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Research at the interface of marketing and entrepreneurship has emphasized digital entrepreneurship and how entrepreneurs pursue business opportunities centered on new technologies. However, a different type of entrepreneurship focused on opportunities involving consumers’ (re)adoption of displaced analog technologies when digital alternatives are dominant – analog entrepreneurship – is a trend and counter phenomenon to digital entrepreneurship that is receiving intense practitioner interest but limited scholarly attention. The purpose of this article is to present a theoretical framework that explains the role of analog entrepreneurship in technology revitalization.

In this conceptual paper, the authors use the microfoundations perspective to develop a multilevel theory of analog entrepreneurship. The authors define and delineate the “analog entrepreneurship” concept and formulate a midrange theory explaining how entrepreneurs influence the reemergence of analog technologies.

The theory’s main insight is that the renewal of analog technologies is not confined to consumers. Entrepreneurs are creating businesses that stimulate demand for analog technologies. As a result of entrepreneurs’ activities, legacy analog technologies do not fade into nonexistence in the face of rival digital technologies.

The theory of analog entrepreneurship contributes to research at the intersection of entrepreneurship and marketing by expanding its focus to consider the entrepreneurs who revitalize displaced analog technologies when digital alternatives are dominant. The authors provide insight into the potential trajectories of technologies after their initial displacement and the role entrepreneurs play in shaping the late stages of technology lifecycles. The theory draws attention to an underexplored phenomenon made increasingly prevalent by recent technological disruptions and suggests an agenda for studying how entrepreneurs renew analog technologies.

Religious and secular pilgrimages present rich opportunities for investigating information activities in an original and intriguing context. While the Information Science community has previously shown interest in digital expressions of religion and spirituality, discussion on pilgrimage is at a nascent stage. The purpose of this study is to conduct an in situ investigation of how pilgrims record, curate, and share their experiences.

A field ethnography was conducted while walking with, observing and interviewing pilgrims along the Camino de Santiago, a popular European pilgrimage and UNESCO World Heritage route. Data collected from 25 semi-structured interviews and participant observations were thematically analysed within a theoretical framework combining Stebbins' contemplation and Nature Challenge Activity in serious leisure and Hektor's model of information behaviour.

This study expands the interpretation of pilgrimage by introducing new insights into pilgrims, different types of mobilities, spaces and objects, and social interactions. By using field ethnography and close-up observations of praxis, pilgrimage is analysed as a socio-technical process and discussed literature within and beyond Information Science. The work presents new understandings of the interplay between spirituality, embodied information practices, physical and online social interactions, analogue and digital media before, during and after these journeys and legacy aspirations.

The study is original in its combination of theoretical models and their ethnographic in situ application. It contributes to a more in-depth, in-the-field understanding of how pilgrims document their experiences via a rich palette of old and new media, the dynamics of using digital technologies during such physical and inner journeys and pilgrims' sharing practices. Implications for serious leisure and information practices are discussed, from theoretical to practical challenges and opportunities offered by pilgrimage experiences.

This paper aims to review in-memory computing (IMC) for machine learning (ML) applications from history, architectures and options aspects. In this review, the authors investigate different architectural aspects and collect and provide our comparative evaluations.

Collecting over 40 IMC papers related to hardware design and optimization techniques of recent years, then classify them into three optimization option categories: optimization through graphic processing unit (GPU), optimization through reduced precision and optimization through hardware accelerator. Then, the authors brief those techniques in aspects such as what kind of data set it applied, how it is designed and what is the contribution of this design.

ML algorithms are potent tools accommodated on IMC architecture. Although general-purpose hardware (central processing units and GPUs) can supply explicit solutions, their energy efficiencies have limitations because of their excessive flexibility support. On the other hand, hardware accelerators (field programmable gate arrays and application-specific integrated circuits) win on the energy efficiency aspect, but individual accelerator often adapts exclusively to ax single ML approach (family). From a long hardware evolution perspective, hardware/software collaboration heterogeneity design from hybrid platforms is an option for the researcher.

IMC’s optimization enables high-speed processing, increases performance and analyzes massive volumes of data in real-time. This work reviews IMC and its evolution. Then, the authors categorize three optimization paths for the IMC architecture to improve performance metrics.

Reference voltage or current generators are an important requirement for an analog or digital circuit design. Bandgap reference circuits (BGR) are most common way of generating the reference voltage. This paper aims to provide a detailed insight of design of a folded cascode operational amplifier (FC op amp) and a BGR circuit. The complete study flow from design to layout of the circuits on 180 nm semiconductor laboratory (SCL) process leading to bonding diagram for possible tape-out is discussed. This study work has been supported by MeitY, Govt. of India, through Special Manpower Development Project Chip to System Design.

This paper provides a detailed insight in design of a FC op amp and a BGR circuit. The complete study flow from design to layout of the two circuits on 180 nm SCL process leading to bonding diagram for possible tape-out is discussed. Section 2 shows the design of FC op amp, beta-multiplier circuit and their simulation results. Section 3 describes the comparison of design of conventional BGR and the proposed BGR with other state-of-art BGR circuits. Section 4 gives the comparison of their performance. The conclusion is given in Section 5.

The post-layout simulation of FC op amp show an open-loop gain of 64.5 dB, 3-dB frequency of 5.5 KHz, unity-gain bandwidth of 8.7 MHz, slew rate of 8.4 V/µs, CMRR of 111 dB and power of 25.5µW. Among the two BGR designs, the conventional BGR generated 693 mV of reference voltage with a temperature coefficient of 16 ppm/°C the other BGR, with curvature correction generated 1.3 V of reference voltage with a temperate coefficient of 6.3 ppm/°C , both results in temperature ranging from −40°C to 125°C. The chip layout of the circuits designed on 180 nm SCL process ensures design rule check (DRC), Antenna and layout versus schematic (LVS) clean with metal fill.

Slew rate, stability analysis, power are important parameters which should be taken care while designing an op amp for a BGR. Direct current gain should be kept higher to reduce offset errors. Input common mode range is decided by the operating temperature range. A higher power supply rejection ratio will reduce BGR sensitivity to supply voltage variations. Input offset should be kept low to reduce BGR error in reference voltage. However, this paper emphasis on the flow from schematic to layout using simulation tools. As part of the study, the bonding diagram for tape-out of BGR and FC design in the given SCL frame size with seal ring is also explored, for possible tape-out.

Reference voltage or current generators are an important requirement for an analog or digital circuit design. BGR are most common way of generating the reference voltage. This paper provides a detailed insight in design of a FC op amp and a BGR circuit. The complete study flow from design to layout of the circuits on 180 nm SCL process leading to bonding diagram for possible tape-out is discussed. The chip layout of the circuits was designed on 180 nm SCL process ensuring DRC, antenna and LVS clean with metal fill using Cadence virtuoso and Mentor Graphics Calibre simulation tools.

BGR are most common way of generating the reference voltage. This paper gives a detailed insight of a BGR design using a folded-cascode operational amplifier. The FC op amp is biased using a beta multiplier circuit and high-swing cascode current mirror circuit. The paper discuss FC circuit design flow from schematic to layout.

FC op amp is biased using a beta multiplier circuit and high-swing cascode current mirror. The paper discusses FC design flow from schematic to layout. The circuits were designed on 180 nm SCL technology with 1.8 V of power supply. The post-layout simulation show an open-loop gain of 64.5 dB, 3 dB frequency of 5.5 KHz, unity-gain bandwidth of 8.7 MHz, slew rate of 8.4 V/µs, CMRR of 111 dB and power of 25.5 µW. BGR were designed using FC op amp. The proposed BGR generated 1.3 V of reference voltage with a temperature coefficient of 6.3 ppm/°C in the range from −40°C to 125°C in schematic simulation.

Maintaining the safety of the human is a major concern in factories where humans co-exist with robots and other physical tools. Typically, the area around the robots is monitored using lasers. However, lasers cannot distinguish between human and non-human objects in the robot’s path. Stopping or slowing down the robot when non-human objects approach is unproductive. This research contribution addresses that inefficiency by showing how computer-vision techniques can be used instead of lasers which improve up-time of the robot.

A computer-vision safety system is presented. Image segmentation, 3D point clouds, face recognition, hand gesture recognition, speed and trajectory tracking and a digital twin are used. Using speed and separation, the robot’s speed is controlled based on the nearest location of humans accurate to their body shape. The computer-vision safety system is compared to a traditional laser measure. The system is evaluated in a controlled test, and in the field.

Computer-vision and lasers are shown to be equivalent by a measure of relationship and measure of agreement. R² is given as 0.999983. The two methods are systematically producing similar results, as the bias is close to zero, at 0.060 mm. Using Bland–Altman analysis, 95% of the differences lie within the limits of maximum acceptable differences.

In this paper an original model for future computer-vision safety systems is described which is equivalent to existing laser systems, identifies and adapts to particular humans and reduces the need to slow and stop systems thereby improving efficiency. The implication is that computer-vision can be used to substitute lasers and permit adaptive robotic control in human–robot collaboration systems.

Both technological and human-centric perspectives need to be acknowledged when combining lean production practices and Industry 4.0 (I4.0) technologies. This study aims to explore and explain how lean production practices and I4.0 technologies may coexist to enhance the human-centric perspective of manufacturing operations in the era of Industry 5.0 (I5.0).

The research approach is an explorative and longitudinal case study. The qualitative data collection encompasses respondents from different job functions and organizational levels to cover the entire organization. In total, 18 interviews with 19 interviewees and five focus groups with a total of 25 participants are included.

Identified challenges bring forth that manufacturing organizations must have the ability to see beyond lean production philosophy and I4.0 to meet the demand for a human-centric perspective in socially sustainable manufacturing in the era of Industry 5.0.

The study suggests that while lean production practices and I4.0 practices may be considered separately, they need to be integrated as complementary approaches. This underscores the complexity of managing simultaneous organizational changes and new digital initiatives.

The research presented illuminates the elusive phenomena comprising the combined aspects of a human-centric perspective, specifically bringing forth implications for the co-existence of lean production practices and I4.0 technologies, in the transformation towards I5.0.

The study contributes to new avenues of research within the field of socially sustainable manufacturing. The study provides an in-depth analysis of the human-centric perspective when transforming organizations towards Industry 5.0.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

This book chapter focuses on firstly social innovation and tools used to address the social needs and foster social innovation initiatives. Looking at the world economic forum and how it supports the social innovations, currency swings, low paying jobs growing rapidly, rapid change and growth as a result of high volatility and high returns, respectively. Secondly looking at the emerging market brought about by the social innovations and how they interconnect. Leading innovation emerging market has three main industries semiconductors, fin-tech, and electric cars. It also looks at the significance of technology in the development of business emerging markets, the role of technology in the emerging market and activities over the decades. Small firms in emerging areas face three major challenges which technology might help overcome. The challenges are trust, sustainability, and network. The role of technology replacing analog chip used for power supply, sensors, wideband signal make up the large semiconductors in the United States replaced with digital chip such as logical operations, data storage, computer information management all this have given birth to artificial intelligence, autonomous machines, self-driving cars, supply-chain management, cloud computing, and software-as-a-service (SaaS) applications are all made possible by digital chips. These are also used for e-commerce, mobile payment, fine-tech, 5G telecom, health-care advancement, remote learning, online entertainment, and cloud computing. Technical advancements that has sparked a revolution that would be especially advantageous for emerging market and small-cap enterprises are the causes of these benefits of how it has affected countries such as Europe, the United States, China, and India to mention a few.

Digitization is the process of transforming analog systems into digital ones, and it has become a crucial factor in the sustainable development of emerging economies. Although implementing digitization may be challenging due to limited budgets, missing strategies, pushback from employees, and the existing organizational structure, it can bring multifaceted benefits to the economy, such as improved employment and income, enhanced access to knowledge and education, and reduced costs for companies and countries. Moreover, digitization can significantly impact economic growth, as it can create new job opportunities, foster innovation, and improve infrastructure, among other benefits. The United Nations Sustainable Development Goals (SDGs) provide a global agenda for creating a more sustainable, equitable, and prosperous future by 2030, and digital technologies have become increasingly important in addressing the challenges of achieving these goals, particularly for emerging economies. However, achieving these ambitious goals presents significant challenges, particularly for emerging economies. Hence, this literature review aims to discuss the potential impact of digital technologies on the implementation of the SDGs in emerging economies, supported by scholarly research and opinions. In conclusion, digital technologies have significant potential to contribute to the achievement of the SDGs by promoting economic growth and innovation while also promoting sustainability, creating a more prosperous and equitable world for all.

This study aims to improve the complexity of chaotic systems and the security accuracy of information encrypted transmission. Applying five-dimensional memristive Hopfield neural network (5D-HNN) to secure communication will greatly improve the confidentiality of signal transmission and greatly enhance the anticracking ability of the system.

Chaos masking: Chaos masking is the process of superimposing a message signal directly into a chaotic signal and masking the signal using the randomness of the chaotic output. Synchronous coupling: The coupled synchronization method first replicates the drive system to get the response system, and then adds the appropriate coupling term between the drive The synchronization error and the coupling term of the system will eventually converge to zero with time. The synchronization error and coupling term of the system will eventually converge to zero over time.

A 5D memristive neural network is obtained based on the original four-dimensional memristive neural network through the feedback control method. The system has five equations and contains infinite balance points. Compared with other systems, the 5D-HNN has rich dynamic behaviors, and the most unique feature is that it has multistable characteristics. First, its dissipation property, equilibrium point stability, bifurcation graph and Lyapunov exponent spectrum are analyzed to verify its chaotic state, and the system characteristics are more complex. Different dynamic characteristics can be obtained by adjusting the parameter k.

A new 5D memristive HNN is proposed and used in the secure communication