Belden – Belden to acquire RUCKUS Networks from Vistance Networks, accelerating its transformation into a full-stack networking solutions provider

EMR Analysis

More information on Belden: See the full profile on EMR Executive services

More information on Dr. Ashish Chand (President and Chief Executive Officer, Belden): See the full profile on EMR Executive services

More information on Jeremy Parks (Executive Vice President, Chief Financial Officer, Belden): See the full profile on EMR Executive Services 

 

 

More information on RUCKUS Networks by Belden: https://www.ruckusnetworks.com/ + At RUCKUS Networks, we build connectivity that powers possibility. From the earliest days of Wi-Fi innovation to today’s most demanding enterprise environments, we’ve never settled for average—and neither should you.

Born from a rebellious spirit and a relentless drive to redefine wireless, RUCKUS emerged as the antidote to slow, unreliable networks. We pioneered technologies that shattered expectations—like adaptive antenna arrays and beamflex—and we’ve been pushing boundaries ever since.

RUCKUS Networks builds and delivers purpose-driven networks that perform in the tough environments of the industries we serve. RUCKUS delivers high-performance wireless LAN, wired LAN, IoT, assurance and security solutions that enable exceptional connection experiences for guests, students, residents, citizens and employees. Best-in-class hardware and AI-driven software reduce complexity and improve business outcomes for the world’s most demanding industries.

More information on Bart Giordano (President, RUCKUS Networks, Belden): https://www.ruckusnetworks.com/blog/author/bart%20giordano/ + https://www.linkedin.com/in/bartgiordano/ 

 

 

 

More information on Vistance Networks: https://www.vistancenetworks.com/ + Vistance Networks (NASDAQ: VISN) shapes the future of communications technology, pushing past what is possible. We deliver solutions that bring reliability and performance to a world always in motion. Our global team of innovators and employees are trusted advisors who listen to customers first, then deliver value.

More information on Charles L. Treadway (President and Chief Executive Officer, Vistance Networks): https://www.vistancenetworks.com/about-us/management-team/ + https://www.linkedin.com/in/charles-treadway-092a29140/ 

 

 

 

More information on J.P. Morgan Chase & Co.: https://www.jpmorgan.com/global + We aim to be the most respected financial services firm in the world, serving corporations and individuals in more than 100 countries.

JPMorgan Chase & Co. (NYSE: JPM) is a leading financial services firm based in the United States of America (“U.S.”), with operations worldwide. JPMorganChase had $4.2 trillion in assets and $346 billion in stockholders’ equity as of September 30, 2024. The Firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing and asset management. Under the J.P. Morgan and Chase brands, the Firm serves millions of customers in the U.S., and many of the world’s most prominent corporate, institutional and government clients globally.

More information on Jamie Dimon (Chairman and Chief Executive Officer, J.P. Morgan Chase & Co.): https://www.jpmorganchase.com/about/our-leadership/jamie-dimon + https://www.linkedin.com/in/jamiedimon/

 

 

 

 

 

 

 

 

 

 

 

EMR Additional Notes:

• Wi-Fi and Z-Wave: 
  • A Wi-Fi network is simply an internet connection that’s shared with multiple devices in a home or business via a wireless router. The router is connected directly to your internet modem and acts as a hub to broadcast the internet signal to all your Wi-Fi enabled devices.
  • Wi-Fi, which most of us are familiar with, operates on either 2.4 GHz or 5 GHz frequencies, providing wireless internet to any connected devices. Z-Wave operates on a much lower frequency — between 800 and 900 MHz — and is primarily for home automation.
  • On 8 January 2024, the Wi-Fi Alliance introduced its Wi-Fi Certified 7 (IEEE 802.11be) program to certify Wi-Fi 7 devices. While final ratification is not expected until the end of 2024, the technical requirements are essentially complete, and as of February 2024 there are already products labeled as Wi‑Fi 7. WiFi 7 will be much faster than WiFi 6. For the same WiFi radio configuration, the speeds will be 2.4x faster. So, maximum speeds with a typical mobile phone with WiFi 7 can reach up to 5Gbps.
  • If you want better range, use 2.4 GHz. If you need higher performance or speed, use the 5GHz band.
  • Z-Wave operates on a completely different wireless frequency that will not conflict with your Wi-Fi network signal. Z-Wave is a mesh technology that strengthens the network with several connected devices. Z-wave is popular as smart-property technology, powering locks, lights, sensors, thermostats, etc.
  • Z-wave uses much less power than WiFi. That means that it’s possible to use battery-powered Z-wave devices without worrying about having to change the batteries frequently. Z-wave is also more secure since it’s more of a closed system and can offer some additional layers of protection.

 

 

 

 

 

• Information Technology (IT) & Operational Technology (OT):
  • Information Technology (IT): 
    • Refers to anything related to computer technology, including hardware and software. Your email, for example, falls under the IT umbrella. IT forms the technological backbone of most organizations and companies by managing data, communications, and business processes. These devices and programs have little autonomy and are updated frequently.
  • Operational Technology (OT): 
    • Refers to the hardware and software used to change, monitor, or control physical devices, processes, and events within a company or organization. This form of technology is most commonly used in industrial settings, where these systems are engineered for safety, reliability, and precision control. An example of OT includes SCADA (Supervisory Control and Data Acquisition).
  • => The main difference between OT and IT devices:  OT devices control the physical world, while IT systems manage data.

 

 

• Earning Per Share (EPS):
  • Company’s net income subtracted by preferred dividends and then divided by the average number of common shares outstanding. The resulting number serves as an indicator of a company’s profitability. It is common for a company to report EPS that is adjusted for extraordinary items and potential share dilution.
  • The higher a company’s EPS, the more profitable it is considered to be.
  • Earnings per share value is calculated as net income (also known as profits or earnings) divided by available shares. A more refined calculation adjusts the numerator and denominator for shares that could be created through options, convertible debt, or warrants. The numerator of the equation is also more relevant if it is adjusted for continuing operations.

 

 

• EBIT:
  • Earnings Before Interest and Taxes (EBIT) is a measure of a company’s operating profitability before accounting for interest expenses and income taxes. It is also known as operating profit and shows how effectively a company’s core business is generating profit from its operations.
• EBITA:
  • Earnings before interest, taxes, and amortization (EBITA) is a measure of company profitability used by investors. It is helpful for comparing one company to another in the same line of business.
  • EBITA = Net income + Interest + Taxes + Amortization
• EBITDA: 
  • Earnings before interest, taxes, depreciation, and amortization (EBITDA) is an alternate measure of profitability to net income. By including depreciation and amortization as well as taxes and debt payment costs, EBITDA attempts to represent the cash profit generated by the company’s operations.
  • EBITDA and EBITA are both measures of profitability. The difference is that EBITDA also excludes depreciation.
  • EBITDA is the more commonly used measure because it adds depreciation—the accounting practice of recording the reduced value of a company’s tangible assets over time—to the list of factors.
• EV/EBITDA (Enterprise Multiple):
  • Enterprise multiple, also known as the EV-to-EBITDA multiple, is a ratio used to determine the value of a company.
  • It is computed by dividing enterprise value by EBITDA.
  • The enterprise multiple takes into account a company’s debt and cash levels in addition to its stock price and relates that value to the firm’s cash profitability.
  • Enterprise multiples can vary depending on the industry.
  • Higher enterprise multiples are expected in high-growth industries and lower multiples in industries with slow growth.

 

• Cash Flow (CF):
  • Cash flow (CF) is the net amount of money (cash and cash equivalents) moving into and out of a business over a given period. It is a critical indicator of a company’s financial health and liquidity, revealing its ability to pay expenses, service debt, and fund growth, independent of non-cash accounting items like depreciation.
• Free Cash Flow (FCF):
  • Free cash flow (FCF) is a company’s available cash repaid to creditors and as dividends and interest to investors. Management and investors use free cash flow as a measure of a company’s financial health. FCF reconciles net income by adjusting for non-cash expenses, changes in working capital, and capital expenditures. Free cash flow can reveal problems in the financial fundamentals before they become apparent on a company’s income statement. A positive free cash flow doesn’t always indicate a strong stock trend. FCF is money that is on hand and free to use to settle liabilities or obligations.

 

• Leveraging and De-leveraging:
  • Leveraging is the use of borrowed capital to amplify potential investment returns, boosting gains in good times but magnifying losses during downturns. Deleveraging is the opposite process, where individuals or companies reduce their total debt levels—often by selling assets or restructuring—to lower financial risk and restore stability after excessive borrowing.

 

 

• AI – Artificial Intelligence:
  • Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
  • As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but several, including Python, R and Java, are popular.
  • In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
  • AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
  • The 4 types of artificial intelligence?
    • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
    • Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
    • Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
    • Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
  • Machine Learning (ML):
    • Developed to mimic human intelligence, it lets the machines learn independently by ingesting vast amounts of data, statistics formulas and detecting patterns.
    • ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
    • ML algorithms use historical data as input to predict new output values.
    • Recommendation engines are a common use case for ML. Other uses include fraud detection, spam filtering, business process automation (BPA) and predictive maintenance.
    • Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
  • Deep Learning (DL):
    • Subset of machine learning, Deep Learning enabled much smarter results than were originally possible with ML. Face recognition is a good example.
    • DL makes use of layers of information processing, each gradually learning more and more complex representations of data. The early layers may learn about colors, the next ones about shapes, the following about combinations of those shapes, and finally actual objects. DL demonstrated a breakthrough in object recognition.
    • DL is currently the most sophisticated AI architecture we have developed.
  • Generative AI (GenAI):
    • Generative AI technology generates outputs based on some kind of input – often a prompt supplied by a person. Some GenAI tools work in one medium, such as turning text inputs into text outputs, for example. With the public release of ChatGPT in late November 2022, the world at large was introduced to an AI app capable of creating text that sounded more authentic and less artificial than any previous generation of computer-crafted text.
  • Small Language Models (SLM) and Large Language Models (LLM):
    • Small Language Models (SLMs) are artificial intelligence (AI) models capable of processing, understanding and generating natural language content. As their name implies, SLMs are smaller in scale and scope than large language models (LLMs).
    • LLM means Large Language Models — a type of machine learning/deep learning model that can perform a variety of natural language processing (NLP) and analysis tasks, including translating, classifying, and generating text; answering questions in a conversational manner; and identifying data patterns.
    • For example, virtual assistants like Siri, Alexa, or Google Assistant use LLMs to process natural language queries and provide useful information or execute tasks such as setting reminders or controlling smart home devices.
  • Computer Vision (CV) / Vision AI & Machine Vision (MV):
    • Field of AI that enables computers to interpret and act on visual data (images, videos). It works by using deep learning models trained on large datasets to recognize patterns, objects, and context.
    • The most well-known case of this today is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language.
    • Machine Vision (MV) :
      • Specific application for industrial settings, relying on cameras to analyze tasks in manufacturing, quality control, and worker safety. The key difference is that CV is a broader field for extracting information from various visual inputs, while MV is more focused on specific industrial tasks.
      • Machine Vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine Vision is similar in complexity to Voice Recognition.
      • MV uses the latest AI technologies to give industrial equipment the ability to see and analyze tasks in smart manufacturing, quality control, and worker safety.
  • Multimodal Intelligence and Agents:
    • Subset of artificial intelligence that integrates information from various modalities, such as text, images, audio, and video, to build more accurate and comprehensive AI models.
    • Multimodal capabilities allows AI to interact with users in a more natural and intuitive way. It can see, hear and speak, which means that users can provide input and receive responses in a variety of ways.
    • An AI agent is a computational entity designed to act independently. It performs specific tasks autonomously by making decisions based on its environment, inputs, and a predefined goal. What separates an AI agent from an AI model is the ability to act. There are many different kinds of agents such as reactive agents and proactive agents. Agents can also act in fixed and dynamic environments. Additionally, more sophisticated applications of agents involve utilizing agents to handle data in various formats, known as multimodal agents and deploying multiple agents to tackle complex problems.
  • Agentic AI:
    • Agentic AI is an artificial intelligence system that can accomplish a specific goal with limited supervision. It consists of AI agents—machine learning models that mimic human decision-making to solve problems in real time. In a multiagent system, each agent performs a specific subtask required to reach the goal and their efforts are coordinated through AI orchestration.
    • Unlike traditional AI models, which operate within predefined constraints and require human intervention, agentic AI exhibits autonomy, goal-driven behavior and adaptability. The term “agentic” refers to these models’ agency, or, their capacity to act independently and purposefully.
    • Agentic AI builds on generative AI (gen AI) techniques by using large language models (LLMs) to function in dynamic environments. While generative models focus on creating content based on learned patterns, agentic AI extends this capability by applying generative outputs toward specific goals.
  • Edge AI Technology:
    • Edge artificial intelligence refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
    • Simply stated, edge AI, or “AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
    • Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.
  • High-Density AI: 
    • High-density AI refers to the concentration of AI computing power and storage within a compact physical space, often found in specialized data centers. This approach allows for increased computational capacity, faster training times, and the ability to handle complex simulations that would be impossible with traditional infrastructure.
  • Explainable AI (XAI) and Human-Centered Explainable AI (HCXAI): 
    • Explainable AI (XAI) refers to methods for making AI model decisions understandable to humans, focusing on how the AI works, whereas Human-Centered Explainable AI (HCXAI) goes further by contextualizing those explanations to a user’s specific task and understanding needs. While XAI aims for technical transparency of the model, HCXAI emphasizes the human context, emphasizing user relevance, and the broader implications of explanations, including fairness, trust, and ethical considerations.
  • Physical AI & Embodied AI: 
    • Physical AI refers to a branch of artificial intelligence that enables machines to perceive, understand, and interact with the physical world by directly processing data from a variety of sensors and actuators.
    • Physical AI provides the overarching framework for creating autonomous systems that act intelligently in real-world settings. Embodied AI, as a subset, focuses on the sensory, decision-making, and interaction capabilities that enable these systems to function effectively in dynamic and unpredictable environments.
  • Federated Learning and Reinforcement Learning:
    • Federated Learning is a machine-learning technique where data stays where it is, and only the learned model updates are shared. “Training AI without sharing your data”.
    • Reinforcement Learning is a type of AI where an agent learns by interacting with an environment and receiving rewards or penalties. “Learning by trial and error”
    • Federated Learning (FL) and Reinforcement Learning (RL) can be combined into a field called Federated Reinforcement Learning (FRL), where multiple agents learn collaboratively without sharing their raw data. In this approach, each agent trains its own RL policy locally and shares model updates, like parameters or gradients, with a central server. The server aggregates these updates to create a more robust, global model. FRL is used in applications like optimizing resource management in communication networks and enhancing the performance of autonomous systems by learning from diverse, distributed experiences while protecting privacy.
  • AI Factories:
    • AI Factories are specialized, high-performance computing centers designed to train, tune, and deploy artificial intelligence models at scale.
    • Companies and organizations involved in AI factory infrastructure and development include Nvidia, AWS, Microsoft, OpenAI, CoreWeave, Lambda, Nebius, Supermicro, and HPE. The European Union is also establishing AI Factories through its EuroHPC Joint Undertaking to foster regional innovation.

 

 

• Cloud Computing:
  • Cloud computing is a general term for anything that involves delivering hosted services over the internet. It is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each location being a data center.
• Edge Computing:
  • Edge computing is a form of computing that is done on site or near a particular data source, minimizing the need for data to be processed in a remote data center.
  • Edge computing can enable more effective city traffic management. Examples of this include optimising bus frequency given fluctuations in demand, managing the opening and closing of extra lanes, and, in future, managing autonomous car flows.
  • An edge device is any piece of hardware that controls data flow at the boundary between two networks. Edge devices fulfill a variety of roles, depending on what type of device they are, but they essentially serve as network entry — or exit — points.
  • There are five main types of edge computing devices: IoT sensors, smart cameras, uCPE equipment, servers and processors. IoT sensors, smart cameras and uCPE equipment will reside on the customer premises, whereas servers and processors will reside in an edge computing data centre.
  • In service-based industries such as the finance and e-commerce sector, edge computing devices also have roles to play. In this case, a smart phone, laptop, or tablet becomes the edge computing device.
  • Edge Devices:
    • Edge devices encompass a broad range of device types, including sensors, actuators and other endpoints, as well as IoT gateways. Within a local area network (LAN), switches in the access layer — that is, those connecting end-user devices to the aggregation layer — are sometimes called edge switches.

 

• Hybrid Computing: 
  • A hybrid cloud integrates private, on-premises infrastructure with public cloud services, offering flexibility to distribute workloads between these environments. Hybrid models often incorporate edge computing, allowing organizations to run critical workloads locally at the edge while using the cloud for other tasks, thereby optimizing performance, cost, and data management for various business needs.
• HPC (Hight-Performance Computing):
  • Practice of aggregating computing resources to gain performance greater than that of a single workstation, server, or computer. HPC can take the form of custom-built supercomputers or groups of individual computers called clusters.

 

• Data Centers:
  • A data center is a facility that centralizes an organization’s shared IT operations and equipment for the purposes of storing, processing, and disseminating data and applications. Because they house an organization’s most critical and proprietary assets, data centers are vital to the continuity of daily operations.
• Hyperscale Data Centers:
  • The clue is in the name: hyperscale data centers are massive facilities built by companies with vast data processing and storage needs. These firms may derive their income directly from the applications or websites the equipment supports, or sell technology management services to third parties.
• White Space and Grey Space in Data Centers:
  • White space in a data center refers to the area where IT equipment is placed. It typically houses servers, storage, network gear, and racks.
  • Gray space, on the other hand, is the area where the back-end infrastructure is located. This space is essential for supporting the IT equipment and includes areas for switchgear, UPS, transformers, chillers, and generators.

 

• Edge & Cloud Services: 
  • Edge services perform data processing on local devices and servers near the data source, reducing latency for time-sensitive operations, while cloud services centralize large computations and storage in remote datacenters, offering massive scalability and flexibility for general workloads.
  • Most organizations use both, creating an “edge-to-cloud” architecture where edge devices handle immediate tasks, and the cloud manages large-scale data processing and complex applications, providing a seamless and efficient experience.

 

• Edge Devices:
  • An edge device is a hardware component that provides an entry point into enterprise or service provider core networks, acting as the interface between the physical world and a digital network.

 

 

• Enterprise Switching:
  • Enterprise switching involves high-performance, managed network devices designed to connect computers, printers, and phones within large corporate offices, campuses, or branch locations.

 

• Full-stack:
  • Full stack development is the process of developing both the frontend and backend of applications. Any application has a frontend (user-facing) component and a backend (database and logic) component.
  • Enterprise switching refers to the use of high-performance network switches designed to handle the complex, heavy-traffic demands of large organizations like corporate offices, schools, and hospitals.

 

 

• Generally Accepted Accounting Principles (GAAP): 
  • GAAP is the set of accounting rules set forth by the Financial Accounting Standards Board (FASB) that U.S. companies are expected to follow when putting together their financial statements.
  • The goal of GAAP is to ensure that a company’s financial statements are complete, consistent, and comparable.
  • GAAP may be contrasted with pro forma accounting, which is a non-GAAP financial reporting method.
  • GAAP is used mainly in the U.S., while most other countries follow the International Financial Reporting Standards (IFRS). IFRS is currently used in 168 jurisdictions around the world.
  • GAAP is also used by states and other government entities in the U.S. in preparing their financial statements.

 

• International Financial Reporting Standards (IFRS):
  • Alternative to GAAP that is currently used in 168 jurisdictions around the world, including those in the European Union.
  • Set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.
  • They were issued by the London-based Accounting Standards Board (IASB) and address record keeping, account reporting, and other aspects of financial reporting.
  • The IFRS system replaced the International Accounting Standards (IAS) in 2001.
  • IFRS fosters greater corporate transparency.
  • Chinese companies do not use IFRS or GAAP. They use Chinese Accounting Standards for Business Enterprises (ASBEs).
  • There are differences between IFRS and GAAP reporting.
    • For example, IFRS is not as strict in defining revenue and allows companies to report revenue sooner. A balance sheet using this system might show a higher stream of revenue than a GAAP version of the same balance sheet.
    • One major issue is the treatment of inventory. IFRS rules ban the use of last-in, first-out (LIFO) inventory accounting methods, while GAAP rules allow for LIFO. Both systems allow for the first-in, first-out method (FIFO) and the weighted average-cost method.

 

 

• Share Buyback:
  • A buyback, also known as a share repurchase, occurs when a company purchases its own outstanding stock shares to reduce their number on the open market. This strategic move aims to enhance the value of remaining shares by decreasing supply. Companies often undertake buybacks to signal confidence in their financial stability and to counter the risk of a major shareholder gaining a controlling interest, which could lead to an unwanted takeover.