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Climate change has become one of the most imperative problems of this century. Climate change is a grave global threat, and Pakistan has been ranked eighth on the list of countries most vulnerable to climate change by the German watch Global Climate Risk Index. The increasing amount of carbon dioxide (CO2), the foremost contributor to environmental degradation, seems to provoke this problem. Agriculture-based economies have always been dependent on the vagaries of nature and climate. Present study analyses the climate change, environmental degradation, input index and economic growth impact on crops production in Pakistanfrom 1977 to 2016. Our study employsAuto Regressive Distributed Lag Model (ARDL) to check the long run association among the variables. The results shows that positive association among climate change and crops production in the longrun.On the behalf of results policymakers must drive attention to adopt advanced cultivation processes using farming practice through international research institutions' assistance to further improve crops production in Pakistan.

Anthropogenic actionsamplify the greenhouse gas concentration in the atmosphere since the 19th century which lead to noticeable variation in climate that has a significant influence on different regions across the globe. World-wildly, Pakistan is participatinginsignificantly in greenhouse gas emissions but facing significant effects on several segments of the economy. The green revolution boosts agriculture production and a significant increase in productivity against to before it. The agriculture sector is considered to be lifeline of Pakistan's economy it contributes significantly to the gross domestic product, gives employment opportunitiesto huge number of the working population and provides raw materials to other sectors. A drastic increase in population had led to rising demand for agricultural products in Pakistan and it highlights the importance of agriculture sector in Pakistan. Several current studies had concentrated on greenhouse gas emissions in the agriculture sector in Pakistan but ignored the green revolution aspect. It is a pioneer study which has a central aim to explore the influence of greenhouse gases emission and the green revolution on agriculture production with the help of Cobb Douglas Production Function (CDPF). The current study uses the four decades’time seriesdata usingAuto Regressive Distributed Lag (ARDL) approach with the help of Bound Test run to observe the long and short-run impact of greenhouse gas emissions and green revolution on agriculture sector in Pakistan. Findings revealed that greenhouse gas emissions have a negative while the green revolution has a positive influence on agriculture production in the long and short run. These results have some policy implication,government should spend more budget on research and development related to the agriculture sector and levy high taxes on those sectors which are emitting greenhouse gases and these policies can dramatically boost agriculture production which will boost Pakistan's economy.

An electroencephalogram (EEG)-based non-invasive 2-channel neuro-feedback SoC is presented to predict and report negative emotion outbursts (NEOB) of Autistic patient. The SoC incorporates area-and-power efficient dual-channel Analog Front-End (AFE), and a deep neural network (DNN) emotion classification processor. The classification processor utilizes only the two-feature vector per channel to minimize the area and overfitting problems. The 4-layers customized DNN classification processor is integrated on-sensor to predict the NEOB. The AFE comprises two entirely shared EEG channels using sampling capacitors to reduce the area by 30%. Moreover, it achieves an overall integrated input-referred noise, NEF and crosstalk of 0.55VRMS, 2.71 and -79dB, respectively. The 16mm2 SoC is implemented in 0.18um 1P6M, CMOS process and consumes 10.13J/classification for 2 channel operation while achieving an average accuracy of >85% on multiple emotion databases and real-time testing.

Autism Spectrum Disorder (ASD) is the prevalent child neurological and developmental disorder causing cognitive and behavioral impairments. The early diagnosis is an urgent need for treatment and rehabilitation of ASD patients. This work presents an electroencephalogram (EEG) based ASD classification processor that targets a patch-form factor sensor that can be used for long time monitoring in a wearable environment. The selection of frontal and parietal lobe electrodes causes minimum uneasiness to the children. The proposed and implemented algorithm utilizes only four EEG electrodes. The processor is implemented and validated on Artix-7 FPGA which requires only 26K lookup tables and 15K flip flops. The hardware efficient implementation of the complex kurtosis value and Katz fractal dimension (KFD) features using kurtosis value indicator and KFD indicator with 54% and 38% efficient implementations, respectively, is provided. A hardware feasible shallow neural network architecture is used for the ASD classification. The system classifies the ASD with a high classification accuracy of 85.5% using the power and latency of $8.62 \mu \mathrm{W}$ and 2.25ms, respectively.

Autism spectrum disorder (ASD) is the chronic ailment of the central nervous system that causes the degradation of emotional and cognitive abilities. Long-term continuous monitoring with neurofeedback of human emotions for patients with ASD is crucial in mitigating its harmful effect. ASD is highly intimidating chronic disorder because of its sharp increase in the number of patients due to late detection. Lack of emotions’ regularization worsens their daily life by certain negative outbursts of emotions. These negative emotion outbursts cause self-injuries and degrade learning abilities. The timely emotion’s prediction can be used to regulate the emotions by controlling these outbursts. Noninvasive electroencephalogram (EEG) can be used for the emotion’s prediction using the valence and arousal scales. This work proposes a real-time wearable on-chip processor for the early prediction of the emotions. The miniaturized low-power processor can be embedded in a headband (patch sensor) for timely prediction of negative emotion’s outbursts. A linear support vector machine classifier is used with power spectral density, logarithmic interhemispheric power spectral ratio and the interhemispheric power spectral difference of eight EEG channel locations suitable for a wearable noninvasive classification system. A lookup table–based logarithmic division unit (LDU) is proposed to represent the division features in machine learning (ML) applications. The proposed LDU minimizes the cost of integer division by 34% for ML applications. The proposed emotion’s classification processor achieved accuracies of 72.96% and 73.14%, respectively, for the valence and arousal classification on multiple publicly available databases.

Long-term, continuous monitoring of human brain activity with closed-loop precise neurostimulation can potentially help to treat conditions such as epilepsy and Parkinson. These implantable devices are used to sense the brain signal, detect an abnormality, and stimulate once the abnormal activity is detected to mitigate the adverse effect. The target is to achieve the maximum efficacy while balancing the signal acquisition and intelligent processing to ensure minimize battery replacement frequency. This paper presents the design of the implantable Electrocorticography (ECoG) based system for intractable epileptic seizure treatment. The challenges, design choices, and trade-offs, related to the neurological disorder in the implantable environment are discussed. A multi-channel ultra-low-power instrumentation amplifier (IA) with digital electrode offset rejection loop (EORL) with a cutoff frequency of < 0.5Hz to mitigate the electrode offset effect with a fast settling of < 0.1 Sec to ensure real-time recording. The implantable system is realized in 180nm CMOS process to ensure area-and-power efficient design.

Chronic neurological disorders (CND's) are lifelong diseases and cannot be eradicated, but their severe effects can be alleviated by early preemptive measures. CND's, such as Alzheimer's, Autism Spectrum Disorder (ASD), and Amyotrophic Lateral Sclerosis (ALS), are the chronic ailment of the central nervous system that causes the degradation of emotional and cognitive abilities. Long term continuous monitoring with neuro-feedback of human emotions for patients with CND's is crucial in mitigating its harmful effect. This paper presents hardware efficient and dedicated human emotion classification processor for CND's. Scalp EEG is used for the emotion's classification using the valence and arousal scales. A linear support vector machine classifier is used with power spectral density, logarithmic interhemispheric power spectral ratio, and the interhemispheric power spectral difference of eight EEG channel locations suitable for a wearable non-invasive classification system. A look-up-table based logarithmic division unit (LDU) is to represent the division features in machine learning (ML) applications. The implemented LDU minimizes the cost of integer division by 34% for ML applications. The implemented emotion's classification processor achieved an accuracy of 72.96% and 73.14%, respectively, for the valence and arousal classification on multiple publicly available datasets. The 2 x 3mm2 processor is fabricated using a 0.18 μm 1P6M CMOS process with power and energy utilization of 2.04 mW and 16 μJ/classification, respectively, for 8-channel operation.

This live demonstration showcases a noninvasive glucose monitoring device based on a single wavelength near-infrared (NIR) spectroscopy. The analog frontend record the Photoplethysmographic (PPG) signal from the fingertip. Ten discriminating features are extracted from the PPG signal to predict the blood glucose level using (Exponential Gaussian Process) machine learning regression. Visitors can easily insert their fingers into the finger-clip part of the system to measure their blood glucose level and display it on a mobile phone.