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Hikvision Introduces eDVR 1TB, for Enhanced Storage Capacity, eSSD Technology Helps in 45% Energy Savings

Hikvision has introduced eDVR 1TB, for enhanced video surveillance storage capacity in the India market. Its eSSD Technology helps in 45% energy savings. Since the launch of eDVRs, the market response has exceeded the expectation. There were specific demands for enhancing the storage capacity of eDVR, it has come from the end-users and system integrators. The latest eDVR 1TB is Hikvision India’s latest offering with many value added features like Enhanced Capacity, Enduring Storage and Efficient Video Analytics. Around the world, energy costs have been rising, and the need to lower carbon emissions is more pressing than ever. With Hikvision’s range of eDVRs, homeowners and businesses can achieve annual, per-device energy savings of 40-45kWh compared to traditional DVRs: which is the equivalent to more than 3,000 iPhone charges. Traditional digital video recorders (DVRs) consume a significant amount of power due to their reliance on hard disk drives (HDDs). These drives are comprised of numerous moving parts, such as spindles, actuator arms, and the drive heads, which contribute to high energy consumption for data storage. Enhanced Storage Capacity Hikvision eDVR 1TB offers enhanced storage capacity for the diverse surveillance storage requirements. It is available in six models namely – DS-E16HGHI-B, iDS-E08HQHI-B, iDS-E04HUHI-B, DS-E08HGHI-D, iDS-E04HQHI-D, iDS-E04HGHI-E. Furthermore, thanks to its exceptional storage efficiency, a 1TB eDVR can provide up to 8 weeks of storage, easily meeting customers’ storage requirements. A 1TB 4 channel model (iDS-E04HQHI-D) can store up to 4 weeks of continuously recorded footage by all channels in 1080P, while a DS-E04HGHI-E model supports up to 8 weeks of continuous recording in 1080p lite (960×1080 resolution). Enduring Storage Hikvision patented technology offers convenience of adaptability on the storage front. It applies H. 265-based scene-adaptive bitrate coding on eDVRs to ensure sufficient storage and quality video compression. Thus H. 265-based scene-adaptive bitrate control Storage allocation is enabled to ensure 2/4/8 weeks’ recording. Event Judgement and Storage Prediction The algorithms can detect how complicated and active the actual scene is, and then assign certain bitrate to the scene according to the level of activeness, so as to guarantee storage allocation. Storage Allocation The storage is pre-allocated into about 2/4/8 weeks with minor differences in each day according to actual scene and events. Efficient Video Analytics Intelligent human/ vehicle classification is in-built in the eDVRs, enabling smart Motion Detection and quick target search and playback. Deep Learning Based Motion Detection Deep learning-based motion detection 2.0 it can classify human and vehicle, and extremely reduce false alarms deep-learning algorithms are trained to classify objects in videos into three categories – persons, vehicles, and other. The algorithms can automatically detect persons and vehicles. Video Compression In comparison to H.265, H.265 Pro option offers better coding efficiency, which improves video compression capacity by 20%. Application Scenarios Hikvision eDVR 1TB is applicable for residential complexes, villas, retail shops, restaurants and hotels, etc. Reducing Power Consumption for DVRs with Hikvision To address this challenge, and to help organizations and individuals reduce their energy costs and emissions, Hikvision has created a range of new eDVRs. They use embedded solid state drive (eSSD) technology rather than traditional HDDs, which eliminates the need for moving parts in the storage drive and delivering major improvements in energy efficiency. The eSSDs incorporate a highly integrated ‘NAND’ and a solid-state controller, which offer improved memory capacity, heat dissipation, and overall electrical performance compared with traditional HDDs. 10 Times more Energy Efficient eDVR In an in-house lab test, Hikvision has found that the energy consumption of the eSSD storage chip for the DS-E04HQHI-B model is 0.4-0.5 Watts. This is an impressive 10 times more energy efficient than the 4.0-5.0 Watts demanded by the more traditional technology. When considering the green performance of the devices as a whole, eDVRs with eSSD storage are around 45% more energy efficient than traditional DVR+HDD. The new eDVRs register a power consumption of 5.42- 5.82 Watts, rather than the 9.48-11.00 Watts that traditional DVR+ HDD use to perform a similar function (albeit with lower performance). These tests show that replacing traditional DVR with Hikvision eDVRs could save typical homeowners and small businesses up to 45 Kilowatt-Hours (kWh) of electricity per year, per device: the equivalent of around 3,000 iPhone charges. In a time of energy cost and supply uncertainties around the world, Hikvision eDVRs can help in terms of reducing costs and carbon emissions. Hikvision eDVR Energy Savings in Numbers Accounts for only 10 percent of power consumption of a traditional HDD. Typical energy consumption per eDVR of 5.42-5.82 Watts (for model DS-E04HQHI-B), compared with 9.48-11.00 Watts for traditional DVRs. Per-device energy savings of 40-45 kWh per year (equal to 3,000 iPhone charges). Sustainability Beyond Energy Efficiency As well as offering major energy savings, Hikvision’s new eDVRs are more sustainable for a number of other important reasons. Firstly, by eliminating moving parts from the storage drive, eSSD technology reduces internal wear and tear in the eDVR, and extends the product’s useful lifecycle. This optimizes customers’ investments in Hikvision technology and reduces carbon emissions related to replacing end-of-life equipment. Secondly, the highly compact nature of the eSSD chips means that the new Hikvision eDVRs are smaller than traditional DVR devices. This reduces shipping costs and packaging requirements, and all the related environmental impacts. Finally – and importantly – Hikvision uses green packaging for the new eDVRs instead of conventional plastic packaging and EPE foam. This ensures that waste is minimized across the entire delivery chain.  

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BFSI Sector’s Trust Factor Depends on Customers Feeling Safe About Their Data

Himanshu Gautam, Technical Director – West & South India, Radware The BFSI (Banking, Financial Services, and Insurance) sector has seen rapid adoption of digital transformation tools, which has made the sector vulnerable to intense, evolving and repeated cybersecurity attacks from threat actors. CERTIn’s report said that just in the 1st half of 2022 the number of ransomware attacks rose by 51% when compared to 2021. India has been one of the leading countries in going digital with the BFSI sector taking the lead. But this means the vertical should protect the data associated with its customers as trust is the No. 1 factor that needs to be considered. The BFSI sector handles sensitive and confidential information of its customers such as financial transactions, personal details, and credit scores. Therefore, ensuring data security is crucial to maintain customer trust and protecting them from potential threats. But why is data security of utmost importance for the BFSI sector? BFSI institutions hold valuable data, making them a prime target for cybercriminals. Data breaches can lead to identity theft, financial fraud and reputational damage. This is why they must comply with various regulations such as the Payment Card Industry Data Security Standard (PCI DSS) and the General Data Protection Regulation (GDPR). Non-compliance can result in hefty fines and legal actions. If this is not met and cyberattacks are countered then they compromise financial assets, leading to monetary losses for both the institution and its customers. The BFSI sector is highly competitive and maintaining data security can be a competitive advantage. Customers are more likely to trust institutions that prioritize data security. Data breaches can erode customer trust, leading to reputational damage and loss of business. To stay immune to the threats, its necessary to know some common security threats: Phishing attacks. Ransomware attacks. Insider threats. DDoS attacks. Social engineering attacks. Third-party risks. To fight these cyberattack methods the individual players in the sector need to take the necessary measures. By following these measures, the BFSI sector can secure its data and protect against cyber threats. It is important to stay vigilant and proactive in order to ensure the safety and security of sensitive financial data. Implement strong access controls: Limit access to sensitive data only to those who need it. Use multi-factor authentication, strong passwords, and other security protocols to ensure that only authorized personnel can access data. Encrypt data: Use encryption to protect data both in transit and at rest. This will make it more difficult for cybercriminals to steal data if they manage to gain access. Monitor and detect: Monitor the network and systems for suspicious activity and detect potential threats early to prevent data breaches. Implement intrusion detection and prevention systems to identify and block unauthorized access. Implement cybersecurity policies and procedures: Develop and implement cybersecurity policies and procedures to ensure that all employees understand their roles and responsibilities in maintaining data security. Regularly test and update systems: Regularly test and update systems to ensure that they are secure and up to date with the latest security patches and updates. Conduct employee training and awareness programs: Conduct regular training and awareness programs to educate employees on how to identify and prevent cybersecurity threats. Have a disaster recovery plan: Develop a disaster recovery plan that includes a backup and recovery strategy in case of a data breach or other security incident. Failure to comply with data security can lead to severe consequences for the BFSI sector. Legal penalties are levied when BFSI organizations fail to comply with data security regulations. These penalties can be significant and may result in monetary losses for the organization. They suffer from a loss of reputation. Legal penalties can result in financial losses for BFSI organizations. Apart from legal fees, losses can arise from regulatory fines, customer compensation and reputational damage. Data breaches and security incidents can damage the reputation of BFSI organizations, eroding customer trust and confidence. A tarnished reputation can lead to reduced business opportunities and revenue losses. Security incidents can disrupt the operations of BFSI organizations, causing significant downtime and loss of productivity. This can lead to delays in customer service, impacting customer satisfaction and loyalty. Also, non-compliance with data security can increase the vulnerability of BFSI organizations to cyberattacks. This can lead to data breaches and other security incidents, resulting in the consequences outlined above. These aspects of data and security in the BFSI sector prove that trust is an exceptional factor for customers. Overall, compliance with data security regulations is essential for the BFSI sector to protect sensitive customer data, maintain trust and confidence, and avoid the severe consequences of non-compliance. Radware® is a global company in cyber security and application delivery solutions for physical, cloud, and software-defined data centers. Its award-winning solutions portfolio secures the digital experience by providing infrastructure, application, and corporate IT protection, and availability services to enterprises globally. Radware’s solutions empower enterprise and carrier customers worldwide to adapt to market challenges quickly, maintain business continuity, and achieve maximum productivity while keeping costs down.  

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Harnessing the Power of Edge AI

Visionbot™ – the pioneer in Cloud AI to launch its appliance for EDGE AI The Importance of Automated Visual Monitoring with Edge AI and Cloud Reporting for Enterprise Analysis Prakash Prabhu – Chief Business Officer & Co-Founder, VisionBot VisionBot was incorporated with a focus to address the demand for Video driven Digital Enterprise. VisionBot AI enabled Computer Vision helps analyse & augment performance of workflow processes and enables improvement of overall Operational efficiency of an Enterprise. Generative AI will be the adopted tool for generating adaptive video content to boost engagement and drive collaboration across enterprises and social interactions. The reality of Video becoming the primary driver of the creator economy is inevitable and Visionbot™ intends to be in the forefront of the technology curve.Visionbot™ is one inclusive platform which encompasses Cloud Computer Vision, Subscription AI, Video Forensics, Cloud Storage and Streaming (Media/ Video Collaboration/ Digital content) and Generative AI for the creator economy. 1.1  What is Edge AI? Edge AI is the deployment of AI applications in devices throughout the physical world. It’s called ‘edge AI’ because the AI computation is done near the user at the edge of the network, close to where the data is located, rather than centrally in a cloud. Advances in edge AI have opened opportunities for machines and devices, wherever they may be, to operate with the ‘intelligence’ of human cognition. AI-enabled smart applications learn to perform similar tasks under different circumstances, much like real life. The efficacy of deploying AI models at the edge arises from three recent innovations. Maturation of neural computing: Neural networks and related AI infrastructure have finally developed to the point of allowing for generalized machine learning. Availability of no of trained datasets for computer vision. Advances in compute infrastructure: Powerful distributed computational power is required to run AI at the edge. VisionBot edge application can be on CPU, GPU or CPU+GPU. Adoption of IoT devices: The widespread adoption of the Internet of Everything has fuelled the explosion of big data. With the sudden ability to collect data in every aspect of a business – from industrial sensors, smart cameras, automation systems and more – we now have the data and devices necessary to deploy AI models at the edge. 1.2 Why deploy AI at the Edge? What are the benefits of Edge AI? Edge AI represents a paradigm shift in the way visual monitoring is implemented. It involves the deployment of AI algorithms and models directly on edge devices, such as cameras, sensors, or gateways, allowing data analysis to be performed locally without relying solely on cloud infrastructure. This local analysis brings several significant advantages. Since AI algorithms are capable of understanding video, sounds, and other sensor inputs which are forms of unstructured information. These AI applications would be challenging or even impossible to deploy in a centralized cloud or enterprise data center due to issues related to latency, bandwidth and privacy. 1.3 What are the benefits of Edge AI & Cloud Reporting? The benefits of edge AI include: Localized Intelligence: AI applications are more powerful and flexible than conventional applications. Real-time Insights: Since edge technology analyzes data locally rather than in a faraway cloud delayed by long-distance communications, it responds to users’ needs in real time. Reduced Cost: By bringing processing power closer to the edge, applications need less internet bandwidth, greatly reducing networking costs. Increased Privacy: Edge AI further enhances privacy by containing that data locally, uploading only the analysis and insights to the cloud. Even if some of the data is uploaded for training purposes, it can be anonymized to protect user identities. By preserving privacy, edge AI simplifies the challenges associated with data regulatory compliance. High Availability: Decentralization and offline capabilities make edge AI more robust since internet access is not required for processing data. This results in higher availability and reliability for mission-critical, production-grade AI applications. Persistent Improvement: AI models grow increasingly accurate as they train on more data. When an edge AI application confronts data that it cannot accurately or confidently process, it typically uploads it so that the AI can retrain and learn from it. Reduced Latency: By processing visual data at the edge, near the source of its generation, edge AI minimizes latency, ensuring real-time insights and prompt action. This is particularly critical for time-sensitive applications, such as security monitoring, where immediate responses are crucial to prevent potential risks or threats. Bandwidth Optimization: Transmitting large volumes of visual data to the cloud for analysis can strain network bandwidth and result in increased costs. Edge AI reduces the amount of data that needs to be transmitted by performing initial analysis locally and sending only relevant information to the cloud. This optimization of bandwidth allows for cost-effective and efficient data transmission. Cloud Reporting for Enterprise Access: While local analysis provides immediate insights and enables real-time decision-making, cloud reporting complements edge AI by providing comprehensive and centralized access to visual monitoring data. Centralized Data Management: Cloud reporting allows enterprises to store, manage, and analyze visual monitoring data from multiple edge devices in a centralized manner. This centralized approach facilitates holistic analysis, cross-device comparisons, and long-term. Scalability and Flexibility: Cloud-based platforms provide the scalability required to accommodate growing datasets and increasing demand for visual monitoring. Enterprises can easily expand their infrastructure and adapt to evolving needs without the limitations of on-premises solutions. Cloud reporting also enables remote access, empowering stakeholders to monitor operations and make informed decisions from anywhere at any time. Advanced Analytics and AI: Cloud reporting platforms often integrate advanced analytics and AI capabilities, enabling enterprises to leverage powerful algorithms and models for deeper insights and predictive analytics. By combining local edge AI analysis with cloud-based AI, organizations can unlock the full potential of visual monitoring, identifying patterns, anomalies, and optimizing processes with a higher degree of accuracy.   Real-World Applications and Benefits: Manufacturing: Edge AI allows real-time monitoring of production lines, detecting faults or anomalies immediately, reducing downtime, and enhancing overall productivity. Retail: Visual monitoring combined with edge AI…

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