From small, single-camera systems to large, scalable deployments with hundreds of cameras, Konntek has the ideal solution for all application from big commercial projects to small retail solutions. Our product line is popular for its simplicity and flexibility. We improve surveillance by allowing end users to increase the protection of both people and property.
If you're waiting for Amazon Prime Day to kick off tomorrow, you may want to take advantage of the deals that other retailers already have going on. Walmart has already kicked off its own "anti-Prime Day" savings event and with it comes the best price we've seen on the Lenovo Smart Clock. Right now, Walmart has the smart alarm clock for $39, which is $1 cheaper than its previous low and 50 percent off its normal price. This little gadget has gotten quite popular since its release last year. We gave it a score of 87 for its charming design, ambient light sensor, sunrise alarm feature and lack of camera.
Wireless Sensor Networks (WSNs) and Internet of Things (IoT) often suffer from error-prone links when deployed in resource-constrained industrial environments. Reliability is a critical performance requirement of loss-sensitive applications, and Signal-to-Noise Ratio (SNR) is a key indicator of successful communications. In addition to the improvement of the physical layer through modulation and channel coding, machine learning offers adaptive solutions by configuring various communication parameters dynamically. In this paper, we apply a Deep Neural Network (DNN) to predict SNR and Packet Delivery Ratio (PDR). Analysis results based on a real dataset show that the DNN can predict SNR and PDR at the accuracy of up to 96% and 98%, respectively, even when trained with very small fraction (≤10%) of data. Moreover, a common subset of features turns out to be useful in predicting both SNR and PDR so as to encourage considering both metrics jointly. We may control the transmission power in the dynamic and adaptive manner when we have predictable SNR and PDR, and thus fulfill the reliability requirements with energy conservation. This can help in achieving sustainable design for the communication system.
Ambient Intelligence is a result of steady progress in technology over the years. Technology not only created plenty of devices to use, but it also developed a computing power into many aspects of our lives. This transformation enables us to drive towards Computer Science and a wide range of embedded computing devices. Technologies nowadays can wash our dishes and clothes, cook food for us, and some can drive a car for us. There are some major successful applications also present like RFID, Networking, Ubiquitous Computing, and many more.
It's a powerful enabling technology for a new generation of use cases that will leverage edge computing to make IoT more effective and efficient. In many ways, the narrative of 5G is the interaction between two inexorable forces: the rise in highly reliable, high-bandwidth communications, and the rapid spread of available computing power throughout the network. End-point devices that connect to the network are also getting smarter and more powerful. The increasingly dynamic and powerful computational environment that's taking shape as telcos begin to redesign their networks for 5G will accelerate the uptake of IoT applications and services throughout industry. We expect that 5G will enable new use cases in remote monitoring and visual inspection, autonomous operations in large-scale remote environments such as mines, connected vehicles and more.
Artificial Intelligence has begun to impact industries in a big way but nowhere has the impact been as profound as it has been in the security vertical. New and innovative solutions are being released not just be established security vendors but also by smaller startups and together these solutions are adding value to the security operations of an organization. Let's discuss how AI shall impact the video surveillance industry in the near future. Initially, in the days of CCTV cameras the video used to be streamed live on the TV screens but very little effort was made to make any meaningful analysis of a possible security incident. The video surveillance solutions in those days were always reactive and continue to remain so in large parts of the world. Most agencies as for CCTV footage only when an incident has occurred or when there is a massive threat perception.
Recent advances in the Internet of Things (IoT) technology have led to a surge on the popularity of sensing applications. As a result, people increasingly rely on information obtained from sensors to make decisions in their daily life. Unfortunately, in most sensing applications, sensors are known to be error-prone and their measurements can become misleading at any unexpected time. Therefore, in order to enhance the reliability of sensing applications, apart from the physical phenomena/processes of interest, we believe it is also highly important to monitor the reliability of sensors and clean the sensor data before analysis on them being conducted. Existing studies often regard sensor reliability monitoring and sensor data cleaning as separate problems. In this work, we propose RelSen, a novel optimization-based framework to address the two problems simultaneously via utilizing the mutual dependence between them. Furthermore, RelSen is not application-specific as its implementation assumes a minimal prior knowledge of the process dynamics under monitoring. This significantly improves its generality and applicability in practice. In our experiments, we apply RelSen on an outdoor air pollution monitoring system and a condition monitoring system for a cement rotary kiln. Experimental results show that our framework can timely identify unreliable sensors and remove sensor measurement errors caused by three types of most commonly observed sensor faults.
With digital transformation in full swing, the number of connected devices is increasing at a fast pace. IDC Data predicts 152,200 connected IoT devices every minute by the year 2025. While this translates to more data and, subsequently, more avenues to improve efficiency, a robust network is necessary for this data exchange. The fifth-generation wireless technology has features that will not only support high-speed mobile communication but also make IoT data transfer more efficient. Let's look at these features in contrast with the existing 4G network: All these features make the 5G network adaptable to the external environment, unlike its predecessors, which has limited network flexibilities.
Dublin, July 28, 2020 (GLOBE NEWSWIRE) -- The "Global Video Surveillance Market: Focus on Ecosystem, Application (Infrastructure, Commercial Residential, Industrial, Institutional, Others), and Region - Analysis and Forecast, 2020-2025" report has been added to ResearchAndMarkets.com's offering. The video surveillance industry analysis projects the market to grow at a significant CAGR of 10.06% on the basis of value during the forecast period from 2020 to 2025. Asia-Pacific region dominated the global video surveillance with a share of 58.12% in 2019. The constantly expanding infrastructure in China and India has been a significant driver in promoting the growth of video surveillance in these countries. The decline in the costs of the overall CCTV-based security system packages due to the reduction in the prices of video cameras has hiked the price competitiveness of these surveillance systems, especially in the Chinese, Indian, and South Korean markets.
Safehub, whose platform enables businesses to monitor their buildings for signs of earthquakes, today closed a $5 million seed round. The company says it will use the capital to accelerate deployment to Fortune 500 customers as it expands its engineering team. A recent FEMA study pegged U.S. losses from earthquakes at $4.4 billion per year. In spite of the risk, more than 60% of U.S. small businesses don't have a formal emergency-response plan and fail to back up their sensitive data offsite. Safehub aims to close the gap with a real-time, building-specific earthquake damage data-gathering solution.