions are easy to analyze quantitively, but the limited options to select from for a given question may cause bias. Open questions are not as subject to bias, but are difficult to analyze quantitatively at scale. Wiki surveys allow for open responses by the users' contribution of survey questions (also called 'items'), and uses machine learning techniques to (at least partially) automate the quantitative analysis of the responses to those questions.

Behavior selection algorithm

In artificial intelligence, a behavior selection algorithm, or action selection algorithm, is an algorithm that selects appropriate behaviors or actions for one or more intelligent agents. In game artificial intelligence, it selects behaviors or actions for one or more non-player characters. Common behavior selection algorithms include: Finite-state machines Hierarchical finite-state machines Decision trees Behavior trees Hierarchical task networks Hierarchical control systems Utility systems Dialogue tree (for selecting what to say) == Related concepts == In application programming, run-time selection of the behavior of a specific method is referred to as the strategy design pattern.

Proof of authority

Proof of authority (PoA) is a category of consensus protocols used with blockchains based on reputation and identity as a stake that delivers comparatively fast and efficient transactions (compared to proof-of-work and proof-of-stake). The most notable platforms using PoA are VeChain, Bitgert, Palm Network and Xodex. == Description == Proof-of-authority is a category of consensus protocols for networks and blockchains where transactions and blocks are built and validated by approved entities known as validators. Their permissions are often granted through a centralized authority, but they can also be granted through a council or decentralized organization. The term "proof-of-authority" was coined by Gavin Wood, co-founder of Ethereum and Parity Technologies. With PoA, validators are incentivized to maintain good behavior and honesty when validating blocks to avoid developing a negative reputation. PoA can have higher security than PoW and even PoS due to validators wanting to avoid damaging their reputation. Because PoA is permissioned, it is not fully trustless. Validators without good reputation may risk having their validator permissions removed. PoA is generally more efficient than PoW and PoS because it operates with fewer nodes and validators, thus requiring fewer duplicated resources.

Local ternary patterns

Local ternary patterns (LTP) are an extension of local binary patterns (LBP). Unlike LBP, it does not threshold the pixels into 0 and 1, rather it uses a threshold constant to threshold pixels into three values. Considering k as the threshold constant, c as the value of the center pixel, a neighboring pixel p, the result of threshold is: { 1 , if p > c + k 0 , if p > c − k and p < c + k − 1 if p < c − k {\displaystyle {\begin{cases}1,&{\text{if }}p>c+k\\0,&{\text{if }}p>c-k{\text{ and }}p

List of algorithm general topics

This is a list of algorithm general topics. Analysis of algorithms Ant colony algorithm Approximation algorithm Best and worst cases Big O notation Combinatorial search Competitive analysis Computability theory Computational complexity theory Embarrassingly parallel problem Emergent algorithm Evolutionary algorithm Fast Fourier transform Genetic algorithm Graph exploration algorithm Heuristic Hill climbing Implementation Las Vegas algorithm Lock-free and wait-free algorithms Monte Carlo algorithm Numerical analysis Online algorithm Polynomial time approximation scheme Problem size Pseudorandom number generator Quantum algorithm Random-restart hill climbing Randomized algorithm Running time Sorting algorithm Search algorithm Stable algorithm (disambiguation) Super-recursive algorithm Tree search algorithm