Diese Eierlikörmousse-Torte mit lockerem Schokoboden und zarter Eierlikörmousse ist ein echter Traum und perfekt für die Osterfeiertage.

The ability of workplaces to adapt to their employees’ wishes regarding flexibility is becoming an expectation rather than just a benefit. According to the 2022…

The First Practical Guide to Advanced Wireless Development with ZigBee Technologies Supported by more than a hundred companies, the new ZigBee standard enables powerful new wireless applications for safety, security, and control, ranging from smart energy to home automation and medical care to advanced remote control. ZigBee Wireless Sensor and Control Network brings together all the knowledge professionals need to start building effective ZigBee solutions. The only simple, concise guide to ZigBee architecture, concepts, networking, and applications, this book thoroughly explains the entire ZigBee protocol stack and covers issues ranging from routing to security. It also presents detailed, practical coverage of ZigBee features for home automation, smart energy networking, and consumer electronics. Topics include • Fundamental wireless concepts: OSI Model, error detection, the ISM Band, modulation, WLAN, FHSS, DSSS, Wireless MANs, Bluetooth, and more • ZigBee essentials: applications, characteristics, device types, topologies, protocol architecture, and expanded ZigBee PRO features • Physical layer: includes frequency bands, data rate, channels, data/management services, transmitter power, and receiver sensitivity • MAC layer: data/management services, MAC layer information base, access methods, and frames • Network layer: data entities, NIB, device configuration, starting network, addressing, discovery, channel scanning, and more • Application support sublayer and application layer: includes profiles, cluster format, attributes, device discovery, and binding • ZigBee network security: includes encryption, trust center, security modes, and security management primitives • Address assignment and routing techniques • Alternative technologies: 6lowpan, WirelessHART, and Z-wave

With the strengthening of global anti-terrorist measures, it is increasingly important to conduct security checks in public places to detect concealed objects carried by the human body. Research in recent years has shown that deep learning is helpful for detecting concealed objects in passive terahertz images. However, previous studies have failed to achieve superior accuracy and performance for real-time labeling. Our research aims to propose a novel method for accurate and real-time detection of concealed objects in terahertz images. To reach this goal we trained and tested a promising detector based on deep residual networks using human image data collected by passive terahertz devices. Specifically, we replaced the backbone network of the SSD (Single Shot MultiBox Detector) algorithm with a more representative residual network to reduce the difficulty of network training. Aiming at the problems of repeated detection and missed detection of small targets, a feature fusion-based terahertz image target detection algorithm was proposed. Furthermore, we introduced a hybrid attention mechanism in SSD to improve the algorithm’s ability to acquire object details and location information. Finally, the Focal Loss function was introduced to improve the robustness of the model. Experimental results show that the accuracy of the SSD algorithm is improved from 95.04 to 99.92%. Compared with other current mainstream models, such as Faster RCNN, YOLO, and RetinaNet, the proposed method can maintain high detection accuracy at a faster speed. This proposed method based on SSD achieves a mean average precision of 99.92%, an F1 score of 0.98, and a prediction speed of 17 FPS on the validation subset. This proposed method based on SSD-ResNet-50 can provide a technical reference for the application and development of deep learning technology in terahertz smart security systems. In the future, it can be widely used in some public scenarios with real-time security inspection requirements.

The PoE smart light pole is an important source of data collection. Data is the future of Internet of Things and an integral part of the

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Machine learning is the subfield of computer science that "gives computers the ability to learn without being explicitly programmed" (Arthur Samuel, 1959). Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is unfeasible; example applications include spam filtering, optical character recognition (OCR), search engines and computer vision.

If you like Spirograph and the idea of 3D printed textiles, then you'll love Flexible Textile Structures by the [trans]LAB team – Negar Kalantar and Alireza Borhani.

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Recently, with the construction of smart city, the research on environmental sound classification (ESC) has attracted the attention of academia and industry. The development of convolutional neural network (CNN) makes the accuracy of ESC reach a higher level, but the accuracy improvement brought by CNN is often accompanied by the deepening of network layers, which leads to the rapid growth of parameters and floating-point operations (FLOPs). Therefore, it is difficult to transplant CNN model to embedded devices, and the classification speed is also difficult to accept. In order to reduce the hardware requirements of running CNN and improve the speed of ESC, this paper proposes a resource adaptive convolutional neural network (RACNN). RACNN uses a novel resource adaptive convolutional (RAC) module, which can generate the same number of feature maps as conventional convolution operations more cheaply, and extract the time and frequency features of audio efficiently. The RAC block based on the RAC module is designed to build the lightweight RACNN model, and the RAC module can also be used to upgrade the existing CNN model. Experiments based on public datasets show that RACNN achieves higher performance than the state-of-the-art methods with lower computational complexity.

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