Evaluation of Organochlorine Pesticide Residues in Fish and Water from Lake Geriyo in Yola North Local Government Area of Adamawa State, Nigeria

Pesticides usage in agricultural fields to control pests is extremely toxic to non-target organisms like fish and affects fish health through deterioration of metabolism, and sometimes leading to death. The present study was carried out to determine the level of organochlorine (OC) pesticide residues in, water and fish (Catfish and Tilapia) from Lake Geriyo, in Adamawa State, Nigeria, in order to find out the extent of pesticide contamination and accumulation in the lake. Soxhlet extraction process was used for the extraction of fish using a mixture of hexane and acetone, while water sample was extracted using dichloromethane liquid- liquid extraction method. The extracts were cleaned-up and analyzed using a gas chromatograph mass spectrometer (GC-MS). The result shows that organochlorine pesticides residues such as endrin, aldrin, dieldrin, heptachlor were detected in the water and the fish samples of the study area. Dieldrin was the predominant residue in all the fish samples analysed, at the concentration range of 0.36mg/kg to 0.57mg/kg and 0. 051mg/kg to 0.047mg/kg in Tilapia and catfish respectively, while Lindane was of  higher concentration in water samples, (0.33mg/L) in point p1 upstream and 0.27mg/L in point p2 downstream.  The lowest levels of OC pesticides were related to Endrin, Aldrin and Heptachlor in which Endrin and Heptachor were not found in water samples. The result of the investigation shows that matured fish contain higher concentration of the OC pesticide residues than the immature fish. Dieldrin and Lindane were found to be the predominant pesticide residues in all the analysed water and fish samples. Therefore the result indicates that Lake Gariyo is contaminated by pesticide from the nearby vegetable gardens which affects the water quality and non target organisms like fishes thereof. Keywords: Organochlorine, Pesticides, Lake Geriyo, Adamawa State, Fish and Water DOI: 10.7176/CMR/11-2-0

Performance Analysis of a Hardware Implemented Complex Signal Kurtosis Radio-Frequency Interference Detector

In the field of microwave radiometry, Radio Frequency Interference (RFI) consistently degrades the value of scientific results. Through the use of digital receivers and signal processing, the effects of RFI on scientific measurements can be reduced depending on certain circumstances. As technology allows us to implement wider band digital receivers for radiometry, the problem of RFI mitigation changes. Our work focuses on finding a detector that outperforms real kurtosis in wide band scenarios. The algorithm implemented is a complex signal kurtosis detector which was modeled and simulated. The performance of both complex and real signal kurtosis is evaluated for continuous wave, pulsed continuous wave, and wide band quadrature phase shift keying (QPSK) modulations. The use of complex signal kurtosis increased the detectability of interference

Wideband Digital Signal Processing Test-Bed for Radiometric RFI Mitigation

Radio Frequency Interference (RFI) is a persistent and growing problem experienced by spaceborne microwave radiometers. Recent missions such as SMOS, SMAP, and GPM has detected RFI in L, C, X, and K bands. To proactively deal with this issue, microwave radiometers must (1) Utilize new algorithms for RFI detection (2) Utilize fast digital back-ends that sample at hundreds of MHz. The wideband digital signal processing testbed (WB-RFI) is a platform that allows rapid deelopment and testing various RFI detection and mitigation algorithms

Performance Analysis of a Hardware Implemented Complex Signal Kurtosis Radio-Frequency Interference Detector

Radio-frequency interference (RFI) is a known problem for passive remote sensing as evidenced in the L-band radiometers SMOS, Aquarius and more recently, SMAP. Various algorithms have been developed and implemented on SMAP to improve science measurements. This was achieved by the use of a digital microwave radiometer. RFI mitigation becomes more challenging for microwave radiometers operating at higher frequencies in shared allocations. At higher frequencies larger bandwidths are also desirable for lower measurement noise further adding to processing challenges. This work focuses on finding improved RFI mitigation techniques that will be effective at additional frequencies and at higher bandwidths. To aid the development and testing of applicable detection and mitigation techniques, a wide-band RFI algorithm testing environment has been developed using the Reconfigurable Open Architecture Computing Hardware System (ROACH) built by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) Group. The testing environment also consists of various test equipment used to reproduce typical signals that a radiometer may see including those with and without RFI. The testing environment permits quick evaluations of RFI mitigation algorithms as well as show that they are implementable in hardware. The algorithm implemented is a complex signal kurtosis detector which was modeled and simulated. The complex signal kurtosis detector showed improved performance over the real kurtosis detector under certain conditions. The real kurtosis is implemented on SMAP at 24 MHz bandwidth. The complex signal kurtosis algorithm was then implemented in hardware at 200 MHz bandwidth using the ROACH. In this work, performance of the complex signal kurtosis and the real signal kurtosis are compared. Performance evaluations and comparisons in both simulation as well as experimental hardware implementations were done with the use of receiver operating characteristic (ROC) curves. The complex kurtosis algorithm has the potential to reduce data rate due to onboard processing in addition to improving RFI detection performance

Wideband Digital Signal Processing Test-Bed for Radiometric RFI Mitigation

Radio Frequency Interference (RFI) is a persistent and growing problem experienced by spaceborne microwave radiometers. Recent missions such as SMOS, SMAP, and GPM have detected RFI in L, C, X, and K bands. To proactively deal with this issue, microwave radiometers must (1) Utilize new algorithms for RFI detection (2) Utilize fast digital back-ends that sample at hundreds of MHz. The wideband digital signal processing testbed (WB-RFI) is a platform that allows rapid development and testing various RFI detection and mitigation algorithms

An RFI Detection Algorithm for Microwave Radiometers Using Sparse Component Analysis

Radio Frequency Interference (RFI) is a threat to passive microwave measurements and if undetected, can corrupt science retrievals. The sparse component analysis (SCA) for blind source separation has been investigated to detect RFI in microwave radiometer data. Various techniques using SCA have been simulated to determine detection performance with continuous wave (CW) RFI

Defining Functional Illiteracy to Empower Inclusive Technology Design

Limited literacy presents a significant challenge in HCI research, yet the field lacks consistent definitions and measurement criteria. Researchers often interchange terms such as 'functional illiterates,' 'low literates,' and 'semi-literates,' further complicating the field. This paper conducts a systematic literature review (SLR) of 33 HCI studies, revealing concerns about the absence of a definition in 41% of the studies and the lack of measurement technique in 74%. Based on the results from our SLR and relevant research beyond HCI, we propose the following work-in-progress definition. 'Functional illiterates are motivated adults with some familiarity with text but insufficient to fully comprehend meanings and low skills in the measured digital skill, with enough language proficiency in the study language if they are literate in their native language. This understanding, coupled with addressing the identified issues, will empower the HCI4D community to design more inclusive technology solutions for functionally illiterate users in developing countries

Improved Calibration through SMAP RFI Change Detection

Anthropogenic Radio-Frequency Interference (RFI) drove both the SMAP (Soil Moisture Active Passive) microwave radiometer hardware and Level 1 science algorithm designs to use new technology and techniques for the first time on a spaceflight project. Care was taken to provide special features allowing the detection and removal of harmful interference in order to meet the error budget. Nonetheless, the project accepted a risk that RFI and its mitigation would exceed the 1.3-K error budget. Thus, RFI will likely remain a challenge afterwards due to its changing and uncertain nature. To address the challenge, we seek to answer the following questions: How does RFI evolve over the SMAP lifetime? What calibration error does the changing RFI environment cause? Can time series information be exploited to reduce these errors and improve calibration for all science products reliant upon SMAP radiometer data? In this talk, we address the first question

Soil Moisture Active Passive (SMAP) Project Algorithm Theoretical Basis Document SMAP L1B Radiometer Data Product: L1B_TB

The purpose of the Soil Moisture Active Passive (SMAP) radiometer calibration algorithm is to convert Level 0 (L0) radiometer digital counts data into calibrated estimates of brightness temperatures referenced to the Earth's surface within the main beam. The algorithm theory in most respects is similar to what has been developed and implemented for decades for other satellite radiometers; however, SMAP includes two key features heretofore absent from most satellite borne radiometers: radio frequency interference (RFI) detection and mitigation, and measurement of the third and fourth Stokes parameters using digital correlation. The purpose of this document is to describe the SMAP radiometer and forward model, explain the SMAP calibration algorithm, including approximations, errors, and biases, provide all necessary equations for implementing the calibration algorithm and detail the RFI detection and mitigation process. Section 2 provides a summary of algorithm objectives and driving requirements. Section 3 is a description of the instrument and Section 4 covers the forward models, upon which the algorithm is based. Section 5 gives the retrieval algorithm and theory. Section 6 describes the orbit simulator, which implements the forward model and is the key for deriving antenna pattern correction coefficients and testing the overall algorithm