CSI-Based Human Activity Recognition using Convolutional Neural Networks

Human activity recognition (HAR) as an emerging technology can have undeniable impacts on several applications such as health monitoring, context-aware systems, transportation, robotics, and smart cities. Among the main research methods in HAR (sensor, image, and WiFi-based), the WiFi-based method has attracted considerable attention due to the ubiquity of WiFi devices. WiFi devices can be utilized to distinguish daily activities such as “walk”, “run”, and “sleep”. These activities affect WiFi signal propagation and can be further used to recognize activities. This paper proposes a Deep Learning method for HAR tasks using channel state information (CSI). A new model is developed in which CSI data are converted to grayscale images. These images are then fed into a 2D-Convolutional Neural Network (CNN) for activity classification. We take advantage of CNN's high accuracy on image classification along with WiFi-based ubiquity. The experimental results demonstrate that our proposed approach achieves acceptable performance in HAR tasks

A CSI-Based Human Activity Recognition Using Deep Learning

The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics ofWiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities

Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques

Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human–Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representations, and its applications include smart homes and buildings, action prediction, crowd counting, patient rehabilitation, and elderly monitoring. Traditionally, HAR has been performed through vision-based, sensor-based, or radar-based approaches. However, vision-based and sensor-based methods can be intrusive and raise privacy concerns, while radar-based methods require special hardware, making them more expensive. WiFi-based HAR is a cost-effective alternative, where WiFi access points serve as transmitters and users’ smartphones serve as receivers. The HAR in this method is mainly performed using two wireless-channel metrics: Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). CSI provides more stable and comprehensive information about the channel compared to RSSI. In this research, we used a convolutional neural network (CNN) as a classifier and applied edge-detection techniques as a preprocessing phase to improve the quality of activity detection. We used CSI data converted into RGB images and tested our methodology on three available CSI datasets. The results showed that the proposed method achieved better accuracy and faster training times than the simple RGB-represented data. In order to justify the effectiveness of our approach, we repeated the experiment by applying raw CSI data to long short-term memory (LSTM) and Bidirectional LSTM classifiers

A CSI-based Human Activity Recognition using Canny Edge Detector

Human Activity Recognition (HAR) is one of the hot topics in the field of human-computer interaction. It has a wide variety of applications in different tasks such as health rehabilitation, smart houses, smart grids, robotics, and human action prediction. HAR can be carried out through different approaches such as vision-based, sensor-based, radar-based, and Wi-Fi-based. Due to the ubiquitous and easyto-deploy characteristic of Wi-Fi devices, Wi-Fi-based HAR has gained the interest of both academia and industry in recent years.WiFi-based HAR can be implemented by two channel metrics: Channel State Information (CSI) and Received Signal Strength Indicator (RSSI). Recently, converting the CSI data to images has led to increasing the accuracy level of activity prediction. However, none of the previous research has focused on extracting the features of converted images using image-processing techniques. In this study, we investigate three available datasets, gathered using CSI property, and took advantage of Deep Learning (DL), with convolutional layers and edge detection technique to increase overall system accuracy. The canny edge detector extracts the most important features of the image, and giving it to the DL model empowers the prediction of activities. In all three datasets, we witnessed an improvement of 5%, 27%, and 37% in terms of accuracy

Genome-wide screening of mouse knockouts reveals novel genes required for normal integumentary and oculocutaneous structure and function.

Oculocutaneous syndromes are often due to mutations in single genes. In some cases, mouse models for these diseases exist in spontaneously occurring mutations, or in mice resulting from forward mutatagenesis screens. Here we present novel genes that may be causative for oculocutaneous disease in humans, discovered as part of a genome-wide screen of knockout-mice in a targeted single-gene deletion project. The International Mouse Phenotyping Consortium (IMPC) database (data release 10.0) was interrogated for all mouse strains with integument abnormalities, which were then cross-referenced individually to identify knockouts with concomitant ocular abnormalities attributed to the same targeted gene deletion. The search yielded 307 knockout strains from unique genes with integument abnormalities, 226 of which have not been previously associated with oculocutaneous conditions. Of the 307 knockout strains with integument abnormalities, 52 were determined to have ocular changes attributed to the targeted deletion, 35 of which represent novel oculocutaneous genes. Some examples of various integument abnormalities are shown, as well as two examples of knockout strains with oculocutaneous phenotypes. Each of the novel genes provided here are potentially relevant to the pathophysiology of human integumentary, or oculocutaneous conditions, such as albinism, phakomatoses, or other multi-system syndromes. The novel genes reported here may implicate molecular pathways relevant to these human diseases and may contribute to the discovery of novel therapeutic targets

On the presence of humpback whales in the Persian Gulf: rare or rarely documented? Report of the IWC Scientific Committee Meeting SC/67A/CMP/14, Bled, Slovenia, May 2017

We critically review the evidence for humpback whale presence in the Persian Gulf. Five specimen records, assumed to belong to the endangered Arabian Sea population, are confirmed in the period 1883- 2017: Bassore Bay, Iraq; Doha, Qatar; Kuwait Inner harbour, Kuwait; Qeshm Island, Iran; and Akhtar, Bushehr Province, Iran. The two Iranian cases, both juveniles, are newly recorded. With accumulating reports, an alternate hypothesis to 'rare stragglers' deserves consideration, one in which Arabian Sea humpback whales may enter the Persian Gulf with some regularity, perhaps as normal visitors, if not permanent residents. Deficiency of records may reflect a general sparsity of whale research effort in the Persian Gulf. The historical description of Megaptera indica Gervais, 1883 is translated from French

HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations

Publicly available collections of drug-like molecules have grown to comprise 10s of billions of possibilities in recent history due to advances in chemical synthesis. Traditional methods for identifying ``hit'' molecules from a large collection of potential drug-like candidates have relied on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug to its protein target. A major drawback of the approaches is that they require exceptional computing capabilities to consider for even relatively small collections of molecules. Hyperdimensional Computing (HDC) is a recently proposed learning paradigm that is able to leverage low-precision binary vector arithmetic to build efficient representations of the data that can be obtained without the need for gradient-based optimization approaches that are required in many conventional machine learning and deep learning approaches. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated for a range of application areas. We consider existing HDC approaches for molecular property classification and introduce two novel encoding algorithms that leverage the extended connectivity fingerprint (ECFP) algorithm. We show that HDC-based inference methods are as much as 90 times more efficient than more complex representative machine learning methods and achieve an acceleration of nearly 9 orders of magnitude as compared to inference with molecular docking. We demonstrate multiple approaches for the encoding of molecular data for HDC and examine their relative performance on a range of challenging molecular property prediction and drug-protein binding classification tasks. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools

Biological and immunological characteristics of Brucella abortus S99 major outer membrane proteins

Introduction and objective: Outer membrane proteins (OMPs) of Brucella are considered as immunogenic structures which can be used to design and develop a subunit vaccine for human brucellosis. Brucella abortus S99 OMPs promote the synthesis of high levels of specific anti-Brucella IgG molecules in rabbits when administrated with lipopolysaccharide (LPS). The objective of this study is evaluation of the efficacy of B. abortus major OMPs with LPS in the induction of immune response against brucellosis. Materials and methods: OMPs were derived from B. abortus by sequential extraction of sonicated cells with ultracentrifugation and predigestion with lysozyme. Proteins could be separated by anion-exchange chromatography and gel-filtration. Based on SDS-PAGE profiles, porins have been dominantly purified among three different classes of B. abortus OMPs. Sera of immunized rabbits against B. abortus porins were analyzed by enzymelinked immunosorbent assay (ELISA). LPS of B. abortus and complete Freud's adjuvant (CFA) were also applied to elicit higher levels of anti-Brucella antibodies. Results: ELISA confirmed the potency of porins and porins combination with CFA and LPS to promote humoral specific response. Among the above-mentioned compounds, a combination of porins + LPS or porins + CFA has been the most potent immunogenic compound to induce higher titer of antibody against B. abortus S99 in the animal model. Conclusion: The application of a complex of Brucella LPS and porins as an effective method to elicit protective and long-lasting immunity against Brucella infection and would be studied to design and develop a subunit vaccine for human brucellosis

Identification of a Small Molecule Anti-biofilm Agent Against Salmonella enterica

Biofilm formation is a common strategy utilized by bacterial pathogens to establish persistence in a host niche. Salmonella enterica serovar Typhi, the etiological agent of Typhoid fever, relies on biofilm formation in the gallbladder to chronically colonize asymptomatic carriers, allowing for transmission to uninfected individuals. S. enterica serovar Typhimurium utilizes biofilms to achieve persistence in human and animal hosts, an issue of both clinical and agricultural importance. Here, we identify a compound that selectively inhibits biofilm formation in both S. Typhi and S. Typhimurium serovars at early stages of biofilm development with an EC50 of 21.0 and 7.4 μM, respectively. We find that this compound, T315, also reduces biofilm formation in Acinetobacter baumannii, a nosocomial and opportunistic pathogen with rising antibiotic resistance. T315 treatment in conjunction with sub-MIC dosing of ciprofloxacin further reduces S. enterica biofilm formation, demonstrating the potential of such combination therapies for therapeutic development. Through synthesis of two biotin-labeled T315 probes and subsequent pull-down and proteomics analysis, we identified a T315 binding target: WrbA, a flavin mononucleotide-dependent NADH:quinone oxidoreductase. Using a S. Typhimurium strain lacking WrbA we demonstrate that this factor contributes to endogenous S. enterica biofilm formation processes and is required for full T315 anti-biofilm activity. We suggest WrbA as a promising target for further development of anti-biofilm agents in Salmonella, with potential for use against additional bacterial pathogens. The development of anti-biofilm therapeutics will be essential to combat chronic carriage of Typhoid fever and thus accomplish a meaningful reduction of global disease burden