This survey covers more than 150 published papers related to sub-6 GHz wideband LNAs from IEEE publications such as ISSCC, JSSC, TMTT, RFIC, MWCL, TCAS and NEWCAS published in the last 20 years. The considered LNAs are classified according to the technology node and its topology. The presented database is a useful tool for investigating technology trends and comparing the performance of common LNA design styles. 

Instructions: 

- The database is organized by technology and topology. 

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Sugarcane vegetation on path-loss between CC2650 and CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)".

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on path-loss between CC2650 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy rice crop monitoring from period 03/07/2019 to 18/11/2019.

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on received signal strength between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy Rice crop monitoring from period 01/07/2020 to 03/11/2020.

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Millet vegetation on path-loss between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for millet crop monitoring from period 03/06/2020 to 04/10/2020.

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This dataset is captured from a Mirai type botnet attack on an emulated IoT network in OpenStack. Detailed information on the dataset is depicted in the following work. Please cite it when you use this dataset for your research.

  • Kalupahana Liyanage Kushan Sudheera, Dinil Mon Divakaran, Rhishi Pratap Singh, and Mohan Gurusamy, "ADEPT: Detection and Identification of Correlated Attack-Stages in IoT Networks," in IEEE Internet of Things Journal.

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This dataset contains the database of the transport block (TB) configurations .

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Message Queuing Telemetry Transport (MQTT) protocol is one of the most used standards used in Internet of Things (IoT) machine to machine communication. The increase in the number of available IoT devices and used protocols reinforce the need for new and robust Intrusion Detection Systems (IDS). However, building IoT IDS requires the availability of datasets to process, train and evaluate these models. The dataset presented in this paper is the first to simulate an MQTT-based network. The dataset is generated using a simulated MQTT network architecture.

Instructions: 

The dataset consists of 5 pcap files, namely, normal.pcap, sparta.pcap, scan_A.pcap, mqtt_bruteforce.pcap and scan_sU.pcap. Each file represents a recording of one scenario; normal operation, Sparta SSH brute-force, aggressive scan, MQTT brute-force and UDP scan respectively. The attack pcap files contain background normal operations. The attacker IP address is “192.168.2.5”. Basic packet features are extracted from the pcap files into CSV files with the same pcap file names. The features include flags, length, MQTT message parameters, etc. Later, unidirectional and bidirectional features are extracted.  It is important to note that for the bidirectional flows, some features (pointed as *) have two values—one for forward flow and one for the backward flow. The two features are recorded and distinguished by a prefix “fwd_” for forward and “bwd_” for backward. 

 

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A Indústria enfrenta desafios graves e fracassa sem competitividade. Atacando esta problemática, conferiu-se o oferecimento de maior eficiência a processos industriais para promover a produtividade, elevar a qualidade e impulsionar mudanças. A solução desenvolvida incluiu dispositivos com sensores não invasivos, simples de instalar, que contabilizam os itens sendo transportados em linhas de produção.

Instructions: 

Os dados foram coletados utilizando o dispositivo IoT da EnergyNow Tecnologias denominado Prodbox™, o qual opera como um equipamento empregado para intensificar a produtividade e apontar maneiras estratégicas de modificar variáveis que interferem na visão de gestão sobre a produção.

O dispositivo utiliza sensores não obstrutivos para contabilizar o número de itens que atravessam a linha de detecção gerada entre o transmissor e o receptor instalados.

Notadamente, os dados coletados são enviados para a nuvem, onde podem, quando integrados a uma plataforma de análise, ser processados para apresentar indicadores de acompanhamento de produtividade. Um sistema inteligente pode processar os dados coletados e apresentar métricas que permitem ao gestor identificar formas de aumentar a produção, bem como etapas que estão prejudicando a produtividade. Além disso, alertas customizados podem ser configurados para prover informação sobre a parada ou inatividade detectada pelo dispositivo.

Os dados gerados através do dispositivo podem ser utilizados para entender melhor variáveis sobre o ritmo de produção e, a partir delas, fomentar projeções de produção, calculando-se a relação entre itens produzidos e período de tempo necessário (segundos, minutos, horas, dias, semanas, etc).

 

Algumas sugestões sobre abordagens a serem consideradas:

  • Verifique se políticas de aumento de produtividade estão sendo efetivas.

  • Distribuia melhor os funcionários em etapas diferentes de uma linha de produção.

  • Correlacione etapas de produção com variáveis que estejam interferindo na produtividade para resolver problemáticas internas.

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The  database contains the raw range-azimuth measurements obtained from mmWave MIMO radars (IWR1843BOOST http://www.ti.com/tool/IWR1843BOOST) deployed in different positions around a robotic manipulator.

Instructions: 

The database that contains the raw range-azimuth measurements obtained from mmWave MIMO radars inside a Human-Robot (HR) workspace environment. 

 

The database contains 5 data structures:

i) mmwave_data_test has dimension 900 x 256 x 63. Contains 900 FFT range-azimuth measurements of size 256 x 63: 256-point range samples corresponding to a max range of 11m (min range of 0.5m) and 63 angle bins, corresponding to DOA ranging from -75 to +75 degree. These data are used for testing (validation database). The corresponding labels are in label_test. Each label (from 0 to 5) corresponds to one of the 6 positions (from 1 to 6) of the operator as detailed in the image attached.

 

ii) mmwave_data_train has dimension 900 x 256 x 63. Contains 900 FFT range-azimuth measurements used for training. The corresponding labels are in label_train.

 

iii) label_test with dimension 900 x 1, contains the true labels for test data (mmwave_data_test), namely classes (true labels) correspond to integers from 0 to 5. 

 

iv) label_train with dimension 900 x 1, contains the true labels for train data (mmwave_data_train), namely classes (true labels) correspond to integers from 0 to 5. 

 

v) p (1 x 900) contains the chosen random permutation for data partition among nodes/device and federated learnig simulation (see python code).

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