Multiple-input-multiple-output (MIMO) array antenna integrated with the double negative metamaterial superstrate is presented. The triangular metamaterial unit cell is designed by combining two triangular elements positioned in complementary on the same plane at different sizes. Such design with more gaps is used to excite rooms for more capacitance effects to shift the resonance frequency thus enlarging the bandwidth of the MIMO antenna. The unit cell is arranged in 7 × 7 periodic array created a superstrate metamaterial plane where the Cstray exists in parallel between the two consecutive cells. It is found that the existence of Cstray and gaps for each unit cells significantly influenced the bandwidth of the MIMO antenna. The higher value of the capacitance will lead to the negativity of permittivity. The superstrate plane is then located on top of the 4 × 2 MIMO with a gap of 5 mm. The integration resulted in improving the bandwidth to 12.45% (5.65-6.4GHz) compared to only 3.49% bandwidth (5.91-6.12GHz) of the MIMO antenna itself. Moreover, the negative permeability characteristic is created by a strong magnetic field between the complementary unit cells to have 14.05-dBi peak gain. Besides that, the proposed antenna managed to minimize the mutual coupling and improve the mean effective gain, envelope correlation coefficient, and multiplexing efficiency.

This article presents a newly circularly polarized (CP) antenna for V2X's dedicated short range communications applications. Its CP characteristic is enabled by a 70 Ω sequential phase feeding network and sequential rotation technique designed on top of the substrate. It has features of ≈90° phase difference in sequence between ports of S21 = 2.4°, S31 = −87°, S41 = −180°, and S51 = −276°, resulting in a 2.19 dB axial ratio centered at 5.9 GHz. The length of the SP feeding network to each ports designed in the different form of meander lines are the key to control the generated phase at the center frequency It also contributes to the smaller final size of 0.59λ × 0.59λ. The proposed antenna operated from 5.850 to 5.925 GHz with a gain between 4 and 6 dBi. The gains are radiated in bidirectional mode due to the presence of the complimentary dipoles located on the opposite side of the substrate. These features indicate the suitability of the proposed antenna in compliance to the ITS-G5 OBU V2X standard.

Typhoid fever, also known as typhoid, is a life-threatening bacterial infection that remains a global health concern. The infection is associated with a significant morbidity and mortality rate, resulting in an urgent need for specific and rapid detection tests to aid prevention and management of the disease. The present review aims to assess the specificity and sensitivity of the available literature on the immunodiagnostics of typhoid fever. A literature search was conducted using three databases (PubMed, ProQuest and Scopus) and manual searches through the references of identified full texts to retrieve relevant literature published between 1 January 2011 and 31 December 2020. Of the 577 studies identified in our search, 12 were included in further analysis. Lipopolysaccharides (LPS) and hemolysin E (HlyE) were the most frequently studied antigens. The specimens examined in these studies included serum and saliva. Using blood culture as the gold standard, anti-LPS IgA gave the highest sensitivity of 96% (95% CI: 93–99) and specificity of 96% (95% CI: 93–99) for distinguishing between typhoid cases and healthy controls, whereas the combination of anti-LPS and anti-flagellin total IgGAM gave the highest sensitivity of 93% (95% CI: 86–99) and specificity of 95% (95% CI: 89–100) for distinguishing typhoid cases and other febrile infections. A comparably high sensitivity of 92% (95% CI: 86–98) and specificity of 89% (95% CI: 78–100) were shown in testing based on detection of the combination of anti-LPS (IgA and IgM) and anti-HlyE IgG as well as a slightly lower sensitivity of 91% (95% CI: 74–100) in the case of anti-50kDa IgA. Anti-50kDa IgM had the lowest sensitivity of 36% (95% CI: 6–65) against both healthy and febrile controls. The development of a rapid diagnostic test targeting antibodies against lipopolysaccharides combined with flagellin appeared to be a suitable approach for the rapid detection test of typhoid fever. Saliva is added benefit for rapid typhoid diagnosis since it is less invasive. As a result, further studies could be done to develop additional approaches for adopting such samples.

The development of rapid, accurate, and efficient detection methods for Salmonella can significantly control the outbreak of salmonellosis that threatens global public health. Despite the high sensitivity and specificity of the microbiological, nucleic-acid, and immunological-based methods, they are impractical for detecting samples outside of the laboratory due to the requirement for skilled individuals and sophisticated bench-top equipment. Ideally, an electrochemical biosensor could overcome the limitations of these detection methods since it offers simplicity for the detection process, on-site quantitative analysis, rapid detection time, high sensitivity, and portability. The present scoping review aims to assess the current trends in electrochemical aptasensors to detect and quantify Salmonella. This review was conducted according to the latest Preferred Reporting Items for Systematic review and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A literature search was performed using aptamer and Salmonella keywords in three databases: PubMed, Scopus, and Springer. Studies on electrochemical aptasensors for detecting Salmonella published between January 2014 and January 2022 were retrieved. Of the 787 studies recorded in the search, 29 studies were screened for eligibility, and 15 studies that met the inclusion criteria were retrieved for this review. Information on the Salmonella serovars, targets, samples, sensor specification, platform technologies for fabrication, electrochemical detection methods, limit of detection (LoD), and detection time was discussed to evaluate the effectiveness and limitations of the developed electrochemical aptasensor platform for the detection of Salmonella. The reported electrochemical aptasensors were mainly developed to detect Salmonella enterica Typhimurium in chicken meat samples. Most of the developed electrochemical aptasensors were fabricated using conventional electrodes (13 studies) rather than screen-printed electrodes (SPEs) (two studies). The developed aptasensors showed LoD ranges from 550 CFU/mL to as low as 1 CFU/mL within 5 min to 240 min of detection time. The promising detection performance of the electrochemical aptasensor highlights its potential as an excellent alternative to the existing detection methods. Nonetheless, more research is required to determine the sensitivity and specificity of the electrochemical sensing platform for Salmonella detection, particularly in human clinical samples, to enable their future use in clinical practice.

Large-scale food-borne outbreaks caused by Salmonella are rarely seen nowadays, thanks to the advanced nature of the medical system. However, small, localised outbreaks in certain regions still exist and could possess a huge threat to the public health if eradication measure is not initiated. This review discusses the progress of Salmonella detection approaches covering their basic principles, characteristics, applications, and performances. Conventional Salmonella detection is usually performed using a culture-based method, which is time-consuming, labour intensive, and unsuitable for on-site testing and high-throughput analysis. To date, there are many detection methods with a unique detection system available for Salmonella detection utilising immunological-based techniques, molecular-based techniques, mass spectrometry, spectroscopy, optical phenotyping, and biosensor methods. The electrochemical biosensor has growing interest in Salmonella detection mainly due to its excellent sensitivity, rapidity, and portability. The use of a highly specific bioreceptor, such as aptamers, and the application of nanomaterials are contributing factors to these excellent characteristics. Furthermore, insight on the types of biorecognition elements, the principles of electrochemical transduction elements, and the miniaturisation potential of electrochemical biosensors are discussed.

A rapid and accurate detection of pathogens is crucial in disease diagnosis and quality monitoring of food and water supply. Salmonella species is a well-known food-borne pathogen that can cause typhoid fever and salmonellosis and its fast detection is of great importance to prevent a major outbreak. This study aims to develop an aptamer-based electrochemical biosensor (aptasensor) using a printed circuit board gold electrode for Salmonella hemolysin E protein detection. The aptasensor was developed by modifying the gold working electrode with chitosan-graphene oxide nanomaterials to improve their electrical conductivity. Subsequently, the electrode was immobilized with hemolysin E specific aptamer and tested for target protein detection. The optimum chitosan and nanomaterials oxide nanomaterials concentration was found at 0.01% (w/v) and 4 mg/mL respectively to give almost 35 % of peak current increase. The developed aptasensor was able to detect the presence of hemolysin E target protein at 1 μg/mL concentration within 1 hour of incubation time.

Land Mobile Radios (LMRs) are a two-way consumer radio communication system, popularly used for public safety operations. An unintentional strong far-out interfering signal causes the LMR receiver to be overloaded and reduces the gain of the weak desired signal. The conventional non-learning based methods to mitigate the effects of interference require prior knowledge of the interferer or additional filtering components at the RF front-end of the receiver. In this paper, we propose a novel data-driven unsupervised Deep Learning-based approach for joint interference detection, interference cancellation and signal detection of narrowband LMR signals that we refer to as DeepLMR. The DeepLMR uses a Variational Autoencoder (VAE)-based framework known as Recovery VAE (Re-VAE), with a Gumbel-Softmax distribution that encodes the input to a lower dimensional representation as the latent space representations. The latent space representations are sampled from a categorical distribution and classified to the corresponding symbols of the transmitted signal. Experimental results with real-world signals distorted by a strong far-out interfering signal showed that our proposed DeepLMR architecture has bit error rate (BER) performance improvements as compared to the conventional frequency discriminator and other state-of-the-art Deep Learning-based architectures.

Around View Monitoring (AVM) system uses multiple input cameras mounted on different positions of a vehicle to display 360° bird-eye-view around the vehicle that is not readily visible to the driver. The development of this system will contribute to the reduction of parking accidents by monitoring its surroundings, detecting lanes and identifying obstacles. With AVM, we can significantly decrease the number of minor accidents. AVM will not only be used for parking assistance but can also assist navigation in the narrow path area. Conventional AVM systems developed in the market using four or six cameras and requires an additional sensor for detection in order to minimise stitching error or to reduce the time to calibrate the output display image. The procedure is time-consuming and increases the cost of development. We propose to develop two ultra-wide-angle cameras located on the front and rear vehicle integrated with the motion estimation (ME) algorithm to produce a parking bird eye view and forward/backward trajectory lines. From our ablative analysis, optical flow is not suitable to be used for real-time ADAS systems as it fails at least 25.5% of the time. However, block matching algorithm based on normalized cross-correlation (CCORR NORMED) and normalised correlation coefficient (CCOEFF NORMED) were able to detect all templates correctly with 0% of false detection on our dataset.